Volatile organic compounds in the Pearl River Delta: Identification of source regions and recommendations for emission-oriented monitoring strategies

Atmospheric Environment 76 (2013) 162e172

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Volatile organic compounds in the Pearl River Delta: Identification of source regions and recommendations for emission-oriented monitoring strategies Zibing Yuan a, b, Liuju Zhong c, Alexis Kai Hon Lau a, b, *, Jian Zhen Yu a, b, Peter K.K. Louie d a

Atmospheric Research Center, HKUST Fok Ying Tung Graduate School, Nansha IT Park, Nansha, Guangzhou 511458, PR China Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China Guangdong Environmental Monitoring Center, Guangzhou 510045, PR China d Hong Kong Environmental Protection Department, Revenue Tower, 5 Gloucester Road, Wanchai, Hong Kong, PR China b c

h i g h l i g h t s < A grid study was initiated with VOC samples simultaneously taken at 84 sites over the PRD. < PMF model was applied to identify major VOC sources and their spatiotemporal variations. < Hotspot areas with significant VOC contributions were identified over the PRD. < Emission-oriented monitoring network, such as PAMS, is recommended to establish in the PRD.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 September 2012 Received in revised form 10 November 2012 Accepted 20 November 2012

For the purpose of systematically characterizing the ambient volatile organic compound (VOC) levels and their emission sources in the Pearl River Delta (PRD) of China, a grid study with VOC samples simultaneously taken at 84 sites over the PRD was conducted in summer and winter of 2008 and 2009. Positive Matrix Factorization (PMF) model was applied to identify the major VOC contributing sources and their temporal and spatial variations. Nine sources were identified, with gasoline exhaust, industrial emission and LPG leakage & propellant emission the top three significant sources. They accounted for 23%, 16% and 13% of the ambient VOC levels, respectively. Control measures should be therefore targeted on mitigating the VOC emissions from the traffic-related and industrial-related sources. The total VOC level did not show strong increase from 5 a.m. to 10 a.m. during all the four sampling campaigns, which may result from stronger wind and higher mixing height at 10 a.m. Three hotspot areas with significant VOC contributions were identified by source apportionment analysis: (1) the Pearl River Estuary; (2) an area from Central Dongguan to North Shenzhen; and (3) the ZhuhaieZhongshaneJiangmen area. For better characterizing the roles of VOC and NOx in producing the secondary pollutants and to identify specific sources emitting excessive concentrations of precursors, the emission-oriented Photochemical Assessment Monitoring Station (PAMS) network is recommended to be established in the PRD. Three PAMS networks are suggested in correspondence to the three identified hotspot areas. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Volatile organic compounds Positive matrix factorization Photochemical assessment monitoring station Pearl River Delta

1. Introduction In the past two decades, the Pearl River Delta of China has undergone fast economic development. Large-scale industrial zones and highways have linked the small- and medium-scale * Corresponding author. Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China. Tel.: þ86 852 2358 6944; fax: þ86 852 2358 1582. E-mail address: [email protected] (A.K.H. Lau). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.11.034

cities together, accelerated the urbanization process, changed lifestyles, and at the same time stimulated a huge energy demand and raw material consumption. With the rapid development and lax pollution control, air pollution in the PRD deteriorated rapidly. Air pollution in the PRD is characterized by high level of ozone and secondary particulate matter (PM) (Chan and Yao, 2008). With high NOx emissions from vehicles, power plants and marine vessels, reduction of volatile organic compounds (VOCs) emission becomes to be the key and should be prioritized in the air pollution control efforts in the PRD (Zhang et al., 2008; Shao et al., 2009). In

Z. Yuan et al. / Atmospheric Environment 76 (2013) 162e172

particular, some VOC species, e.g. benzene, hexane and chlorinated VOCs, are associated with adverse health impact, or even carcinogenic (http://www.epa.gov/ttn/atw/nata/34poll.html). Sources with high emissions of these VOC species should be strictly banned. Having developed as one of the largest workshops in the world, the PRD is concentrated with a diversified range of industries as well as their supporting facilities. In addition, the main industries also differ among cities. For example, furniture, shoe-making and electronic industries are concentrated in Dongguan, while printing and painting industries are mainly distributed in Guangzhou/ Foshan area (Zheng et al., 2009a). Considering the various VOC emission profiles among different sources and substantial diversity in chemical reactivities and the associated ozone and PM forming potentials among different VOCs, we need to formulate locationspecific VOC control strategies tailor for the emission characteristics in a particular locale. It is therefore important to identify the major VOC sources at different locations of the PRD region. A set of receptor modeling techniques has been applied in VOC source apportionment worldwide (e.g. Xie and Berkowitz, 2006; Brown et al., 2007; Song et al., 2007; Yuan et al., 2009; Lau et al., 2010). Specifically, Miller et al. (2002) used the same set of VOC data to assess the performance of four receptor models, Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), Chemical Mass Balance (CMB) and Unmix. They found that PMFextracted factor profiles most closely represent the major sources used to generate the simulated data, and concluded that PMF is the most appropriate model to explain the results. In PMF, error estimated for each data value is determined as point-by-point weight. Such weighting scheme allows the inclusion of uncertain data in the analysis by assigning them with lower weights. In addition, constraints on the results such as non-negativity of the factors are integrated into the computational process. These features make physical sense in receptor modeling of environmental data. We therefore choose to use PMF to investigate the VOC source contribution in this study. The combination of intense VOC and NOx emissions and high temperature, relative humidity and strong solar radiation favors photochemical oxidation of VOCs in the PRD. The average and peak concentrations of ozone and PM are far higher than the WHO Air Quality Guideline (GDEMC and HKEPD, 2012; WHO, 2005). Therefore, it is urgently needed to develop effective control strategies targeting on the precursors of these secondary pollutants. Experiences in the U.S. (http://www.epa.gov/ttn/amtic/pamsmain.html) and Taiwan (Yang et al., 2005) demonstrated that the establishment of the Photochemical Assessment Monitoring Station (PAMS) network is helpful in characterizing the levels and variations of photochemical reactants, tracking precursor emissions and identifying key constituents and parameters involved in photochemical processes. In the U.S., 23 PAMS networks including 78 sites have been developed in serious non-attainment areas. In Taiwan, 3 PAMS networks were established in northern, central and southern part of the island (Wang, 2008). Currently, O3, NO, NOx, 56 targeted VOCs and three carbonyl species (formaldehyde, acetaldehyde and acetone) as well as a standard set of surface meteorological parameters are measured in the PAMS network in the US and Taiwan. There is no regular measurement of VOC and carbonyls in the PRD. With the limited resources, the VOC and carbonyl monitoring stations should be strategically selected to reflect the distribution of major emission sources over the PRD and the impacts to the downwind areas. The major objective of this article is to identify major VOC sources and their spatial distributions by utilizing the speciated data collected in a first-ever grid sampling campaign in the PRD. Based on the VOC source characteristics, strategies in setting up emissionoriented monitoring network over the PRD are recommended.

