Volatile organic compounds in Tijuana during the Cal-Mex 2010 campaign: Measurements and source apportionment

Atmospheric Environment 70 (2013) 521e531

Contents lists available at SciVerse ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Volatile organic compounds in Tijuana during the Cal-Mex 2010 campaign: Measurements and source apportionment Jun Zheng a, b, Jessica P. Garzón b, c, María E. Huertas b, c, Renyi Zhang a, b, *, Misti Levy b, Yan Ma a, José I. Huertas c, Ricardo T. Jardón d, Luis G. Ruíz d, Haobo Tan e, Luisa T. Molina f a

School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, PR China Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843-3150, USA Tecnológico de Monterrey, Eduardo Monroy Cardenas No. 2000, Toluca, Mexico d Universidad Nacional Autónoma de México, Mexico City, Mexico e Institute of Tropical and Marine Meteorology, CMA, Guangdong 510080, PR China f Molina Center for Energy and the Environment, La Jolla, CA 92037, USA b c

h i g h l i g h t s < Major VOCs at the San DiegoeTijuana border area included OVOCs and aromatics. < VOCs were attributed to solvent usage, gas/diesel vehicle exhausts, and aged plume. < Diesel vehicle emission contributed to 87% of SO2 and 75% of NOx. < Aged plumes were associated with NW wind from air masses of the San Diego area.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 September 2012 Received in revised form 7 November 2012 Accepted 12 November 2012

As part of the Cal-Mex 2010 air quality study, a proton transfer reactionemass spectrometer (PTR-MS) was deployed at the San DiegoeTijuana border area to measure volatile organic compounds (VOCs) from 15 May to 30 June 2010. The major VOCs identified during the study included oxygenated VOCs (e.g., methanol, acetaldehyde, acetone, and methyl ethyl ketone) and aromatics (e.g., benzene, toluene, C8- and C9-aromatics). Biogenic VOCs (e.g., isoprene) were scarce in this region because of the lack of vegetation in this arid area. Using an U.S. EPA positive matrix factorization model, VOCs together with other trace gases (NOx, NOz and SO2) observed in this border region were attributed to four types of sources, i.e., local industrial solvent usage (58% in ppbC), gasoline vehicle exhaust (19% in ppbC), diesel vehicle exhaust (14% in ppbC), and aged plume (9% in ppbC) due to regional background and/or long-range transport. Diesel vehicle emission contributed to 87% of SO2 and 75% of NOx, and aged plume contributed to 92% of NOz. An independent conditional probability function analysis of VOCs, wind direction, and wind speed indicated that the industrial source did not show a significant tendency with wind direction. Both gasoline and diesel engine emissions were associated with air masses passing through two busy crossborder ports. Aged plumes were strongly associated with NW wind, which likely brought in aged air masses from the populated San Diego area. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: VOCs Trace gases PTR-MS Positive matrix factorization Conditional probability function Tijuana

1. Introduction Volatile organic compounds (VOCs) are ubiquitous in the atmosphere, due to both natural and anthropogenic emissions. Although biogenic VOC emission (e.g., isoprene) is the dominant

* Corresponding author. Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843-3150. E-mail address: [email protected] (R. Zhang). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.11.030

VOC source on a global scale (Guenther et al., 2006), anthropogenic activities are responsible for most of the VOC emissions in and around heavily populated urban areas (Li et al., 2007; Wang et al., 2009; Apel et al., 2010), which often host tens of millions of residents, numerous factories, and millions of automobiles. For instance, Brown et al. (2007) found that 37e49% of total VOC emissions in Los Angeles results from gasoline-related emissions, with a substantial portion of aromatic compounds, such as benzene, toluene, C8- and C9-aromatics. A similar situation is also identified in Mexico City (Rogers et al., 2006).

522

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

Once in the atmosphere, VOCs play critical roles in various photochemical chemical and multi-phase processes (Calvert et al., 2002; Fan and Zhang, 2004; Wang et al., 2010a,b; Zhang et al., 2002). VOC can undergo reactions initiated by hydroxyl radicals (OH) to form peroxy radicals (RO2), which react rapidly with nitric oxide (NO) to form nitrogen dioxide (NO2), an essential step in the formation of ground level ozone (O3) (Calvert et al., 2002; Zhang et al., 2003). As one of the six criteria air pollutants, O3 is strictly regulated by the ‘Clean Air Act’ because of its adverse affects on the ecosystem and public health, such as reducing agricultural crop yield and causing severe damage to the respiratory system. In addition, oxidation products of VOCs can contribute substantially to secondary organic aerosol (SOA) formation through nucleation and growth processes (Levitt et al., 2006; Zhang et al., 2012; Zhao et al., 2005a, 2006). It is well established that SOA accounts for a significant portion of atmospheric particulate matters (PM) (Carlton et al., 2009; Fan et al., 2006; Hallquist et al., 2009; Zhang et al., 2009; Zhao et al., 2009; Ziemann, 2011), which exert a significant impact on climate (IPCC, 2007) by directly interacting with solar and terrestrial radiations and modifying cloud microphysical properties (Li et al., 2005; Tie et al., 2003, 2005; Zhang et al., 2007). Hence, a thorough characterization of VOCs, including chemical speciation and source identification, is fundamental to fully comprehend ambient O3 and SOA formation mechanisms, and thus to develop effective emission control strategies. Conventionally, the receptor model is often used to quantitatively apportion the measured VOC concentrations to the specific emission sources (Jorquera and Rappenglück, 2004; Liu et al., 2005; Scheff and Wadden, 1993; Song et al., 2008; Watson et al., 2001). However, in practice both the number of sources and the specific profile of each source are often not well defined in an area of research interest. Another type of the factor analysis method, i.e., positive matrix factorization (PMF), has been developed by Paatero (1997), capable of identifying both the source type and source strength under the assumptions that there exist strong statistical correlations among the species in each profile. The receptor models typically do not account for the chemical loss of the modeled species during transport from the source to receptor site, which is an unrealistic assumption. To address this deficiency, it has suggested that only VOCs with low reactivities are included in the receptor model (Fujita, 2001). However, highly reactive species play key roles in ozone and SOA formation in the atmosphere (McGivern et al., 2000; Zhang et al., 2000, 2004a). To account for the chemical loss of reactive VOCs, Na and Kim (2007) have incorporated a decay factor into the standard chemical mass balance (CMB) model and found that the modified model well describes the VOC source profile, but does not significantly affect the source contributions. Similarly, Shao et al. (2011) have suggested that photochemical initial concentrations, instead of measured concentrations, should be employed in the receptor models to account for the chemical losses. In an inter-comparison study among the CMB, UNIMIX, and PMF models, Song et al. (2008) have found that CMB was more suitable for fresh emissions, but the other two factor models were more suitable for aged emissions. It has also been indicated that the PMF factors are usually associated with aged profiles (Lanz et al., 2008). Hence, PMF can be used not only to identify the emission sources, but also to derive the information regarding the photochemical processing (Slowik et al., 2010). In this work, we present VOC measurements obtained using proton transfer reaction mass spectrometry (PTR-MS) at the San Diego-Tijuana border area during the Cal-Mex 2010 field study (Bei et al., 2013). The purpose of this field campaign is to characterize the chemical and physical properties of the major air pollutants emitted from this border region that are highly affected by the international trade and commerce activities between the two

