About the Air & Waste Management Association The Air & Waste Management Association (A&WMA) is a not-for profit, nonpartisan professional organization that enhances knowledge and expertise by providing a neutral forum for technology exchange, professional development, network opportunities, public education, and outreach to more than 8,500 environmental professionals in 65 countries. Established in 1907, A&WMA is the longest existing international air pollution association in the world. A&WMA also promotes global environmental responsibility and increases the effectiveness of organizations to make critical decisions that benefit society.
Joining A&WMA Air & Waste Management Association One Gateway center, 3rd Floor 420 Fort Duquesne Blvd. Pittsburgh, PA 15222-1435 Phone: +1-412-232-3444; +1-800-270-3444 Fax: +1-412-232-3450 E-mail:
[email protected] Membership rates and Options (see website for full details for each level of membership): Electronic only (for qualifying international countries): U.S.$10 - 25 based on location Individual: U.S.$180 Young professional (30$m in over 30 size channels or three PM values. Furthermore, compared with other nanoparticle measurement instruments, such as electron microscope, condensation particle counter, and scanning mobility particle sizer, the NanoCheck is robust, sophisticated, cost-effective and fast, and doesn’t need radioactive source or butanol during the measurement. Real-time monitoring of airborne particles helps find exposure sources, and then some effective measures can be taken to control and minimize the nanoparticle exposure so as to reduce the risk to human health. Thus this method is very utile for occupational health study, inhalation toxicology research, epidemiological research, production processes, workplace exposure monitoring, and outdoor measurements of the ambient airborne nanoparticles. References C. Arden Pope III,“Review: Epidemiological Basis for Particulate Air Pollution Health Standards,”Aerosol Science and Technology, 2000, 32, 4-14. G. Oberdoerster, J. Finkelstein, J. Ferin, J. Godleski, L.Y. Chang, R. Gelein, C. Johnston, J.D. Crapo,“Ultrafine particles as a potential environmental health hazard. Studies with model particles,” Chest, 1996, 109, 68S-69S. C. Sioutas, R. Delfino, and M. Singh,“Exposure assessment for atmospheric ultrafine particles (UFPs) and implications in epidemiologic research,” Environ. Health Persp., 2005, 113, 947-955. W. C. Hinds, Aerosol Technology, first edition, Wiley Interscience, 1982. Brugge D, Durant JL, Rioux C.: Near-highway pollutants in motor vehicle exhaust: a review of epidemiologic evidence of cardiac and pulmonary health risks. Environmental health: a global access science source., 6:23, 2007.
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Development of the Aerosol Focusing-Laser Induced Breakdown Spectroscopy (AF-LIBS) for the Determination of Metals in Atmospheric Aerosols in Real Time Jihyun Kwak, Gibaek Kim, and Kihong Park* Department of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea E-mail :
[email protected] Introduction In recent years, growing concern about air pollution has resulted in a number of regulatory actions in trying to ensure quality of atmospheric aerosols in terms of concentration limits. In particular, heavy metals in particulate matters (PM) are currently receiving increased attention because of their high toxicity and adverse health effects on human being despite their small fraction in PM mass.1-3 As the sources of atmospheric particles are highly heterogeneous and they only lasts for several hours to several days in atmosphere, continuous monitoring of atmospheric particles is required. However, conventionally the determination of metal concentrations in atmospheric particles has been carried out by off-line analytical methods such as Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) and Atomic Absorption Spectroscopy (AAS). Those methods require long sampling time (12-24 hours) and complicated sample preparation procedures such as chemical extraction and dissolution, which are expensive and labor intensive.4-5 For real-time measurement of heavy metals, a Laser Induced Breakdown Spectroscopy (LIBS) can be used due to less-destructive, rapid, and highly selective in-situ analysis with little or no sample preparation.6-8 The LIBS technique uses a powerful pulsed laser to generate microplasma on a sample. As the plasma cools down, the excited atomic, ionic, and molecular fragments within the plasma emit light with specific wavelengths as signatures of elements in the sample.8-10 Several works have been reported on in-situ determination of elemental composition for aerosols using LIBS technique.11-13 However, no studies have yet been reported in characterizing physical and chemical properties of atmospheric aerosols including seasonal meteorological phenomenon by using LIBS. In this study, we have applied the Aerosol Focusing-Laser Induced Breakdown Spectroscopy (AF-LIBS) to determine metal concentrations (Ca, Mg, K, Fe, Mn, Pb, Cr, Al, and Co) in atmospheric particles in real time. Furthermore, the Asian dust (AD) event occurred on October 19, 2009 has also been studied using the AF-LIBS.
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Experimental Methods LIBS system A schematic of the AF-LIBS system employed in this study is shown in Figure 1. Details of the technique was described previously.14 In brief, a high power pulsed
Figure 1. A schematic of the AF-LIBS system with (a) an aerosol focusing with a collection substrate and (b) sheath air focusing.
Nd:YAG laser (Continuum Inc., USA) (1064 nm wavelength, 5 ns pulse width, 2 Hz) with 90 mJ/pulse energy was used to create a microplasma onto particles. The laser beam was tightly focused into the center of an aerosol sampling chamber using a plano convex lens (focal length=75 mm). After each laser shot, the emitted light was measured through a fiber optic cable with a collecting lens (focal length = 150 mm). The broadband spectrometer (LIBS+2000, Ocean Optics Inc., USA), which is connected to the fiber optic cable measured spectrum of the emitted light within the wavelengths of 200-980 nm range. The broadband spectrometer is equipped with seven charge-coupled device (CCD) detectors having a slit width of 10 $m and 2048 element linear CCD array with a spectral resolution of 0.1 nm. A delay generator (BNC565, Berkeley Inc., USA), which is coupled to a Q-switched laser trigger signal, was used to control the delay time which decides when the spectrometer receives the emitted light after the laser fires. The gate delay time plays an important role in minimizing the continuum emission and in enhancing the atomic lines of the LIBS spectra. Gate delay time and integration time in the spectrometer were optimized to 2 $s and 2.1 ms, respectively. Aerosol sampling The experimental site for this study is located in urban Gwangju, Korea (35°13’ [latitude] and 126°50’ (longitude)). The PM10 inlet was placed on the roof of the 3rd floor, Gwangju MoE/NIER Super-site. PM10 metal concentration was measured by
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using AF-LIBS for every 1~2 hr during 10/07/2009~10/30/2009. Atmospheric aerosols were dried out by diffusion dryer after passing through a PM10 inlet. Then the aerosols were sent into the AF-LIBS sampling chamber through an aerosol focusing nozzle with an inner diameter of 1 mm. In the aerosol chamber, Nylon membranecoated collection substrate, which does not interfere with metal emission lines, was placed at the 50 mm distance from the end of the aerosol focusing nozzle and aerosol particles were collected onto the substrate. For the single particle detection of atmospheric particle, a sheath air focusing nozzle was used. After collection of particles for every 1~2 hr, the laser beam was focused onto the collected samples to create microplasma. Results and Discussion Figure 2 shows LIBS spectra for metal emission lines of atmospheric aerosols (black solid line) and blank (red solid line) detected by the AF-LIBS during the sampling period.
Figure 2. LIBS spectra for metals in aerosols (black solid line) and blank (red solid line) detected by the AF-LIBS. It can be seen from the result that various metals such as Al, Ca, Mg, K, Na, Mn, Ba, Co, Pb, Cr, Fe, Cu, and Zn were detected. For the purpose of calibration, the relationship between intensity ratio and mass concentration of Ca, Fe, Mg, Al, and K obtained from Minivol sampler (PM10) with linear dynamic range and lower detectable mass concentration was determined. Temporal variation of hourly metal concentrations of Ca, Fe, Mg, Al, and K was determined by using the calibration curves. From Figure 3, the temporal variation of Al and Ca, which are major elemental constituents in atmospheric aerosols, can be seen along with PM10 mass concentrations. During the occurrance of AD event, the levels of Al, Ca, Fe, and Mg were 2-3 times higher than those determined in non-AD period, while those of 77
anthropogenic species (Cr, Co, and Mn) did not increase except Pb. This result were consistent with that of Park and co-workers.15 In the future study, impact of transport pathways on the changes of pollutants‘ composition from single particle detection by using AF-LIBS with a sheath air focusing will be investigated.
Figure 3. Temporal variation of hourly metal concentrations of Ca, Fe, Mg, Al, and K determined by AF-LIBS during 10/07/2009~10/30/2009.
References 1. Dockery, D. W.; Pope, C. A., Ann. Rev. Public Health 1994, 15, 107-132. 2. De Vries, W.; Bakker, D. J., Wageningen. DLO Winand Staring Centre Rep 1996, 114, 173. 3. Berggren, D.; Bergkvist, B.; Falkengren-Grerup, U.; Folkeson, L.; Tyler, G., Science of the Total Environment 1990, 96, (1-2), 103-114. 4. Mader, B. T.; Pankow, J. F., Environmental Science and Technology 2001, 35, 3422-3432.
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5.
6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
Park, K.; Chow, J. C.; Watson, J. G.; Trimble, D. L.; Doraiswamy, P.; Arnott, W. P.; Stroud, K. R.; Bowers, K.; Bode, R.; Petzold, A.; Hansen, A. D. A., Journal of Air and Waste Management Association 2006, 56, 474-491. Jensen, L. C.; Langford, S. C.; Dickinson, J. T.; Addleman, R. S., Spectrochim. Acta Part B 1995, 50, 1501-1519. Corsi, M.; Cristoforetti, G.; Hidalgo, M.; Legnaioli, S.; Palleschi, V.; Salvetti, A.; Tognoni, E.; Vallebona, C., Applied Geochemistry 2006, 21, (5), 748-755. Wainner, R. T.; Harmon, R. S.; Miziolek, A. W.; McNesby, K. L.; French, P. D., Spectrochimica Acta Part B-Atomic Spectroscopy 2001, 56, (6), 777-793. Capitelli, F.; Colao, F.; Provenzano, M. R.; Fantoni, R.; Brunetti, G.; Senesi, N., Geoderma 2002, 106, (1-2), 45-62. Harmon, R. S.; DeLucia, F. C.; McManus, C. E.; McMillan, N. J.; Jenkins, T. F.; Walsh, M. E.; Miziolek, A., Applied Geochemistry 2006, 21, (5), 730-747. Lithgow, G. A.; Robinson, A. L.; Buckley, S. G., Atmospheric Environment 2004, 38, (20), 3319-3328. Carranza, J. E.; Fisher, B. T.; Yoder, G. D.; Hahn, D. W., Spectrochimica Acta Part B-Atomic Spectroscopy 2001, 56, (6), 851-864. Hettinger, B.; Hohreiter, V.; Swingle, M.; Hahn, D. W., Applied Spectroscopy 2006, 60, (3), 237-245. Park, K.; Cho, G.; Kwak, J. H., Aerosol Science and Technology 2009, 43, (5), 375-386. Park, M. H.; Kim, Y. P.; Kang, C. H., Water, Air, and Soil Pollution: Focus 2003, 3, (2), 117-128.
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Quantification of Fine and Ultrafine Particles Suspended in Seawater for Studying Marine Aerosol Formation Jiyeon Park, Sungil Lim, and Kihong Park* Gwangju Institute of Science and Technology (GIST), Korea E-mail:
[email protected] Abstract It is essential to determine number, size, and composition of submicron particles in seawater to better understand their contribution on production of marine aerosols. In this study, we applied the membrane filtration-differential mobility analyzer (MFDMA) counting technique to determine both number concentration of suspended particles in sizes of 20-600 nm (#/ml) and mass concentration of dissolved solids (ppm) in the MF membrane (0.45 $m pore size)-filtered seawater in real time. By using the MF-DMA technique with a variety of solution and their mixture, we established a relationship between particles in water (#/ml) and in air (#/cm3). The effect of their mixing states on quantitative performance of the MF-DMA was tested using mixtures of artificial seawater (25,000 ppm) (dissolved) and silica particles (suspended). Then we applied this technique to determine sub-mircon particles in seawater sampled in Korea. The number concentrations of suspended particles in the MF membrane-filtrated seawater (10 fraction in Figure 1b observed a reverse tendency from the distance of various emission sources, and these fractions in the near-source and countryside were relatively high (0.56 and 0.32) and in suburban and harvest-season background were rather low (0.27 and 0.19). This might be in part involving to dry deposition of the large coarse particles during transport of smoke from fields into suburban areas. A differing Table 1 Levoglucosan fractions in three particle-size ranges from rice straw burnings at different sampling sites. Levoglucosan fractions in PM Sampling sites PM2.5 Near-source
PM2.5
PM
-10
>10
0.41 ± 0.13
0.03 ± 0.02
0.56 ± 0.14
Countryside
0.60 ± 0.13
0.09 ± 0.01
0.32 ± 0.13
Sub-urban
0.71 ± 0.07
0.02 ± 0.01
0.27 ± 0.08
0.76 ± 0.23
0.05 ± 0.03
0.19 ± 0.20
Intensive Burning
Harvest-season Background a
a. The average of countryside and suburban background.
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fraction trend (Figure 1a) was found in PM2.5 particles. Furthermore, the levoglucosan fraction in coarse particles (PM2.5-10) at the countryside location was 3 times higher than that at the suburban site, likely due to transport and coagulation of smoke particles generated during rice straw burning process (Figure c). Thus, levoglucosan tracer presented strong spatial dependence in the ambient concentrations as well as in its size distributions. On the other hand, diagnostic ratios of levoglucosan to mannosan (Levo/Mann) were especially augment in large coarse particles (PM>10) during intensive burning at the countryside (Figure 2), indicating the presence of fresh smoke aerosol derived from rice straw burning. This study derived from straw burning, obtained the similar high ratios (Levo/Mann) as the literature of chamber studies reported (40-60)10, 11. Lower ratios in both fine and coarse particles might be combined with ambient aerosol from different emission sources, such as typical lower ratio particles (ratio 4-5) derived from soft wood emission 5, 6, 7, 8, resulting in two particle ratios relative low, specifically during winter dry season in southern Taiwan (any of fine ambient particles easily to retain longer in less precipitation environment). Meanwhile, seasonal soil samples were examined for the biomass burning tracer content, presenting a negative correlation with the precipitation (Figure 3). Because of high solubility of anhydrosugar tracer, the abundance of levoglucosan from rice field during wet season has shown a lower tracer content in soils.
SUMMARY Possible transport and transformation mechanisms, explaining the observed particle472
size characteristics of the biomass smoke aerosol is presented. Those smoke particles from rice straw burning might together with certain atmospheric processes (Figure 4), including transport process in fine particles, deposition in large coarse particles, and coagulation in secondary aerosol of coarse particles, and that may partially explain the observation of varying tracer concentrations in this field investigation. Therefore, we have shown that the influence of atmospheric processes on particle-size anhydrosugars associating with rice straw burning practices in subtropical regions of Asia may give rise to obviously different chemical and physical characteristics of the resulting smoke aerosols compared to the features in chamber or other ambient observations. ACKNOWLEDGEMENT This work was supported in part by National Science Council (Taiwan) grant NSC-982221-E-224-005.
REFERENCES 1. Simoneit, B.R.T.; Schauer, J.J.; Nolte, C.G.; Oros, D.R.; Elias, V.O.; Fraser, M.P.; Rogge, W.F.; and Cass, G.R. Atmos Environ. 1999, 33, 173-182. 2. Leithead, A.; Li, S.M.; Hoff, R.; Cheng, Y.; and Brook, J. 3. Atmos Environ. 2006, 40, 2721-2734. 4. Puxbaum, H.; Caseiro, A.; Sanchez-Ochoa, A.; Kasper-Giebl, A.; Claeys, M.; Gelencser, A., Legrand, M.; Preunkert, S.; and Pio, C. 473
5. 6. 7. 8.
9.
10. 11. 12. 13. 14. 15.
J. Geophysical Research, 2007, No. D23S05, 10.1029/2006JD008114 Ward, T.J.; Hamilton, R.F.; Dixon, R.W.; Paulsen, M.; and Simpson, C.D. Atmos Environ. 2006a, 40, 7005-7017. liveira, C.; Pio, C.; Alves, C.; Evtyugina, M.; Santos, P.; Goncalves, V.; Nunes, T.; Silvestre, A. J. D.; Palmgren, F.; Wahlin, P.; and Harrad, S. Atmos Environ. 2007, 41, 5555-5570. Oliveira, C.; Pio, C.; Alves, C.; Evtyugina, M.; Santos, P.; Goncalves, V.; Nunes, T.; Silvestre, A. J. D.; Palmgren, F.; Wahlin, P.; and Harrad, S. Atmos Environ. 2007, 41, 5555-5570. Pio, C. A.; Legrand, M.; Alves, C. A.; Oliveira, T.; Afonso, J.; Caseiro, A.; Puxbaum, H.; Sanchez–Ochoa, A.; and Gelencser, A. Atmos Environ. 2008, 42, 7530-7543. Schmidl, C., Marra, I. L., Caseiroa, A., Kotianova’, P., Berner, A., Bauer, H., Kasper-Giebl, A., Puxbaum, H., Atmospheric Environ. 2008, 42 126–141 Lee, J. J.; Engling, G.; Lung, S. C. C.; Lee, K. Y. Atmos Environ. 2008, 42, 83008308. Sheesley, R. J., Schauer, J. J., Chowdhury, Z., Cass, G. R. and Simoneit, B. R. T., 2003. Journal of Geophysical Research 108: Art. No. 4285 Zhang, Y. X., Shao, M., Zhang, Y. H., Zeng, L. M., He, L. Y., Zhu, B., Wei, Y. J. and Zhu, X.L., 2007. Journal of Environmental Sciences - China 19:167-175
KEYWORDS Biomass burning, Aerosol mechanism, Anhydrosugar, Levoglucosan, Size distribution.
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IIg: Source Characterization
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On-board Emission Measurement from the World’s Largest Heavy Haulers Xiaoliang Wang, Steven D. Kohl, Judith C. Chow, John G. Watson, Steven Gronstal Desert Research Institute, 2215 Raggio Pkwy, Reno, NV 89512 Principal Contact: Xiaoliang Wang, Assistant Research Professor, Desert Research Institute, 2215 Raggio Pkwy, Reno, NV 89512. Phone: 1-775-674-7177, Fax: 1-775674-7009, E-mail:
[email protected] . Abstract An on-board emission measurement system was developed and applied to measure emissions from the 2648 kW diesel engines in Caterpillar 797B, arguably the largest heavy hauler in the world, under real-world operating conditions. This system draws a sample of the exhaust gas from the tailpipe, dilutes it with filtered air and quantifies CO, CO2, NO, NO2, O2, SO2 and polycyclic aromatic hydrocarbon (PAH) concentrations, particle size distribution, number and mass concentration, and black carbon concentration at 1 second interval. Integrated samples by filter packs and canisters are acquired for laboratory analysis of particle mass concentration, light absorption characteristics, elements, isotope, ions, NH3, H2S, SO2, carbon, organic compounds and VOCs. Chemical source profiles and fuel-based emission factors were measured from the exhaust of two 400-ton capacity 797B trucks during working cycles, including idling, loading, dumping and transit with and without load. This paper describes the on-board system, emission factor and profile variability, and differences with certification values. On-board measurements present a leapfrogging opportunity for determining non-road engine emissions.
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Design of a compact dilution sampler for stationary combustion sources Xinghua Li1, Shuxiao Wang1, Lei Duan1 and Jiming Hao1 1 Department of Environmental Science and Engineering, Tsinghua University, Beijing, China, 100084 Principal Contact: Xinghua Li, Ph.D., Department of Environmental Science and Engineering, Tsinghua University, Beijing, China, 100084, Phone: 86-1062796731, fax: 86-10-62773650, E-mail:
[email protected] Abstract Dilution sampler simulates the cooling and dilution processes after hot flue gas leaving the stack. Recently, it has been used for characterizing emissions from stationary combustion sources. In order to make this method more suitable for field investigation, a compact dilution sampler was designed. The sampler includes the following units: inlet part, dilution and mixing part, residence chamber and sampling part. The main features of the design are illustrated as the following: (1) Dilution part consists of two stage diluters. The first diluter is based on ejection type dilution. A regulator valve, attached at the exhaust pipe of the first diluter, is used to regulate gas flow rate of outlet into the second diluter. A venture flowmeter is equipped to record gas flow rate of outlet of the first diluter. The second diluter is an enclosed cylinder with a perforated cone inside. The sample gas from the first diluter is introduced into the inside of the cone. The second dilution air is forced through the apertures of the cone into the inside and then mixes with the sample flow. A mass flowmeter and a venture flowmeter are equipped to regulate and record the dilution air flowrate in the second diluter, respectively. The total dilution ratio of the two stage diluters ranges from 20 to 50. (2) The combination of two stage diluters shortens the length of mixing section. The size of the residence chamber is reduced by decreasing the nominal flow rate through the aging section to 100 L/min. The decreased size of the sampler is suitable for field test. (3) The sampling gas is pressured into the residence chamber and the air pressure in the chamber is micro-positive. The un-collected redundant gas was discharged through pressure-equalizing port attached at the lower part of chamber automatically, which will keep the unit stable.