163

2. Methodology 2.1. VOC sampling and experiment For the identification of the VOC characteristics over the entire PRD region, the PRD, with a land area of w40,000 km2, was divided into 100 grid cells each at 20 km in length. VOC samples were taken by canisters simultaneously at one site in each of the 84 grid cells over land. A total of four sampling campaigns were conducted for two different seasons (summer and winter) in two consecutive years (2008 and 2009). The sampling dates were selected to be under synoptic conditions favorable for the photochemical production of secondary pollutants. The two summer campaigns were conducted before the approach of tropical cyclone Jangmi and Ketsana, respectively, which induced stagnant conditions over the PRD region. The two winter campaigns were conducted before and during the passage of weak cold front. The weather during all the campaigns was generally fine and the wind speeds were calm, favoring pollutant accumulation and interaction. On each sampling day, two samples were collected at each site, one at 5 a.m. and the other at 10 a.m. to capture the VOC level changes as a result of human activities (e.g., morning rush hours) and solar radiation. A total of eight sampling events were therefore conducted. Detailed information about the VOC sampling, chemical analysis and quality assurance (QA)/quality control (QC) procedures is provided in Louie et al. (2013) of this Issue. A brief description is given below. The canisters were analyzed by gas chromatography (GC) with a flame ionization detector (FID) for carbon monoxide (CO) and methane (CH4), and after pre-concentration analyzed by GC with a multi-detector system (consisting of a mass spectrometer detector (MSD), two flame ionization detectors (FIDs) and two electron capture detectors (ECDs)) for non-methane VOC species. The chemical analysis is subjected to rigorous quality assurance (QA)/quality control (QC) procedures. A total of 645 field samples, excluding 58 samples that did not comply with the QA/QC requirements, have valid data of CO, CH4, 39 non-methane hydrocarbons (NMHCs), 4 halocarbons, 5 alkyl nitrates and DMS. 2.2. Data processing and analysis Software package USEPA PMF v3.0 was applied for VOC source apportionment analysis (USEPA, 2008). The procedure of uncertainty determination reported by Polissar et al. (1998) is applied. For outliers (reported values larger than the maximum acceptable concentrations of respective species), uncertainty was set as four times the concentration. In this way, the outliers in the PMF run were down-weighted. Values reported as below detection limit were replaced by half of the detection limit (Table S1 of the Appendix). The corresponding uncertainty was set as 2 times the detection limit. Species with more than 5% of all samples as outliers or below detection limit were excluded from analysis. As a result of this data screening, CO, CH4, 30 NMHCs, 4 halocarbons and 4 alkyl nitrates are retained in the analysis, as shown in Fig. 1 and Table S1 of the Appendix. The uncertainty of each element is determined as,

Uij ¼ 0:05  cij þ 0:05 

X

cj =i

i

where Uij is the uncertainty for the sample i and parameter j, and cij is the concentration for the sample i and parameter j. As each sample is associated with its own inherent uncertainty, PMF generally requires a large number of samples to make stable and reliable source identification, especially for minor sources. Data collected at all sampling sites and in all the campaigns were lumped together for PMF source apportionment analysis. By doing this, one

164

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100

Combustion

50 0 100

Diesel Exhaust

50 0 100

Gasoline Exhaust

50 0 100

Gasoline Evaporation

50 0 100

LPG Leakage & Propellant Emission

50 0 100

Mixed Solvents

50 0 100

Industrial Emission

50 0 100

Secondary Formation & Aged Air Mass

50 0 100

Biogenic Emission

0

CH4 CO CHCl3 CH2Cl2 C2HCl3 C2Cl4 MeONO2 EtONO2 i-PrONO2 2-BuONO2 Ethane Ethene Ethyne Propane Propene i-Butane n-Butane 1-Butene i-Butene t-2-Butene c-2-Butene i-Pentane n-Pentane Isoprene n-Hexane n-Heptane n-Octane 2,3-DMB 2-Mpentane 3-Mpentane 2-Mhexane 3-Mhexane Benzene Toluene Ethylbenzene m/p-Xylene o-Xylene n-Pbenzene 3-Etoluene 1,2,4-TMB