countries and to investigate the chemical processes that are responsible for the transformation and aging of PM originated from and around this region. Using the PMF model, along with traditional trace gases measurements, we have determined the major VOC sources that contribute to the observations during the campaign period. Our results are further validated with an independent conditional probability function (CPF) analysis. 2. Experimental 2.1. Observation site From 15 May to 30 June 2010, a series of ground sites were established along the San DiegoeTijuana border to monitor the chemical and physical properties of both gaseous and particulate air pollutants emitted from this region. The observation site of current work was located near the urban center of Tijuana, Baja California, Mexico, inside the Parque Morelos (32
290 49.3300 N, 116
560 31.2100 W). A detailed map of this site was provided in a companion paper of this special issue (Bei et al., 2013; Zheng et al., 2013). The site was about 3.5 miles to the south of Mexicoe California border and about 10 miles to the west coastline. There was no dense vegetation around the site. Instruments related to this work were housed inside three air-conditioned trailers. All sample inlets were mounted at least 5 m above the ground. During the observation period, the site was dominated by northerly wind with scarce precipitation. Two cross-border ports, i.e., San Ysidro and Otay Mesa, where 1.4 millions of automobiles crossed annually (El Colegio de la frontera del Norte, 2007) were within 5 miles to the northwest and north of the site. 2.2. VOC and other trace gases measurements A commercial PTR-MS (Ionicon Analytik) was used in this work to measure VOCs. The PTR-MS consisted of an ion source, a drift tube reactor and a quadrupole mass spectrometer (QMS). In principle, any VOC with a proton affinity (PA) higher than water (166.5 kcal mol1) can be ionized through proton transfer reaction with hydronium ion (H3Oþ) and detected by the QMS (Fortner et al., 2004; Hansel et al., 1995; Lindinger et al., 1998). The drift tube was operated at 1.98 mbar and 50
C, and the electric field was maintained at 50 V cm1, equivalent to an E/N ratio of 113 Townsend (1Td ¼ 1017 V cm2). The typical ion-molecular reaction time was about 1.3  104 s. Water vapor flow in the ion source was maintained at 8 standard cubic centimeters per minute (sccm). The PTRMS was operated in the selected ion monitor mode (SIM), and 28 species were sequentially measured. Typically, one cycle took about 100 s. A custom made catalytic converter (Fortner et al., 2009) was used to perform the background check for every 10 cycles. Ambient air was sampled by a diaphragm pump at about 10 standard liters per minute (slpm) through a 0.25 OD, w20-ft long PFA tubing, about 150 sccm of which was introduced into the PTR-MS inlet. The PTR-MS was routinely calibrated with a commercial VOC mixture (Spectra Gases Inc.), including propene, acetaldehyde, butenes, isoprene, acetone, benzene, methyl ethyl ketone (MEK), toluene, xylene, and 1,3,5-trimethyl benzene. For VOCs without calibration standards, kinetic calculations were applied to obtain their concentration using previously determined ion-molecule reaction rate coefficients with the hydronium ion (Zhao and Zhang, 2004; Zhao et al., 2004, 2005b; Zheng et al., 2010). For a 10-min average time, the typical detection limit of VOCs was less than 0.5 ppbv, except for methanol with a value of 3.0 ppbv. The mass assignments of the VOCs were based on our previous observations in Mexico and literature results (de Gouw and Warneke, 2007; Fortner et al., 2009; Rogers et al., 2006).

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

NOx (NO þ NO2), nitric acid (HNO3), NOy (NOx þ NOz), SO2, as well as meteorological parameters, were measured using the CCA-UNAM air quality mobile monitoring station. NOx were measured with a chemiluminescence NO/NOx/NO2 analyzer (Thermo Environmental Instruments, Model 42C), equipped with a heated molybdenum (Mo) catalytic converter maintained at w325
C. The operation principle of this commercial instrument has been given in Joseph and Spicer (1978). A pulsed-fluorescence analyzer (Thermo Environmental Instruments, Model 43C) was used to measure SO2, which was excited by ultraviolet (UV) light and then decayed to a lower energy state while emitting light at a longer wavelength. All instruments were calibrated following the U.S. EPA protocols before, during and after the campaign using NIST certified gas mixtures, which were prepared with a Teledyne API 700 Calibrator equipped with an internal ozone generator and a zero air supply (Thermo Environmental Instruments, Model 111). To calibrate the NOy/HNO3 analyzer, a set of mixtures of NO and NO2 were prepared to obtain an equivalent of total NOy. It was assumed that nitric acid was efficiently scrubbed by the nylon filter (Joseph and Spicer, 1978). Several quantitative tests were performed during the calibration to check the response of the filter with diluted nitric acid fumes. The data of all instruments were achieved at 1-min time resolution and averaged to 5 min for reporting and analysis. Wind speed was measured with a three-cup rotating anemometer (Climatronics F460, Model 100075) and wind direction measurements were conducted using a potentiometer-type sensor (Climatronics F460, Model 100076) with a counterbalanced wind vane assembly. A LICOR LI-200 pyranometer was used for measuring solar radiation. This device featured a silicon photovoltaic detector mounted in a fully cosine-corrected miniature head, which produced a current output that was directly proportional to the solar radiation in W m2. A Vaisala HMP45 sensor was used for measuring both relative humidity and air temperature and was mounted inside a naturally ventilated 10plate plastic radiation shield located on the roof of the mobile station. A Vaisala PTB101B barometer was used to measure barometric pressure. The barometric pressure sensor was mounted inside the mobile station. Five-minute averages of ambient temperature, barometric pressure and solar radiation were computed on the basis of 1-s time resolution raw data. 2.3. PMF model The PMF model used in this work was obtained from U.S. EPA (Version 3.0). Detailed descriptions of the PMF model have been given elsewhere (Paatero and Tapper, 1994; Paatero, 1997). The measured VOC concentration can be expressed as

xij ¼

p X

gik fkj þ eij

523

between the observed and the predicted concentration, it indicates that gik and fik are the best estimates to reproduce xij when Q reaches the minimum. The PMF calculates Q values with all data points (Qtrue) and without the outliers (Qrobust). The theoretically value, Qtheory, is estimated as

Qtheory ¼ nm  pðn þ mÞ

(3)