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Transformation and Toxicity Assessment of PAH Emissions from a Municipal Solid Waste Incinerator/Power Plant in the Pearl River Delta, China Boguang Wang1, Jie He1, Tao Zhang1, Yan Zhou2, Huixuan Liu1, Jian Peng3 1Department 2Guangdong 3 OC
of Environmental Engineering, Jinan University, Guangzhou 510632, China
Province Environmental Monitoring Center, Guangzhou 510045, China
Watersheds Program 2301 N. Glassell St, Orange, CA 92865, USA
INTRODUCTION Polycyclic aromatic hydrocarbons (PAHs) are widely distributed in the atmosphere and well-known for their carcinogenic and mutagenic properties. Previous studies have mostly focused on their atmospheric behavior, environmental effects, and source apportionment, which indicated that biomass burning, coal combustion and vehicle exhaust are the major sources., However, in the past decades, the amount of Municipal Solid Waste (MSW) has been dramatically increasing worldwide. Municipal Solid Waste Incineration (MSWI), which reduces waste volume and recovers energy from the exothermic incineration, is becoming one of the most important techniques for MSW treatment. However, the fact that this process emits hazardous substances, such as PAHs, have not received adequate attention. Zhang and Tao’s study (2009) indicated that the contribution of MSWI to atmospheric PAHs could have been underestimated in USA as well as in UK in absence of individual PAH Emission Factors (EFs). In China, an emission inventory of sixteen PAHs was established by Xu et al. (2006), but MWSI source was not included. Furthermore, the exisitng MWSI emission profiles have significant variability in China. Therefore, it is essential to get a better understanding on the emission profile of MSWI, and to complete the PAHs emission inventory. Due to fast economic development in the past two decades, air quality in the Pearl River Delta (PRD) of Guangdong Province, China has severely deteriorated. Regional haze events accompanied by high concentrations of particle matters and PAHs has occurred frequently in recent years. A lot of efforts have been made in solving the complex air pollution problems in PRD. To investigate the ‘missing’ source associated with MSWI, this paper investigated LiKeng (LK), a typical MSWI power plant in the northeast of Guangzhou, the primary megacity of PRD with over 10 million population in 2007. Since its startup in 2006, the power plant was the target of frequent complaints by residents for the odor and the perceived adverse effects on human
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health. Through two-day field observations in the MWSI power plant, this study focused on assessing the transformation of species and toxicity of PAHs contributed to the plant and ambient atmosphere. Experimental Methods Sampling procedure The area surrounding LK it is free of other emission sources except for a minor road about 1km away. Source samples were collected from the chimney (site A) and discharge unit (site B) of the plant. Ambient air samples were taken at the upwind boundary site (C1) and the downwind residential receptors (C2 and C3), about 200m, 800m, 1000m from the plant, respectively. Sampling was performed on December 17 and 18, 2009. At site A, the total suspended particle (TSP) samples were collected for PAHs, and the sampling time was conducted 4 times a day of 30 minutes each with the flux of 50mL/min. The samples of respirable particulate matter (PM10) were collected for PAHs at site B, C1, C2 and C3 in the flux of 100mL/min. Samples in site B were collected 2 times each day, 2 hours for each collection, while the collection at site C2 and C3 were conducted for just 1 period of 6 hours. Duplicate sample was collected for each field sample at site B, and one for each receptor site. A total of 25 samples were collected in the study. Sample pretreatment and analysis The samples were ultrasonically extracted with 20ml of mixed solvent (methanol, dichloromethane and n-hexane, v:v:v=3:3:4) twice. The extracts were combined and concentrated by a rotary evaporator, followed by column chromatography using 60100 mesh silica gel column eluted with 25mL mixed solvent. The eluent was concentrated to 1ml for analysis. A gas chromatography coupled with mass spectrometer (GC/MS, SHIMAZDU QP2010Plus) was used for the 16 PAHs analysis in selective ionization mass (SIM) mode. The recoveries of all target compounds were between 76% and 118%, averaging 93 ± 8.42%. The detection limits ranged from 0.12ng/&L to 1.58ng/&L. Results and discussion Concentration distribution of PAHs at all sampling sites Eleven of the sixteen priority PAHs were detected, including naphthalene(Nap), phenanthrene(Phe), anthracene (Ant), fluoranthene(Fl), chrysene(Chr), benzo[a]anthracene(BaA), pyrene(Pyr), benzo[k]fluoranthene(Bkf), benzo[a]pyrene(BaP), benzo[b]-fluoranthene(BbF) and indene[1, 2, 3-cd] pyrene (IndP) (see Table 1). The average total PAH concentrations were in the range of 19.28 ng/m3 to 218.13 ng/m3, with the order of chimney (site A) >discharge unit (site B)>receptor site (C3)> receptor site (C2)> receptor site (C1).
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Fingerprints of PAHs In chimney (site A), the major PAHs were Nap, BaA, Fl, Phe, and Ant. Nap was the most abundant species at 127.73 ng/m3, accounting for 58.56% of the total PAHs, followed by BaA which accounted for 23.74% at 51.78 ng/m3. Fl and Phe accounted for 5.2% and 5.0% respectively. PAHs in chimney were dominated (98.18%) by the 2ring to 4-ring analogs. Two factors might explain the high level of Nap in chimney samples. First, large number of Nap may have formed in the incineration process of MWS; second, the volatile Nap might more readily adsorbed by the TSP, in the case of TSP contained certain numbers of organic compounds., As for the discharge unit, Nap also had the highest concentration of all the measured PAHs at 12.45 ng/m3, accounting for 27.5% of the total detection. The next most abundant component was Chr at 9.13 ng/m3 with a weight of 20.1%. PAHs detected in discharge unit ranged from 2-ring to 6-ring, suggesting that PAHs in raw waste materials may have been destroyed during the incineration process. The study also showed the PAHs contribution to the atmosphere of the plant was significant. Table 1. PAHs detected in each sampling points Sampling Sites (mean ± SD) Nap Phe Ant Fl Chr BaA Pyr BbF BkF BaP IndP receptor site C2> receptor site C1. The major PAHs in chimney plume were dominated (98.18%) by 2-4-ring PAHs including Nap, BaA, Fl, Phe and Ant; In the discharge unit, 2-6-ring PAHs such as Nap, Pyr, IndP, BbF, Phe were predominant (81%). The PAHs emissions of the chimney and discharge unit indicated PAHs in raw waste materials might have been destroyed during the incineration processes. The study showed that the boundary receptor appeared to be more impacted by discharge unit than by the chimney. In contrast, the downwind areas of the plant were more significantly influenced by the chimney plume of the plant. Nerveless, Health Risk Assessment of the five sampling sites using BaPeq and 1&m). When the fog is forming, density of fine particles below 1&m decrease continuously, however number concentration of particles in 1-2.5&m size range had a trend of fluctuating growth, and its change period is about 2.5h. During the fog strengthening stage, the number concentration of particles below 1&m diameter was on a declining trend, whereas the number concentration of paticles above 1&m diameter had a increasing tendency. By checking the surface specific humidity during particle growth time, we found that whether there is fog or not, the number concentration of coarse particles above 1&m will increase, as long as the specific humidity increases continuously. There are three possible reasons for the number decrease of particles below about 1&m size and number increase of particles in 1-5&m size range in the development of fog, sea salt coarse mode particles were transported by marine atmosphere, the coagulation and hygroscopic growth of about 1&m size particles. To find the most possible reasons, the aerosol size distribution before and after the wind veering in the afternoon of July 6 was compared. It can be seen that after the wind shifting from south west to south east the number of particles below 0.7&m diameter decreased, particles in 0.7-
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0.8&m size range have the smallest variation extent, particles above 0.8&m increased, and particles in 1-3&m size range had the largest variation range of concentration. But the concentration of particles above 5&m was nearly unaffected. This means that predominant modes of aerosols from the sea was mostly in the 0.8-5&m size range. The wind velocity under 120m altitude was mostly below 2m/s around the wind shifting from southwest to southeast, so the coarse aerosol particles may be produced by the sea wave’s friction crusher on beach, or may be the pre-existing particles in the polluted marine atmosphere. The growth rate of particles(&m/s) in the fog developing stage was estimated. Particles with 1.1&m and 2.2&m diameter had a highest growth rate when the sea fog is forming. In the maintance stage, the growth rate of particles in 2.2-3.7&m size range decreased slightly, other particles decreased obviously, and particles below 1.1&m and particles above 5.7&m show a negative growing. The possible reason is that small particles are consumed through growing, and large particles are scavenged by fog droplet. When the fog was very thick, particles in 0.5-5.7&m size range all growed up, the growth rate of particles above 4.5&m was the highest in the fog event. This indicates the growing speed of coarse particles is higher than that of scavenging by fog droplet in the fog developing stage. Good relationship was found between the occurring time of low visibilities and number concentrations of coarse mode particles in the 1-5$m size range. One extreme case is that the visibility was below 300m from 9 o’clock to 16 o’clock on July 9, which is the lowest visibility in this sea fog, number concentration of particles in 15$m size range was nearly 6 times of it in other times. Summary The persistent increasing of the specific humidity lead to the growth of the number concentration of coarse particles under the influence of the warm humid air flow. Moreover, the number concentrations increase much more when the increase speed of the specific humidity becomes faster. After fog formation, the number of particles below 1&m size decreased , and the number of particles in 1-5&m size range increased. Particles in 0.5-5.7&m size range growed up in the forming and strengthening stage of the fog, however only particles in 2.2-3.7&m size range kept growing in the maintenance stage of the fog, and the grow rate was lower than that in the forming and strengthening stage of the fog. The occurring time of poor visibility was coincident with the increasing of number concentration of particles in 1-5&m size range, especially, an inverse correlation was identified between the visibility and the number concentration of the particles in 1–2.5 &m.
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References 1. Gultepe, I., and Milbrandt, J. Pure and Applied Geophysics, 2007, 164, 11611178.Li,Z.H., Yang, J., Shi C. E.and Pu. M. J. The physics of regional dense fog, China meteorology Press. Beijing, 2008. Zhou, J. W., Li, L., Wang, Y. Q. Journal of Anhui Agricultural Sciences, 2009, 37(20):9561-9565㧚 Yang, Z. Q., Xu, S. Z. Acta Oceanologica Sinica, 1982,11(4),431-438. Huang,H. J., Huang, J., Liu, C. X. et al. Acta Oceanologica Sinica, 2009,31(2),17-23. Li, X. N., Huang, J. , Shen, S. H. et al. journal of tropical meteorology, 2010,26(2): 5966. Jiang, D.S., Zhang, S.P., Lu, W.S. Transactions of Oceanology and Limnology,2008. 3,7-12. Wu, D.. Guangdong Meteorology , 2009, 31(2),1-3. Shi,C. E., Yang, J. , Qiu, M. Y., Xie,W. and Zhang, H. Climatic and Environmental Research , 2008, 3,327-336. Yang L. S. Marine Sciences 1985, 9(4),49-50. Xu, J.Q., Zhang, Z., Wei, H. Transaction of Oceanology and Limnology, 1994, 2,174178. Sheng, L. F., Liang, W. F., Wang, D., Gao, S. H. Periodical of Ocean University of China, 2010,40(6).
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Application of WRF/Chem in the Beijing-Tianjin Region: Simulation of Fog and Haze Chengming Pang1, Ning Niu2, Yaodong Li1, and Jianwen Liu1 1 Beijing Institute of Aviation Meteorology, An Ning Zhuang Road 29#, Hai Dian District, Beijing, 100085 2 China Meteorological Administration Training Centre, Zhong Guan Cun South Street 46#, Hai Dian District, Beijing, 100086 Principal Contact: Chengming Pang, Doctor, Beijing Institute of Aviation Meteorology, An Ning Zhuang Road 29#, Hai Dian District, Beijing, 100085, 01066919739-810,
[email protected] Abstract The fog and haze events are adversely affecting transportation due to worsening visibility, affecting the air quality and health through accumulation of pollutants near the ground. Forecasting the fog and haze events successfully is helpful to reduce their disadvantageous impact on visibility, air quality and health etc. The forecasting ability of numerical model for fog and haze is currently limited. Successful simulation of fog and haze events leading to an improvement of the forecasting ability of numerical model is the focus of the paper. As a meteorological and chemical coupled model, WRF/Chem is advantaged to simulate the air quality and weather. The WRF/Chem model is used to simulate the fog and haze in Beijing-Tianjin region during the period of Dec. 6th-9th of 2009. The fog and haze events form in atmospheric boundary layer (ABL), even in a hundreds-meters layer near surface. So 9 " levels are added in ABL mainly below 200 meters and appropriate parameterization schemes are adopted to localize WRF/Chem in North China. In order to simulate the fog and haze events in Beijing-Tianjin region, a new emission dataset is developed according to a new method (Zhang, 2009) based on a new Asian emissions (Zhang et al., 2009) which is not adapt to run a simulation in the region because of its coarse resolution and a highresolved pollutants emission data distilling from Chinese statistic yearbooks of 2008. Using GFS 0.5ºx0.5º forecasting dataset as the initial and boundary conditions a simulation of fog and haze events during the period of Dec. 6th-9th of 2009 is run using localized WRF/Chem model. The comparisons between the model results and observing data including both meteorological factors and pollutants concentration show a good potential forecasting ability of localized WRF/Chem model for fog and haze events. Zhang Wei. Simulations of seasonal changes in air pollution and emission source control scenarios in the Beijing-Tianjin-Hebei region. Master Thesis. Institute of Atmospheric Physics, Beijing, 2009 (in Chinese).
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Zhang, Q. et al. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. Discuss, 2009, 9: 4081–4139.
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Study on the Haze Pollution Index System for Shanghai Yusen Duan1, Jiong Shu2,Yihua Zhang1,Yanmin Huang1,Song Gao1 and Dongfang Wang1 1 2
Shanghai Environmental Monitoring Center, No.1 Nandan Road, Shanghai, 200030 East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200062
Principal Contact: Yusen Duan, Engineer, Shanghai Environmental Monitoring Center, No. 1 Nandan Road, Shanghai, 200030, 86-21-24011580, 86-21-64393639,
[email protected] Abstract With the global warming and the overspreading of industrial pollutant emissions, regional visibility deterioration is becoming an increasingly serious problem in the more developed area in eastern China. Large-scale fog and haze occur frequently. Based on the theoretical analysis of relative humidity (RH) and air pollutants which affect visibility, we proposed the concept of haze pollution of the environmental category and established the haze pollution index system which included visibility, particle mass concentration and speciation. The RH factor was parameterized based on the monitoring data of particle speciation. Analyzing one-year monitoring data, we found that the haze pollution calculated from haze pollution index had good corresponding relation to the haze of meteorological category. The haze pollution index was significant to evaluate regional air pollution and establish control strategies.
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Algorithm Development of Visibility Monitoring Technique Using Digital Image Analysis Rajib Pokhrel, Heekwan Lee, Ph.D. Department of Civil and Environmental Engineering, University of Incheon 167 Sookgol-Gil, Nam-Gu, Incheon, 402-749, South Korea Abstract Atmospheric visibility is one of the indicators to evaluate status of air quality. With the support of such conceptual definition of visibility, it is the maximum distance at which outline of the selected target can be recognized, image analysis technique was introduced. Although there are various measurement techniques, covering from bulk and precise instruments to naked eye observation technique, the techniques have their own limitations. In this study, a series of image analysis techniques were introduced and examined in-situ application. Imaging system was built up using a digital camera and it was installed on the study sites such coastal area in Incheon. Visual range was monitored by using Visibility Sensor and ambient air quality was monitored by using Air quality monitoring system. The Sobel mask filter was used to detect the edge line of object by extracting the high frequency from the digital image. RMS index and Mean index of processed image were calculated then regression analysis was done. RMS index was substantially correlated with visual range with correlation R2=0.88. The equation of regression line was VR=2.36e0.46× (RMS). Further visual range (VR) could be enhanced with known RMS index. Similarly, the fine particles (PM2.5) were substantially correlated with visual range with correlation R2=0.66 while coarse particles had low correlation. From the result, It is concluded that fine particles were more responsible for the impairment of visibility than coarse particles.
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Impact of Atmospheric Pollutants on Visibility in Chongqing Main City Zhou Zhien1 Zhang Can Zhang Dan Chongqing Academy of Environmental Science. No.252, Qishan Road, Ran Jia Ba, YuBei District, Chongqing, P.R.China. 401147. Principal Contact: Zhou Zhien. Department Director of Atmospheric and Remote Sense Research Department, Chongqing Academy of Environmental Science. No.252, Qishan Road, Ran Jia Ba, YuBei District, Chongqing, P.R.China. 401147. Tel: 86-2367726829. Fax: 86-23-67855069. Email:
[email protected]. Abstract Historical data show that annual average visibility level in Chongqing since 2000 is 2~6km less than Beijing, Shenyang and other northern cities. In addition to its own meteorological conditions, air pollution is also an important reason in reducing the visibility by light absorption and scattering. With the economic and social development as well as effective control of sulfur dioxide (SO2) , SO2 concentrations had fallen significantly in the past years, but particulate matter pollution has gradually become a highlighted issue of Chongqing main city, as other domestic cities, particulate matter has become a primary atmospheric pollutant in Chongqing main city. SO42-, NO3-and NH4 + are important compounds in PM, and they are closely linked with the precursor gases SO2 and NO2. In order to find the relations between atmospheric pollutants and visibility in Chongqing main city, this paper explores the main air pollutants, which are particulate matter (PM10, PM2.5), SO2, nitrogen dioxide (NO2) and ozone (O3) and looks at their impact on the degree of visibility by correlation analysis.
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IIIb: Air Pollution Impacts on Ecosystems
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The Athabasca Oil Sands Region: Effects-based Environmental Monitoring in a Multistakeholder Air Quality Management Context Kevin E. Percy1, 2 1K.E. Percy Air Quality Effects Consulting Ltd., 32-230 Wilson Drive, Fort McMurray, Alberta, Canada, T9H 0A4 2Wood Buffalo Environmental Association, 100-330 Thickwood Blvd., Fort McMurray, Alberta, Canada, T9K 1Y1 Principal Contact: Kevin Percy, 32-230 Wilson Drive, Fort McMurray, Alberta, Canada T9K 1Y1, 780-748-1187,
[email protected] Abstract To control ground level concentrations of air pollutants and mitigate against adverse effects to health and welfare, both risk-based and technology-based approaches have been used. Regardless of approaches employed singly or mixed, it is clear that standard’s-based air quality management has achieved a large degree of success. Bachmann (2007), in his seminal review of the US NAAQS experience, cites cumulative benefits from the Clean Air Act and amendments at between $6 trillion to $50 trillion as compared with compliance costs of $520 billion. Essentially, effectsbased science and monitoring for human health, and a lesser extent vegetation and materials, has underpinned air quality management in the US, and to some extent, Canada. The Alberta Oil Sands of north-eastern Alberta, Canada, contain an estimated initial volume in-place of approximately 1.7 trillion barrels of crude bitumen. The AOSR produced 1.32 Mbbl d-1 of crude bitumen in 2007. By 2020, AOSR production is predicted to reach 3-4 Mbbl d-1. The Wood Buffalo Environmental Association (WBEA) is a multistakeholder, not-for-profit organization that monitors air quality and terrestrial environmental effects from AOSR energy sector development. Recently, significant investment has been made by members to enhance effects-based vegetation monitoring in order to facilitate more informed decisions on air shed planning and management. This monitoring strategy supports a risk-based approach to air quality management. It is expected to be a more utilitarian and ultimately more effective at protecting against adverse effects to the region’s terrestrial ecosystems than caps on emissions.
