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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | table | Table 1 Summary of the mean concentrations of $\mathrm{PM}_{2.5}$ , OC, and EC $\left(\upmu\mathrm{g}\:\mathsf{m}^{-3}\right)$ in Shenzhen during the controlled and uncontrolled periods at two sampling sites, along with the concentrations of SOA for isoprene, $\mathfrak{x}_{}$ -pinene, $\upbeta.$ -caryophyllene and toluene $(\mathrm{ng~m}^{-3}$ ). P-values correspond to t-test comparisons of the controlled and uncontrolled periods at each site, with $\mathsf{p}<0.05$ considered to be statistically significant. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 1. Wind rose plots (a) showing the frequency of wind directions in Shenzhen during the controlled period and uncontrolled periods, along with time series of the daily ambient concentrations of $\mathrm{PM}_{2.5}$ at Longgang and Peking University and visibility (b); time series of carbonaceous aerosol (OC, EC, and OC:EC) for Longgang (c) and Peking University (d). |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 2. Source apportionment of $\mathrm{PM}_{2.5}\ \mathrm{OC}$ in Shenzhen at Longgang (LG) and Peking University (PU), reported as the average relative source contributions to OC $(\%)$ during controlled and uncontrolled periods. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 3. Ambient concentrations of secondary organic tracers: A) sum of three isoprene SOA tracers, B) the sum of four $\mathfrak{x}$ -pinene SOA tracers, and C) one toluene SOA tracer at LG and PU during the controlled and uncontrolled periods. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | image | Fig. 4. Comparison of the fossil and contemporary sources of total carbon (equivalent to the sum of organic and elemental carbon) estimated by a) radiocarbon $(^{14}\mathrm{C})$ analysis, and b) chemical mass balance (CMB) modeling. |
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atmosphere | 0000 | 10.1016/j.envpol.2018.04.071 | text | Not supported with pagination yet | Source apportionment of fine particulate matter organic carbon in Shenzhen, China by chemical mass balance and radiocarbon methods\*
Ibrahim M. Al-Naiema a, Subin Yoon b, Yu-Qin Wang c, d, Yuan-Xun Zhang c, e, Rebecca J. Sheesley b, \*, Elizabeth A. Stone a, f, \*\*
a Department of Chemistry, University of Iowa, Iowa City, IA 52242, USA
b Department of Environmental Science, Baylor University, Waco, TX 76798, USA
c College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
d School of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an, Shaanxi 710021, China
e Huairou Eco-Environmental Observatory, Chinese Academy of Sciences, Beijing 101408, China
f Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 15 December 2017
Received in revised form
28 March 2018
Accepted 16 April 2018
Keywords:
Air quality
Aerosol
Emission control
Secondary organic aerosol
Universiade
Chemical mass balance (CMB) modeling and radiocarbon measurements were combined to evaluate the sources of carbonaceous fine particulate matter $(\mathsf{P M}_{2.5})$ in Shenzhen, China during and after the 2011 summer Universiade games when air pollution control measurements were implemented to achieve air quality targets. Ambient $\mathsf{P M}_{2.5}$ filter samples were collected daily at two sampling sites (Peking University Shenzhen campus and Longgang) over 24 consecutive days, covering the controlled and uncontrolled periods. During the controlled period, the average $\mathrm{PM}_{2.5}$ concentration was less than half of what it was after the controls were lifted. Organic carbon (OC), organic molecular markers (e.g., levoglucosan, hopanes, polycyclic aromatic hydrocarbons), and secondary organic carbon (SOC) tracers were all significantly lower during the controlled period. After pollution controls ended, at Peking University, OC source contributions included gasoline and diesel engines $(24\%)$ , coal combustion $(6\%)$ , biomass burning $(12.2\%)$ , vegetative detritus $(2\%)$ , biogenic SOC (from isoprene, $\mathfrak{x}$ -pinene, and $\upbeta$ -caryophyllene; $7.1\%)$ , aromatic SOC $(23\%)$ , and other sources not included in the model $(25\%)$ . At Longgang after the controls ended, similar source contributions were observed: gasoline and diesel engines $(23\%)$ coal combustion $(7\%)$ , biomass burning $(17.7\%)$ , vegetative detritus $(1\%),$ biogenic SOC (from isoprene, $\pmb{\mathrm{\Omega}}$ - pinene, and $\upbeta{}_{\cdot}$ -caryophyllene; $5.3\%$ ), aromatic SOC $(13\%)$ , and other sources $(33\%)$ . The contributions of the following sources were smaller during the pollution controls: biogenic SOC (by a factor of 10e16), aromatic SOC (4e12), coal combustion (1.5e6.8), and biomass burning (2.3e4.9). CMB model results and radiocarbon measurements both indicated that fossil carbon dominated over modern carbon, regardless of pollution controls. However, the CMB model needs further improvement to apportion contemporary carbon (i.e. biomass burning, biogenic SOC) in this region. This work defines the major contributors to carbonaceous $\mathrm{PM}_{2.5}$ in Shenzhen and demonstrates that control measures for primary emissions could significantly reduce secondary organic aerosol (SOA) formation.