Time series of all species are reviewed in the PMF model to identify outliers, so that fairly well correlations (0.4 < R2 < 0.9) can be found between the predicted and measured concentrations. By examining the rose-plots of both daytime and nighttime wind conditions for each day, we found that northerly wind dominated about 83% during the observation period, while the rest time westerly wind occurred from the coastline, which might carry clean air mass to the site and was excluded in the analysis. PMF was executed with different number of factors. The best result of the model was selected according to several criteria. The ratio between Qrobust and Qtrue was lower than 1.5; otherwise, the outliers were affecting the results. There was no correlation among factors, i.e., there were no two or more factors to represent the same emission source. This was achieved by examining the G-space plots, included in the PMF software. Also, Qrobust was close to Qtheory (Kim et al., 2003). In this study, the ratio of Qtheory to Qrobust ratio was 0.4. The residual distributions for all of the VOCs and other pollutants were within 3. Any one of the identified factors could be related to a known physical source. Accordingly, it was found that the four factors represented the best solution. The assignments of specific VOC sources will be further discussed in the following section. Theoretically, there can be several combinations of G and F matrixes (i.e., linear transformations or rotations of G and F) that produce the same Q minimum. In environmental applications, it is necessary to assume that fkj  0 and gik  0 and this non-negativity constraint limits the rotation. If a sufficient number of elements of G and F are known to be zero, there will be no rotational ambiguity in the solution (Gamero-Castano and de la Mora, 2000). FPEAK is a built-in function of PMF that performs rotations of the F and G matrices by a user-defined value (Kim et al., 2003). In this study, the FPEAK value varied from 1 to 1, with a step-size of 0.2. It was found that the difference between the base and FPEAK solutions was less than 7%, indicating that the base solution represented the best result. Bootstrapping is a statistical technique that quantifies the precision of PMF results, i.e., the reproducibility of the solution. Bootstrapping analysis was implemented with the multi-linear engine (ME) of the PMF model. One hundred iterations of bootstrapping were performed in this work and a minimum correlation (R2 ¼ 0.6) was obtained for bootstrap-factors mapping, indicating that all factors were mapped and a stable, robust solution was acquired.

(1)

k¼1

2.4. CPF analysis where xij is the element of the data matrix X, i.e., the ith species in the jth sample, gik is the element of the source profile matrix G, i.e., the ith species in the kth source, fkj is the element of the source contribution matrix F, i.e., the kth source’s contribution to the jth sample, and eij is the residual. To locate the optimal solution, PMF minimizes the objective function Q:

Q ¼

" #2 n X m X eij u ij i1 j1

(2)

where n is the number of species, m the number of samples, and uij is the error estimate (two times of the measurement standard deviation) of the data point of xij. Since Q represents the differences

The conditional probability function (CPF) is a well-developed tool that identifies the physical locations associated with the identified groups of VOCs (Grosjean et al., 1996; Kriger et al., 1995; McDermott and Ockovic, 2000). Mathematically, CPF function is described as

CPF ¼

mq nq

(4)

where mq represents the number of samples within the winddirection sector q with mixing ratios greater than the 75th percentile of all the observations, and nq is the total number of samples in the same sector.

524

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

3. Results and discussion 3.1. Diurnal variations of VOCs Diurnal variations of NOx, NOz and SO2 during the sampling period are shown in Fig. 1. It is shown that NOx is substantially enhanced during the morning and evening rush hours, consistent with the fact that vehicular exhaust is the dominant NOx emission source in this area. NOx concentration increases after sunrise and reaches its maximum of 38 ppbv between 7:00 to 8:00 AM. The evening peak (around 6:00 PM) is smaller in magnitude (w30 ppbv) than the morning peak because of a higher planetary boundary layer height in the afternoon. NOz exhibits a higher concentration in the early morning than at nightime, indicating greater NOx oxidation in the early morning hours (Grosjean et al.,1993; Lei et al., 2000; Zhang et al., 2004b; Zheng et al., 2008). The concentration of SO2 ranges from 0.6 to 1.0 ppbv and shows a similar pattern as NOx, with a higher peak in the morning rush hour than in the afternoon. The results indicate both NOx and SO2 share the same source from automobile emissions, consistent with previous studies along the U.S.eMexico border (US EPA, 2007) suggesting diesel engine vehicles as one of the major sources of air pollution. Fig. 2 illustrates the diurnal patterns of oxygenated VOCs (OVOCs). Similar diurnal cycles are identified for acetone, acetaldehyde and C5-carbonyl, alkyl acetate and MEK, with the concentration ranging from 0.4 to 15 ppbv and a daily maximum around midday. All OVOCs, except for methanol, show a strong photochemical production during the daytime. Alkyl acetates, such as ethyl acetate, have been detected in Mexico City (Fortner et al., 2009) and are often used as a solvent in the lithography process

in the electronic industry to remove unnecessary coatings or resist material that remains on the semiconductors (US EPA, 1995). MEK increases with the sunrise but decreases after sunset, indicating the influence from the secondary formation. However, MEK is also directly emitted as solvent used by electronic industry (US EPA, 1995). Despite of the secondary sources, considerable methanol is released into the atmosphere by vegetation (Atkinson and Arey, 2003) and electronic industry (US EPA, 1995), resulting in a high level of background methanol. The hourly average of methanol varies from 4.0 to 6.1 ppbv and its diurnal pattern is mainly affected by the development of the planetary boundary layer. The most abundant aromatic VOCs observed during the measurement period are toluene, benzene and C8-aromatics (the sum of xylene, ethylbenzene and benzaldehyde) and their diurnal patterns are shown in Fig. 3. Hourly mean concentrations of aromatics range from 0.3 to 1.3 ppbv. Diurnal variations of the aromatics are strongly correlated with each other, indicating similar emission sources. Aromatic compounds, such as toluene and benzene, are mainly emitted from vehicle exhausts because of incomplete combustion and fugitive fuel evaporation (Na et al., 2003), which explains the pronounced rush hour peak at about 8:00 AM. Since aromatics are relatively reactive with OH (at 298 K, ktoluene ¼ 5.63  1012 cm3 molecule1 s1, kbenzene ¼ 1.22  1012 cm3 molecule1 s1, and kC8 aromatics ¼ ð7:0  23:1Þ  1012 cm3 molecule1 s1 ) (Calvert et al., 2002; Molina et al., 1999; Suh et al., 2002), all aromatics reach daily maximum before noon and are consumed rapidly in the afternoon. Fig. 4 shows the diurnal profiles of isoprene and acetonitrile. Isoprene is the most important biogenic VOC in the atmosphere

Fig. 1. Diurnal variations of NOx, NOz and SO2 at Parque Morelos, Tijuana during the Cal-Mex 2010 campaign.

Fig. 2. Diurnal variations of the oxygenated VOCs (OVOC) at Parque Morelos, Tijuana during the Cal-Mex 2010 campaign.

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

525

Fig. 3. Diurnal variations of aromatic VOCs at Parque Morelos, Tijuana during the Cal-Mex 2010 campaign.