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The Impact of Sulphur Dioxide on Rice Yield Jin-Heng Zhang Contact Email㧦
[email protected] (Institute of Eco-environment & Agriculture Information㧘College of Environment and Safety Engineering㧘Qingdao University of Science and Technology, Qingdao㧘 Shandong 266042, China) A field trial was conducted to investigate yield componentes changes of different rice varieties to SO2 stress at five growing stages in the year 2007. Plots were arranged in a randomized complete block and consisted of same fertilizer applications level. Three varieties of rice ( Lindao10 , Shengdao13 and Yangguang200) were conducted with the similar growth circle. Sulfur dioxide at concentrations of the background concentration (CK), C1㧔13.09mg/m3㧕, C2㧔26.18mg/m3㧕, C3㧔39.26mg/m3㧕 and C4㧔52.35mg/m3㧕were provided by an open-top fumigation device. Compared with the normal growth rice, difference of SO2 impacting on yield components depended on genotypes and growth stages of rice. When rice were exposed to SO2, ear weight, thousand grain weight and yield per ear reduced at tiller stag, and thousand grain weight reduced at heading stage according to the elevated SO2 concentrations. Difference of ear weight and thousand grain weight existed between rice exposed to SO2 and CK at jointing stage. The reduction of average yield of 18.5%,32.3% and 29.3% at tiller stage, 18.5% ,21.4% and 16.0% at jointing stage, 8.7%,14.6% and 12.1% at heading stage, 18.4%,24.1% and 11.4% at grain filling stage for Lin Dao10, Sheng Dao13 and Yang Guang200 respectively. The ratio of average yield reduction of Lin Dao10 near by 18%㧘but the ratios of average yield reduction of Sheng Dao13 and Yang Guang200 were 32.3% and 29.3% respectively. This investigation showed that the effects of sulphur dioxide on the yield components of rice exposed to SO2 at early growth stages more than at later growth stages. Key words: sulphur dioxide; rice;yield components
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Atmospheric Nutrients in Southeast Asia: Identification, Quantification and Impact Assessment Jun He 1,2, Sundarambal Palani3 and Rajasekhar Balasubramanian 2,1 1Singapore-Delft
Water Alliance, National University of Singapore, Singapore 117576 2 Division of Environmental Science & Engineering, National University of Singapore, Singapore 117576 3Tropical Marine Science Institute, National University of Singapore, Singapore 117576 Principal Contact: R. Balasubramanian, Division of Environmental Science & Engineering, National University of Singapore, Singapore 117576. Phone: (65) 65165135; Fax: (65) 6774-4272; e-mail:
[email protected] Introduction In recent years, atmospheric nitrogen deposition has received considerable attention, particularly in the context of eutrophication of aquatic systems. Rapidly increasing global industrialization and automotive transport use are of main concern towards the cultural modification of atmospheric nitrogen cycle.1-3 Until now, most of nitrogen budgets have been focused on inorganic nitrogen and therefore our understanding on anthropogenic sources and deposition patterns of inorganic nitrogen are well established.4-6 However, the implications of organic nitrogen are still being appraised despite over a century of published report on dissolved organic nitrogen (DON).5, 7, 8 An improved qualitative and quantitative understanding of organic nitrogen component is needed to compliment the well-established knowledge-base pertaining to nitrate and ammonium deposition. Therefore, a number of studies focus mainly on organic nitrogen deposition in recent years to understand its contribution to the global nitrogen budget. It is clear that organic nitrogen is ubiquitous yet still poorly characterized component of atmospheric precipitation.7 It is mainly because no single analytical technique can analyze the entire range of organic forms of nitrogen present. The limited results available to-date indicate that the organic nitrogen can be ~ 10 – 40 % of total nitrogen depending on their sources and locations. Aliphatic amines, free amino acids, total hydrolysable amino acids, urea, nitrogen containing aromatics have been reported to be present in wet and bulk deposition samples. Amorphous largely uncharacterized macromolecules like humic material also contribute significantly to DON. Nevertheless, the current practice is only measuring bulk DON through difference between the total nitrogen and inorganic nitrogen.5 An alternative method for the determination of organic nitrogen is the alkaline persulfate digestion technique which promotes efficient hydrolysis and oxidation of most nitrogenous compounds
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resulting in nitrate ions. The final step requires the analysis of nitrate either by ionchromatography (IC) or colorimetric technique. In this study, a rapid microwave assisted persulfate oxidation followed by ion chromatography for total nitrogen was optimized on various parameters such as reagent concentration, microwave power, extraction time, etc. As mentioned above, one of the most rapidly growing sources of N loading is atmospheric deposition. Average percentages of atmospheric N deposition as part of total external N loading into coastal waters such as Mediterranean Sea, North Pacific Ocean and New York were up to 10-60% 9, 40-70% 10 and 38% 11, respectively. It has been estimated that 48.8% of new N into Lake Taihu of China is of atmospheric origin based on a field study from 2002 to 2003 12. In Europe, the increasing concentrations of nutrients emissions derived from combustion or biomass burning have led to a large input of these products into the ocean from the atmosphere, in some cases even equaling or exceeding the input from rivers 13, 14. In the aquatic system, increased N loading has been recognized to cause major shifts in ecological structure and function structure and deterioration of ecosystem conditions, such as reduced water transparency, algal bloom growth and bottom water oxygenation 15, 16. In Southeast Asia (SEA), it has been reported that nutrient species such as N are associated with aerosol particles17 and they also contribute nutrients significantly onto the Singapore Strait through dry and wet atmospheric depositions.18 In addition, during the Southwest monsoon season (June-September), the prevailing southeast/ southwest winds are the dominant control on the transport of atmospheric pollutants in SEA and Singapore’s watershed can be affected by biomass burning frequently occurring in Indonesia.19 In the present study, an extensive atmospheric monitoring of N in dry and wet depositions at three monitoring stations within the Marina Catchment area of Singapore was conducted from April 2007 to March 2008. Kinematic 3D air mass backward trajectories and cluster analysis were computed by using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide an accurate description of the history of air masses transported to the sampling sites for possible atmospheric nutrients source identification. Nutrient budgets for dissolved inorganic nitrogen (DIN, as nitrate, nitrite and ammonium), organic nitrogen (ON) and total nitrogen (TN) were estimated and temporal trends of these loadings were studied. In addition, the influence of atmospheric deposition of nutrients on receiving aquatic ecosystems has been investigated. Experimental Microwave Digestion system A closed vessel microwave digestion system (MLS- 1200 mega, Milestone, Italy) was
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used. It consists of compact terminal touch-screen display with operator selectable 0~800 W output, temperature control up to 300 oC, five-layer PTFE coated microwave cavity, HPR/1000/10S Rotor, and 100-mL Teflon vessels (ten vessels). The Teflon vessels were cleaned with 2 % hydrochloric acid and washed with deionized water three times before use. Field Sampling Both dry and wet deposition samples were collected from April 2007 to March 2008 in Marina Catchment area of Singapore by using mini-vol aerosol samplers and automated wet-dry samplers. Extraction and Chemical Analysis For aerosol samples, quartz filters were cut into two equal parts. One of the two parts was extracted for water-soluble ions such as NO2-, NO3-, and NH4+. Ten milliliters of 18.2 M' ultrapure water were added to the filter samples, which were then ultrasonicated at 60 oC for 1 h. The extract was subsequently filtered through a 0.45 $m PTFE membrane filter and stored in a refrigerator at 4 oC until analysis. The other part was used to determine total nitrogen (TN) by microwave conditions optimized in this study. All the prepared samples were analyzed for nitrate concentration by ion chromatography (IC) within seven days. Results and Discussion Optimization of microwave persulfate oxidation
Figure 1. Optimization of quantity of persulfate and sodium hydroxide
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Figure 2. Optimization of microwave power and extraction time Air Mass Categorization The 96 h backward trajectories of air masses arriving at the study site during this oneyear sampling period were computed by using U.S.National Atmospheric and Oceanic Administration (NOAA) latest HYSPLIT-4 (HYbrid Single-Particle Lagrangian Integrated Trajectory) model and are categorized as follows. (1) Southeast – a category of continental air masses for Southwest monsoon season (Jun-Sept 2007). This category accounted for 81.2 % of all the air masses in the examined days during the SW monsoon season with high mixing depth and high wind speed. These air masses were partly terrestrial and partly oceanic in origin, passing through islands of southern Indonesia such as Sumba, Lombok and Sumbawa, Java Sea, mass islands of Java and southwestern Sumatra, and even on some cases crossing Kalimantan Island before arriving at Singapore. (2) Northeast – a category of continental air masses for Northeast monsoon season (Dec 2007 – Mar 2008) coming from Northeast areas with high mixing depth and high wind speed. This category accounted for 86.2 % of all the air masses investigated during the NE monsoon season. Most of air masses sampled were of oceanic character, traveling over the South China Sea; some originated from the Philippines and some were from east coast of China, Southern China such as Hainan, Canton province and Taiwan Island/strait as well. (3) Inter-monsoon seasons – this category of air masses for pre-NE (Apr-May 2007) and pre-SW (Apr-May 2008) monsoon seasons with no dominant long-range transport pathway. During the inter-monsoon period, air mass origins were scattered and from various directions; in some cases, air masses were located in low altitude and had a short pathway or with a loop trajectory with most time spending over the sampling 548
point vicinity. Low mixing depth and low wind speed were typical meteorological characteristics during both pre-monsoon seasons.More details on this study will be presented and discussed in the oral presentation. Conclusion This study optimized a simple rapid microwave assisted persulfate oxidation followed by ion chromatography for determination of total nitrogen in atmospheric samples. The optimum conditions include 0.012 M K2S2O8 and 0.024 M NaOH were used and digested at 350 W for 7 min using microwave. The method was employed for estimating the nitrogen flux in wet and dry deposition sample collected in Singapore. Both wet and dry deposition fluxes showed seasonal variations and have been found to be affected by meteorological conditions. Acknowledgements The authors, HJ and RB, gratefully acknowledge the support and contributions of this project to the Singapore-Delft Water Alliance (SDWA). The research presented in this work was carried out as part of the Singapore-Delft Water Alliance (SDWA)’s research programme (R-264-001-013-272). References 1. Galloway, J. N.; Schlesinger, W. H.; Levy, H.; Michaels, A.; Schnoor, J. L. Global Biogeochem. Cycles 1995, 9, 235-252. 2. Vitousek, P. M.; Aber, J. D.; Howarth, R. W.; Likens, G. E.; Matson, P. A.; Schindler, D. W.; Schlesinger, W. H.; Tilman, D. G. Ecol. Appl. 1997, 7, (737-750). 3. Smil, V. Global Biogeochem. Cycles 1999, 13, 647-662. 4. Rejesus, R. M.; Hornbaker, R. H. Agr. Ecosyst. Environ. 1999, 75, 41-53. 5. Cornella, S. E.; Jickellsa, T. D.; Capeb, J. N.; Rowlandc, A. P.; Duced, R. A. Atmos. Environ. 2003, 37, 2173-2191. 6. Asman, W. A. H.; Sutton, M. A.; Schjorring, J. K. New Phytol. 1998, 139, 2748. 7. Neff, J. C.; Holland, E. A.; Dentener, F. J.; McDowell, W. H.; Russell, K. M. Biogeochemistry 2002, 57/58, 99-136. 8. Seitzinger, S. P.; Sanders, R. W. Limnol. Oceanogr. 1999, 44, 721-730. 9. Martin, J. M.; Elbaz-Poulichet, F.; Gwue, C.; Loye-Pilot, M. D.; Han, G. Mar. Chem. 1989, 28, 159-182. 10. Prospero, J. M.; Savoie, D. L. Nature 1989, 339, 687-689. 11. Hinga, K. R.; Keller, A. A.; Oviatt, C. A. Ambio 1991, 20, 256-260. 12. Yang, L. Y.; Qin, B. Q.; Hu, W. P. Oceanol. Limnol. Sin. 2007, 38, 104-110. 13. Spokes, L., et al. Environ. Pollut. 2006, 140, 453-462. 14. Hicks, B. B.; Valigura, R. A.; Courtight, F. B. Estuaries 2000, 23, 854-863. 15. Hutchinson, G. E. Amer. Sci. 1973, 61, 269-279.
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16. Hecky, R. E.; Bugenyi, F. W. B.; Ochumba, P.; Talling, J. F.; Mugidde, R.; Gophen, M.; Kaufman, L. Limnol. Oceanogr. 1994, 39, 1476-1481. 17. He, J.; Balasubramanian, R. J. Atmos. Chem. 2008, 60, 205-220. 18. Sundarambal, P.; Balasubramanian, R. Water Sci. Technol. 2009, 59, 22872295. 19. Balasubramanian, R.; Victor, T.; Begum, R. J. Geophys. Res-Atmos. 1999, 104, 26881-26890. 20. Stohl, A. Atmos. Environ. 1998, 32, 947-966. 21. Stohl, A.; Seibert, P. Quart. J. Roy. Meteor. Soc. 1998, 124, 1465-1484. 22. Erel, Y.; Kalderon-Asael, B.; Dayan, U.; Sandler, A. Environ. Sci. Technol. 2007, 41, 5198-5203. 23. Dvorská, A.; Lammel, G.; Klanova, J.; Holoubek, I. Environ. Pollut. 2008, 156, 403-408.
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Respiration Rate Characteristics of Carbon Dioxide at Coastal Ecosystems in Suncheon Bay Park Sa Kim1, Dong Hwan Kang1, Byung Hyuk Kwon1, Kwang Ho Kim1, Min Seong Kim1 and Hun Sun Yu2 1 2
Pukyong National University, Busan, 608-737 Dong-eui Institute of Technology Dong-eui Analysis Center, Busan, 614-715
Principal Contact: Dong Hwan Kang, Full time researcher, Pukyong National University, 599-1 Daeyeon3-Dong Nam-Gu, Busan, 608-737, +82-51-629-6686, +8251-629-6638,
[email protected] Abstract This paper was studied CO2 respiration rate with physicochemical properties of soils at wetland, paddy field and forest in Nongju-ri, Haeryong-myeon, Suncheon city, Jeollanam-do. Soil temperature and CO2 respiration rate were measured at the field, and soil pH, moisture and soil organic carbon were analyzed in laboratory. Field monitoring was conducted at 6 points (W3, W7, W13, W17, W23, W27) for wetland, 3 points (P1, P2, P3) for paddy field and 3 points (F1, F2, F3) for forest in 10 January 2009. CO2 concentrations in chamber were measured 352 㨪 382 ppm for wetland, 364 㨪 382 ppm for paddy field and 379 㨪 390 ppm for forest, and the average values were 370 ppm, 370 ppm and 385 ppm, respectively. CO2 respiration rates of soils were measured -73㨪44 mg/m2/hr for wetland, -74㨪24 mg/m2/hr for paddy field and -55㨪 106 mg/m2/hr for forest, and the average values were -8 mg/m2/hr, -25 mg/m2/hr and 38 mg/m2/hr. CO2 was uptake from air to soil in wetland and paddy field, but it was emission from soil to air in forest. CO2 respiration rate function in uptake condition increased exponential and linear as soil temperature and soil organic carbon. But, it in emission condition decreased linear as soil temperature and soil organic carbon. CO2 respiration rate function in wetland decreased linear as soil moisture, but its in paddy and forest increased linear as soil moisture. CO2 respiration rate function in all sites increased linear as soil pH, and increasing rate at forest was highest.
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Mass Balance of Mercury in a Modern Municipal Solid Waste Landfill in China Zhonggen Li1, Xinbin Feng1*, Ping Li1, Xuewu Fu1, Shunlin Tang1, Shaofeng Wang1 and Lian Liang2 1 State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, P R China 2 Cebam Analytical, Inc., 18804 North Creek Parkway, Suite 110, Bothell, WA 98011, USA *Corresponding author: Xinbin Feng; Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, P.R. China; Tel.: +86-851-5891356; Fax: +86-8515891609; E-mail:
[email protected](X. Feng) Abstract Introduction Mercury (Hg) enters into the municipal solid waste (MSW) mainly from a variety of Hg-containing products, such as batteries, fluorescent lumps, thermometers, switches, etc. The treating of MSW by incinerators has resulted in a great deal of Hg lost into the atmosphere.1 While, due to the low cost and low maintenance, the majority of MSW (around 70%) in the world is treated by landfills.2 As a consequence, quantities of mercury are ended up in landfills. There are 88 and 390 tonnes of Hg been buried in the landfills of 15 European Union countries and USA in 1995 and 2000, respectively.3-4 In China, there are hundreds of tones Hg discarded into the MSW associated with the Hg-containing products, such as batteries5 and fluorescent lamps and thermometers.6 However, the behavior of Hg in the landfill site has rarely systematically studied as that of MSW incinerators. Once Hg enters into the landfill, it could be re-mobilized into the surrounding environment, as shown in Fig. 1. To provide a detailed insight into Hg cycling in the Chinese landfills, we carried out a series of field campaigns at a modern sanitary MSW landfill (Gaoyan landfill) in Guiyang city, the capital of Guizhou province, SW China, during the year of 2003-2006. Experimental methods The studied landfill, Gaoyan MSW sanitary landfill, started operation since 2001 and is the biggest and highest standard landfill in Guiyang. Gaoyan landfill, treats MSW on a rate of 1300 tonnes per day, is a reprehensive of modern landfills in China, i.e., it covers the MSW by daily soil coverings, collects and treats the leachate by the waste water treatment plant, and discharges the landfill gas (LFG) into the atmosphere directly by vent pipe system. 552
Hg distribution in MSW, covering soil, leachate and LFG in Gaoyan landfill was investigated, the Hg surface-air exchange was also determined, and finally, a picture of the fate of Hg in this landfill was obtained by a means of mass balance methodology.
Fig. 1. Sketch map showing the cycling of Hg in the MSW landfill site (Modified from reference 7) Results Hg in MSW and covering soil Hg content in MSW, as shown in Fig. 2A, ranged from 0.170 to 46.222 mg kg-1 (N=40), with an arithmetic mean and geometric mean of 1.870 and 0.603 mg kg-1, respectively. The distribution was highly skewed, with over half the samples having concentrations less than 0.5 mg kg-1, only three had concentrations exceeding 2.0 mg kg-1, these samples maybe contaminated by Hg-containing products. The Hg distribution pattern in MSW at Gaoyan was similar to that of a MSW landfill in Florida, USA, where the range and the geometric mean are 0.033-16.800 mg kg-1 (N=106) and 0.178 mg kg-1, respectively.8 While, Hg levels in covering soil, as shown in Fig. 2B (range: 0.130-0.215 mg kg-1; arithmetic mean: 0.175 mg kg-1; geometric mean: 0.173 mg kg-1, N=16), was more convergent, which reflect a background value for Guiyang.
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Fig. 2. Hg content in MSW (A) and covering soil (B) at Gaoyan landfill Hg speciation in LFG Three Hg species in LFG emitted from the vent pipe system were determined, i.e., total gaseous mercury (TGM), monomethylmercury (MMHg) and dimethylmercury (DMHg). The range of TGM was 2.0-1406.0 ng m-3 and the mean was 89.8 ng m-3. Large variations were observed among different pipes, which reflect the difference in Hg content in MSW. MMHg in LFG of some vent pipes varied between 0.14 and 6.37 ng m-3, with an average of 1.93 ng m-3. The percentage of MMHg to TGM ranged from 0.14 to 1.68%, with an average of 0.51%. For the same vent pipes sampled for MMHg, DMHg ranged from 2.54-19.05 ng m-3, with an average of 9.21 ng m-3. DMHg comprised 0.27 to 3.64% of TGM in the LFG, with an average of 1.79%. The global background concentrations of MMHg and DMHg in the atmosphere are generally below 10 pg m-3,9 the high concentrations of MMHg DMHg found in LFG hints that landfill is an important methylated Hg source. Hg in Leachate The distribution of Hg in the raw leachate and the treated samples at different stages of the on-site treatment plant, are shown in Fig. 3. Total Hg in the raw leachate was 79.4 ng l-1, which was at the lower end of the worldwide landfills (50-160000 ng l-1).10 Total Hg and particulate Hg declined significant along the treatment process, while the dissolved Hg remained relative constantly (Fig. 3).
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Fig. 3. Mercury concentrations in leachate along different process in the on-site waste water treatment plant Hg surface-air flux Hg surface-air flux at the non-working area was measured by a dynamic flux chamber (DFC) method, while for the working face area, where the MSW was dumped, spreaded, crushed and covered with the soil covering, a ISCST3 model based on the Gaussion plume model was applied.11 The results turned out (as see in Table 1) that the Hg surface-air flux was lowest at the soil covering area (generally less than 200 ng m-2 h-1), highest at the working face area (nearly 60000 ng m-2 h-1), showing the effective of soil covering in the reduction of mercury lost from the surface. The data also revealed that the weather conditions intensively affected Hg emissions from the landfill surface, leading to more Hg lost from the surface at warm and sunny conditions. Mass balance of Hg in Gaoyan landfill Based on the aforementioned studies, a rough picture can be achieved for Hg cycling in the landfill and its environmental lost (Table 2). There are around 172 kg Hg enters into Gaoyan landfill each year, while 3.36 kg Hg (1.96% of the total) was lost into the surrounding environment at the same time, of which, 97.83% was emitted into the atmosphere, and 2.17% was leached into the surface water. The working face area was the largest pathway for Hg lost from the landfill, accounting for 96.22% of the total loss. Table 1. Statistical summary of Hg surface-air emissions at different landfill surface
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sites Site No. F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11
Site description Soil covering area Soil covering area Soil covering area Soil covering area Soil covering area Soil covering area Sporadic uncovered MSW Sporadic uncovered MSW Sporadic uncovered MSW Sporadic uncovered MSW Working face area
Mean±Std
Weather condition
(ng m-2 h-1)
Rainy
78.8±77.9
DFC
Sunny
183.3±191.3
DFC
Sunny
133.3±65.8
DFC
Cloudy
27.8±16.5
DFC
Cold season
Sunny
29.1±17.5
DFC
Cold season
Sunny
-1.4±26.2
DFC
Cold season
Sunny
57.5±83.4
DFC
Cold season
Sunny
84.5±88.5
DFC
Sunny
664.6±1341.2
DFC
Sunny
537.7±485.1
DFC
Sunny
57651
ISCST3 Model
Season Warm season Warm season Warm season Warm season
Warm season Warm season Warm season
Method
Table 2. Annual environmental loss of Hg from different pathways in Gaoyan landfill Hg Emission pathways Soil covers Sporadic uncovered MSW Working face Vent pipes Leachate Total
Receptor
emission
Atmosphere
quantities (g yr-1) 42.03
Atmosphere
10.95
Atmosphere Atmosphere Surface water
3231.35 0.93 73.00 3358.25 556
Percentage of each pathway (%) 1.25 0.33 96.22 0.03 2.17 100.00
Conclusions Our research for the first time revealed the Hg fate in a real, large and modern landfill in China. The most Hg lose from the landfill was evaporated into atmosphere through the surface, only a minor was discharged into the leachate. The emission patterns of Hg was totally different from other heavy metals (such as Cd, Ni, Pb Cu, Zn),12 which lost mainly through the leachate, and the lost percentage (0.16%-0.99% for Cd, Ni, Pb Cu, Zn)12 was obviously lower than that of Hg (1.96%). Due to the high concentrations of methylated mercury in the LFG and their potent toxicology to the creatures, it is highly recommended that the LFG being properly treated before being discharged into the atmosphere. Although, the percentage of Hg lost into the environment each year was relatively small than that of incinerators, however, for a long run, landfill will impact the ecosystem for a longer time. So, to reduce the environmental risk of Hg in landfill, it’s better to ban the Hg-containing waste throwing into the MSW, and a recycling and management system for the waste Hgcontaining products must be set up in China. Acknowledgments This study was financially supported by the National Natural Science Foundation of China (No. 40703023, 40721002). References 1. Pirrone, N.; Keeler, G. J.; Nriagu, J. O. Atmos. Environ.1996, 30,2981-2987. 2. OECD. OECD Environment Directorate 1999-2001 Programme on Sustainable Development, Paris, France, 2001, pp. 56-83. 3. Yang, F. 4. ; Liu, J.; Wang, R. Shanghai Environ. Sci., 2003,22, 322-328. (in Chinese with abstract in English). 5. Hao, C.; Shen, Y. 6. Res. Environ. Sci., 2006, 19,18-21.(in Chinese with abstract in English). 7. Mukherjee, A. B.; Zevenhoven, R.; Brodersen, J.; 8. Hylander, L. D; Bhattacharya, P. Resour. Conserv. Recycling, 2004, 42,155-182. 9. SWANA. The SWANA (Solid Waste Association of North America) Applied Research Foundation report. Silver Spring, MD. 2004. 10.Oygard, J. K; Måge, A.; Gjengedal, E. 11.Water Res., 2004, 38, 2851-2858. 12.Earle, C. D. A.; Rhue, R. D.; Earle, J. F. K. 13.Waste Manage. Res., 1999, 17,305-312. 14.Prestbo, E. M.; Bloom, N. S.; Pontgratz, R.; Heumann, K. G. In 15.the Proceedings Fourth International Conference on Mercury as an environmental Conference on Mercury as an Environment Pollution, Hamburg. 1996. 16.Christensen, T. H.; Kjeldsen, P.; Bjerg, P. L.; Jensen, D. L.; Christensen, J. B.;
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Baun, A.; Albrechtsen, H. J.; Heron, G. 17.Appl. Geochem., 2001,16, 659-718. 18.U.S. EPA. 19.User’s guide for the industrial source complex (ISC3) dispersion models, Volume I-user instructions, Washington, DC., 1995. 20.Qu, M.; He, P.J.; Shao, L.M.; Lee, D. J. Chemosphere, 21.2008, 70, 769-777. Keywords: Mercury; Municipal solid waste; Landfill; Mass balance; Behavior
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The Seasonal Burden of Dimethyl Sulphide-Derived Aerosols in the Arctic and the Impact on Global Warming Bo Qu1*, Albert Gabric2, Patricia Matrai3 and T. Hirst4 1 Bo Qu: Lecturer, 19 Qi Xiu Road, Nantong University, Science Faculty, 226007, China . * Email:
[email protected] 2Associate Professor, School of Australian Environmental Studies, Griffith University,
Nathan, 4111, Australia 3 Principal Scientist, Bigelow laboratory for Ocean Sciences, West Boothbay Harbor, ME, USA 4 Principal Scientist, CSIRO atmospheric Research, Private Bag 1, Aspendale, 3195, Australia Global climate changes have led to remarkable environmental changes in the Arctic. On the other hand, Dimethyl sulphide (DMS) emission in Arctic Ocean plays an important role for the global warming. The ice cover as the special feature of Arctic Ocean has significant effect on regulation of the large distribution of phytoplankton production. Chlorophyll-a (CHL), as the primary production of phytoplankton, has its strong relationship with DMS derived aerosol in the ocean surface. This paper will describe the physical and phytoplankton data (based on the past 5 years SeaWiFS satellite data recorded 1998-2002) in the Barents Sea region (30-35°E and 70-80°N). The relationship between temperatures, photosynthetic available irradiance (PAR), cloud cover, ice cover and CHL were also analysed. The field data was based on the three Cruises gathered biological and physical measurement on vertical potential density, temperature, salinity, CHL as well as sulful compounds. The field data is compared with the satellite data within the study region and the good agreement was achieved before calibrating parameters of the developed DMS model using Genetic Algorithm. The significant inter-annual variation of CHL leads significant inter-annual production of DMS in this study region. The DMSPd field data is used for further DMS calibration. We finally applied the CSIRO GCM forcings to the calibrated DMS model to predict sea-to-air flux of DMS for enhanced greenhouse conditions (from 1xCO2 to 3xCO2) in the zonal 70°- 80°N global belt.