50

Fig. 1. Explained variations of the nine source profiles identified by PMF.

has to assume that the profile of each source category remain unchanged at all sampling sites during the eight sampling events. We understand that VOC is a family within which species are associated with different reactivities with OH radical and other atmospheric oxidants. As a result, source profiles tend to change with respect to time and locations during transport of emission plumes, so called “photochemical aging”. Therefore, a compromise must be reached between the demanding requirement on the

sample size by PMF, and the possible change of source profiles that were assumed to be unvaried temporally and spatially. By examining the spatial characteristics of VOC species, Louie et al. (2013) concluded that the VOC levels were significantly affected by freshly emitted pollutants from local sources during sampling days. In this regard, chemical losses of VOC species were likely not significant. Hence, no adjustment on VOC losses was conducted in this study.

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Table 1 Identified source categories by PMF and their corresponding markers. Source

Source category

Markers

Reference

1 2 3 4 5 6 7 8 9

Combustion Diesel exhaust Gasoline exhaust Gasoline evaporation LPG leakage & propellant emissions Mixed solvents Industrial emission Secondary formation & Aged air mass Biogenic emission

Ethene, ethyne, C3eC4 alkenes C2 species, 1,2,4-trimethylbenzene CO, C2 species, C4eC5 alkanes Isopentane, n-butane, isobutene Propane, n-butane, i-butane Toluene, ethylbenzene, xylenes C2HCl3, CCl4, C6eC7 alkanes Alkyl nitrates, ethane Isoprene

Watson et al. (2001) Gertler et al. (1996) Watson et al. (2001) Watson et al. (2001) Blake and Rowland (1995); Liu et al. (2008a) Seila et al. (2001), Song et al. (2007) Tsai et al. (2008) Bertman et al. (1995), Simpson et al. (2003) Guenther et al. (1995)

In the PMF analysis, 10% extra modeling uncertainty was imposed to account for temporal changes in the source profiles and other sources of variability. Twenty base runs were performed and the run with the minimum Q value was selected as the base run solution. 100 bootstrap runs were conducted with minimum correlation R-value of 0.6 to examine the stability and to estimate the uncertainty of the base run solution. All of the bootstrapped factors were uniquely matched with a factor from the base solution. It is noted that the bootstrap method accounts for some but not all sources of uncertainty and thus likely provides a lower bound on the true uncertainties in the modeling results. For the Q values derived from the twenty base runs, the standard deviation was only w0.10% of the mean, indicating a very stable solution. For this modeling, an Fpeak value of zero resulted in the most meaningful

Fig. 2. Overall contribution percentages for the nine identified source by PMF analysis.

results. Modeling was performed for four to ten factors and the nine-factor solution was deemed to be most representative.

3. Results and discussion 3.1. VOC source identification Putative emission source categories were mapped onto the factors by identifying the tracer(s) for each source that are typically exclusively or largely reside in the source. The sourceetracer links are based on studies in the PRD or elsewhere in China as far as practical (e.g. Tsai et al., 2006; Liu et al., 2008a). A total of nine sources were identified by the PMF analysis. They are gasoline exhaust, diesel exhaust, aged air mass from traffic, gasoline evaporation, LPG leakage and propellants, mixed solvents, industrial emission, biomass burning, biogenic emission and aged air mass. Fig. 1 illustrates the percent of each species apportioned to the nine factors, also known as “explained variation” (EV). EV is a dimensionless quantity describing how much each computed factor explained a row (EV of G) or a column (EV of F) of the input data matrix. By normalizing the species concentration in a particular factor to its total concentration, the markers, sometimes with small or tiny amounts, can be discovered to facilitate identifying the associated source categories. Table 1 summarizes the nine source categories and the corresponding markers for their identification. Source 1 has significant amounts of ethane and ethyne. The alkenes percentage is also highest among all identified sources, therefore this source is believed to be combustion that may be from different origins, e.g. coal combustion from industrial plants. Source 2 is characterized by a significant presence of heavy alkanes (C7eC9) and 1,2,4-trimethylbenzene, tracer of diesel exhaust (Gertler et al., 1996). Tsai et al. (2006) reported that 1,2,4-trimethylbenzene was found in diesel fuels collected at different locations over the PRD. Liu et al. (2008a) also found significant presence of trimethylbenzenes in diesel source profile. Source 3 is associated with significant

Table 2 Source contributions and contribution percentages derived by PMF analysis. Sources

Combustion Diesel exhaust Gasoline exhaust Gasoline evaporation LPG leakage & propellant emissions Mixed solvents Industrial emissions Secondary formation & aged air mass Biogenic emission Total (ppbv)

1st campaign

2nd campaign

3rd campaign

4th campaign

5 a.m.

10 a.m.

5 a.m.

10 a.m.

5 a.m.

10 a.m.

5 a.m.

10 a.m.