Fig. 4. Diurnal variations of isoprene and acetonitrile at Parque Morelos, Tijuana during the Cal-Mex 2010 campaign.

and its emission is strongly influenced by solar radiation and ambient temperature (Guenther et al., 2000). The isoprene profile is clearly following the solar radiation intensity. The increase of isoprene concentration after midday indicates a natural biogenic contribution (Borbon et al., 2001). However, isoprene concentration in Tijuana is significantly lower than the concentration of a few ppbv, observed by Fortner et al. (2009) in Mexico City, likely attributable to the lack of vegetation coverage in this arid area. Acetonitrile is frequently used as a tracer of biomass burning emissions (Sanhueza et al., 2004). The acetonitrile profile shows little variation during the day with an average value close to the background level in the free troposphere (0.1e0.2 ppbv) (Warneke et al., 2006), indicating that the

site is not affected by biomass burning events during the observation period. 3.2. PMF factors and source profiles The data used in the PMF analysis include NOx, NOz, SO2, and 13 VOCs. The measurement statistics of these species are listed in Table 1. Also shown in Table 1 for comparison are similar measurements conducted in Houston (Leuchner and Rappengluck, 2010) and Mexico City (Velasco et al., 2005, 2007). The concentrations of most VOCs in the Mexico City Metropolitan Area (Molina et al., 2010) and in the urban area of Houston (Lei et al., 2004; Li et al., 2007; Zhang et al., 2004b) are higher than in Tijuana.

Table 1 VOCs, NOx, NOz and SO2 measured at Parque Morelos in Tijuana, Mexico. Also shown here are similar measurements conducted in Houston and Mexico city. Species

NO1x NO2z SO2 Methanol Propene Acetaldehyde Acetone Alkyl acetate Isoprene MVK/MCR MEK Benzene C5-carbonyl Toluene C8-aromatics C9-aromatics a b c d

Tijuana, Mexicoa May 15theJun 30th, 2010 (ppbv)

Houstonb Aug. 7theSept. 20th, 2006 (ppbv)

Valley of Mexicoc MCMA 2002e2003 campaign (6e10 AM) (ppbv)

Median

Mean

s

Range

Mean

Mean

22.27 2.02 0.71 3.51 2.18 1.78 3.90 5.55 0.23 0.62 0.97 0.33 0.44 0.69 0.70 0.34

23.83 2.90 0.76 4.27 2.79 2.28 4.70 7.53 0.27 0.72 1.22 0.39 0.57 0.85 0.82 0.40

12.22 2.64 0.25 2.92 2.52 1.80 3.35 6.03 0.19 0.55 1.41 0.32 0.43 0.73 0.60 0.30

4.01e104.7 DLe42.18 DLe1.98 DLe25.62 DLe27.06 DLe19.84 DLe27.41 DLe36.98 DLe1.27 DLe8.34 DLe35.23 DLe3.33 DLe2.83 DLe6.04 DLe5.93 DLe3.17

e e e 6.18d 0.84 1.94d 3.25d e 1.74 e e 1.81 e 4.70 1.23 e

e e e 23.2d 5.93  3.17 6.47d 8.01d e 0.33  0.27 e e 3.17  1.75 e 13.45  9.33 1.62  1.43 0.78  0.50 (0.57  0.38)

This study. Leuchner and Rappengluck (2010). Velasco et al. (2007). Lamb et al. (2004).

526

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

Slightly higher acetone concentration is found in Tijuana than in Houston, which may be due to the usage of acetone as solvent in the electronic industry in Tijuana (US EPA, 1995). Four factors, i.e., industrial emission (IE), aged plume (AP), gasoline vehicle exhaust (GVE), and diesel vehicle exhaust (DVE), are identified by the PMF analysis, and the source profile of each factor is shown in Fig. 5. The IE profile is distinguished mainly for the high concentrations of alkyl acetate, acetone and MEK, referred to by the U.S. EPA as the mostly released VOCs from the electronic industry. In the last two decades rapid growth in the ‘Maquilas’ sector (i.e., the original equipment manufacturers of the electronic industry in the free trade zone) in the Tijuana economy has exerted a considerable impacts on the local environment and has promoted the cross-border transport. In this work, the portions of acetone and MEK contributed by IE are 86.3% and 92.2%, respectively. The AP profile consists mainly of oxidized primary pollutants, such as NOz and OVOCs. AP contributes to the biggest portion of the observed NOz (75%), but only to 7.6% of the observed NOx. Acetone and methanol are also present in the aged plume, since they are the oxidation products of primary VOCs and have relatively long lifetimes of 53 and 12 days (assuming OH ¼ 2.0  106 molecules cm3) (Atkinson, 2000), respectively. Both GVE and DVE mainly consist of internal combustion products and unburned fuels, which can be used to distinguish them from other factors. The typical key compounds used to identify diesel and gasoline exhaust emissions are listed in Table 2. Since diesel fuel has a higher sulfur content than gasoline and

Table 2 Summary of compounds used to identify gasoline and diesel engine emissions. Reference

Jiang et al. (2011)

Thornhill et al. (2010)

Yuan et al. (2009)

Watson et al. (2001)

Location

Santiago de Chile, Chile Benzene Toluene n-Pentane

Mexico City, Mexico Benzene Toluene C8-aromatics C9-aromatics CO NOx CO Formaldehyde

Beijing, China

Urban areas, U.S.

Benzene Toluene n-Hexane

Acetylene iso-Butene iso-Pentene n-Hexane

n-Nonane n-Decane

Propene Propane n-Decane n-Undecane

Gasoline

Diesel

Toluene Xylene Styrene Nonane Undecane

diesel engines generally emit more NOx than gasoline ones (Lloyd and Cackette, 2001), the profile with the highest concentrations of SO2 and NOx is assigned as DVE, which also contains some minor components of acetaldehyde, propene, C8- and C9-aromatics commonly found in diesel vehicle exhausts (Lloyd and Cackette, 2001). According to the latest emission inventory of Tijuana reported by the Secretariat of the Environment and Natural Resources of Mexico (L.T. Consulting, 2010) 76% of SO2 emission is due to diesel vehicle emission, consistent with the result of this work. Hence, DVE dominates SO2 at the observation site. Due to the higher aromatics content in gasoline than diesel, GVE contains

Fig. 5. Concentration profiles of industrial emission (in red), aged plume (in green), diesel (in black) and gasoline (in blue) exhaust sources with error bars of 95% confidence intervals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

much more aromatics than DVE (Schroder and Dannecker, 1994). In this work, GVE contributes to 96.2% and 73.5% of the observed toluene and benzene, respectively. NOx is present in both vehicle emission factors and contributes to a substantial portion to the AP profile, but does not appear in the industrial factor. This is explained because the principal manufacturing activities in Tijuana do not directly consume fossil fuels, such as the electronic industry. Fig. 6 shows the source contributions of VOCs, SO2, NOx and NOz measured at Parque Morelos, Tijuana. As a striking feature in Fig. 6, IE is responsible for 58% (in ppbC) of the VOC emission, DVE contributes to 87% of SO2 and 75% of NOx, and AP contributes to 92% of NOz. The expansion of electronic industry in Tijuana increases the consumption of solvents, which are released in greater quantities to the atmosphere. The growth in industry activity demands more cross-border transport, leading to more NOx and SO2 emissions. The time series of the contributions of all four factors during the entire campaign are shown in Fig. 7. The gaps correspond to the time periods when clean air is blown from the west and thus are excluded from the analysis. The magnitude of the factor contribution in Fig. 7 is normalized by the average contribution in the same factor during the entire period. The labels AeG in Fig. 7 denote the events of abnormally high contribution. The detailed information, including the exact time period, the dominated factor and wind conditions, of each event is listed in Table 3. The two AP events are