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Introduction Dimethyl sulphide (DMS) is the main sulphur released during the decay of ocean biota (Lovelock et al [1]). Aerosols formed from the conversion of DMS can exert a climate cooling effect by scattering and absorbing solar radiation and promoting the formation of cloud condensation nucleii (CCN) and hence increase the albedo of clouds, reflecting more solar radiation back to space, thus change the climate (Charlson et al [2]). The main source of DMS is the dimethyl sulphoniopropionate (DMSP) (Kiene and Bates [3]). As Arctic Ocean is mostly covered by ice, the ice algae have high DMSP content when there is a high salinity and low temperature associated Kirst et al [4]. High biomass of Phaeocystis sp. and Emiliania huxleyi, known to occur in Arctic Ocean, could lead to subsequent production of large quantities of DMS, just as icealgal communities are likely to be an important source of DMS for Arctic Ocean (Bouillon et al [5]). As most Arctic Ocean is covered by ice, the steady decreasing rate of ice cover would give rise to a stratified and nutrient-rich euphotic zone, which supports pronounced spring bloom in the area (Olli et al [6]). Ice melting phenomenon also affects the sea levels and ocean circulations, hence it has significant impact on global climate. Here we investigate the impact of simulated climate change on the DMS in the year 1998 to 2002 based on the calibrated satellite data (CHL) in the same study region and then apply the CSIRO GCM forcings to the calibrated DMS model to predict sea-to-air flux of DMS for enhanced greenhouse conditions (from 1xCO2 to 3xCO2) in the zonal 70- 80°N global belt. Model, Calibrations and Results 1 The DMS model and calibrations The DMS model was firstly introduced by Gabric et al [7] which was adapted the ecological structure of the nitrogen-based plankton community model of (Moloney et al., [8]). The model was a depth averaged model and was developed and used for subAntarctic southern ocean modelling (Gabric et al [9],[10]). The DMS model is divided to two sub-models: A PZN sub-model uses following equations:
(1) (2)
(3) 560
where P is the phytoplankton (CHL), Z is the zooplankton, N is the nitrogen (as nitrate). The surphur sub-model is described by the following equations:
(4) (5) Parameters ki (1< i 99.99%) was used as a carrier gas to obtain a certain concentration of formaldehyde. Formaldehyde was then mixed with oxygen and nitrogen gas as to simulate the gas stream. High-purity oxygen and nitrogen gases were acquired from a compressed gas cylinder without pretreatment. Four mass flow controllers were used to ensure a constant flow rate of gas stream including formaldehyde, oxygen and nitrogen in the photocatalytic reaction system. The water vapor content of gas stream was controlled by passing nitrogen and/or oxygen gas through a humidification chamber. The temperature and the relative humidity of gas stream entering the photocatalytic reactor were measured with a dew point monitor (General Eastern, Model Hygro-M2). The simulated gas stream was then flowed into the photocatalytic reactor. The concentration of formaldehyde in the effluent gas of the photocatalytic reactor was measured.
1- nitrogen cylinder, 2- oxygen cylinder, 3-dynacalibrator, 4-mass flow controller, 5-needle valve, 6-oneway valve, 7-humidifier, 8-hygrometer, 9-15-watt black lamp, 10-photocatalytic reactor, 11-temperature controller, 12-heating tape
Fig. 1 Schematics of the photocatalytic reaction system. 574
In this study, the sampling and analysis of formaldehyde followed a standard procedure promulgated in the Chinese Air Quality-Spectrophotometric Determination of Formaldehyde, Method Acetylacetone (GB/T 15516-1995). An UV-visible spectrophotometer (Shimadzu Corporation, UV 160-A) was applied to determine the concentration of formaldehyde. Results and Discussion The variation of formaldehyde reaction rate with influent formaldehyde concentration was investigated. Firstly, the black lamp was turned on while the photocatalytic reaction system became stable. Each experiment was conducted for 150 mins and the formaldehyde concentration of effluent was detected at an interval of 30 mins after the black lamp was turned on. The experimental data was obtained for the experimental tests conducting at the humidity of 853 ppmv (equivalent to 1% relative humidity) and the oxygen content of 21%. According to the detected data, the effluent formaldehyde concentration became stable in approximately 30 mins after the black lamp was turned on. The variation of the formaldehyde reaction rate with influent formaldehyde concentration was shown in Fig. 2. The results showed that the reaction rate of formaldehyde increased with influent formaldehyde concentration for all experimental temperatures.
Fig. 2 Variation of formaldehyde reaction rate with influent formaldehyde concentration. Several kinetic models for exploring the mechanism of photocatalytic reaction of VOCs, including Langmuir-Hinshelwood model and power-rate law model have been widely applied in previous studies8-10. The following equation is the LangmuirHinshelwood model describing the photocatalytic reaction rate of formaldehyde. (1) where r is the reaction rate, mg/g-hr; k is the reaction rate constant, mg/g-hr; K is the 575
Langmiur adsorption equilibrium constant, mg-1; C is the concentration of gas-phase formaldehyde, mg/m3. Eq. (1) can be further rearranged to a differential equation and thus simplified to a linear function: (2) The plots of 1/r versus 1/C can be used to determine k and K from the slope and the intercept of each linear fitting line as shown in Fig. 3.
Fig. 3 Determination of k and K by linear fitting of formaldehyde kinetic data for Langmuir-Hinshelwood model. As illustrated in Fig. 3, the reaction rate for influent formaldehyde concentration of 5~20 mg/m3 can be accounted for by using the rate expression of L-H model. But the value of k for the Langmuir-Hinshelwood model with the influent formaldehyde concentration of 0.6~1.2 mg/m3 was negative, which disagreed with the theory. Thus, the Langumir-Hinshelwood model might not be suitable for simulating the photocatalytic reaction rate as the influent formaldehyde concentration ranged from 0.6 to 1.2 mg/m3. The dependence of formaldehyde reaction rate upon influent formaldehyde concentration was also validated by the power-rate law equation: (3) where r is the reaction rate, mg/g-hr; k and n are constants; C is the concentration of gas-phase formaldehyde, mg/m3. Eq. (3) can be further rearranged to a differential equation and thus simplified to a linear function: (4) The value of k and n at different reaction temperature can be determined by linearly plotting of ln(r) versus ln(C). The variation of ln(r) with ln(C) at different temperature is shown in Fig. 4. 576
Fig. 4 Linear fitting of formaldehyde kinetic data for the power-rate law model. Fig. 4 showed that the Power-rate model was feasible for describing the photocatalytic oxidation for influent formaldehyde concentration of 0.6~1.2 mg/m3. The constant k for the L-H model and the Power-rate law model decreased with the reaction temperature. The correlation between the constant k for these two models and the reaction temperature agrees with the Arrhenius equation. Conclusions This investigation revealed that the decomposition of formaldehyde was apparently affected by influent formaldehyde concentration. The reaction rate for influent formaldehyde concentration of 5~20 mg/m3 can be accounted for by using the rate expression of L-H model. However, Power-rate model was feasible for describing the photocatalytic oxidation for influent formaldehyde concentration of 0.6~1.2 mg/m3. These results illustrated that the concentration variation process for high and low influent formaldehyde concentration was different. The conclusion obtained from high influent formaldehyde photodegradation didn’t suit low influent formaldehyde photodegradation. Therefore, in order to enhance the removal efficiency of indoor formaldehyde, the theoretical research of formaldehyde photodegradation should be taken with indoor level formaldehyde concentration. References 1. Wang K, Li Y, Zhao Q. Indoor air formaldehyde measurement analysis and its prediction model. China Environmental Science 2004; 24(6): 658-661. 2. Li S, Zhang X, Zheng W. An investigation of indoor air pollution in newly decorated rooms in Luohe. Journal of Environment & Health 2006; 23(1):49-50. Conolly R B, Kimbell J S, Janszen D B. Dose response for formaldehyde-induced cytotoxicity in the human respiratory tract. Regulatory Toxicology and Pharmacology 2002; 35: 32-43. Collins J J, Lineker G A. A review and meta-analysis of formaldehyde exposure and 577
leukemia. Regulatory Toxicology and Pharmacology 2004; 40: 81-91. Qi H, Sun D, Chi G. Affecting factors and kinetics of formaldehyde degradation by photocatalytic oxidation process. Journal of Harbin Institute of Technology 2006; 38(7): 1051-1054. Huang W, Sun Z, Wu J. Photocatalytic degradation of formalehyde by nanometer TiO2. Chinese Journal of Rare Metals 2005; 29(1): 34-38. Peng J, Wang S. Performance and characterization of supported metal catalysts for complete oxidation of formaldehyde at low temperatures. Applied Catalysis B: Environmental 2007; 73: 282-291. Zhao J, Yang X. Photocatalytic oxidation for indoor air purification: a literature review. Building and Environment 2003; 38 (5) : 645-654. Kim S B, Hong S C. Kinetic study for photocatalytic degradation of volatile organic compounds in air using thin film TiO2 photocatalyst. Applied Catalysis B : Environmental 2002; 35(2): 305-315. Lou J C, Lee S S. Destruction of trichloromethane with catalytic oxidation. Applied Catalysis B: Environmental 1997; 12: 111-123.
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Analysis of VOCs and Aldehydes in the Classrooms of Elementary Schools in Ulsan, Korea Byeong-Kyu Lee1, Do-Soon Kim2, Kyeong-Hwa Lee2, Kwang-Won Lee2, Dae-Shin Kang2, Bon-Gon Koo2, Se-Mok Kwon2, Soo-Geun Jung2, Yoo-Sik Hham2, Bo-Soon Seo2, Bong-Kwan Yu2 1Department
of Civil and Environmental Engineering, University of Ulsan, Mugeo-dong, Nam-gu, Ulsan, Korea 2Ulsan Institute of Health and Environmental Research, Ulsan, Korea Principal Contact: Byeong-Kyu Lee, Professor, Department of Civil and Environmental Engineering, University of Ulsan, Mugeo-dong, Nam-gu, Ulsan, Korea Tel: 82-52-259-2864, Fax: 82-52-259-2629, E-mail:
[email protected], Abstract This study analyzed concentration distribution of volatile organic compounds (VOCs) and aldehydes in the elementary school classrooms with different environment in Ulsan, a typical industrial city, Korea. Indoor air samples for 40 mins of a class hour were collected from the selected 28 classrooms from 7 schools during 3 seasons including spring, summer, and early winter. This study compared the classroom concentrations of VOCs and aldehydes from 7 schools having different construction years of 2 schools newly built within a year, 3 schools with medium ages from 3 to 5 years, and 2 schools with old ages above 10 years. The indoor concentrations in the classrooms, located in the 3rd – 5th floors, for higher school year students of the 5th or 6th grades were compared to those, located in the 1st – 2nd floors, for lower school year students of the 1st or 2nd grades. The mean values of formaldehyde in the new built, medium and old aged classrooms were 65.3, 40.7, and 31.0 &g/m3, respectively. Also, the mean values of benzene and toluene in the new built, medium and old aged classrooms were 2.8 and 182.6 &g/m3, 3.7 and 65.0 &g/m3, and 3.7 and 67.0 &g/m3, respectively. Concentrations of formaldehyde and the sum of the identified VOCs in the elementary school classrooms during the summer period were much higher than those during the fall or the winter periods. The concentrations in the classrooms for the higher school year students were higher than those for the lower ones. INTRODUCTION School classrooms are also important indoor facilities that a lot of young students can be exposed to high concentrations of VOCs, HCHO, and particular matter.1,2 In Korea, a lot of students ranging from 32 - 40 students are staying their classrooms during their school hours. Most of the elementary students spend their time for 5 to 7 579
hours, depending upon their school year, in a normal school day. Thus they spend approximately 25% of their daily life cycle inside their classrooms during school periods in Korea. A large amount of VOCs and HCHO are generated through their class activities and from indoor materials such as curtains, paint coating, and exhibition inside their classrooms. However, a lot of classrooms in elementary schools are closed during school hours resulting in poor ventilation and deteriorating indoor air quality. According to Smedje and Norbäck,3 the students attending the schools that have higher concentrations of formaldehyde and total molds in the classroom air showed the more common incidence of asthma diagnosis. However, most of the students and even teachers do not recognize the high exposure to VOCs, HCHO, and PM inside classrooms through their school life. This study tried to analyze the VOCs and HCHO concentrations inside the classrooms that have different indoor environment such as built age, height from ground, and school year in typical urban elementary schools of Korea. METHODS 7 typical elementary schools, located in urban areas of Ulsan metropolitan city in Korea, were selected for this study. The indoor air samples were obtained from the center of total 28 classrooms during class hours of 7 selected schools with 3 categories of construction ages (10 years). The indoor air concentrations in 2 classrooms, located in the 3rd – 5th floors, for higher school year students of the 5th or 6th grades were compared to those, located in the 1st – 2nd floors, for lower school year students of the 1st or 2nd grades. 4 indoor air samples in 4 classrooms for one season were collected from each selected school. The seasonal indoor air samplings were conducted for three seasons including spring (April), summer (July), and winter (November – December) for a seasonal comparison. Air sampling pump and adsorption cartridges including 2,4-dinitophenyl hydrazine were used for air sampling of aldehydes and ketones analysis. Silcosteel coated canisters were used for air sampling of VOCs. A total of 92 samples for aldehydes and ketones analysis and a total of 92 samples for VOCs analysis were obtained from the classrooms air sampling during 3 seasons. Alldehydes and ketones were identified using a high performance liquid chromatography (HPLC, Agilent 1100Series) analysis with a column (25 cm x 4.6 mm, 5 &m, SUPELCOSILTM LC-18) and a UV-VIS absorbance detector (at 360 nm). 5 VOCs were analyzed using a gas chromatography with a mass detector (GCMS, Varian Saturn 2000) with a cryogenic and a temperature programming methods. The used GC column was C-SIL 5CB (60 m x 0.32 mm x 1 &m). RESULTS AND DISCUSSION Concentration Ranges of VOCs, Aldehydes and Ketones The highest mean concentration out of aldehydes and ketones during the whole study periods was identified in formaldehyde of 44.9 &g/m3, followed by acetone of 39.4 &g/ 580
m3. The identified mean concentration of formaldehyde in Ulsan was lower than that (52.6 &g/m3) from the classrooms of elementary schools in Daegu metropolitan city.1 The formaldehyde concentrations identified in the classrooms in France and Hong Kong ranged from 6.1 to 126.6 &g/m3 and from 18.3 to 43.7 &g/m3, respectively.4,5 The major component of the identified VOCs was toluene which ranged from 7.4 to 357.2 &g/m3 with a mean value of 93.8 &g/m3, which was much lower than the World Health Organization (WHO) guideline of 260 &g/m3. The mean and maximum benzene concentrations in the investigated classrooms were 3.5 &g/m3 (1.1 ppb) and 14.3 &g/m3 (4.5 ppb), respectively, which were much lower the temporary evacuation level (100 ppb) specified in the Indoor Air Quality Guidelines for Pennsylvania School 6. Construction Age Effects As the construction age of schools increases, the concentrations of the aldehydes and ketones in the classrooms decrease. In particular, their concentrations in the classrooms from the schools newly built within a year were much higher than those with 3-5 years and over 10 years. The mean formaldehyde levels for the new built, 3-5 years, and over 10 years classrooms were 65.3, 40.7, and 31.0 &g/m3, respectively. The time spent for 3-5 years and over 10 years after the schools were newly constructed resulted in decreasing formaldehyde levels of 53 and 24%, respectively, as compared the new built one. Even though the decrease trend of acetaldehyde levels as a function of construction age was similar to that of formaldehyde, the decrease rate after 3-5 years was lower than that of formaldehyde. This concentration decrease of the aldehydes was consistent with the results of other studies (5). As the construction age of schools increases, in general, the concentrations of the VOCs in the classrooms decrease like those of the aldehydes and ketones. In the classrooms the time spent for 3-5 years and over 10 years after the schools were newly constructed, however, the average benzene concentrations seem to increase as compared the new built one. Also, their standard deviations also showed a little higher than that in the new built classrooms. This fact is maybe because of increased classroom activities, according to the new directions of the ministry of education to improve education quality, which use a lot of new products or education tools that may contain benzene. Seasonal Concentrations Table 1 shows seasonal mean concentrations of the VOCs, aldehydes and ketones in the investigated school classrooms. The highest concentrations of aldehydes and ketones were observed in summer sampling periods followed by spring and fall, in turn. The increased summer concentrations would be due to the increased emissions from interior or coating materials and school supplies by the increased air temperature. Also, the ambient concentrations of aldehydes and ketones would be increased in summer because of the increased activity of photo-oxidation of VOCs under increased 581
radiation intensity and duration time of sunlight during summer periods. Table 1. Seasonal concentrations of the VOCs, aldehydes and ketones in the investigated school classrooms (unit: &g/m3, n=84)
Compounds Formaldehyde Acetaldehyde Acetone Acrolein Propionaldehyde Methyl Ethyl Ketone Benzene Toluene Ehtyl Benzene m,p-Xylene Styrene
Spring mean±SD 37.9±16.5 7.4±2.2 39.5±22.2 0.0±0.0 1.8±0.8 16.9±23.3 2.5±1.0 88.5±79.7 7.5±47 13.6±9.6 4.0±5.3
Summer mean±SD 79.5±59.3 9.4±9.9 44.1±52.7 0.0±0.0 1.9±2.4 13.2±15.3 1.2±0.7 115.0±124.5 16.6±20.2 8.9±10.1 10.4±164
Winter mean±SD 17.4±4.0 4.8±1.4 34.6±29.0 0.0±0.0 0.2±0.5 5.6±2.9 6.8±3.5 76.6±54.4 19.5±12.3 24.1±13.1 3.9±2.9
Thus the frequent air exchange or increased infiltration of the aldehydes and ketones from the ambient air into the classrooms could lead to increase in their classroom levels during the summer. The seasonal levels of toluene and styrene in the classrooms were a similar to the seasonal trends of the aldehydes and ketones. However, it is not easy to find a change trend in the seasonal concentrations of other investigated VOCs in the classrooms. The highest mean concentrations of benzene, ethyl benzene, and m,p-xylenes was identified in the winter sampling periods. CONCLUSIONS The highest mean concentrations of formaldehyde and benzene were 44.9 and 3.5 &g/ m3, respectively, in the investigated classrooms of elementary schools in Ulsan, Korea. As the construction age of schools increases, the concentrations of the aldehydes and ketones in the classrooms decrease. The highest concentrations of aldehydes, ketones, toluene and styrene were observed in summer sampling periods followed by spring and fall, in turn. The summer value of the TVOCs was the highest concentration followed by the winter one. The concentrations of the aldehyde and ketone group and the VOC group (ehtyl benzene, m,p-xylenes, and styrene) in the high grade classrooms were 1.2 ~ 1.5 times and 1.4 ~ 3.4 times, respectively, as high as those in the low grade ones. REFERENCES
1. Whang, Y.J.; Park, H.S.; Jang, S.I., No, K.C., Shon, T.J., Han, T.U., Bae, K.S.,
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Choi, I.J. Characteristics evaluation of the indoor air concentrations of carbonyls in the classrooms of new built schools. J. KOSAE. 2006, 22, 831841. 2. Son, J.Y., Noh, Y.M., Son, B.S., Yang, W.H. The Assessment of Survey of the Indoor Air Quality at Schools in Korea . Proceeding of the 39th Meeting of KOSAE, 2005. 3. Smedje G., Norbäck, D. Incidence of asthma diagnosis and self-reported allergy in relation to the school environment- a four-year follow-up study in schoolchildren. Inter. J. Tuberculosis and Lung Disease. 2001, 5(11), 1059-1066. 4. Meinin, G.R., Kouniali, A., Mandin, C., Cicolella, A. Risk assessment of sensory irritants in indoor air-A case study in a French school. Environ. Inter. 2003, 28, 223-557. 5. Lee, S.C., Guo, H., Li, W.M., Chan, L.Y. Intercomparison of air pollutant concentrations in different indoor environments in Hong Kong. Atmos. Environ. 2002, 36, 1929-1940. Pennsylvania Department of Health. Indoor Air Quality Guidelines for Pennsylvania School. 2002, 1-17. http:// www.health.state.pa.us/pdf/hpa/epi/revised_indoorair.pdf.