1.4 (8%) 1.3 (8%) 2.3 (14%) 1.6 (10%) 4.5 (26%) 3.0 (17%) 0.6 (4%) 1.8 (10%) 0.6 (3%) 17.2

1.7 (8%) 1.8 (9%) 3.9 (19%) 0.9 (5%) 3.8 (19%) 4.1 (20%) 1.1 (5%) 1.3 (7%) 1.8 (9%) 20.5

4.3 (17%) 0.9 (4%) 7.6 (31%) 2.0 (8%) 3.5 (14%) 1.2 (5%) 3.7 (15%) 1.4 (6%) 0.2 (1%) 24.8

4.9 (17%) 1.5 (5%) 9.0 (32%) 1.7 (6%) 3.4 (12%) 2.0 (7%) 4.1 (15%) 1.4 (5%) 0.1 (0.4%) 28.0

2.4 (9%) 0.5 (2%) 2.3 (9%) 4.7 (18%) 3.3 (13%) 4.0 (16%) 6.2 (24%) 2.2 (8%) 0.2 (1%) 25.7

1.3 (5%) 0.2 (1%) 4.8 (19%) 4.1 (16%) 2.6 (10%) 3.7 (14%) 5.9 (23%) 1.8 (7%) 1.5 (6%) 25.9

4.2 (10%) 7.3 (17%) 9.8 (23%) 2.5 (6%) 3.9 (9%) 6.4 (15%) 7.9 (18%) 1.6 (4%) 0.1 (0.2%) 43.6

2.0 (6%) 4.9 (16%) 9.3 (30%) 1.2 (4%) 3.2 (11%) 3.2 (10%) 4.9 (16%) 1.7 (6%) 0.2 (1%) 30.5

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portion of CO and C2 species and C4eC5 alkanes, therefore determined as gasoline exhaust (Watson et al., 2001). Source 4 shows significant presence of i-pentane, n-pentane and toluene, signature of gasoline evaporation. Source 5 is dominated by propane and ibutane. They are the signatures of LPG and propellants like hair sprays (Blake and Rowland, 1995; Liu et al., 2008a). One should note that the propellants may be contained in the wider category as “consumer products” of which the current findings could not pinpoint between these two sources. Nevertheless, this source is categorized as LPG leakage & propellant emission in this study. Source 6 is characterized by dominant presence of toluene, ethylbenzene and xylenes, therefore is considered to be from mixed solvents (Seila et al., 2001; Song et al., 2007). Consumer products like paints are included in this category. Some VOC major emission sources like car manufacturing, printing, furniture manufacturing, shoe-making and toy making are mainly associated with painting and uses of adhesives during their manufacturing processes. These emissions are included in this source category of mixed solvent. Source 7 is identified as industrial emission by the exclusive presence of chlorinated VOCs, tracers of industries such as plastic manufacturing and automobile degreasing (Scheff and Wadden, 1993; Tsai et al., 2008). This source is also significantly associated with alkanes with 6 or 7 carbon numbers. Source 8 is identified as secondary formation for the dominant presence of methyl nitrate and ethyl nitrate (Bertman et al., 1995; Simpson et al., 2003). The associated primary VOC species are unreactive; therefore, this source was named as secondary formation & aged air mass. Source 9 is distinguished by a large presence of isoprene, the indicator of biogenic emissions (Guenther et al., 1995). 3.2. Estimation of source contributions Fig. 2 illustrates source contribution percentages the overall VOC source contributions in the eight sampling events. Gasoline exhaust was the largest contributor (23%) to the VOC concentration in the PRD, followed by industrial emission (16%) and LPG leakage & propellant emission (13%). The vehicle-related emission sources, defined as the sum of gasoline exhaust, diesel exhaust and gasoline evaporation, accounted in total for 40% of the VOC level in the PRD. As discussed later, the largest contributing source to LPG is residential energy use. Therefore, LPG is regarded as domestic emissions here. Industrial emission and solvent uses including some consumer products like paints also accounted for the other quarter of the ambient VOC levels. Table 2 lists the source contributions and contribution percentages during the eight sampling events (methane and CO not included). The total VOC did not show strong increase from 5 a.m. to

10 a.m. during all the four sampling campaigns. The largest increase occurred in the first sampling campaign, with an increase of around 20%. Stronger wind speed and higher mixing height at 10 a.m. may contribute to the greater dispersion capacity of the pollutants, reducing their ambient concentration to some extent. Seasonally, LPG leakage & propellant emission, mixed solvents, secondary formation & aged air mass and biogenic emission accounted for higher portions during the summertime sampling campaigns, while gasoline exhaust and combustion accounted for higher portions during the wintertime sampling campaigns. Higher temperature in summer leads to more fugitive emission (i.e., evaporative loss) from solvents. Weaker background wind and more stagnant air mass before the approach of tropical cyclone results in enhanced secondary formation & aged air mass, while higher temperature and stronger solar radiation in summer elevated photosynthesis and more biogenic emissions. It is noted that the source contributions are prone to the change of meteorological conditions; therefore, the inter-campaign differences of source contributions are not as evident as the inter-event differences in the same sampling campaign. 3.3. Comparison of source contributions with other studies and emission inventory Guo et al. (2007) and Lau et al. (2010) applied receptor models to identify the prevailing VOC sources in Hong Kong. Despite applying different models, they both found that vehicular emissions made a significant contribution to ambient non-methane VOC levels in urban and in sub-urban areas. Other sources such as gasoline evaporation, industrial emissions and mixed solvent also played important roles in the VOC emissions. Liu et al. (2008b) applied CMB model for source apportionment and also discovered significant contribution from the vehicle-related sources (42.6e74.5%) which was slightly higher than the present findings. A compilation of VOC source apportionment results from this study and 6 other recent studies is shown in Table 3. Although samples were taken in different locations, time periods, and the different numbers and types of VOCs measured in the studies, this comparison illustrates the importance of different sources at difference places. Vehicle-related emissions were consistently the major sources in various urban locations such as Beijing, Hong Kong, several sites in the PRD Region, Seoul and Los Angeles (Na et al., 2005; Brown et al., 2007; Liu et al., 2008b; Song et al., 2008; Cai et al., 2010; Lau et al., 2010). The contributions from LPG leakage were also significant. Its contribution to ambient VOCs level ranged from around 10e30% in the PRD and Beijing. Although the contribution of mixed solvents in Hong Kong was only 5%, its