527

exclusively associated with NW wind and high wind speed (>3.1 m s1), implying possible long-range transport from the populated San Diego area. The other events are dominated by either GVE or DVE, indicating that these factors are more localized in nature. Industry contributions do not exhibit strong variations over the time series, since typical industrial manufacturing operates continuously throughout the day and week (24/7) and can be treated as a constant area source. 3.3. Wind conditions and source profiles The CPF analysis is performed to explore the correlations between the source profiles and the wind direction and wind speed. The CPF values are calculated according to Eq. (4), where wind direction is divided into ten sectors between 0
and 360
(q ¼ 36
) and calm winds with speed of less than 1 m s1 are excluded. The results are displayed in Fig. 8 in polar coordinates. Although the IE factor appears to be predominant in the SW direction, because of its small value (less than 0.14 in all direction) IE shows insignificant association with any wind direction. Hence, industrial emission represents an area source in Tijuana. The aged plume plot shows a prevalent direction from NW with a CPF value of 0.35, indicating that the aged plume is originated from San Diego and its surrounding area. This is also consistent

Fig. 6. Source apportionment of VOCs (top left), NOx (top right), SO2 (bottom left) and NOz (bottom right) at Parque Morelos, Tijuana during the Cal-Mex 2010 campaign.

Fig. 7. Time series of all four PMF factor contributions. The magnitude of each series is normalized by its average of the entire period.

Table 3 Detailed description of the peak assignments labeled in Fig. 7. Peak

Date and time

Predominant factor

Wind speed (ms1) and direction

A B C D E F

05/24/2010 10:50e11:10 AM 05/26/2010 7:00e7:30 AM 05/29/2010 0:40e1:30 AM 06/05/2010 8:30e9:20 AM 06/12/2010 2:50e3:20 AM 06/15/2010 9:00e9:30 AM 11:10e11:40 AM 06/25/2010 8:20e9:10 AM

AP DVE/GVE GVE AP GVE GVE

WS WS WS WS WS WS

DVE

WS (1.6e2.3) and WD coming from NW (317
)

G

(2.6e2.8), and WD coming from N (350
) (0.8e1.1) and WD coming from N (359
) (0.4e0.5) and WD coming from SE (120
) (1.4e1.6) and WD coming from NW (335
) (0.9e1.2) and WD coming from E (285
) (2.8e3.7) and WD coming from NW (318
)

Fig. 8. Polar CPF plots for each emission source: Industry emission (top left), aged plume (top right), diesel vehicle exhaust (bottom left) and gasoline vehicle exhaust (bottom right).

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

with the observations of high levels of NOz and long-lived OVOCs in the aged plume events listed in Table 3. Both DVE and GVE factors are strongly associated with the NE wind direction, corresponding to locations of the two most important cross-border ports, i.e., San Ysidro and Otay Mesa. In particular, Otay Mesa is one of the busiest cargo ports along the U.S.eMexico border, with heavy-duty diesel trucks. 4. Summary and conclusions As part of the Cal-Mex 2010 air quality study, from 15 May to 30 June 2010 an Ionicon PTR-MS together with other instruments were deployed at the San DiegoeTijuana border area to measure VOCs, HNO3, NOx, NOy, SO2 and other air pollutants. The major VOCs identified during the study include OVOCs (e.g., methanol, acetaldehyde, acetone, and MEK) and aromatics (e.g., benzene, toluene, C8- and C9-aromatics). Biogenic VOCs (e.g., isoprene and pinenes) are consistently lower in Tijuana than other U.S. urban areas, since Tijuana is located in an arid area with only scarce vegetation pockets. Using the EPA PMF model, VOCs together with other trace gases (NOx, NOz and SO2) observed in this border region can be attributed to four sources, i.e., local industrial solvent usage (58% in ppbC), gasoline vehicle emission (19% in ppbC), diesel vehicle emission (14% in ppbC), and aged plume (9% in ppbC) due to regional background and/or transport. The aged plume consists mainly of NOz (92% of the total) and long-lived OVOCs, such as methanol and acetone. Diesel vehicle emission contributes to 87% of SO2 and 75% of NOx, with minor components of acetaldehyde, propene and C8-, C9-aromatics. Gasoline vehicle emission contains more aromatics but significantly less NOx and SO2 than diesel vehicle emission because of a higher aromatics content in the gasoline fuel. Since ‘Maquilas’ sector was the major economic activity in the Tijuana area, large quantities of organic solvents (such as alkyl acetate, acetone, MEK and methanol) were consumed and eventually released into the atmosphere. It should be pointed out that chemistry may play an important role in the PMF analysis. In this work, most carbonyls (e.g., acetone and MEK) are associated with industrial solvent usage in the Tijuana area, according to the local economic activities. These carbonyls are relatively stable and their photochemical aging is negligible during transport. NOz is solely due to secondary formation and thus is typically associated with aged air masses. Therefore, NOz represents a good tracer for aged plumes. Under a typical atmospheric condition (PBL ¼ 1000 m), the dry deposition velocity of NOz (e.g. [HNO3] ¼ w2 ppbv) is about 5 cm s1 (Zheng et al., 2008) and the corresponding deposition rate of HNO3 is about 0.08 ppb h1. Since the San Diego urban center is fairly close to the observation site, it is reasonable to assume that significant amount of NOz is transported to the Tijuana site. Therefore, the chemical reaction may not significantly affect the PMF results in this work. An independent conditional probability function analysis of the four source profiles, wind direction and speed indicated that: (1) industrial source did not show significant tendency in any wind direction; (2) both gasoline and diesel vehicle emissions were associated with air masses passing through the two busy crossborder ports, i.e., San Ysidro and Otay Mesa; (3) the aged plume was strongly associated with NW wind, which may bring the aged air mass from the populated San Diego area. With regarding to the implication for air pollution reduction, our results indicated that VOC emissions in Tijuana were fundamentally decided by its economic structure, i.e., the blooming ‘Maquilas’ manufacturing had significantly boosted the demand for cross-border transport of spare parts and end products, which led to more primary emissions and caused further deterioration of air quality in this area.