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Status and Source Identification of VOCs in Hospital Waiting Areas Na Luo1, Xiaoyun Liu1, Peng Xie1, Chaowen Luo1, Zhaorong Liu1 1
College of Environmental Sciences and Engineering, Peking University, No.5 Yiheyuan Road Haidian District, Beijing, 100871 *
Corresponding author. E-mail:
[email protected]
Introduction Volatile organic compounds (VOCs) may result in adverse health effects and are regarded as important indoor air pollutants. Although there are many reports about indoor VOCs, little is associated with VOCs pollution in the hospitals, especially waiting areas. Lü et al. reported the BTEX pollution status of hospital in Guangzhou. 1 They focused on the clinic, ward, emergency room and injection room and the mean concentration of BTEX varied from 10.61 to 253.35$g/m3. Takigawa et al. investigated a newly constructed hospital of Japan so as to demonstrate possible relationships between environment, personal and occupational factors and 39 VOCs were identified and qualified.2 The sources of indoor VOCs are quite numerous, including construction materials, furnishings, paints, air freshers, cleaning products, combustion by-products, adhesives, solvents and potable water.3 Certainly, outdoor air is also an important source. Another study pointed out that presence of an attached garage, residence type, recent renovation, and residence age and three occupant activities (indoor smoking, door opening, and window opening) and ventilation will affect indoor VOC levels.4 1. Methods Samples were collected from three hospitals (H1(waiting areas A,B,C), H2(waiting areas D,E,F) and H3(waiting areas G,H,I)) in Beijing.Air samples were analyzed by gas chromatography mass spectrometry (7890AGC-5973CMS).The analytical columns included a DB-624 capillary column (60m×0.32mm×1.8&m) and a PLOT capillary column (30m×0.32mm×3.0&m). The oven temperature program was initially at 30° for 7min,which then increased at a rate of 5 min-1 to 120° held for 5min,and increased at a rated of 6 min-1 up to 180° which temperature was maintained for 7min. Over 100 VOC species were identified by the National Institute of Standard and Technology (NIST) mass spectra library. 2. Results and Discussion 2.1VOCs concentration in indoor and outdoor waiting areas The rane of mean concentration of VOCs was 123.64-713.22 $g/m3 for waiting areas 584
as illustrated in Fig.1. Aromatics, alkanes and alkenes were the main components of both indoor and outdoor air, accounting for 61~98% of the total contents.H2 and H3 displayed higher percentage of aromatics than H1. Toluene, xylenes, ethylene and benzene were the most abundant aromatics. E and F waiting areas had higher levels of ethylbenzene, xylenes and decane than other waiting ares. The mean concentration of xylenes reached up to 218.87$g/m3 in F waiting area, exceeding the guideline (200$g/ m3) of indoor air standard of China (2003). Xylenes were commonly used in pathology department.Therefore, it was a possible reason why higher levels of xylenes were obtained in F, a waiting area for pathology department. Similar to previous studies in other microenvironments, 1,2,3,5,6 toluene was the most prevalent VOCs in our study with the mean concentration of 55.60$g/m3. In addition, toluene concentration in waiting area G and H was close to the guideline (200$g/m3) during sampling. Long chain alkanes with high concentration were recorded in F waiting areas when the workers were painting. Meanwhile, VOC concentration was nearly 2~5 times higher than that of non-painting days.
Fig 1.VOC concentration in indoor and outdoor air.
Fig 2.VOC composition in waiting areas
2.2 Correlation between indoor and outdoor waiting areas The I/O values of selected VOCs in nine waiting areas and correlation between indoor and outdoor air were listed in table 1.High correlations between indoor and outdoor air for benzene, n-hexane and toluene was found in each site except B and C. For B and C waiting areas, I/O values exceeded unit greatly and low correlation between inoor and outdoor air concentration was found for most VOCs. The I/O values %1 for n-hexane, benzene, and toluene were observed in D waiting areas, while a significant correlation existed between indoor and outdoor air. In this area, VOCs like C9~C11 alkanes, m/p-xylene and tetrachloroethylene were about 3~4 times the concentration of outdoor air with a weak correlation. It demonstrated that 585
indoor sources dominated, while weak outdoor sources also existed. The I/O values of C6~C9 alkanes, toluene and cyclohexane in E and F waiting areas and decane, benzene and tetrachlorethylene in F were low and a high correlation existed between indoor and outdoor air.This revealed that outdoor sources determined the indoor air for these compounds. The levels of ethylene and xylenes were found to be higher than outdoor air with the I/O over 7 in F and negative correlations were obtained. Thus, the indoor sources dominated for ethylbenzene and xylenes in F. Long-chain alkanes in G and H showed similar characteristics. The indoor concentrations were much higher than that of outdoors.The relationships for C6~C8 alkanes and benzene were relatively strong between indoor and outdoor air.But weak correlations were obtained for C8~C10 alkanes and BTEX except for benzene.Similar sources possibly existed for these compounds. The concentrations of C6~C8 alkanes in I waiting area were close to outdoor concentrion with relatively high correlations. However, the higher levels of C9~C11 alkanes, toluene and halohydrocarbons in indoor air were detected with low correlations. Thus, outdoor sourcers determined the indoor air for C6~C8 alkanes, while C9~C11 alkanes, toluene and halohydrocarbons were due to indoor sources. Table 1. I/O values and correlation of selected VOCs between indoor and outdoor air ޓ
A
B
n-Hexane Heptane n-Octane n-Nonane Decane Undecane Benzene Toluene Ethylbenzene o-Xylene m/p-Xylene Dichloromethane Chloroform Trichloroethylene Tetrachloroethylene
1.07
3.31
1.24
4.27
4.58
1.25
1.26
4.54
2.50
1.10
3.37
1.52
C D I/O concentration ratios 2.28 0.95
E
F
G
H
I
1.51
1.04
9.60
10.57
2.18
1.44
1.26
9.28
11.07
1.83
1.68
1.86
1.74
8.72
9.46
1.85
1.56
3.66
4.74
1.95
2.72
2.11
3.58
4.08
2.32
4.30
5.03
1.69
1.63
1.33
4.52
2.40
5.78
6.57
4.85
5.92
3.78
1.95
1.44
5.45
1.82
2.70
4.47
0.98
2.21
1.00
1.57
1.33
1.28
1.34
4.27
2.90
1.04
1.24
1.14
12.52
12.83
3.04
1.19
4.23
1.91
2.73
9.02
11.97
4.89
4.27
3.02
1.22
3.88
2.51
4.01
7.81
24.88
2.52
2.15
2.15
1.19
4.26
2.48
4.13
7.04
25.46
0.16
0.15
2.05
2.03
3.65
2.28
1.23
5.70
2.04
2.51
12.17
4.20
5.82
3.92
4.50
1.61
2.13
2.81
0.97
0.88
3.43
1.55
7.25
3.08
3.01
8.91
3.74
16.01
16.22
6.92
1.08
2.36
1.17
1.36
2.62
1.06
0.35
0.19
5.24
ޓ
ޓ
ޓ
ޓ
ޓ
ޓ
ޓ
ޓ
ޓ
ޓ
586
Indoor and outdoor selected VOC correlation
coefficents
n-Hexane Heptane n-Octane n-Nonane Decane Undecane Benzene Toluene Ethylbenzene o-Xylene m/p-Xylene Dichloromethane Chloroform Trichloroethylene Tetrachloroethylene *P
0.97**
0.06
0.16
0.98**
0.60
0.71
0.64
0.64
0.74
0.85*
-0.06
-0.07
0.72
0.64
0.62
0.77
0.77
0.80
0.83*
-0.47
-0.01
0.93**
0.90*
0.55
0.63
0.62
0.67
0.85*
-0.15
-0.13
0.07
0.41
0.71
0.23
0.30
0.20
0.82*
0.12
-0.17
0.21
-0.24
0.52
0.27
0.55
-0.20
0.24
-0.33
-0.28
0.10
0.92*
0.64
0.25
0.71
-0.36
0.25
0.66
0.88*
0.89*
0.88*
0.55
0.14
-0.26
0.90*
0.96**
0.07
-0.20
0.82*
0.95*
0.74
0.59
0.58
0.64
0.99**
0.04
-0.24
0.19
0.96*
-0.38
0.25
0.06
0.70
0.99**
-0.17
-0.29
0.63
0.51
-0.23
0.51
0.44
0.64
0.79
-0.25
-0.30
0.25
-0.57
-0.17
0.15
0.11
0.67
0.45
0.20
-0.27
0.33
0.07
0.08
0.84*
0.54
0.49
0.89*
0.64
0.56
-0.08
0.25
-0.45
-0.42
-0.59
0.84*
0.98*
0.15
-0.53
0.48
0.37
0.60
0.49
0.45
0.59
0.92**
0.17
-0.12
0.47
0.15
0.73
-0.20
0.41
0.86*
㧨 0.05;** P 㧨 0.01.
2.3Principal component analysis (PCA) Principal component analysis (PCA) was performed separately on indoor and outdoor concentration of waiting areas to identify possible VOC sources. Table 2 and table 3 presented the factors identified by PCA in outdoor and indoor samples. For outdoor samples, three factors were extracted by PCA and accounted for 62.03%, 17.70% and 10.23% of the variace, respectively. BTEX, and aliphatic hydrocarbons like n-hexane, heptane and n-octane were the typical pollutants emitted from gasoline. They were loaded on F1out.Thus, F1out was estimated to associate with gasoline emission. F2out had a strong correlation with C9~C11 alkanes which were generally discharged by diesel in ambient air.F3out was comparatively weakly correlated with C6~C8 alkanes, chloroform and benzene. C6~C8 alkanes were correlated with F1out and F3out. Chloroform seemed to determine by three independent factors. These compounds as common solvents were widely used for industry.Thus, F3out probably standed for industrial sources. For the indoor samples, the PCA identified four factors,which explained 85.16% of the total variance.F1in explained about 36.24%, while F2in,F3in and F4in explained 21.86%, 18.01% and 9.05% of the variance, respectively.F1in was strongly correlated with C6~C9 alkanes, trichloroethylene and toluene.These compounds were identified from construction materials and furnishings, such as wallpaper,insulation foam,chipboard and paint.3,7 Decane, undecane and tetrachloroethylene had the highest loadings for F2in in similar manner to factor 2 in the outdoor samples.F3in was correlated most strongly with ethylbenzene and xylenes.Ethylbenzene and xylenes 587
were traditionally associated with cleaning products in indoor air.6 Different from other microenvironments, it’s very frequent to clean and disinfect skin, instruments, matierials, ground and surfaces in hospital. F4in was composed of benzene and chloroform with high factor loadings and C6~C8 with weak correlations, similar to factor 3 identified in outdoor samples. Meanwhile, benzene and cholorform were shared between F2in and F4in.
3. Conclusions This study gave a clear picture of the characteristics of VOCs in different waiting areas of hospitals. The average VOC concentration was 123.64~713.22 $g/m³. Aromatics, alkanes, alkenes contributed to VOCs greatly, accounting for 61~98% of the total contents.Toluene, xylenes, ethylene and benzene were the most abundant aromatics with indoor concentrations significantly higher than that of outdoors except benzene. High levels of toluene and xylenes were found exceeding the indoor air standard of China (2003) in some waiting areas. The I/O values of most VOCs exceeded 1, indicating that indoor sources existed. Variability in indoor waiting areas was dominated by compounds associated with construction materials and furnishings followed by diesel emission, cleaning products and industrial emission. Reference Huixiong Lü;Sheng Wen;Yanli Feng;Xinming Wang;Xinhui Bi;Guoying Sheng;Jiamo Fu, Science of the Total Environment.2006, 368,574–584. • Tomoko, T.; Tokushi, H.; Ohashi, Y. et al., Environmental Toxicology. 2004, 19, 4, 280-290. • Jones, A. P. Atmospheric Environment. 1999, 33, 4535-4564.
•
588
• • • •
Chunrong Jia; Stuart Batterman; Christopher Godwin, Atmospheric Environment.2008, 42, 2101-2116. Chan, D.W. T; Tam, C. S.Y; Jones, A. P., Indoor Built Environ. 2007, 16, 4,376382. Guo, H; Shuncheng, L.; W.M.Li; Cao, J.J., Atmospheric Environment. 2003, 37, 73-82. Burney,P.M.;Cox,C.;Fernandes,E.O.;Fanger,P.O.;Groes,L.;Clausen,G.;Roulet,C.A.;Valbjorn,O.,Final Report,Contract JOU2-CT92-002,1995.
Keywords: Indoor air quality; Volatile organic compounds (VOCs); Hospital waiting area; Sources.
589
Determination of Biogenic Volatile Organic Compounds (BVOCs) from Cleaning Products via SPME/GC-FID Analysis Yu Huang1, K. F. Ho1, S. C. Lee1,* Department of Civil and Structural Engineering, Research Center for Environmental Technology and Management, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China Principle Contact: Prof. S.C.Lee, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China E-mail:
[email protected] Phone:+852-2766 6011 Fax:+852-2334 6389 Abstract The indoor use of cleaning products, such as floor cleaners and detergents, can yield high levels of biogenic volatile organic compounds (BVOCs) which can react with ozone to form secondary organic aerosols (SOA). In this study, a solid phase microextraction (SPME) coupled to Gas Chromatograph with Flame Ionization Detector (GC-FID) method was developed for the determination of BVOCs in cleaning products commonly used in Hong Kong. Five common BVOC species have been tested, including 8-pinene, 6-pinene, camphene, 3-carene and d-limonene. 7 floor cleaners and 5 detergents were selected in this study based on production quantity of the materials and extent of use at home. For the seven categories of floor cleaners, dlimonene was detected in all the samples with the concentrations varying from 2.55&g/ mL to 70.96 &g/mL. 8-pinene and 3-carene were detected in two categories of the tested floor cleaners as well. However, only d-limonene was detected in the five categories of detergents with the concentrations varying from 24.07&g/mL to 740.58&g/mL. The findings obtained in this study will significantly enhance our understanding on the levels and sources of VOCs which can contribute to the formations of SOA in indoor environment. The experimental results can also assist home residents to improve IAQ by means of selecting appropriate clean household products, building materials, and proper life styles.
590
Modeling of Indoor Air Quality in Pardisan Biodiversity and Persian Carpet Museums in Iran Majid Shafie-Pour1, Khosro Ashrafi2, Azadeh Tavakoli3 1, 2 Assistant professor, Faculty of Environment, University of Tehran 3 Ph.D student, Faculty of Environment, University of Tehran, Principal Contact: Azadeh Tavakoli, Ph.D student, Faculty of Environment, University of Tehran, +989132076023,
[email protected] Abstract In many peoples’ minds, air pollution is associated with the contamination of urban air from automobile exhausts and industrial emissions. Growing number of scientific evidence has indicated that in industrialized societies, air within buildings can be more seriously polluted than the outdoor air. In the last couple of decades, with the widespread availability of personal computers, mathematical modelling has also begun to be used to understand air pollution behaviour and effects on cultural heritages and personal collections kept in museums. The conservation of invaluable items such as cultural heritages exhibited inside museums is influenced considerably by the indoor environment, particularly indoor air quality with emphasis on gaseous and particulate matters. These air pollutants play an important role in the deterioration of these items. Therefore, this paper foresees an examining of the air quality in museums using mathematical modelling techniques for Indoor Air Quality, IAQ. Two case-studies are carried out in Pardisan Biodiversity Museum and Persian Carpet Museum in Tehran-Iran. These museums are situated in the central part of the city and adjacent to crowded streets and highways. The major driving pollutants such as NO, NO2 and SO2 in and outside of the museums have been measured, analysed and the results have been used for modelling purposes. This research focuses on examining a masterwork IAQ model namely IMPACT to compare its ability in estimating concentration of pollutants and the amount of error with real measured concentrations. The results confirm that there is good agreement between predictions made by IMPACT model with real concentrations. This agreement is in the range of 77% to 94%. This paper ends with some recommendations for improving museum’s IAQ management. Keywords: Air Pollution, Indoor, Modeling, IMPACT, Museum.
591
Carbonaceous Aerosol During Summer 2009 in Outdoor and Indoor Environments of Nanchang City, China Huang Hong1,2, Cao Junji2, Zou Changwei1 1. School of Environmental and Chemical Engineering, Nanchang University, Nanchang, China 330031; 2. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China 710075; Principal Contact: Huang Hong, Associate Professor, School of Environmental and Chemical Engineering, Nanchang University, No. 999, Xuefu Avenue, Honggutan, Nanchang, China 330031, 86-13755697035, 86-791-3969594,
[email protected] A study of carbonaceous aerosol was initiated in Nanchang, a city in eastern China for the first time. Daily and semidiurnal (daytime and nighttime) PM2.5 were collected at an outdoor site and three different indoor environments (common office, special printing & copying office and student dormitory) in campus of Nanchang university during summer (from 5th through 20th June) 2009. Daily PM10 samples were collected only at outdoor site corresponding to outdoor PM2.5, simultaneously. Loaded PM2.5 and PM10 samples were analyzed for organic carbon and elemental carbon by thermal/ optical reflectance following IMPROVE-A protocol. PM2.5 accounted for about 80% of PM10, and OC and EC accounted for 28.1% and 5.8% of PM2.5, respectively. Average concentrations of PM2.5 (87.7&g/m3), PM10 (108.1&g/m3) and OC (25.7&g/ m3) and EC (5.2&g/m3) in PM2.5 were orders of magnitude higher than concentrations measured at locations representative of USA and Europe. Except EC, PM2.5, PM10 and OC were even higher than urban aerosols in many Chinese cities including Beijing, Shanghai and Guangzhou, indicating serious and complicated organic aerosol pollution in Nanchang. OC/EC ratios were used to quantify secondary organic aerosol. The difference of carbonaceous between daytime and nighttime was helpful for interpreting secondary formation mechanism. Factor analysis of eight carbon species reflected sources of carbonaceous in Nanchang ambient environment mainly from industry emission, biomass burning, motor vehicle exhaust, transportation and transformation. By simplified model, indoor sources contributing little while outdoor sources contributing the most to indoor carbonaceous in common office and student dormitory, with special printing & copying office opposite result. PM2.5 and OC/EC concentrations in special printing & copying office were higher than that in common office and even outdoor environment, indicating printer and copycat as one of main indoor emission sources truly resulting in adverse indoor air pollution and health hazard.
592
Key words: PM2.5; PM10; Organic carbon (OC); Elemental carbon (EC); Source; indoor air;
593
Assessment of Indoor Air Quality in Schools in Mumbai, India Srinidhi Balasubramanian1 and Rashmi S. Patil2 1 Graduate 2
Scholar, Indian Institute of Technology Bombay, Powai, Mumbai 400076 Professor, Indian Institute of Technology Bombay, Powai, Mumbai 400076
Principal Contact: Srinidhi Balasubramanian, Graduate Scholar, Center for Environmental Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, +91-98676-27232,
[email protected] Abstract Poor air quality in school environments can have severe impacts on health and performance of children. Immature immunological development and large volume of air inspired by children make them more vulnerable to respiratory disorders as compared to adults. Schools in India are characterised by high occupant densities, poor ventilation and proximity to heavy-density traffic roads, which is responsible for poor air indoor quality (IAQ). No guideline is available and literature is limited for the status of IAQ in school microenvironments in India. The primary objective of this study is to assess IAQ in schools by monitoring pollutant levels and various comfort parameters of temperature, relative humidity and air change rates. Three types of schools were chosen to represent the existing wide diversity in terms of governing body, infrastructure, ventilation systems and economic levels. Parallel assessment of comfort parameters and particulate matter was done in four different microenvironments of classrooms, corridors, restrooms and outdoor under occupancy and non-occupancy conditions. Wide variations in temperature, RH and levels of particulate matter were observed in these schools. Preliminary studies have identified large occupant densities and poor infrastructure to be prime causes of poor air quality in schools. Importance of good housekeeping practices has also been highlighted. Personal exposure measurements indicate higher exposure levels (662 $g/ m3) in naturally ventilated schools as compared to mechanically ventilated schools (31.34 $g/m3). Greater proportion of students attend government schools with poorest infrastructure and they are the worst affected. This study indicates that simple and cost-effective measures of adequate ventilation and proper housekeeping can mitigate poor indoor air quality. Further studies are being done to develop mitigation measures and toolkit strategies for maintenance of good IAQ in schools.
594
Existence and Source Analysis of Volatile Aromatic Pollutants in Hospital Waiting rooms Liu Xiao-yun1, Luo Na1, Xie Peng1, Luo Chao-wen1, Liu Zhao-rong1* 1College
of Environmental Sciences and Engineering, Peking University, No.5 Yiheyuan Road Haidian District, Beijing, 100871 Introduction As a special public place, hospitals receive large numbers of patients every day, its indoor air quality has a strong influence on health of patients and staffs. Few researches involved air pollution measurement in hospitals, especially in hospital waiting rooms so far. P. Aarnio measured mould, TVOC and ammonia pollution in the healthcare centre of Helsinki area, and found that air pollution correlated with certain health symptoms1. Huixiong Lü measured carbonyl compounds and BTEX concentrations in clinical and ward areas of four hospitals in Guangzhou, results showed that indoor concentrations were higher than outdoor and pollutants were mainly from indoor sources2. C.G. Helmis assessed the status of air quality in a dentistry clinic, and found concentrations of TVOC, CO2 and particulate matter were higher during operation hours than non-working periods3. Elena G. analyzed air conditions in hospital operation rooms, and found that air pollution especially anaesthetic gases pollution were severe4. As we all know, large numbers of patients and their families spend much time in hospital waiting rooms, air condition of such places has a strong relationship with their health. So we collected air samples from typical waiting rooms of three hospitals, analyzed concentration of fifteen aromatic compounds (benzene, toluene, ethylbenzene, o/m/p - xylene, styrene, isopropyl benzene, propylbenzene, o/m/p - ethyl toluene, 1,2,3 - trimethylbenzene, 1,2,4 trimethylbenzene, 1,3,5 - trimethylbenzene), in order to analyze air quality of these places. 1.