Table 3 A comparison of average results from some recent source apportionment studies (contribution in % of VOC mass concentration). Study (year)

Source apportionment method

No. of sources resolved

No. of VOCs used for source apportionmenta

Average VOC concentration

Vehicle tailpipe emissions (%)

This study 84 sites (2008e2009)

PMF

9

41 (51)

26.98 ppbv

31.1

Shanghai, 1 site (2007e2010) Beijing, 1 site (Aug 2005)

7 7 8 6 9

18 23 31 22 37

32.35 ppbv 101.8 mg m3

Hong Kong, 4 sites (2002e2003, 2006e2007)

PMF CMB PMF UNMIX PMF

19.32 ppbv

25.0 46.6 42.9 35.6 20.8

Hong Kong, 4 sites (2002e2003, 2006e2007) PRD Region, 7 sites (OcteNov 2004)

PCA CMB

3e5 12

22 (51) 58 (134)

NA NA

48e65 31.2e52.6

Seoul, 1 site (Nov 1997eJul 1999) Los Angeles, 2 sites (summers of 2001e2003)

PCA PMF

2 5e6

7 (8) 56 (56)

99.2 ppbC 79e237 ppbC

52.0 22e24

a

Figures in brackets indicate the no. of VOCs measured in the study. NA, not available.

(31) (45) (45) (45) (51)

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167

monitoring stations ranged from 230 mg m3 to 430 mg m3. The non-attainment rate ranged from 0.13% at Tsuen Wan, Hong Kong to 4.70% at Wanqingsha, Guangzhou (GDEMC and HKEPD, 2012). It is noted that this non-attainment rate is calculated based on the current 1-h ozone National Ambient Air Quality Standard (NAAQS) of 200 mg m3. As the ozone level kept increasing in the past 5 years (Zhong et al., submitted for publication) and a more stringent 8-h ozone NAAQS of 160 mg m3 will be implemented, we may expect a substantial rise of ozone non-attainment rate. Prompt measures are therefore needed to curb the increasing trend of ozone and evaluate the effectiveness of control measures that have been and to be implemented. In this regard, the philosophy of setting up PAMS network in the U.S. with comprehensive characterization of secondary pollutants and their precursors upwind, within, and downwind of the major emission areas can be adopted over the PRD.

contributions in other sites in the PRD Region were over 10%, and sometimes even reached 40%, this revealed the importance of solvent uses to the ambient VOC level in the region. Oil refinery, dry cleaning and aged air masses were also the common sources identified in the PRD Region. It is noticed that the identified source contributions are different to some extent from the VOC emission inventory in Hong Kong (CH2M, 2002) and the PRD (Zheng et al., 2009b). In the emission inventory, the contribution of vehicular emissions to the VOC levels in Hong Kong was 25% and in the PRD was 39%. The present results were higher than the inventory but very close to those obtained in previous studies by using receptor models. In the emission inventory, the contribution of solvents to the VOC levels in Hong Kong was 50% and in the PRD was 25%. The present results were lower than the inventory. While the VOC emission inventory is associated with significant levels of uncertainties, this discrepancy may also arise from the losses of some reactive species during transport. Shao et al. (2011) has demonstrated clearly that the chemical abundances of measured concentrations (MCs) and photochemical initial concentrations (PICs) of VOC species differed substantially during the period of 2008 Beijing Olympics. This would inevitably lead to different source apportionment results. In Shao et al. (2011), the PICs were derived by using the evolution of the ratio of i-butene and propene in photochemical processes and assuming the ages of all VOC species and contributing sources are unified. Such an assumption is problematic in our study, considering the samples were collected during different seasons at different stations spanning over the entire PRD. Indeed, even for a particular sample, the chemical ages of different VOC species may be different due to the great variety of sources and the mixing in the atmosphere. Therefore, in this study we did not attempt to derive the PICs and we understand that the source apportionment results may underestimate sources more abundant in reactive species and overestimate sources more abundant in unreactive species (Wittig and Allen, 2008). With continuous monitoring of VOC species in the PAMS network, the derivation of and source apportionment on PICs are possible and reasonable (Shao et al., 2011).