529

Acknowledgments This work was supported by the National Science Foundation (AGS-1009727 and AGS-1009393), the Robert A. Welch Foundation (A-1417), the Mexican Ministry of the Environment and Natural Resources and California Air Resources Board. We acknowledged Alejandro Torres-Jaramillo and José Santos García-Yee for their support with the CCA-UNAM air quality mobile monitoring station during the campaign. J.Z. and Y.M. acknowledged support from the National Natural Science Foundation of China (40905057 and 41275142), Jiangsu University Natural Science Research Foundation (09KJB170004), and Jiangsu Natural Science Foundation (BK2012861). References Apel, E.C., et al., 2010. Chemical evolution of volatile organic compounds in the outflow of the Mexico City Metropolitan area. Atmos. Chem. Phys.10, 2353e2376. Atkinson, R., 2000. Atmospheric chemistry of VOCs and NOx. Atmos. Environ. 34, 2063e2101. Atkinson, R., Arey, J., 2003. Atmospheric degradation of volatile organic compounds. Chem. Rev. 103, 4605e4638. Bei, N., Li, G., Zavala, M., Barrera, H., Torres, R., Grutter, M., Gutiérrez, W., García, M., Ruiz-Suarez, L.G., Ortinez, A., Guitierrez, Y., Alvarado, C., Flores, I., Molina, L.T., 2013. Meteorological overview and plume transport patterns during Cal-Mex 2010. Atmos. Environ. 70, 477e489. Borbon, A., Fontaine, H., Veillerot, M., Locoge, N., Galloo, J.C., Guillermo, R., 2001. An investigation into the traffic-related fraction of isoprene at an urban location. Atmos. Environ. 35, 3749e3760. Brown, S.G., Frankel, A., Hafner, H.R., 2007. Source apportionment of VOCs in the Los Angeles area using positive matrix factorization. Atmos. Environ. 41, 227e237. Calvert, J.G., Atkinson, R., Becker, K.H., Kamens, R.M., Seinfeld, J.H., Wallington, T.J., 2002. The Mechanisms of Atmospheric Oxidation of Aromatic Hydrocarbons. Oxford University Press, New York, NY. Carlton, A.G., Wiedinmyer, C., Kroll, J.H., 2009. A review of secondary organic aerosol (SOA) formation from isoprene. Atmos. Chem. Phys. 9, 4987e5005. de Gouw, J., Warneke, C., 2007. Measurements of volatile organic compounds in the Earth’s atmosphere using proton-transfer-reaction mass spectrometry. Mass. Spectrom. Rev. 26, 223e257. El Colegio de la frontera del Norte, 2007. Estudio de puertos de entrada MéxicoEstados Unidos: Análisis de capacidades y recomendaciones para incrementar su eficiencia, Tijuana. Fan, J.W., Zhang, R.Y., 2004. Atmospheric oxidation mechanism of isoprene. Environ. Chem. 1, 140e149. Fan, J.W., Zhang, R.Y., Collins, D., Li, G.H., 2006. Contribution of secondary condensable organics to new particle formation: a case study in Houston, Texas. Geophys. Res. Lett. 33, L15802. http://dx.doi.org/10.1029/2006GL026295. Fortner, E.C., Zhao, J., Zhang, R.Y., 2004. Development of ion drift-chemical ionization mass spectrometry. Anal. Chem. 76, 5436e5440. Fortner, E.C., Zheng, J., Zhang, R., Knighton, B.W., Volkamer, R.M., Sheehy, P., Molina, L., André, M., 2009. Measurements of volatile organic compounds using proton transfer reaction e mass spectrometry during the MILAGRO 2006 campaign. Atmos. Chem. Phys. 9, 467e481. Fujita, E.M., 2001. Hydrocarbon source apportionment for the 1996 Paso del Norte Ozone Study. Sci. Total Environ. 276, 171e184. Gamero-Castano, M., de la Mora, J.F., 2000. A condensation nucleus counter (CNC) sensitive to singly charged sub-nanometer particles. J. Aerosol Sci. 31, 757e772. Grosjean, E., Williams, E.L., Grosjean, D., 1993. Ambient levels of formaldehyde and acetaldehyde in Atlanta, Georgia. J. Air Waste Manage. 43, 469e474. Grosjean, E., Grosjean, D., Fraser, M.P., Cass, G.R., 1996. Air quality model evaluation data for organics .2. C-1-C-14 carbonyls in Los Angeles air. Environ. Sci. Technol. 30, 2687e2703. Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile organic compounds; carbon monoxide, and oxides of nitrogen from North America. Atmos. Environ. 34, 2205e2230. Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 6, 3181e3210. Hallquist, M., Wenger, J.C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N.M., George, C., Goldstein, A.H., Hamilton, J.F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.E., Jimenez, J.L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, T.F., Monod, A., Prevot, A.S.H., Seinfeld, J.H., Surratt, J.D., Szmigielski, R., Wildt, J., 2009. The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmos. Chem. Phys. 9, 5155e5236. Hansel, A., Jordan, A., Holzinger, R., Prazeller, P., Vogel, W., Lindinger, W., 1995. Proton-transfer reaction mass-spectrometry e online trace gas-analysis at the ppb level. Int. J. Mass. Spectrom. 149, 609e619. IPCC, 2007. IPCC Fourth Assessment Report: Climate Change 2007: The Physical Science Basis. Cambridge Univ. Press, United Kingdom and New York.