Methods Air samples were collected from nine typical waiting rooms of hospitals in Beijing, sampling period was from 9:00am to 12:00am every day, lasting for one week. One sample was collected for each sampling location per day. Stainless steel canisters were used, each sampling lasted for about ten minutes. Air samples were then taken back to laboratories and determined by GC/MS system (7890AGC-5973CMS). The analytical columns of GC included a DB-624 capillary column (60m×0.32mm×1.8&m) and a PLOT capillary column (30m×0.32mm×3.0&m). Oven temperature was initially set at 30
for 7 min, then increased to 120
temperature was increased to 180
at a rate of 5 min-1 and hold 5 min, finally the
at a rate of 6 min-1 and hold 7 min. MS was set at
595
scan mode. 2. Results and discussion Concentrations of aromatic compounds Total concentrations of aromatic compounds in three hospitals range from 69.1&g/ m3 to 346.3&g/m3 among which BTEX are main pollutants, accounting for more than 80% of total concentrations. Pathology department of H2 was most seriously polluted, which concentration reached 346&g/m3 followed by stomatology department of H3, reaching 250&g/m3. On the whole, H2 and H3 were more polluted than H1, toluene and xylene concentrations exceeded indoor air quality standard in certain waiting rooms. Table1 Mean concentrations of aromatic compounds in hospital waiting rooms &g/ m3
Sampl ing locatio n H1a** H1b H1c H1d H2a H2b H2c H2d H3a H3b H3c
benze ne
toluene
ethylbe nzene
o/m/p xylene
styrene
total
10.7± 9.7 16.5± 10.5 15.9± 5.7 15.5± 8.0 8.6±4. 9 9.2±4. 3 19.7± 18.0 10.6± 4.5 6.2±2. 8 9.0±3. 9 8.3±4. 3
15.2±1 0.2 19.0±1 2.0 22.8±9. 8 19.0±1 3.6 14.8±9. 7 18.1±8. 6 19.2±2 0.1 19.5±1 0.4 12.5±6. 2 196.1± 257.4 209.6± 2946
7.6±5.6
28.9±40 .0 25.9±48 .6 122.5±1 27.7 5.8±3.9
10.6±6. 2 12.6±7. 5 17.7±5. 7 13.6±1 0.0 11.8±9. 1 46.7±5 1.4 53.7±9 1.9 182.4± 150.1 7.7±4.1
18.4±12 .7 15.8±12 .2
18.8±1 0.7 16.5±1 0.7
1.4±1. 5 14.8±2 4.0 2.5±1. 1 7.9±11 .8 0.6±0. 5 78.7±1 79.0 5.2±22 .8 7.9±11 .9 0.7±0. 5 1.2±0. 6 1.0±0. 5
46.9±3 0.9 74.0±4 4.5 73.1±2 2.6 69.1±5 1.2 44.3±2 6.7 165.9± 184.1 118.5± 122.5 346.3± 158.6 36.3±1 7.3 249.9± 283.6 255.7± 323.2
8.8±6.8 12.4± 7.0 4.4±2.9 4.2±2.6
596
H3d standa rds*
7.0±2. 7 110
39.7±3 7.5 200
13.9±4. 9 -
1.0±5.4 200
**a, b, c, d are different waiting rooms. * China indoor air quality standard GB/T 18883-2002
0.0±0. 5 -
76.4±4 3.6 -
one hour concentration limits.
Pollution characteristic analysis H2 was most severely polluted, total concentration was 3-4 times of concentration in H1. Figure1 shows pollution compositions of typical waiting rooms. H1 represented BTEX integrated pollution style, while H2 and H3 are mainly polluted by toluene. Overall, each hospital has different main pollutants, but pollution compositions in waiting rooms of the same hospital are similar.
Fig.1 Composition of pollutants in typical waiting rooms Source analysis Sources of BTEX are initially analyzed by calculating ratio of indoor and outdoor concentrations (I/O). For almost all the pollutants, I/O are larger than 1, indicating a great contribution of indoor pollution sources. Table2 shows the factor analysis results, combined with I/O analysis results, we conclude that factor1 and factor2 represent indoor and outdoor pollution source respectively. BTEX in H1 and H3 are mainly from indoor sources, while outdoor sources only contribute to benzene. In H2, benzene and toluene are mainly from 597
outdoor sources, ethylbenzene and xylene mainly comes from indoor sources. Talbe2 Factor analysis results of hospital BTEX
H1
H2
H3
pollutants benzene toluene ethylbenzene o/m/p - xylene total % of Variance Cumulative % benzene toluene ethylbenzene o/m/p - xylene total % of Variance Cumulative % benzene toluene ethylbenzene o/m/p - xylene total % of Variance Cumulative %
Factor 1 0.83 0.98 0.96 0.98 3.53 88.13 88.13 -0.42 0.06 0.95 0.92 1.92 48.00 48.00 0.86 0.93 0.98 0.97 3.51 87.64 87.64
Factor 2 0.56 0.16 -0.03 -0.11 0.42 10.51 98.65 0.78 0.93 -0.02 -0.14 1.49 37.25 85.25 0.49 -0.30 -0.15 0.17 0.38 9.39 97.03
Further correlation analysis results showed that BTEX significantly correlated with each other in H1 and H3, indicating common indoor sources for these pollutants. Combined with our investigation, we concluded that BTEX are mainly from indoor decoration materials. Only ethylbenzene and xylene are significantly correlated in H2, indoor medical reagents and decoration materials are possible sources. Outdoor sources only have a contribution to benzene and toluene pollution, possibly the results of construction and traffic release nearby. 3.
Conclusion Our research shows that, although each hospital has different pollution compositions, the main pollutants detected in all three hospitals are benzene, toluene, ethylbenzene and xylene (BTEX). BTEX concentrations in this study were consistent with Guangzhou hospital research2, except for certain special department. Source analysis results showed that BTEX in hospitals come from both indoor and outdoor sources, indoor medical reagents and decoration materials are main sources, while outdoor construction and traffic also have certain contribution. 598
Reference 1. P. Aarnio; H. Mussalo-Rauhamaa; S. Mäkinen-Kiljunen, Indoor Built Environ. 2005, 14, 433–441. 2. Huixiong Lü; Sheng Wen; Yanli Feng, Science of the Total Environment. 2006, 368, 574–584. 3. C.G. Helmis; J. Tzoutzas; H.A. Flocas, Science of the Total Environment. 2007, 377, 349–365. 4. Elena G. Dascalaki; Argyro Lagoudib; Constantinos A. Balaras, Building and Environment. 2008, 43, 1945–1952.
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An Intelligent Expert System for Improvement of Indoor Air Quality in Museum Environments Majid Shafie-Pour1, Khosro Ashrafi2, Azadeh Tavakoli3 1, 2 Assistant professor, Faculty of Environment, University of Tehran 3 Ph.D student, Faculty of Environment, University of Tehran, Principal Contact: Azadeh Tavakoli, Ph.D student, Faculty of Environment, University of Tehran, +989132076023,
[email protected] Abstract Conservation of indoor air quality in healthy conditions has generally important effects on the invaluable objects kept in museums. Some researchers believe that managing indoor air quality is one of the most important issues for museums environment. So, emphasis would be on the evaluation of control equipments use within such environments. In this paper, an intelligent expert system for improvement of air quality in museums, AZTA, is presented. This system, designed by the authors, has abilities to compare indoor concentration of some pollutants, such as Nitrogen dioxide, Sulfur dioxide, Ozone, Particulate Matters and environmental parameter such as temperature and humidity with standard or guideline values and alarm the AZTA user if such limits are exceeded. In addition AZTA offers optimized air change rate per hour as well as most effective equipments with their technical characteristics considered fit for the environment. AZTA has been applied to two museums in Tehran-Iran, namely Pardisan Biodiversity and Persian Carpets museums of Iran. Results show that application of AZTA to offer control systems for indoor air quality, managed to propose alternative which focus on about 3 to 5 folds improvement in energy management at the same time attaining to acceptable indoor air quality.
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Indoor and Outdoor Air Quality at High Schools in Eski$ehir, Turkey Ozlem Ozden, Eftade O. Gaga, Tuncay Dogeroglu Anadolu University, Faculty of Engineering & Architecture,Environmental Engineering Department, Iki Eylul Campus, 26555, Eskisehir, Turkey INTRODUCTION Because of increasing traffic and industrial emissions, ambient air quality has become of growing concern all over the world.1-3 Gaseous and particulate emissions from sources such as power stations, various industries and vehicles, along with their atmospheric transformation products, cause damage to public health and to the environment.4 Nitrogen dioxide (NO2) is one of the main gaseous pollutants with the important effects on both indoor and outdoor air quality. Health impact of NO2 includes different respiratory and lung diseases. Also, ozone is a secondary pollutant primarily formed by the chemical reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of sunlight. It is also among the greenhouse gases and has harmful effects on human health. Most people in the world spend 80-90% of their time indoors.5 Therefore, indoor air quality has become an important issue for the understanding of the impact of air pollution on human health. The indoor concentrations of the pollutants depend on indoor (heaters, smoking, cooking etc.) and outdoor sources (traffic, combustion processes, industry etc.) as well as the air exchange rates. Outdoor pollution is also an important source for the indoor air since the outdoor pollutants may be transferred to indoors by naturally ventilated buildings or by opening windows.6,7 Indoor air pollution affects life quality beside human health in a significant way. In this case, monitoring and controlling of indoor and also outdoor air quality becomes very important. Passive sampling is one of the sampling methodologies for quantifying concentrations of gaseous air compounds such as NO2 and ozone. Passive samplers are inexpensive, easy to use and do not require electricity to operate. Therefore, they are very attractive for use in remote and wilderness areas and in regional scale air quality assessments.8 The major aim of this study was to determine indoor and outdoor NO2 and ozone concentrations in and around three high schools in Eskisehir, Turkey by using passive 601
sampling method to asses the children’s exposure. Indoor/outdoor (I/O) ratios were used to investigate if there is an important source inside the schools for each pollutant. MATERIALS AND METHODS Study Area and Characteristics of the Schools In this study, indoor and outdoor NO2 and ozone measurements were carried out in and around three high schools in Eskisehir, Turkey. The schools were selected according to their locations such as industrial, urban, and urban background areas. School 1 is located in the Eskisehir Organized Industrial area which is approximately 10 km east of the city center and natural gas has been used for any kind of power generation. School 2 is located on a street with medium traffic in the urban zone of the city. School 3 is located in approximately 6 km south of the city center. This location has low traffic density and also far from the major pollution sources, so we can assume as urban background. Figure 1 shows the locations of the schools.
Sampling Methodology In this study, plastic passive sampler with 2.5 cm length and 2 cm inner diameter9 was
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used for the measurements. Passive samplers were left to the sampling points in both indoor and outdoor environments of the schools for two weeks and collected back for the analysis. Indoor samples were obtained from different indoor environments of each school (classroom, library, corridor, dining hall, teacher’s room) and also outdoor sampling was carried out simultaneously at one point at school gardens. During the sampling period, to minimize turbulence effect of wind inside the sampler (especially during outdoor measurements), stainless steel mesh barrier was placed at the open end, and the barrier was replaced with a close cap during the transportation of the sampler. The samplers were mounted vertically with the open end downward and protected from wind and rain during the sampling period in a shelter (outdoor measurements). Preparation and Analyses of the Samples For the preparation of each sample, Whatman GF/A fiber glass filter paper was impregnated with 20% TEA aqueous solution for NO29 and 1% NaNO2 + 1% NaCO3 + 2% glycerol aqueous solution for ozone9 and placed at the bottom of the sampler and then fixed with a ring. The inlet end was closed with a plastic cap. Samplers were left to the sampling points for two weeks and then transferred to the laboratory. Filter papers were removed from the samplers and extracted with ultra pure (Milli Q) water for 15 minutes at room temperature. After extraction, the analyses were carried out by using DIONEX-2,500 Ion Chromatography equipped with a thermal conductivity detector. NO2 and ozone concentrations were then determined by using Fick’s first law of diffusion. 10,11 RESULTS AND DISCUSSIONS Indoor and outdoor measurements were conducted to understand whether school environment is a kind of source for the schoolchildren regarding ozone and NO2. The obtained indoor (for each indoor environment) and outdoor concentrations for each school were shown in Figures 2-4.
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All indoor NO2 concentrations were found higher than ozone concentrations in School 2 and 3. We can say that the effect of indoor NO2 sources (indoor activities and also outdoor air) were much more dominant compared to ozone in these schools. According to the general indoor measurement results, it can be concluded that there were no any important indoor NO2 and ozone sources except for dining halls (for NO2) in the schools. Outdoor NO2 concentrations measured in school 1 (industrial) was the highest (24.82$g/m3) among three schools and followed by the school 2 (urban) (15.29 $g/m3) and school 3 (urban background) measurements (14.93 $g/m3). The highest NO2 levels in the industrial area are mostly related with the heavy traffic since this area is near a highway which has traffic load of especially big trucks carrying different kinds of products. Indoor ozone concentrations were found to be higher than NO2 concentrations in the classroom, library, corridor and teachers room. Since this study was performed in summer time, it is probable that ambient ozone is transferred to inside by natural ventilation of the rooms by opening windows. Higher ozone concentrations were observed in the schools far from the city center. Ozone is a secondary pollutant that is formed when nitrogen oxides (NOx), carbon monoxide (CO) and volatile organic compounds (VOCs) react in the atmosphere in the presence of sunlight. Although these precursors often originate in urban areas, winds can carry NOx hundreds of kilometers, causing ozone formation to occur in less populated regions. So, ozone levels should be lower in places close to the pollution sources and these levels increase as the distance in the case of school 1 which is far from the city center. Highest ozone concentration was measured in school 1 (83.05 $g/m3) and followed by the measurements in school 3 (75.45 $g/m3) and school 2 (60.12 $g/m3), respectively. Indoor/outdoor (I/O) ratios for each indoor environment of the schools were 605
determined. The average I/O ratios for the schools varied from 0.28-3.08 for NO2 and 0.03-0.68 for ozone. Figure 5 shows the average I/O ozone and NO2 ratios for each school. High I/O ratios are an indication of indoor sources. Especially, I/O ratios for NO2 were >1 in the indoor environments such as dining hall (in all schools), teacher’s room (in school 3) where cooking and smoking activities are much more performed. All the I/O ratios for ozone were smaller than one. Lower I/O ratios for ozone as compared to NO2 results shows that there was not an important source for indoor ozone and also the effect of deposition on solid surfaces or decomposition in the indoor environment was much stronger. Also, the building air-tightness may be another reason for lower ratios. It seems that classrooms are not a source of NO2 and ozone considering low I/O (0.6) with Ca2+ and Fe, further demonstrating the importance of fugitive dust. C3/C2 ratios linearly increased as the increase of C4/C2 ratios, indicating C3 and C4 as the precursors of C2. PAHs, sulfate, and Pb correlated one another with r2>0.6 in winter, suggesting an importance of coal burning emissions. Stable carbon compositions of TC ($>3C, ‰) became heavier as the increase of concentration ratios of secondary organic carbon (SOC) to primary organic 683
carbon (POC), and also increased as an increase of concentration ratios of C2/SOC suggesting a release of gaseous products during the secondary organic aerosol formation.
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IIIh: Air Pollution Health Effects (II)
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The Association of Children's Development and Reduction of Prenatal Exposure to Coal-Burning Pollutants – An Environmental Intervention Study in China
Deliang Tang, Ting Yu Li, Judith C. Chow, Sanasi U. Kulkarni, John G. Watson, Steven Sai Hang Ho, Zhang Y. Quan, L.R Qu, Jian Xie, Frederica Perera Department of Environmental Health Sciences, Columbia University, 701 W. 168th Street, New York, NY 10027, USA.
[email protected] Introduction Air pollution is a serious health concern for the population of China living in urban areas, especially for babies in the womb and developing children. Coal-fired power plants, which currently produce nearly 75% of the country’s electricity, are a major source of air pollution[1]. Emissions from the burning of coal contain harmful substances such as particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and mercury. In partnership with Chongqing Children’s Hospital, the Columbia Center for Children’s Environmental Health has carried out prospective cohort studies in Tongliang, China to examine the impact of exposure to emissions from a coal-fired power plant on children’s health. The city of Tongliang has a population of approximately 810,000 and is situated in a small basin approximately 3 km in diameter. A coal-fired power plant located south of the town center operated during the dry season from 1 December to 31 May each year before 2004 to compensate for the insufficient hydroelectric power during that time[1] . This plant was the principal source of local air pollution; in 1995 nearly all domestic heating and cooking units were converted to natural gas, and motor vehicles are not a major source. Air sampling data of 72-hour average concentrations showed that the highest concentrations of PM 2.5 (particles with aerodynamic diameters less than 2.5 &m) and PAHs were found in winter, reflecting the coupling effect of coal-fired power plant operation and meteorological conditions[2] . In May 2004, the power plant was closed and replaced by the national grid system of electrical energy. The Tongliang County Government determined that the shutdown of the power plant would significantly improve local health and have minimal adverse social and economic impacts. The power plant shutdown provided a unique opportunity to compare air monitoring, biomarker, and health outcome data in successive cohorts of children with or without prenatal exposure to coal-fired emissions from the plant. Here we test the hypothesis that the shutdown of the power plant has had a significant 686
beneficial health impact on children born in the Tongliang area. We anticipated that the levels of outdoor emissions decreased following the power plant shutdown. Therefore, we expected to find lower cord PAH-DNA adduct levels and more favorable birth/child neurodevelopment and physical outcomes in the second cohort compared to children in the first cohort. Additional individual susceptibility factors such as the mother’s nutritional status and polymorphic characteristics of PAH metabolism pathways are being evaluated for influence on fetal/child physical and neurodevelopment. Furthermore, a third cohort was recruited in 2007 to supplement our findings thus far on the benefits of reduced emissions by the coal-fired power plant. Our study was limited by the small sample size of 150 mothers for each of the cohorts and by a lack of a few data points for air monitoring due to mechanical glitches. However, because similar values were found within months in each of the two power plant seasons (operational vs. non-operational), we were able to take the average value. A strong positive correlation was found between the operational season and levels of PAH/ PM2.5 as well as an overall higher PAH/ PM2.5 levels during the first 2002 cohort compared to the second 2005 cohort. These correlations were further found to impact cord adducts levels, and subsequent birth/child outcomes. Body of Text Methods Air monitoring. Integrated 72-hour PM 2.5 samples were collected with two Mini-Vol samplers (Airmetrics, Eugene, OR, USA) at three sites in Tongliang between March 2002 and February 2003 and between March 2005 and February 2006[2] . Study subjects. For the 2002 cohort, the subjects are 150 children born to nonsmoking Chinese women who gave birth between 4 March 2002 and 19 June 2002 at four hospitals in Tongliang. For the 2005 cohort, the subjects are 158 children born at the same hospitals from 2 March 2005 to 23 May 2005. The women were selected using a screening questionnaire when they checked in for delivery. Eligibility criteria included current nonsmoking status, 3 20 years of age, and residence within 2.5 km of the Tongliang power plant. Personal interview. A 45-min questionnaire was administered by a trained interviewer after delivery. The questionnaire elicited demographic information, lifetime residential history (location of birth and duration of residence), history of active and passive smoking (including number of household members who smoke), occupational exposure, medication use, alcohol consumption during each trimester of pregnancy, and consumption of PAH-containing meat (frequency of eating fried, broiled, or barbecued meat during the last 2 weeks). Socioeconomic information related to income and education was also collected. Biological sample collection 687
and analysis. Maternal blood (10 mL) was collected within 1 day postpartum, and umbilical cord blood (40–60 mL) was collected at delivery. Samples were transported to the field laboratory at the Tongliang County Hospital immediately after collection processing. Blood samples, the buffy coat, packed red blood cells, and plasma were separated and stored at (70°C[3] . DNA adducts. Benzo[a]pyrene (B[a]P) is a PAH measured to estimate the overall PAH concentration in a sample. B[a]P–DNA adducts were analyzed in extracted white blood cell (WBC) DNA with a modified method using high performance liquid chromatography (HPLC)/fluorescence detection. Measurement of birth outcomes and physical development. Birth weight, birth length, and head circumference were measured immediately after parturition. Information abstracted by the research workers from mothers’ and infants’ medical records after delivery included date of delivery; gestational age at birth (based on the last menstrual period); infant sex, birth weight, length, head circumference, maternal height, pre-pregnancy weight, and total weight gain; complications of pregnancy and delivery; and medications used during pregnancy. Measurement of child neurodevelopment. The experimental approach and methods used in this investigation and the comparison of the two cohorts have been presented elsewhere[4] . Results The demographic, exposure, and birth outcome characteristics for the mothernewborn pairs from cohorts I and II are shown in Table 1. Maternal ETS exposure, maternal height, and gestational age did not differ between the two cohorts. Maternal age (p < 0.001), maternal weight before pregnancy (p = 0.002), maternal head circumference (p < 0.001), and Caesarian delivery status (p < 0.001) were significantly different between the two cohorts and therefore adjusted for in the various models.