4.2. VOC hotspot areas in the PRD region To better design for the location of PAMS network, identification of VOC hotspot areas is essential. Fig. 3 shows the spatial distribution of contributions of gasoline exhaust, the most significant source. The spatial distributions of other sources are presented in the Figure S1 of the Appendix. It is noticed that each identified source is associated with a specific spatial distribution pattern, which may be largely influenced by the varying synoptic conditions. Although the background wind speed is generally weak, the air mass can still be easily transported among grids (with the dimension of 20 km) within a couple of hours. It should therefore be kept in mind that the identified area with significant VOC source contribution (hotspot area) might not necessarily be the emission area. As the prevailing wind was northerly to northeasterly during all sampling campaigns, the VOC emissions tend to locate in the south/southwest of the identified hotspot area. This underlines the need to set up PAMS networks in these hotspot areas to better monitor the movement of emitted pollutants. The spatial distribution of gasoline exhaust is relatively consistent among the four campaigns in comparison with other sources. There exists a westerneeastern gradient in gasoline exhaust, with stronger influence on the western side of the PRD than the eastern side. This shows that major emission sources of gasoline exhaust concentrated on the western PRD Region. The 3rd sampling campaign provided a good illustration. At 5 a.m., the source contribution shows a northernesouthern gradient, which is more influenced by the meteorological conditions. While at 10 a.m., the contribution at western PRD Region increased significantly, likely resulting from fresh emissions during these 5 h. During the 2nd and

4. Strategies in setting up emission-oriented monitoring network in the PRD 4.1. Significance of emission-oriented monitoring network The PRD is experiencing serious ozone and PM pollution. According to the PRD regional air quality monitoring network data in 2011, the maximum ozone concentration in the 16 PRD air quality

Evaporative/fuel distribution (%)

Mixed solvents (%)

Industrial (%)

Biomass burning (%)

Biogenic (%)

LPG (%)

Other sources

References

8.7

12.7

15.9

e

2.1

13.0

This study

15.0 14.1 11.8 5.7 6.2

15.0 2.9 4.7 e 5.0

29.0 e e e 3.5

9.0 e e e e

e 0.6 1.6 2.0 1.9

e 26.4 11.0 19.9 29.0

0e26 5.6e6.5

14e24 16.2e43.7

0e15 e

e 0e14.3

0e2 e

0e15 4.9e16.3

Combustion, propellant emission, secondary formation, aged air masses Coal combustion Natural gas, petrochemical Natural gas, petrochemical Natural gas, petrochemical, painting Consumer and household products, dry cleaning, aged VOC Oil burning Chemical plant, oil refinery, dry cleaning, unidentified

e 13e27

48.0 17

e 15

e

e 1-3

e e

Evaporative emissions, natural gas

Cai et al., 2010 Song et al., 2008

Lau et al., 2010 Guo et al., 2007 Liu et al., 2008b Na et al., 2005 Brown et al., 2007

Fig. 3. Contour plots of the mixing ratio (unit: pptv) of gasoline exhaust.

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4th campaign, gasoline exhaust shows higher contribution at a cluster of stations in the southwestern PRD. Diesel exhaust gave significant contributions to VOCs on the eastern side of the PRD at 10 a.m. in the 1st campaign. The band extends from southern Guangzhou to Dongguan and then western Shenzhen. This band also appears at 10 a.m. in 2nd campaign and 4th campaign. However, during these two campaigns, a significantly higher pollution area appears in the southwestern PRD which coincides exactly with the high pollution area for gasoline exhaust. As high values appear at a cluster of monitoring stations, these outliers unlikely resulted from sample contamination or error in analysis. We speculate that this might be influenced by emissions from boats/ferries in the Gaolan Port of Zhuhai and the surrounding dense waterways. Further site investigations at these sites are needed to discover the underlying contributors for the pollution. For the gasoline evaporation source, a single most significant hotspot appeared at Wanqingsha of Guangzhou in the 1st campaign and 2nd campaign, with the influence weakened at 10 a.m. compared with 5 a.m. A less significant hotspot appeared at Huangpu of Guangzhou at 5 a.m. in the 2nd and 4th sampling campaigns. This source also had significant impact on the southwestern cluster during the 4th campaign. All these hotspot areas are associated with major ports in the PRD (Nansha Port, Huangpu Port and Gaolan Port). Lau et al. (2010) reported the significant influence of emissions from Yantian Port of Shenzhen to the ambient VOC in Hong Kong. As shown in Table 2, the absolute contribution of LPG leakage & propellant was quite consistent throughout the study periods. During the 1st campaign, this source shows relatively smaller spatial variation, with the highest contribution at western Shenzhen and Jiangmen. Obvious emission hotspots exist in urban areas, e.g. Guangzhou, Hong Kong, Zhuhai, during the 2nd, 3rd and 4th sampling campaigns. LPG is used as the main fuel in buses and taxis in the PRD cities during 2008e2009. However, relatively even distribution of this source suggests that LPG-vehicles are not the only main emission source. LPG is the main fuel for cooking in urban household in Guangdong province, accounting for 71e74% of the total residential fuel in 2008e2009 (GSB, 2011). Therefore, we believe the leakages from residential use of LPG are also significant in contributing to ambient VOC in the PRD. Mixed solvent and industrial emission share a similar spatial pattern, with a hotspot band from Dongguan to western Shenzhen. Therefore, this area should be investigated further to discover the specific VOC emission sources responsible for the local pollution rise. A hotspot area for mixed solvent and industrial emission also exist in the southwestern PRD in 5 a.m. of the 4th campaign. In the 1st and 3rd sampling campaigns, contribution of combustion was quite uniform among the study area, however, hotspots appeared in Zhongshan and Zhuhai, and extended to Guangzhou in the 2nd and 4th sampling campaign. It is worth noting that there is a consistent hotspot area for sources of combustion, diesel exhaust, gasoline exhaust, gasoline evaporation and mixed solvent in 5 a.m. of the 4th campaign. Such strong emissions are not captured in the current VOC emission inventory (Zheng et al., 2009a). Although this source would not have potential impact on the air pollution in the PRD due to its downwind location most of the time, it may influence southwestern Guangdong or even Hainan Island. Therefore, targeted in-depth monitoring and analysis is highly recommended to fully understand the VOC emissions in this area. The spatial distribution of secondary formation & aged air mass, mainly oxidation products of LPG precursors, is different from all anthropogenic emissions discussed above. As the reaction of production of alkyl nitrates generally takes hours, the areas with significant fresh emissions tend to be associated with low