530

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531

Jiang, J.K., Chen, M.D., Kuang, C.A., Attoui, M., McMurry, P.H., 2011. Electrical mobility spectrometer using a diethylene glycol condensation particle counter for measurement of aerosol size distributions down to 1 nm. Aerosol Sci. Technol. 45, 510e521. Jorquera, H., Rappenglück, B., 2004. Receptor modeling of ambient VOC at Santiago, Chile. Atmos. Environ. 38, 4243e4263. Joseph, D.W., Spicer, C.W., 1978. Chemiluminescence method for atmospheric monitoring of nitric-acid and nitrogen-oxides. Anal. Chem. 50, 1400e1403. Kim, C.S., Okuyama, K., de la Mora, J.F., 2003. Performance evaluation of an improved particle size magnifier (PSM) for single nanoparticle detection. Aerosol Sci. Technol. 37, 791e803. Kriger, M.S., Cook, K.D., Ramsey, R.S., 1995. Durable gold-coated fused-silica capillaries for use in electrospray mass-spectrometry. Anal. Chem. 67, 385e389. L.T. Consulting, 2010. Inventario de emisiones a la atmósfera en los municipios de Tijuana y Playas de Rosarito, Baja California. Prepared with the Environmental Protection Agency (EPA-USA), Border Environment Cooperation Commission (BECC), Air Resources Board (ARB), Secretaría del Medio Ambiente y Recursos Naturales (SEMARNAT), and Secretaría de Protección al Ambiente de Baja California (SPABC). Lamb, B., Velasco, E., Allwine, E., Westberg, H., Herndon, S., Knighton, B., Grimsrud, E., 2004. Ambient VOC Measurements in Mexico City during the MCMA 2002 & 2003 Field Campaigns. Sixth Conference on Atmospheric Chemistry: Air Quality in Megacities. American Meteorological Society, Seattle, WA. Lanz, V.A., Hueglin, C., Buchmann, B., Hill, M., Locher, R., Staehelin, J., Reimann, S., 2008. Receptor modeling of C2eC7 hydrocarbon sources at an urban background site in Zurich, Switzerland: changes between 1993e1994 and 2005e 2006. Atmos. Chem. Phys. 8, 2313e2332. Lei, W.F., Zhang, R.Y., Tie, X.X., Hess, P., 2004. Chemical characterization of ozone formation in the Houston-Galveston area: a chemical transport model study. J. Geophys. Res. 109, D12301. http://dx.doi.org/10.1029/2003JD004219. Lei, W., Zhang, R., McGivern, W.S., Derecskei-Kovacs, A., North, S.W., 2000. Theoretical study of isomeric branching in the isoprene-OH reaction: implications to final product yields in isoprene oxidation. Chem. Phys. Lett. 326, 109e114. Leuchner, M., Rappengluck, B., 2010. VOC source-receptor relationships in Houston during TexAQS-II. Atmos. Environ. 44, 4056e4067. Levitt, N.P., Zhao, J., Zhang, R.Y., 2006. Heterogeneous chemistry of butanol and decanol with sulfuric acid: implications for secondary organic aerosol formation. J. Phys. Chem. A 110, 13215e13220. Li, G.H., Zhang, R.Y., Fan, J.W., Tie, X.X., 2005. Impacts of black carbon aerosol on photolysis and ozone. J. Geophys. Res. 110, D23206. http://dx.doi.org/10.1029/ 2005JD005898. Li, G.H., Zhang, R.Y., Fan, J.W., Tie, X.X., 2007. Impacts of biogenic emissions on photochemical ozone production in Houston, Texas. J. Geophys. Res. 112, D10309. http://dx.doi.org/10.1029/2006JD007924. Lindinger, W., Hansel, A., Jordan, A., 1998. Proton-transfer-reaction mass spectrometry (PTR-MS): on-line monitoring of volatile organic compounds at pptv levels. Chem. Soc. Rev. 27, 347e354. Liu, Y., Shao, M., Zhang, J., Fu, L.L., Lu, S.H., 2005. Distributions and source apportionment of ambient volatile organic compounds in Beijing city, China. J. Environ. Sci. Health A-Toxic/Hazard. Subst. Environ. Eng. 40, 1843e1860. Lloyd, A.C., Cackette, T.A., 2001. Diesel engines: environmental impact and control. J. Air Waste Manage. 51, 809e847. McDermott, W.T., Ockovic, R.C., 2000. A condensation nucleus counter for chemically reactive gases. J. Aerosol Sci. 31, 1397e1406. McGivern, W.S., Suh, I., Clinkenbeard, A.D., Zhang, R.Y., North, S.W., 2000. Experimental and computational study of the OH-isoprene reaction: Isomeric branching and low-pressure behavior. J. Phys. Chem. A 104 (28), 6609e6616. Molina, M.J., Zhang, R., Broekhuizen, K., Lei, W., Navarro, R., Molina, L.T., 1999. Experimental study of intermediates from OH-initiated reactions of toluene. J. Am. Chem. Soc. 121, 10225e10226. Molina, L.T., Madronich, S., Gaffney, J.S., Apel, E., de Foy, B., Fast, J., Ferrare, R., Herndon, S., Jimenez, J.L., Lamb, B., Osornio-Vargas, A.R., Russell, P., Schauer, J.J., Stevens, P.S., Volkamer, R., Zavala, M., 2010. An overview of the MILAGRO 2006 Campaign: Mexico City emissions and their transport and transformation. Atmos. Chem. Phys. 10, 8697e8760. Na, K., Kim, Y.P., 2007. Chemical mass balance receptor model applied to ambient C2eC9 VOC concentration in Seoul, Korea: effect of chemical reaction losses. Atmos. Environ. 41, 6715e6728. Na, K., Kim, Y.P., Moon, K.C., 2003. Diurnal characteristics of volatile organic compounds in the Seoul atmosphere. Atmos. Environ. 37, 733e742. Paatero, P., 1997. Least squares formulation of robust non-negative factor analysis. Chemometrics Intell. Lab. Syst. 37, 23e35. Paatero, P., Tapper, U., 1994. Positive matrix factorization e a nonnegative factor model with optimal utilization of error-estimates of data values. Environmetrics 5, 111e126. Rogers, T.M., Grimsrud, E.R., Herndon, S.C., Jayne, J.T., Kolb, C.E., Allwine, E., Westberg, H., Lamb, B.K., Zavala, M., Molina, L.T., Molina, M.J., Knighton, W.B., 2006. On-road measurements of volatile organic compounds in the Mexico City metropolitan area using proton transfer reaction mass spectrometry. Int. J. Mass. Spectrom. 252, 26e37. Sanhueza, E., Holzinger, R., Kleiss, B., Donoso, L., Crutzen, P.J., 2004. New insights in the global cycle of acetonitrile: release from the ocean and dry deposition in the tropical savanna of Venezuela. Atmos. Chem. Phys. 4, 275e280.