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Table 1 Demographic, Exposure and Birth Outcome Characteristics of the Population Cohort I (2002) Maternal age (years) Maternal ETS (hr/ day) Maternal height (cm) Maternal prepregnancy weight (kg) Maternal head circumference (cm) Caesarian delivery (%) Gestational age (days) Newborn birth weight (g) Newborn birth length (cm) Newborn head circumference (cm) Sex of newborn (% female) 1Values
(n=150)1 25.3 ± 3.2
Cohort II (2005) (n=158)1 27.8 ± 4.6
p-value2 Ba > Si > K. The ion components showed higher concentration in the order of Cl- > NO3- > SO42-. In the results of modeling based on the analyzed in this study, three contamination sources were analyzed as the contamination sources, while the coefficient of determination (R2) between the two variables was PM10 0.72 as reliable modeling results. Acknowledgment This research was supported by a grant (09 Urban railroad A-01) from Urban Railroad Technology Development Program funded by Ministry of Land, Transport and Maritime Affairs of Korean government. Reference Ki Youn Kim, Yoon Shin Kim, Young Man Roh, Cheol Min Lee, Chi Nyon Kim (2008) Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul
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Metropolitan Subway stations, Journal of Hazardous Materials, Volume 154, Issues 13, 440-443
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Seasonal variation of chemical composition in ambient particulate matter in Tianjin offshore area and its source analysis Zhipeng Bai, Bin Han State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, P R China Principal Contact: Zhipeng Bai, Professor, State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, ankai University, Tianjin 300071, 86-22-23503397,
[email protected] Abstract: Atmospheric particulate matter with different diameter (TSP, PM10 and PM2.5) were collected during four cruises at Tianjin offshore area in 2006-2007 to analyze mass concentrations, main chemical components including elements, ions and carbon. The sources were analyzed using backward trajectory model, enrichment factors, coefficient of divergence and ratios. The results showed that the mass concentrations of TSP, PM10 and PM2.5 were 294.97±3.95, 209.9±13.15 and 154.13±28.6"g/ m3, respectively and it represented obviously seasonal variation, with the highest value in autumn, then coming winter and the lowest in summer. The total element concentrations were 48.754 36.076 and 47.934"g/m3 in TSP, PM10 and PM2.5 with the most abundant elements (Mg, Al, Si, Ca, Fe, Zn, and Pb) accounting for 97% for all the three different diameter particles. The highest ion was Na+ for TSP, occupying 56.8%, 42.4%, 66.5% and 45.7% of the total ions concentration in spring, summer, autumn and winter. Considering PM10 and PM2.5, the richest was Cl-, occupying 41.5, 26.6 and 39.3% of the total ions concentration in spring, autumn and winter for the former and for PM2.5 the values were 26.5% 26.0% and 47.3%. The concentrations of OC for all the three different diameter particles were all higher in autumn and winter than in spring and summer. The sum of concentrations of OC in autumn and winter contributed to 70.9%, 85.7% and 83.1% for TSP, PM10 and PM2.5. The main source was soil for TSP in the study area in spring, but coal combustion translation particles influenced most for PM10 and PM2.5 in autumn and winter according to the Al/Fe value. The backward trajectory model results showed that atmospheric particulate matter in Tianjin offshore area was influenced mainly by continental sources. Higher EFc values for Cu Zn and Pb were found with the highest value (743.1) for Pb in PM10 in winter indicating the influence of human sources. NO3-/SO42- ranged from 0.28 to 0.85, with higher values in spring and summer than autumn and winter, which reflected coal combustion and vehicle emission were both main sources. While the variation range of OC/EC in 2.13-5.58 indicated the existence of secondary organic carbon in this region.
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Key Words: Bohai Sea; Atmospheric particulate matter; inorganic components; Backward trajectory model; sources
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Composition and Secondary Formation of Fine Particulate Material in the Salt Lake Valley: Winter 2009 Delbert J. Eatough1, Roman Kuprov1, Jaron C. Hansen1, Tyler Cruickshank2 and Neal Olson2 1 Department of Chemistry and Biochemistry, Brigham Young University, Provo UT 2State of Utah Department of Environmental Quality, Division of Air Quality, Salt Lake City, UT Principal Contact: Delbert J. Eatough, Professor of Chemistry Emeritus, department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84604. Phone: 1801-375-5535. Fax: 1-801-422-0153. E-mail:
[email protected] The State of Utah maintains an NCore site at the Hawthorne Elementary School in Salt Lake City. During winter inversions the site can exceed the NAAQS for PM2.5. A 7week long intensive study was conducted during the winter of 2009 to determine the chemical composition of the fine particulate material present at the site and to determine the emission and atmospheric conversion factors which lead to exceedences. PM2.5 mass and composition was determined on an hourly averaged basis using a suite of semi-continuous monitors. Mass was determined with an FDMS TEOM and composition was determined using a URG AIM monitor for both gas and particulate phase inorganic components, e.g. particulate sulfate, nitrate, nitrite and other anions and gas phase HNO3, HNO2 and SO2. In addition, the gases NO, NO2, CO, O3 and NH3 were determined on an hourly averaged basis. During inversion conditions, PM2.5 averaged 31 :g/m3 and was 67% ammonium nitrate. Formation of ammonium nitrate was nitric acid limited. The reaction of the OH radical to form nitric acid completed favorably with the photolysis of NO2 and consequently the production of ozone was NOX limited. The presentation will focus on the factors that controlled equilibria between nitric acid, ammonium, and ammonium nitrate as well as the relationships between gas phase precursors, ozone, and the formation of secondary particulate matter.
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Chemical composition and source apportionment of PM1.0 and PM2.5 in the roadside environment Yan CHENG1,2, Shuncheung LEE3, Kinfai HO3, Judith C. CHOW2,4, and John G. WATSON2,4 1 Department of Environmental Science and Technology, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi, 710049, China 2 SKLLQG, Institute of Earth and Environment, CAS, Xi’an, Shaanxi, 710075, China 3 Department of Civil and Structural Engineering, Research Center for Environmental Technology and Management, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China 4 Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA Principle contact: Cheng Yan; Dr.; Department of Environmental Science and Technology, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi, 710049, China; Tel.: +8682664397; fax: +86-82665111; E-mail address:
[email protected] Abstract PM1.0 (particles with an aerodynamic diameter less than 1.0 "m) and PM2.5 (particles with an aerodynamic diameter less than 2.5 "m) were collected simultaneously at an urban roadside site in Hong Kong from September 2004 to October 2005. Organic carbon (OC), elemental carbon (EC), water-soluble ions, and up to 51 elements were determined. The annual average particulate mass concentration was 44.5±19.5 "g m-3 for PM1.0 and 55.4±25.5 "g m-3 for PM2.5. The majority (~80%) of PM2.5 mass was made up by PM1.0, both showing similar chemical characteristics and seasonality. The most abundant species for PM1.0 and PM2.5 in descending rank were EC, sulfate (SO4=), and OC, accounting for ~36%/32%, 24%/23%, 24%/22% of PM1.0/PM2.5 mass, respectively. Low OC/EC ratios (less than 1) in PM1.0 and PM2.5 indicated that fresh vehicle exhausts is the major source for carbonaceous aerosol at the roadside site. Similar seasonal variations for particle mass, OC, SO4=, and ammonium (NH4+) in PM1.0 and PM2.5 were found, with ~50%/68%, 60%/72%, 107%/119%, and 130%/ 136% higher concentrations in winter than summer, showing the impacts of aged aerosols transported from upwind continent in winter. The PM1.0 and PM2.5 EC concentration in summer was affected by ship emission from nearby Victoria Harbor, showing 20% higher concentrations in summer than that in winter. PM1.0/PM2.5 SO4= correlated very well with PM1.0/PM2.5 NH4+ throughout the sampling period, with correlation coefficient (R) of 0.96 for each fraction, suggesting they are in form of ammonium sulfate ((NH4)2SO4) or bisulfate (NH4HSO4). Source apportionment of PM2.5 were estimated using chemical mass balance (CMB) receptor model. 827
Vehicle emission, secondary aerosol, and coal combustion were dominate sources of PM2.5. The contributions of regional sources to the PM2.5 mass was less than 30% when air mass is from marine, and increased to nearly 50% when air mass is from continent. Key word: PM1.0, PM2.5, OC, EC, CMB
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Chemical characteristics of cloud and fog water over Mt. Huang, China Cloud and fog water were sampled at the Huangshan National Basic Meteorological Observing Station (30o08’N, 118o09’E, 1840 m above sea level) from April to July 2008. The pH value and the most important chemical components such as NO3-, NH4+, SO42-, Cl-, Ca2+, K+, Mg2+, and Na+ were analyzed. The results show that the volume-weighted mean pH value is 5.81, with a range from 4.43 to 6.94. About 44.83% of the cloud and fog water samples have pH values larger than 6.0, implying that there might be a quantity of alkaline type species being imported into the cloud and fog in these events. The mean equivalent concentration of components followed the order: NH4+>SO42->NO3- >Ca2+> Cl- > Na+>K+>Mg2+. Among dominant ions in the cloud and fog water there were sulphates, ammonium and nitrates. NH4+ were major neutralization constituents of the cloud and fog. Furthermore, correlation analysis and principal component analysis method were performed to identify possible common sources of the major ions. Author(s): Kui Chen1, Yan Yin1*, Jie Hong2, Qian Chen1, Ling Sun1, Wen Tan1 Affiliation(s): 1 CMA Key Lab for Atmospheric Physics and Environment , Nanjing University of Information Science & Technology, No. 219 Ningliu Road, Nanjing, 210044,
[email protected] 2 Huangshan National Basic Meteorological Observing Station, Huangshan, 242709 1. Introduction Over the past decade, The chemistry of fog and cloud water has become an important topic of investigation.1-6. High elevation fog, usually formed by orographic clouds or interception of stratus clouds, gives a unique opportunity to study cloudwater chemistry without the need of an airborne sampling platform. However, there are fewer studies on fog and cloud water in high elevation South China. Therefore, in this research fog and cloud water collected for spring and summer at the same sampling site. The purpose of this study is to examine the chemical characteristics of fog and to discuss fog chemistry. 2. Experimental 2.1 Research site Measurements were performed in the Mt. Huang of the south of Anhui province. The site is located near the summit of Mt. Huang (the Bright Peak), which is the highest point about 1840 m altitude. Figure 1 shows the geographic locations of the measurements. Of all the notable mountains in China, Mt. Huang, to be found in the south of Anhui province, is probably the most famous. Every year there are many tourists of the world to Mt. Huang tourism. There are one potential pollution sources in 829
the vicinity of the mountain range: the main pollutant is the motor vehicle (transporting tourists) exhaust. The reason why Mt. Huang was selected as the sampling site is its high frequency of fog occurrence. 2.2. Sampling method and analysis Air containing cloud or fog drops is drawn by a fan through the collector where the drops are collected by impaction on 508 "m diameter Teflon strands. Large drops deviate from the flow around the cylinders due to their inertia and are collected by impaction, while smaller drops are able to follow the streamlines around the cylinders. The size cut (droplet diameter collected with 50% efficiency) is predicted to be 3.5"m. One fog event was distinguished from the next by at least a 1-h time interval. The sampling flow rate was controlled at 8.6 m/s by velocity at strands. The fog water collected by the net dripped into the bottle., a part of sample was immediately analyzed for volume, pH, electric conductivity in each bottle. Another part of the samples was stored in the refrigerator and transported about biweekly to the Nanjing University of Information Science & Technology. After transportation, the samples were immediately analyzed major chemical components (Cl%, NO3% ,SO42%, Na+, NH4+, K+, Mg2+, and Ca2+) at the School of Environmental Science and Engineering. The concentrations of chemical species were measured with an ion chromatograph. Meteorological data were sourced routine observation at Mt. Huang meteorological station, such as monthly mean vapor pressure (VP), monthly mean relative humidity (RH), and monthly mean air temperature (AT), were used in the analyses of fog occurrence. 2.3 Quality control and quality assurance QC/QA Criteria based upon ion balance and conductivity (measured vs. calculated) according to other studies, have been used for sample validation5. In order to be included in this study, the percentage difference of the ion balance (P.D.I.) for a given sample had to be lower than 20%:
In addition, the percentage difference of conductivity (P.D.C.) had to be lower than 20%.
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Only samples meeting both criteria have been included. The rejection rate was 20% for bulk cloudwater samples, 10% for small clouddroplets and 30% for rain samples. Sample contamination has been regularly checked by blank values of the collectors. The blanks were generally close to or below detection limit. 3. Results and discussions 3.1. Variation of pH value The frequency distribution of pH for the fog and cloud water samples was plotted in Fig 2 from April to July 2008. The pH values fo individual fog and cloud water varied form 4.43 to 6.94 with volumeweighted mean of 5.81. About 58.62% of total samples had pH values higher than 5.60, the pH of cloud water at equilibrium with atmospheric CO2 7. There were 44.83% the samples with pH values above 6.0. It may suggest a quantity of inputs of alkaline species into fog and cloud in the study area.
Figure 1. Frequency distribution of pH measured in Mt. Huang from April to July 2008 3.2. Fog and cloud water chemical composition 3.2.1. The Influence of sea salt upon fog and cloud water at Mt. Huang The volume-weighted mean concentrations of various ions in the fog and cloud water at Mt. Huang were summarized in Table 1 with other previous studies. Mt. Bamboo and Mt. Rokko is located near the coast and the proportion of ions in fog and cloud water derived from sea salt such as Na+, Mg2+ and Cl% 8 is greater than inland areas. The particle size of sea salt is big for coarse partcles like NaCl. So the sedimentation ratio of sea salt in the atmosphere is high and consequently these salts may be difficult to transport over long distance. The ion concentration in the fog and cloud water is low at Mt. Huang.
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Table 1 Average concentrations for the cloud and fogwater collected at Mt. Huang and comparison with other studies
(1) Mt. Whiteface, U.S.A.9; (2) Mt. Bamboo, Taiwan 10; (3) Mt. Rokko, Japan 11; (4) Mt. Akagi, Japan 12; (5) Mt. Haruna, Japan 12. 3.2.2 Origin of chloride ion Figure 2 shows the correlation between chloride and sodium ion concentrations in fog and cloud water. Correlation coefficient of Na+ versus Cl% (r=0.37) was lower, indicating towards different sources. The average value (Cl-/Na+) was 3.55, higher than those of Na+ versus Cl% (1.17) 13 from sea salt, indicating that the major portion of Cl- was the form of air pollutants. This may be caused automobile exhaust gas of tourist bus and chemical fertilizer utilization around the sampling sites.
Figure 2 Relationship between chloride ion and sodium ion concentrations in cloud and fog water at Mt.Huang
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3.2.3 Major ions in the fog and cloud water Figure 3 shows the average composition of fog and cloud water at Mt. Huang. The average concentrations of ion levels ranked by the order of NH4+ > SO42- > NO3- > Ca2+ > Na+ > Cl- > K+ >Mg2+. Among these ions, NH4+, SO42-, NO3-, Ca2+, were the dominant ions accounting for 85.44% of the total ions. As seen in Table 1, NH4+ having a percent contribution of 40.12% to total ions was the highest concentration cation due to the fact that chemical fertilizer utilization around the sampling sites, while Ca2+ presented a percent contribution of 10.02%. Because that the coal is the major fuel consumption source in China, SO42- was the highest concentration anmong the total anions with a percent contribution of 21.04%, while NO3- presented 14.26%.
Figure 3 Cloud and Fog water compositions at Mt. Huang 3.3 Correlation analysis We analyzed relationships among sulfate, ammonium, and nitrate ions in fog and cloud water at Mt. Huang (Figure 4). Good correlations were observed among them suggesting that ammonium sulfate and ammonium nitrate may be major components of the nucleus in the fog and cloud droplets. Correlation coefficient of NH4+ versus NO3- (0.848) and SO42- (0.895) were higher, showing that NH4NO3, NH4HSO4, (NH4)2SO4 could be more predominant in the atmosphere.
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Figure 4 Relationship between concentrations of ammonium ion and major anions (sulfate ion, nitrate ion) in cloud and fog water at Mt. Huang 4. Conclusions An investigation of fog and cloud water at Mt. Huang was performed from April to July 2008. The most of fog and cloud water was typically alkaline as the pH value ranged from 4.43 to 6.94 with a volume-weighted mean value of 5.81. Dominant ions in the fog and cloud water were NH4+, SO42-, NO3-, Ca2+. A good correlation between NH4+ and SO42-, as well as NH4+ and NO3-, reflected these ions were commonly sources. Correlation coefficient of Na+ versus Cl% (r=0.37) was lower, suggested that most of these ions mainly originated from non-marine source. Acknowledgments This study was partially funded by Key Laboratory of Meteorological Disaster of Ministry of Education (Nanjing University of Information Science & Technology), China, through Grant No. KLME060209, the 973 Program of China through Grant No. 2006CB403706, and the National Natural Sciences Foundation of China through Grant No. 40475003. References 1. Aikawa, M.; Hiraki, T.; Shoga, M.; Tamaki, M., Water, Air, & Soil Pollution 2005, 160 (1), 373-393. 2. Igawa, M.; Tsutsumi, Y.; Mori, T.; Okochi, H., Environ. Sci. Technol 1998, 32 (11), 1566-1572. 3. Winkler, P.; Wobrock, W.; Colvile, R.; Schell, D., Journal of Atmospheric Chemistry 1994, 19 (1), 37-58. 4. Jacob, D.; Waldman, J.; Haghi, M.; Hoffmann, M.; Flagan, R., Review of Scientific Instruments 1985, 56, 1291. 5. Fuzzi, S.; Facchini, M.; Orsi, G.; Bonforte, G.; Martinotti, W.; Ziliani, G.; Mazzalit, P.; Rossi, P.; Natale, P.; Grosa, M., Atmospheric Environment 1996, 30 (2), 201-213.
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6. Collett, J.; Sherman, D.; Moore, K.; Hannigan, M.; Lee, T., Water, Air, & Soil Pollution: Focus 2001, 1 (5), 303-312. 7. Charlson, R. J.; Rodhe, H., Nature, London. Vol. 295 1982, (5851), 683-685. 8. Mamane, Y., Sci. Total Environ. 1987, 61, 1-13. 9. Anderson, J. B.; Baumgardner, R. E.; Mohnen, V. A.; Bowser, J. J., Atmos. Environ. 1999, 33 (30), 5105-5114. 10. LIN, N.; PENG, C. In Chemistry of mountain clouds observed in the northern Taiwan, 1998; p 19¨C24. 11. Aikawa, M.; Hiraki, T.; Shoga, M.; Tamaki, M., Water Air Soil Pollut. 2005, 160 (1-4), 373-393. 12. Tago, H.; Kimura, H.; Kozawa, K.; Fujie, K., Water Air Soil Pollut. 2006, 175 (1-4), 375-391. 13. Keene, W.; A., P.; Galloway J; M., H., J. Geophys. Res. 1986, 6647-6658.
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Track IVf: Receptor Modeling Source Apportionment (II)
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Evaluation of Regional Scale Receptor Modeling Douglas H. Lowenthal1, John G. Watson1, Darko Koracin1, L.-W. Antony Chen1, David Dubois1, Ramesh Vellore1, Naresh Kumar2, Eladio M. Knipping2, Neil Wheeler3, Kenneth Craig3, and Stephen Reid3 1 Desert Research Institute, 2215 Raggio Pkwy, Reno, NV 89512 2 EPRI, 3412 Hillview Ave., Palo Alto, CA 94303 3 Sonoma Technology, Inc., 1455 N Mcdowell Blvd, Petaluma, CA, 94954 Principal Contact: Douglas Lowenthal, Research Professor, Desert Research Institute, 2215 Raggio Pkwy, Reno, NV 89512, 775-674-7047, 775-674-7016 (fax),
[email protected] Topic: Air quality modeling applications Abstract The ability of receptor models to estimate regional contributions to PM2.5 was assessed with synthetic, speciated data sets at Brigantine National Wildlife Refuge (BRIG), NJ and Great Smoky Mountains National Park (GRSM), TN. Synthetic PM2.5 chemical concentrations were generated for summer, 2002 using the Community Multiscale Air Quality (CMAQ) model and chemically speciated PM2.5 source profiles from the EPA’s SPECIATE and DRI’s source profile databases. CMAQ estimated the “true” contributions of seven regions in the eastern U.S. to chemical species concentrations and individual source contributions to primary PM2.5 at both sites. A seven-factor solution by the positive matrix factorization (PMF) receptor model explained ~99% of the variability in the data at both sites. At BRIG, PMF captured the first four major contributing sources (including a secondary sulfate factor), although diesel and gasoline vehicle contributions were not separated. At GRSM, however, the resolved factors did not correspond well to major PM2.5 sources. There were no correlations between PMF factors and regional contributions to sulfate at either site. Unmix produced five and seven factor solutions, including a secondary sulfate factor, at both sites. Some PMF factors were combined or missing in the Unmix factors. The trajectory mass balance regression model (TMBR) apportioned sulfate concentrations to the 7 source regions using HYSPLIT trajectories based on Meteorological Model Version 5 (MM5) and Eta Data Simulation System (EDAS) meteorological input. The largest estimated sulfate contributions at both sites were from the local regions; this agreed qualitatively with the “true” regional apportionments. Estimated regional contributions depended on the starting elevation of the trajectories and on the meteorological input data.
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Analysis of wood combustion aerosols in ambient air using inorganic and organic tracer compounds R.Zimmermann, J.Schnelle-Kreis, J.Orasche, M.Bente, M.Oster Joint Mass Spectrometry Centre of Institute of Ecological Chemistry, Helmholtz Zentrum München (HMGU), 86573 Neuherberg, Germany Chair of Analytical Chemistry, University Rostock, 18051Rostock, Germany Recently, the use of wood as renewable energy source is discussed contradictorily. On one hand the favourable CO2-balance does not contribute to the global warming burden whereas on the other hand biomass combustion is suggested to contribute significantly to PM mass loading of the ambient aerosol and thus may contribute to PM-associated adverse health effects. The influence of wood combustion on the air quality is currently investigated in Augsburg, Germany. For that purpose new analytical techniques are developed and applied. Long-term daily monitoring of organic compounds based on direct thermal desorption – gas chromatography - time of flight mass spectrometry (DTD-GCTOFMS) using a derivatisation technology was established at the HMGU aerosol measurement station in Augsburg. With this approach typical bio-mass combustion indicators such as levoglucosan, retene, phenolic compounds or derivatives of adiebetic acid are detectable. Using positive matrix factorization (PMF) for the statistical investigation of the data set it could be shown that e.g. particle-associated PAH are originated to a large extend from bio-mass burning. In addition singleparticle aerosol mass spectrometric techniques for detection of organic tracer molecules have been developed and are currently under testing. In detail, individual particles are on-line sampled, sized and ranged and subsequently IR-laser desorbed on the fly within the ion source of a time-of-flight mass spectrometer (TOFMS). Some !s after the IR-laser desorption pulse the volatilized aromatic compounds are ionized by an UV-laser pulse (i.e. by resonance enhanced multi photon ionisation – REMPI). Based on the pattern of detected aromatic species and the occurrence of the indicator compounds potassium and retene it can be concluded whether the particle is originated from bio-mass combustion. The current data suggest that the contribution of PM originated from wood burning is surprisingly high in Augsburg.