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photochemical production of alkyl nitrates while the contributions at rural stations are generally higher than those in urban areas. The hotspots for biogenic emissions vary in different sampling campaigns. The contribution of this source at 10 a.m. is generally higher than that at 5 a.m., indicating the photosynthesis leading to biogenic emissions in the 5-h period. 4.3. Potential areas for emission-oriented monitoring network For better management and have an overview of the regional air quality, the advent of the current PRD Regional Air Quality Monitoring Network (Network) is more district-oriented, meaning that the monitoring stations are evenly distributed in every political district (city) over the PRD. Therefore, the network design can, in general, canvass the air quality in the PRD region but may not be specifically targeted more complex air pollution issues such as ozone and PM production processes for a particular city, city cluster or emission hotspot. Source apportionment results revealed that spatial distribution of emission sources in the PRD are not even, especially for different kinds of VOC sources. To accurately estimate the impact of photochemical reason on regional ozone and haze pollution, emission source-oriented network design, such as the design philosophy of PAMS, is important (Demerjian, 2000). The source contribution hotspot stations discussed in Section 4.1 are roughly located in three large areas. The first one is the north-south band in the Guangzhou/Foshan area, roughly located right in the Pearl River Estuary [Central PRD]. Significant contributions of gasoline exhaust, gasoline evaporation and LPG leakage are associated with this hotspot area, which may be due to the significant local emission from the urban Guangzhou/Foshan Metropolis and the Nansha/Huangpu Port. The second hotspot area extends from Central Dongguan to North Shenzhen [Eastern PRD]. This area is associated with significant combustion and fugitive VOC emissions, including diesel exhaust and emissions from mixed solvents and industrial sources. As these sources emit VOCs with significantly adverse health impact, e.g. benzene and chlorinated VOCs, control efforts in this area should be particularly strengthened. The third hotspot area covers the Zhuzhongjiang (ZhuhaieZhongshane Jiangmen) area [Southwestern PRD]. This area is associated with significant VOC emission from traffic-related sources, combustion and mixed solvent, which may be from the vehicles and marine vessels near the Gaolan Port and in the surrounding waterways. We therefore suggest establishing three PAMS networks in correspondence with the three hotspot areas. It is time-consuming and labor-intense to select appropriate locations to establish new monitoring sites, to set up equipment and QA/QC, and to arrange relevant logistic issues. More importantly, some of the PAMS measurement overlaps with the measurements at the 16 Network stations over the PRD (Zhong et al., submitted for publication). Therefore, it is recommended to expand the monitoring capacity of the currently-in-operation Network stations as much as possible in order to maximize resources utilization and saving resources to establish new sites. Considering of the prevailing northeasterly wind in the PRD, all PAMS networks are designed in an NEeSW direction. The Network stations located within the three identified hotspot areas that can be utilized as PAMS are summarized in Table 4. Its station type in PAMS is determined according to the relative direction of the Network station with respect to the hotspot area. At least seven of the Network stations (Tianhu, Luhu Park, Huijingcheng, Xiapu, Haogang, Wanqingsha and Zimaling Park) can be used as PAMS stations with different functionalities (e.g. upwind site, downwind site), monitoring the levels of ozone and PM and their precursors in the PRD region more comprehensively. Two additional sites,

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Table 4 Current PRD air quality monitoring network station to be utilized in the potential PAMS networks. VOC emission hotspot area

VOC emission characteristics

PRD network station

Type in PAMS

Central PRD

Traffic-related emission, combustion

Eastern PRD

Diesel exhaust, mixed solvents and industrial emission Traffic-related emission, combustion Traffic-related emission

Tianhu, Guangzhou Luhu Park, Guangzhou Huijingcheng, Foshan Heshan, Jiangmen Xiapu, Huizhou Haogang, Dongguan Wanqingsha, Guangzhou Zimaling Park, Zhongshan Duanfen, Jiangmen Tap Mun Tsuen Wan Tung Chung

Type Type Type Type Type Type Type Type Type Type Type Type

Southwestern PRD Hong Kong

1 2 2 3 1 2 3 2 4 1 2 3

Notes: Type 1 is the station located in the predominant upwind direction from local emission area; Type 2 is the station located within the local emission area; Type 3 is the station located miles downwind from the edge of the urban area; and Type 4 is the station located in the extreme predominant downwind from the local emission area.