$\copyright$ 2018 Elsevier Ltd. All rights reserved.
1. Introduction
Shenzhen is a rapidly developing and heavily urbanized coastal city in the Pearl River Delta (PRD) region of China. The economic growth of PRD cities has been accompanied by increased emission of air pollutants. Elevated levels of particulate matter (PM) have been attributed to primary emissions from industry and transportation and secondary aerosol formation (He et al., 2011; Huang et al., 2014; Yuan et al., 2006). High concentrations of PM pose risk to public health (Huang et al., 2012) and negatively affect visibility (Tan et al., 2009). Assessment of the chemical composition of PM and its contributing emission sources is therefore crucial to the implementation of effective air quality regulations.
To estimate source contributions to ambient PM in the PRD region, receptor models including positive matrix factorization (Dai et al., 2013; Kuang et al., 2015) and chemical mass balance (CMB) modeling (Wang et al., 2016; Zheng et al., 2011) have both been used. CMB has been widely used to apportion PM to its primary emission sources when source profiles are available (Kong et al., 2010). In Guangzhou, for instance, secondary organic carbon (SOC), coal combustion, and cooking sources were found to have contributed more than $20\%$ , $14\%$ , and $11\%$ of fine particle $(\mathsf{P M}_{2.5})$ organic carbon (OC), respectively (Wang et al., 2016). These results underscore the importance of combustion and secondary sources to $\mathsf{P M}_{2.5}$ OC in the PRD.
To distinguish carbonaceous PM derived from fossil fuels from that devrived from contemporary sources, radiocarbon $(^{14}{\mathsf{C}},\;t_{1/}$ $_{2}=5730$ years) measurements have been used (Gelencser et al., 2007; Gustafsson et al., 2009). $^{14}\!C$ is conserved with respect to emission conditions, atmospheric transport and chemical transformations (Szidat et al., 2004), making it a reliable tracer of modern carbonaceous matter. Using a combination of $^{14}\!C$ measurements and organic tracers in an industrial city in the PRD, Liu et al. (2014) demonstrated that fossil sources contributed $71\%$ of the elemental carbon (EC) and $38\%$ of OC detected. In a Chinese regional background site on Hainan Island ( $550\,\mathrm{km}$ from Shenzhen), radiocarbon analysis demonstrated that the contribution of fossil sources to EC was $51\%$ and OC was $30\%$ (Zhang et al., 2014), with fossil fuel emissions transported from regional industrial cities. These findings demonstrate strong influences from both modern and fossil fuel emissions on carbonaceous $\mathsf{P M}_{2.5}$ in the PRD.
Secondary organic aerosols (SOA) that are important contributors to PM mass are produced in the atmosphere through the photooxidation of VOCs from biogenic and anthropogenic precursors (Kroll and Seinfeld, 2008). The contributions of some precursors to SOA can be estimated using a SOA tracer approach, developed by Kleindienst et al. (2007), in which SOA is estimated based on the ambient concentration of SOA tracers by way of SOA tracer-to-SOA (or tracer-to-SOC) mass ratios determined in smog chambers. In this way, aromatic precursors (i.e. toluene) have been shown to contribute two-thirds of the total estimated SOC in the PRD region (Ding et al., 2012). This dominance reflects the significance of anthropogenic activities on SOA production in the PRD, with a minor contribution from biogenic VOCs like isoprene and monoterpenes.