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Volatile organic compounds (VOCs) observed at a site in the central Pearl River Delta (PRD) region in autumn of 2007 and 2008: Influence of the financial crisis? Yanli Zhang1, Xinming Wang1,*, Hai Guo2 1State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences 2Department of Civil and Structural Engineering, The Hong Kong Polytechnic University Extend Abstract Number: #387 1. Introduction Volatile organic compounds (VOCs) play an important role in tropospheric chemistry. VOCs together with nitrogen oxides (NOx) are important precursors of troposphere ozone and secondary air pollutants1; affecting the global distribution of hydroxyl radical (OH) and the lifetime of many trace species in the troposphere2. In recent years, the PRD region has become one of the most economically developed and industrialized region in China, with a per capita GDP of US$ 2449 in 2000 increased to US$ 7496 in 2008. The average annual rate of GDP growth in the PRD from 2000 to 2007 was 29.2%, which is well above the national GDP growth rate (26.2%). Accompanying the substantial economic development, large amounts of air pollutants are discharged, resulting in a rapid deterioration of the overall air quality. The frequency of ozone episodes in the downwind regions, such as Wanqingsha and Hong Kong has increased significantly3. Elevated carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), and VOCs are frequently found in the PRD region4,5,6. A variety of industries exist in urban, suburban, and rural areas of the PRD. Recently, due to the finical crisis from year 2007 to 2009, the PRD industry has experienced a huge influence especially for Dongguan, a very developed industry city. Many industries were close down or cut sizes, while, at the same time, improved overall air quality was observed in 2008 in PRD region according to GDEPB and HKEPD, i.e. at Wanqingsha (WQS, our sampling site), the O3, PM10, NO2, and SO2 in NOV, 2007 was 0.073, 0.141, 0.058, and 0.085 mg/m3, separately, while, it was decreased to 0.060, 0.094, 0.046, and 0.062 mg/m3 in NOV, 2008, separately (http:// www.epd.gov.hk/epd/english/resources_pub/publications/m_ report.html). In order to investigate what happened to air pollutants and air quality in the PRD region during financial crisis? We present the comparison of key species of air pollutants between 2007 and 2008 at WQS, which is a suburban area located in the central of PRD region, lies in the downwind of Dongguan. Many aspects in comparison including concentrations, percentages, Propy-equiv concentrations, Ozone formation Potential (OFP) and compositions variability of VOCs species were available in this study. Additionally, in this study, we also apply PMF for VOC source apportionment by using speciated VOC data collected at WQS in 2007 and 2008, to 839
identify the major VOC sources that contribute to ambient VOC levels in these two year. This study can help us to better understand influence of the industry on the air quality. Also, this result can help government to make more efficient control strategies. 2. Methodology Ambient air samples were collected using 2-L stainless steel canisters during OctoberDecember 2007 and November-December 2008. VOCs in the air samples were analyzed using a Model 7100 preconcentrator (Entech Instruments Inc., California, USA) coupled with a gas chromatography-mass selective detector (GC-MSD, Agilent 5973N). Detailed cryogenically concentration steps are described elsewhere7,8. The calibration standards were prepared by dynamically diluting the 100 ppbv NMHCs standard mixtures (Spectra Gases Inc., NJ, USA) to 0.5, 1, 5, 15 and 30 ppbv. The calibration curves were obtained by running the five diluted standards plus humidified zero air the same way as the field samples.
Fig. 1. Chemical Composition of VOCs and total NMHCs (TVOC) in 2007 and 2008 3. Results and Discussion 3.1 Changes of levels and comparison of NMHCs From Fig. 1, we can see that the average total NMHCs levels in 2008 (38.19 ± 0.29 ppbv) was higher than that in 2007 (36.63 ± 0.32 ppbv). However, for each VOCs group, aromatics (41.51%) made the greatest contribution to total NMHCs, and followed by alkanes (36.82%), and then by alkenes (10.91%) and ethyne (10.38%) in 2007. While, alkanes (49.66%) was the largest group of VOCs in 2008, Aromatics (21.73%) were the second highest fraction, followed by alkenes (16.21%) and ethyne (9.99%). Biogenic emissions (isoprene accounted only 0.38% and 0.36% in 2007 and 2008, respectively) contributed little to total NMHCs in 2007 and 2008. The percentage of aromatics in 2008 was nearly a half of that in 2007. However, the 840
proportion of alkanes in 2008 was about 1.4 times that in 2007. So, the changes of the most important contributor to total NMHCs from 2007 (aromatics) to 2008 (alkanes) implying that there were something happened. 3.2 Changes of ozone formation potential (OFP) and Propy-Equiv of VOCs From the propy-equiv comparison (Fig. 2a), obviously, the whole propy-equiv levels of the top ten compounds in 2007 were much higher than that in 2008. While, from Fig. 2b, we can see that 1-butene, trans-2-butene and n-butane LPG species were the top 10 species in 2008, but not included in 2007, which mainly involved the aromatic hydrocarbons and C6-C7 solvent species. So, the aromatic species were decreased and C2-C5 alkanes were increased from 2007 to 2008 reflecting that the influence of finical crisis to the industrial and further on the composition of air pollutants in PRD region. Furthermore, there are more aromatic species in the top ten VOCs species in 2007 (8 species) than that in 2008 (4 species). So, from the comparison of OFP and propy-equiv, we can see that the aromatic species were decreased and C2-C5 alkanes were increased from 2007 to 2008. Totally, these comparisons reflect that the influence of finical crisis to the industrial and further on the composition of air pollutants in PRD region.
Figure 2 The ranked top ten species for Propy-Equiv (a) and OFP (b) in 2007 and 2008 3.3 Changes of anthropogenic sources of VOCs In order to get a comprehensive understanding of the anthropogenic VOC emission profile in the PRD region. Six major sources were resolved in 2007 and 2008 using PMF, respectively. They were diesel exhaust, evaporative emissions, gasoline exhaust, industry solvent, biomass/biofuel burning, and LPG/LNG. The average contributions of each source to the total anthropogenic NMHCs in the PRD region are illustrated in Figure 3 and Figure 4 in 2007 and 2008, respectively. From figure 3, we can see that vehicle related sources made the most significant contribution to the total NMHCs in the region (in total 35.6%, of which was 27.7% ± 2.2% for gasoline exhaust, and 7.9% ± 1.2% for diesel exhaust) in 2007, whereas the
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use of solvent was the second largest contributor (30.4% ± 3.2%, mass percentage ± standard error percentage). In addition, LPG/LNG, another important contributor, made 21.5% ± 1.3% to the total NMHCs in the region. The source apportionments of NMHCs in 2008 were illustrated in figure 4. The same as that in 2007, the vehicle related sources were the most important contributors (in total 33.4%, of which was 24.4% ± 1.9% for gasoline and 9.0% ± 0.9% for diesel) to the total NMHCs in 2008. It is interesting to note that the LPG/LNG was the second largest contributor (26.0% ± 3.0% for diesel) in 2008, differently from that in 2007. Solvent was the third largest VOC source, responsible for 20.1% ± 1.1% of total emission. From the comparison of the source contributions in the year of 2007 with that in 2008, the percentage of NMHCs attributed to the LPG/LNG in 2008 was higher than that in 2007. However, the emission of solvent in 2007 was much larger than that in 2008.
Figure 3 Source contributions to TVOCs in 2007 (mean ± Standard error).
Figure 4 Source contributions to TVOCs in 2008 (mean ± Standard error). 842
3.4 Comparison of VOCs concentrations when air masses all across Dongguan To further investigate the impact of regional transport on NMHCs, back trajectories were used to examine the typical air mass arriving at WQS in 2007 and 2008 on sampling days. We selected two days NOV 23th 2007 and NOV 30th 2008, which air mass all came from inland China and across Dongguan during sampling times. In 23th NOV, 2007, the contributions of total aromatics to OFP, propy-equiv, and mixing ratios were 31.52%, 52.79%, and 23.21%, respectively- were 4.8, 3.6, and 5.6 times those measured in 30th NOV, 2008, respectively. Further more, when added other solvents species (i.e. n-hexane, and methylcyclopentane) and vehicle related species (i.e. n-decane), these sources accounted much more in 23th NOV, 2007 than that in 30th NOV, 2008, which were 5.6, 4.4, and 8.2 times for OFP, propy-equiv, and mixing ratios, respectively. From the mixing ratios of the top ten NMHCs, we can see that C2C5 alkanes accounted about 36% of the total NMHCs in 30th NOV, 2008, however, it was only 17.4% in 23th NOV, 2007, which less than a half of that in 2008. Additionally, propylene and butenes were appeared in the top 10 compounds in 30th NOV, 2008 from the OFP and propy-equiv, while not in 23th NOV, 2007. From the comparison of the changes of the top 10 NMHCs from three aspects in those two similarly back trajectories days in 2007 and 2008, also aromatics decreased and C2-C5 alkanes enhanced were found. 4. Summary Toluene was the most abundant compounds in 2007 and 2008. The mean level of toluene in 2007 was much higher than that in 2008. From the comparison, we found that aromatics in 2007 were significantly higher (p autumn > winter. WSOC had a good correlation with OC, and had a poor relationship with EC. Moreover, strong correlation was observed between WSOC and secondary organic carbon (SOC) (R=0.857), which revealed that the main source of WSOC in PM10 is produced by the photochemical reaction. Key words: PM10; water soluble organic carbons (WSOC); OC; EC; SOC
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Heterogeneous Chemistry of Carboxylic Acids On !-Al2O3 Shengrui Tong, Maofa Ge, Weigang Wang State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences (BNLMS), Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, P. R. China Principal Contact: Maofa Ge Phone (including country code):010-62558682 Fax (including country code):010-62559373 E-mail:
[email protected] A study of the atmospheric heterogeneous reactions of formic acid, acetic acid, and propionic acid on dust particles (!-Al2O3) were performed at ambient condition by using a diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) reactor. From the analysis of the spectral features, observations of carboxylates formation provide strong evidence for an efficient reactive uptake process. And the adsorption of organic acids was found to be mostly irreversible on dust particles. The uptake coefficients of formic acid, acetic acid, and propionic acid on !-Al2O3 particles are (2.07 ± 0.26) × 10-3, (5.00 ± 0.69) × 10-3, and (3.04 ± 0.63) × 10-3, respectively (using geometric area). Besides, the effect of various relative humid (RH) on this heterogeneous reactions were studied. The uptake coefficients of organic acids on !Al2O3 particles increase initially (RH < 20%) and then decrease with the increased RH (RH >20%) which due to the effect of water on organic acids salvation, particles surface hydroxylation, and competition on reactive site. On the basis of the results of experimental simulation, the mechanism of heterogeneous reaction of dust with carboxylic acids at ambient condition was discussed. The loss of atmospheric organic acids due to reactive uptake on available mineral dust particles may be competitive with homogeneous loss pathways, especially in dusty urban and desertified environments.
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Vd: Strategies for Controlling Black Carbon Emissions
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Accelerating Climate Benefits While Improving Air Quality: The Co-Benefits of Reducing Black Carbon Dale M. Evarts1 1 US Environmental Protection Agency, (C404-04), Research Triangle Park, NC 27711 Principal contact: Dale M. Evarts, Environmental Protection Agency, (C404-04), Research Triangle Park, NC 27711, 919-541-5535, 919-685-3319,
[email protected] Abstract Much is known about the health and environmental impacts of fine particles, and of the benefits gained for urban and regional air quality by reducing them. For at least the past decade, this has been a major goal of air quality programs in the US, Europe, Japan and other developed countries, and it is increasingly a goal of developing countries faced with rising air pollution resulting from urban and industrial growth. Current scientific evidence indicates that black carbon, a constituent of fine particles that results from incomplete or inefficient combustion, also has significant climate impacts, heating the atmosphere by absorbing energy from the sun, affecting cloud formation and rainfall, and accelerating melting when deposited on snow and ice. While there is uncertainty about the overall effects of black carbon on climate, reducing this “short-lived climate forcer” (SLCF) is being recognized as an important complement to efforts to address longer-lived climate forcers such as CO2. This presentation will examine some key sources of black carbon (e.g. diesel engines, residential heating and cooking, agricultural and forest burning, some industrial sources such as brick kilns, etc.) and how targeted programs to reduce black carbon can have significant and immediate public health and climate co-benefits. It will review confounding issues for climate of co-emitted pollutants such as organic carbon and location of emissions. It will describe the challenges and opportunities for designing optimal strategies for achieving these co-benefits. The presentation will also summarize the major U.S. and multilateral programs to characterize and address black carbon.
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Sources Rich in Black Carbon: Net Radiative Impact on Atmosphere and Snow Tami Bond, Colin Zarzycki University of Illinois-Urbana Champaign; and Mark Flanner, University of Michigan Abstract The possibility of controlling emissions with high black carbon fractions is being discussed as a partial, immediate response to climate change. Reductions in absorbing aerosol that warms the Earth system, the atmosphere, and the surfaces of snow and ice could rapidly benefit both atmospheric warming and sensitive, snow-covered regions such as the Arctic and the Himalayas. To evaluate this possibility, we estimate the net radiative effect of various mitigation actions, considering black carbon, co-emitted aerosols and precursors, and gaseous pollutants. We compare these effects for major sources that are rich in black carbon: diesel engines, domestic wood burning, domestic coal burning, open forest burning, and open field burning. We discuss differences in the atmospheric and snow forcing of black carbon emissions from different regions (South, East, and Southeast Asia, as well as the former USSR). Our modeling with the Community Atmosphere Model (CAM), as well as a synthesis of other atmospheric models, indicates that co-emitted aerosols and precursors are unlikely to fully offset the atmospheric warming by black carbon for most sources, particularly those used for energy rather than uncontrolled open burning. Gaseous, short-lived pollutants from these sources contribute a positive radiative forcing as well. The major outstanding question is whether aerosol-induced cloud changes can offset the positive forcing.
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Historical Trend of Black Carbon Emissions in China: 1990-2007 Wei Wei Song1, Kebin He1, Yu Lei2 1.Dept. of Environmental Science and Engineering, Tsinghua University 2.China Project and School of Engineering and Applied Sciences, Harvard University Principal Contact: Wei Wei Song, Ph.D., Department of environmental Science and Engineering, Tsinghua University, Beijing, China, 100084. Tel: 0086 13401016406. Fax: 0086 62771101. Email:
[email protected]
Abstract To understand the historical trend and characteristics of black carbon (BC) emission in China, a comprehensive black carbon emission inventory was constructed for the years 1990 through 2007, based on the newly updated emission factors and activity data of emitting sources. In particular, ten key source categories in three major anthropogenic emission sources (Stationary combustion, industrial and processing, and mobile source) were addressed. A set of methods were established, composing of building a database of activity data associated with models, setting up a model tool set, and summarizing the functions between technology level and annual emission factors for each source. Result of the study demonstrated the historical trend and spatial distribution of BC emission in China. The estimation of provincial emissions that appeared to represent the BC emission characteristics and historical changes from key emitting sources, including cements, coking, brick, lime, iron, residential biomass burning, residential coal combustion, industry, power station and mobile vehicles. Those changes were mainly driven by the energy consumption patterns and the diversity of sources in provincial level. Uncertainties and recommendations were also identified and summarized for constructing a reliable BC emission inventory for China . Keywords: black carbon, emission inventory, China, historical trend, spatial distribution
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Black Carbon Emission Factors in Residential Stove Combustion Dr. CHEN Yingjun,Yentai Institute, China Academy of Sciences Abstract Large uncertainty in emissions of black carbon results from the complexities of energy use, difficulties in sample collection and analysis, and the effects of control measures. As a consequence, estimates of total emissions of black carbon in China vary among researchers. To improve these estimates, this study introduced a fume diluted sampling device and applied this to the coal/stove burning combination, and analyzed a more effective way of changing old stoves with a simple air cleaner and particulate filter. it compared the difference in emissions between residential burning of coal, biomass and of other fuels. It also proposes an ultimate goal of using compressed natural gas as domestic fuel rather than coal. Studies of different levels of coal emissions in China show that the BC emission factor of coal brick and soft coal reaches 3.81g/kg, which is still higher than anthracite and honeycomb briquette after MVB elimination. Forecasts suggest that BC emissions from residential fuel use may differ by as much as 7 times between the years 2000 and 2020. MVB elimination and honeycomb briquette should rise from 40% to 80%. The emission of compressed biomass fuel is significantly lower than scattered, and is worth popularizing. Emissions of wood burning (such as pine block) is obviously higher than straw and should be controlled. In recent years, the improvement of the stove has raised attention among global biomass research experts, but fuel selection is equally important.
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China’s Research Progress on Black Carbon Aerosols Dr. GAO Jian, China Research Academy of Environmental Sciences Abstract BC aerosol controls have multiple environmental benefits. They not only improve air quality and people’s health, but they also reduces the influence on the ecological system and perhaps more importantly, they play an important role in mitigating climate change. The Atmospheric Environment Institute of the Chinese Research Academy of Environmental Sciences did a 2-year study on aerosols including BC under the leadership of their head, Professor Chai Fahe. This applied the optical method developed at the University Karlsruhe of Germany to analyze black carbon at five sites in Beijing. Experiment results indicate that BC concentration in Beijing varied between 1.8-12.2ug, and varied by time and season. Concentrations were higher in winter due to residential heating and at night due to transport of heavy trucks. Because of different land uses throughout the city, concentrations detected at the southern site were higher. This research suggests that optical immersion technology is recommended. It also suggests that BC time and spatial distribution is extremely important to Beijing particulate pollution and further research should be pursued. This makes a few suggestions for future BC research: to construct a monitoring network to make clear the long-term trend and spatial distribution of black carbon; to develop an emission inventory; to identify emission sources; to determine discharge factors; to set-up a national emission inventory based on GIS; to identify the source, transfer and transform mechanism of BC, as well as a source analysis and transform mechanism of particles characteristics; to develop an online coupling of chemical mode and climate mode; to study the formation process of SOA; and to deeply understand BC environmental and climate effects.
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The Carbon Dioxide-Equivalent Benefits of Reducing Black Carbon Emissions from U.S. Class 8 Trucks Using Diesel Particulate Filters L. Bruce Hill, Ph.D. Senior Scientist, Clean Air Task Force, 18 Tremont St., Suite 530, Boston, MA 02108 USA INTRODUCTION Reducing CO2 emissions is of paramount importance in mitigating long-term climate changes, however short-term benefits may result from reducing black carbon particles. Diesel particulate filter technology may prove to be one of the most promising measures available to capture immediate climate benefits. Targeting black carbon emissions presents a parallel strategy with carbon dioxide (CO2) reductions, with the potential for much faster temperature responses and attendant health co-benefits. As part of an integrated multi-pronged strategy, reducing black carbon can provide net cooling benefits in the near term while carbon dioxide reduction strategies and technologies are developed and implemented. The following paper illustrates a simple method using published global warming potential (GWP) estimates to estimate the CO2- equivalent (CO2e) climate benefits of removing black carbon from the diesel exhaust emissions of U.S. Class 8 (heavy duty) highway diesels using diesel particulate filters (DPFs). The DPF is a proven, off-theshelf technology that can reduce black carbon emissions by 90 percent or more. Large U.S. Class 8 diesel (defined as exceeding 33,000 lbs.), for example, “combination” tractor-trailer trucks, waste haulers, large buses, constitute a significant contributor to U.S. diesel sector pollution from which black carbon emissions can be controlled fleet wide with this readily available technology. However, the simple methodology in this paper could be applied to smaller trucks and other diesel engines as well. Climate scientists have proposed two metrics to calculate CO2 equivalent (CO2e) potencies of black carbon: global warming potential (GWP) and global temperature potential (GTP).1 In this paper, we summarize GWP and GTP estimates from the published literature and uses them to calculate carbon dioxide equivalent benefits from DPFs installed on Class 8 trucks. Using a CO2e approach, we examined four questions that have a bearing on whether installation of DPFs on Class 8 trucks would provide significant climate benefits: I.)
What is the CO2e reduction from a diesel truck equipped with a DPF?
II.) What is the break-even fuel penalty, the point below which use of a DPF to reduce black carbon-related warming is beneficial? III.) How many years would black carbon reduction- related climate benefits from the installation of a DPF exceed the increased CO2e from an assumed fuel penalty of 2 976
percent? IV.) Using the above methods, what would the benefits be of a U.S. Class 8 truck rebuild rule in the United States? A review of the literature finds that fuel penalties associated with retrofit DPF applications range from zero as a best estimate to a few percent. The most comprehensive, controlled field study of 20 retrofit tractor-trailer trucks that travelled approximately 150,000 miles a year/vehicle suggests there may be no measurable fuel penalty associated with the DPF itself. This conclusion is also supported by an analysis of four years worth of fueling records, covering 1.28 million fleet miles, for 10 MTA New York City transit buses that were retrofit with a DPF. Nonetheless, given the uncertainty across studies and to be conservative in this analysis, we assumed a 2 percent fuel penalty. METHOD: CALCULATING GWP20 BENEFITS OF A DIESEL FILTER This approach uses a simple method described in detail in a white paper available on the web at: http://www.catf.us/publications/reports/CATF-BC-DPF-Climate.pdf, to estimate the theoretical reduction in global warming potential (GWP) resulting from reductions in diesel particulate matter using a DPF retrofit. Two principal assumptions simplify the scope of the analysis: 1) DPFs are readily available “off the shelf “ and, 2) ULSD is universally required, as it is in the United States. Results are expressed in CO2 GWP-equivalent grams per gallon of fuel (abbreviated below as CO2e) and are based on assumptions summarized in Table 1. The estimate assumes an incremental 2 percent increase in fuel use attributable to the filter and any corresponding increases in CO2 and black carbon emissions. It is also assumed that 90 percent2,3 of the particulate mass is removed by the filter. It is further assumed that the particle mass is made up of two principal components— organic carbon (OC) (25 percent of the mass) and black carbon (BC) (75 percent of the mass). For simplicity, the sum of black carbon and organic carbon is assumed to be 100 percent, with negligible (