Heshan and Duanfen in Jiangmen, which have been planned to be developed as supersites in the PRD, are also recommended to be part of the network to cover the downwind area of the Central PRD and Southwestern PRD VOC hotspot areas. Fig. 4 shows the proposed locations of the three PAMS networks in the PRD. Besides the three PAMS networks in the PRD, it would be ideal for Hong Kong to have its own PAMS network to provide information on the local contribution to ozone and PM formation in the region and to understand the ozone and PM pollution characteristics in the southeastern PRD. Most of local VOC emissions in Hong Kong is vehicle- and marine vessel-related combustions, and the current three Network stations (Tap Mun, Tsuen Wan and Tung Chung), which roughly lie northeast-southwest, can be expanded to include monitoring capacity as Type 1, Type 2 and Type 3 PAMS, respectively.

4.4. Recommendations in association with setting up emissionoriented monitoring network The PAMS network is originally designed in providing information about the effectiveness of ozone control strategies, precursor emission tracking, trends and exposure. Considering the high level of PM in the PRD and the significance of secondary PM, it is recommended that measurements of PM and its constituents are also included in the PAMS network. Where resources are available, at least one station (Type 2 station is preferred) in each network should be equipped with real-time measurements of VOC species and PM constituents, to capture the variations of emissions and physiochemical processes which are generally in the scale of minutes or hours, rather than days. Recently, Chen et al. (2010) designed a chemical mechanism that is suitable for diagnostic study of VOC species observed in PAMS and embed it into a regional-scale chemical transport model, named PAMS Air Quality Model (PAMS-AQM). PAMS-AQM model directly use the 56 VOC species measured from PAMS stations, thereby avoiding uncertainties generated from the lumping representation of VOC species in the chemical mechanism. PAMSAQM is proven applicable in studying the evolution of PAMS organics in regional and urban environments, resolving the roles of transported and locally emitted ozone and PM precursors in producing an observed exceedance and identifying specific sources emitting excessive concentrations of precursors. Development of such a model is urgently needed is the PRD region. The emissions inventory serves as an essential element of the air management process as well as a fundamental input for photochemical models. Verification of reported inventories and the tracking of changes in the atmospheric VOC profiles can assist in the evaluation of control strategy effectiveness. The ambient speciated data collected in PAMS can gauge the accuracy of estimated changes in emissions. The speciated data can also be used to assess the quality of the speciated VOC and NOx emission inventories.

Fig. 4. Proposed areas for setting up PAMS networks in the PRD.

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5. Conclusions The rapid economic development in the PRD over the past two decades has resulted in changes in the nature of air pollutants emitted from various activities in the region. To identify the ambient VOC characteristics, a grid study was initiated to take VOC samples simultaneously at 84 stations over the PRD. PMF model was applied on the VOC dataset to identify major air pollution sources and their contributions to ambient pollution levels at various regions in the PRD. A total of nine VOC emission sources were found by PMF, including combustion, diesel exhaust, gasoline exhaust, gasoline evaporation, LPG leakage & propellant emission, mixed solvents, industrial emission, secondary formation & aged air mass and biogenic emission. Source contributions at 5 a.m. and 10 a.m. of the 4 sampling campaigns were estimated. Overall, the emission source contributions by gasoline exhaust were found to be the largest contributor (23%) to the ambient VOC concentration in the PRD, followed by industrial emission (16%) and LPG leakage & propellant emission (13%). The PMF results showed that traffic-related emissions (i.e. diesel exhaust, gasoline exhaust and gasoline evaporation) and industrial-related emissions (i.e. industrial emission and mixed solvents) contributed to roughly an average of 40% and 25% of the ambient VOC levels respectively during the four sampling campaigns. More control measures should be developed to mitigate the VOC emissions resulting from these two clusters of sources. Spatial distribution of VOC emission sources in the PRD was not even. To accurately estimate the regional ozone and PM contribution potential of a particular city cluster, emission source-oriented network design, such as the design philosophy of PAMS network, should be applicable in the PRD Region. A more probable strategy would be the integration of these monitoring networks as one in the PRD Region, i.e. having both monitoring capability of photochemical oxidants as well as fine particles. Based on the findings from PMF analysis, the source contribution hotspot stations of VOC are roughly located in three large areas: (1) right in the Pearl River Estuary [Central PRD]; (2) from Central Dongguan to North Shenzhen [Eastern PRD]; and (3) in the Zhuzhongjiang area [Southwestern PRD]. Three PAMS networks in the PRD are suggested to develop in correspondence with the three identified hotspot areas. Within these three hotspot areas, Eastern PRD should receive more stringent control efforts as the emitted VOCs are associated with more significant adverse health impact. Hong Kong should individually develop a PAMS network to provide information on the local contribution to ozone formation in the region and to reflect the ozone and PM pollution characteristics in the southeastern PRD. To maximize current resources, several currently-in-operation PRD Air Quality Monitoring Stations can be utilized and transformed into PAMS. With the continuous measurement of VOC species in the PAMS stations, source apportionment based on the PICs of the VOC species could be conducted, which would be of less uncertainty and greater indication to the formulation of effective VOC, ozone and PM abatement strategies in the PRD. Disclaimer The content of this article does not necessarily reflect the views and policies of the HKSAR and Guangdong Provincial Government, nor does mention of trade names or commercial products constitute an endorsement or recommendation of their use. Acknowledgments The authors would like to thank ENSR Environmental International, Inc (EEII), AECOM Asia Co Ltd., Profs. Yuanhang Zhang and

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