Scheff, P.A., Wadden, R.A., 1993. Receptor modeling of volatile organiccompounds .1. emission inventory and validation. Environ. Sci. Technol. 27, 617e625. Schroder, B., Dannecker, W., 1994. Vehicle emissions as the major source of gaseous aromatic-hydrocarbons at different locations in Germany. Sci. Total Environ. 147, 275e279. Shao, M., Wang, B., Lu, S.H., Yuan, B., Wang, M., 2011. Effects of Beijing Olympics control measures on reducing reactive hydrocarbon species. Environ. Sci. Technol. 45, 514e519. Slowik, J.G., Vlasenko, A., McGuire, M., Evans, G.J., Abbatt, J.P.D., 2010. Simultaneous factor analysis of organic particle and gas mass spectra: AMS and PTR-MS measurements at an urban site. Atmos. Chem. Phys. 10, 1969e1988. Song, Y., Dai, W., Shao, M., Liu, Y., Lu, S.H., Kuster, W., Goldan, P., 2008. Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China. Environ. Pollut. 156, 174e183. Suh, I., Zhang, D., Zhang, R.Y., Molina, L.T., Molina, M.J., 2002. Theoretical study of OH addition reaction to toluene. Chem. Phys. Lett. 364, 454e462. Thornhill, D.A., Williams, A.E., Onasch, T.B., Wood, E., Herndon, S.C., Kolb, C.E., Knighton, W.B., Zavala, M., Molina, L.T., Marr, L.C., 2010. Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City. Atmos. Chem. Phys. 10, 3629e3644. Tie, X.X., Madronich, S., Walters, S., Zhang, R.Y., Rasch, P., Collins, W., 2003. Effect of clouds on photolysis and oxidants in the troposphere. J. Geophys. Res. 108, 4642. http://dx.doi.org/10.1029/2003JD003659. Tie, X.X., Madronich, S., Walters, S., Edwards, D.P., Ginoux, P., Mahowald, N., Zhang, R.Y., Lou, C., Brasseur, G., 2005. Assessment of the global impact of aerosols on tropospheric oxidants. J. Geophys. Res. 110, D03204. http:// dx.doi.org/10.1029/2004JD005359. US EPA, 2007. Analysis of Diesel Emissions in the U.S.eMexico Border Region, pp. 1e 103. US EPA, 1995. Profile of electronic and computer industry. In: Project, E.O.o.C.S.N. U.S. Government Printing Office, Washington, DC, pp. 1e151. Velasco, E., Lamb, B., Pressley, S., Allwine, E., Westberg, H., Jobson, B.T., Alexander, M., Prazeller, P., Molina, L., Molina, M., 2005. Flux measurements of volatile organic compounds from an urban landscape. Geophys. Res. Lett. 32, L20802. Velasco, E., Lamb, B., Westberg, H., Allwine, E., Sosa, G., Arriaga-Colina, J.L., Jobson, B.T., Alexander, M.L., Prazeller, P., Knighton, W.B., Rogers, T.M., Grutter, M., Herndon, S.C., Kolb, C.E., Zavala, M., de Foy, B., Volkamer, R., Molina, L.T., Molina, M.J., 2007. Distribution, magnitudes, reactivities, ratios and diurnal patterns of volatile organic compounds in the Valley of Mexico during the MCMA 2002 & 2003 field campaigns. Atmos. Chem. Phys. 7, 329e353. Wang, M., Zhu, T., Zheng, J., Zhang, R.Y., Zhang, S.Q., Xie, X.X., Han, Y.Q., Li, Y., 2009. Use of a mobile laboratory to evaluate changes in on-road air pollutants during the Beijing 2008 Summer Olympics. Atmos. Chem. Phys. 9, 8247e8263. Wang, L., Khalizov, A.F., Zheng, J., Xu, W., Ma, Y., Lal, V., Zhang, R., 2010a. Atmospheric nanoparticles formed from heterogeneous reactions of organics. Nat. Geosci. 3, 238e242. Wang, L., Lal, V., Khalizov, A.F., Zhang, R., 2010b. Heterogeneous chemistry of alkylamines with sulfuric acid: implications for atmospheric formation of alkylaminium sulfates. Environ. Sci. Technol. 44, 2461e2465. http://dx.doi.org/ 10.1021/es9036868. Warneke, C., de Gouw, J.A., Stohl, A., Cooper, O.R., Goldan, P.D., Kuster, W.C., Holloway, J.S., Williams, E.J., Lerner, B.M., McKeen, S.A., Trainer, M., Fehsenfeld, F.C., Atlas, E.L., Donnelly, S.G., Stroud, V., Lueb, A., Kato, S., 2006. Biomass burning and anthropogenic sources of CO over New England in the summer 2004. J. Geophys. Res. 111 Watson, J.G., Chow, J.C., Fujita, E.M., 2001. Review of volatile organic compound source apportionment by chemical mass balance. Atmos. Environ. 35, 1567e1584. Yuan, Z.B., Lau, A.K.H., Shao, M., Louie, P.K.K., Liu, S.C., Zhu, T., 2009. Source analysis of volatile organic compounds by positive matrix factorization in urban and rural environments in Beijing. J. Geophys. Res. 114. Zhang, R.Y., Suh, I., Lei, W., Clinkenbeard, A.D., North, S.W., 2000. Kinetic studies of OH-initiated reactions of isoprene. J. Geophys. Res. 105, 24627e24635. Zhang, D., Lei, W.F., Zhang, R.Y., 2002. Mechanism of OH formation from ozonolysis of isoprene: kinetics and product yields. Chem. Phys. Lett. 358, 171e179. Zhang, R., Tie, X., Bond, D.W., 2003. Impacts of anthropogenic and natural NOx sources over the U.S. on tropospheric chemistry. Proc. Natl. Acad. Sci. U.S.A. 100, 1505e1509. Zhang, R., Suh, I., Zhao, J., Zhang, D., Fortner, E.C., Tie, X., Molina, L.T., Molina, M.J., 2004a. Atmospheric new particle formation enhanced by organic acids. Science 304, 1487e1490. Zhang, R.Y., Lei, W.F., Tie, X.X., Hess, P., 2004b. Industrial emissions cause extreme urban ozone diurnal variability. Proc. Natl. Acad. Sci. U.S.A. 101, 6346e6350. Zhang, R.Y., Li, G.H., Fan, J.W., Wu, D.L., Molina, M.J., 2007. Intensification of Pacific storm track linked to Asian pollution. Proc. Natl. Acad. Sci. U.S.A. 104, 5295e5299. Zhang, R.Y., Wang, L., Khalizov, A.F., Zhao, J., Zheng, J., McGraw, R.L., Molina, L.T., 2009. Formation of nanoparticles of blue haze enhanced by anthropogenic pollution. Proc. Natl. Acad. Sci. U.S.A. 106, 17650e17654. Zhang, R.Y., Khalizov, A., Wang, L., Hu, M., Xu, W., 2012. Nucleation and growth of nanoparticles in the atmosphere. Chem. Rev. 112, 1957e2011.

J. Zheng et al. / Atmospheric Environment 70 (2013) 521e531 Zhao, J., Zhang, R.Y., 2004. Proton transfer reaction rate constants between hydronium ion (H3O(þ)) and volatile organic compounds. Atmos. Environ. 38, 2177e2185. Zhao, J., Zhang, R.Y., Fortner, E.C., North, S.W., 2004. Quantification of hydroxycarbonyls from OH-isoprene reactions. J. Am. Chem. Soc. 126, 2686e2687. Zhao, J., Levitt, N.P., Zhang, R., 2005a. Heterogeneous chemistry of octanal and 2, 4hexadienal with sulfuric acid. Geophys. Res. Lett. 32, L09802. http://dx.doi.org/ 10.1029/2004GL022200. Zhao, J., Zhang, R.Y., Misawa, K., Shibuya, K., 2005b. Experimental product study of the OH-initiated oxidation of m-xylene. J. Photoch. Photobio. A 176, 199e207. Zhao, J., Levitt, N.P., Zhang, R.Y., Chen, J.M., 2006. Heterogeneous reactions of methylglyoxal in acidic media: implications for secondary organic aerosol formation. Environ. Sci. Technol. 40, 7682e7687. Zhao, J., Khalizov, A., Zhang, R.Y., McGraw, R., 2009. Hydrogen-bonding interaction in molecular complexes and clusters of aerosol nucleation precursors. J. Phys. Chem. A 113, 680e689.

531

Zheng, J., Zhang, R., Fortner, E.C., Volkamer, R.M., Molina, L., Aiken, A.C., Jimenez, J.L., Gaeggeler, K., Dommen, J., Dusanter, S., Stevens, P.S., Tie, X., 2008. Measurements of HNO3 and N2O5 using ion drift-chemical ionization mass spectrometry during the MILAGRO/MCMA-2006 campaign. Atmos. Chem. Phys. 8, 6823e6838. Zheng, J., Khalizov, A., Wang, L., Zhang, R., 2010. Atmospheric pressure-ion drift chemical ionization mass spectrometry for detection of trace gas species. Anal. Chem. 82, 7302e7308. Zheng, J., Zhang, R., Garzón, J.P., Huertas, M.E., Levy, M., Ma, Y., Torres-Jardón, R., Ruiz-Suárez, L.G., Russell, L., Takahama, S., Tan, H., Li, G., Molina, L., 2013. Measurements of formaldehyde at the U.S.eMexico border during the Cal-Mex 2010 Air Quality Study. Atmos. Environ. 70, 513e520. http://dx.doi.org/10.1016/ j.atmosenv.2012.09.041. Ziemann, P.J., 2011. Effects of molecular structure on the chemistry of aerosol formation from the OH-radical-initiated oxidation of alkanes and alkenes. Int. Rev. Phys. Chem. 30, 161e195.