The effects of short-term pollution control on the concentration and composition of atmospheric PM have been the focus of prior field studies in China. Vehicular and industrial emission controls were enforced during the 2008 Beijing Olympic Games, reducing $\mathsf{P M}_{10}$ , nitrogen oxides $\left(\mathsf{N O}_{\mathtt{X}}\right)$ , sulfur dioxide $\left({\mathrm{SO}_{2}}\right)$ , and non-methane VOC by $55\%$ , $47\%$ , $41\%$ , and $57\%$ , respectively (Wang et al., 2010). Simultaneously, black carbon $(45\%)$ , OC $(31\%)$ , and polycyclic aromatic hydrocarbons (PAH) decreased (Wang et al., 2011). Benzene, toluene, ethylbenzene, and xylenes (BTEX) decreased $\mathrm{by}\geq47\%$ (Liu et al., 2009). In addition to emission controls, Gao et al. (2011) suggested that wind direction and precipitation also contributed to air pollutant reductions during this period. In another effort at reducing air pollution during sporting events, during the 16th Asian Games in 2010 in Guangzhou, emissions from power plants, industry, mobile sources, and construction activities were restricted and $\mathsf{P M}_{2.5}$ decreased by $26\%$ , while both $S0_{2}$ and $\Nu0_{\tt x}$ dropped by ${>}40\%$ (Liu et al., 2013). These studies, focused predominantly on primary air pollutants, underscore the importance of controlling emission sources for improving air quality. However, the effect of pollution controls on the relative abundances of SOC from biogenic and anthropogenic origins has not previously been evaluated.
In 2011, Shenzhen hosted the 26th summer Universiade, an international sporting event, during which strict controls on emission sources were implemented to improve air quality, including reduction of: (i) emission of $\Nu0_{\mathtt{x}}$ from power plants, commercial and industrial boilers, and motor vehicles; (ii) $S0_{2}$ emission by controlling fuel sulfur content, the flue gas from desulphurization units, and coal-fired power plants (iii) VOC emissions from industries including printing, adhesives, and furniture; (iv) PM and other air pollutants from construction sites, open biomass burning, and on-road vehicles (Dewan et al., 2016; Wang et al., 2014). In addition to the controls on emission sources in Shenzhen, industrial activities in neighboring cities were minimized. These conditions provided a unique opportunity to examine the effect of anthropogenic activities on the absolute and relative levels of primary and secondary $\mathsf{P M}_{2.5}$ sources in Shenzhen.
In this study, we assess $\mathsf{P M}_{2.5}$ concentrations, composition, and sources, both under strict emission controls (the “controlled period”), and after Universiade, when the controls were lifted (the “uncontrolled period”). Organic molecular markers and SOA tracers were measured in $\mathsf{P M}_{2.5}$ collected over 24 days. $\mathsf{P M}_{2.5}$ OC was apportioned by a molecular marker-driven CMB model (Schauer et al., 1996) and the SOC tracer method (Kleindienst et al., 2007). Tracers included levoglucosan for biomass burning (Simoneit et al., 1999), PAH and hopanes for fossil fuels including coal combustion (Zhang et al., 2008) and vehicle emissions (Schauer et al., 2002), odd-numbered $\mathbf{n}$ -alkanes for vegetative detritus (Rogge et al., 1993), and SOA products identified in chamber studies for biogenic-and aromatic-VOC derived SOA (Kleindienst et al., 2007). The resulting contributions of fossil and modern sources to OC and elemental carbon (EC) were compared to radiocarbon measurements of fossil and modern carbon over the same time period. This study examines differences in $\mathsf{P M}_{2.5}$ and its sources during and after Universiade in 2011, providing new insight to the effect of primary emission controls on SOC.
2. Methods
2.1. Sampling
$\mathsf{P M}_{2.5}$ samples were simultaneously collected from two sampling locations in Shenzhen, China from 12 August to 4 September 2011. Teflon and quartz filters ( $47\,\mathrm{mm}$ , Whatman) were used to collect $\mathsf{P M}_{2.5}$ samples for mass and organic speciation, respectively. The Longgang (LG) site is located in the Longgang district of Shenzhen $\langle22.70^{\circ}\mathrm{N}$ , $114.21^{\circ}\mathrm{E}$ , $161\;\mathrm{m}$ ) on top of a 31-floor residential building at a height of $90\,\mathrm{m}$ from the ground level, about $500\,\mathrm{m}$ north of the main Universiade stadium. The Peking University (PU) site is located at Nanshan district of Shenzhen $.22.60^{\circ}\mathsf{N},$ $113.97^{\circ}\mathrm{E}$ , $50\,\mathrm{m}$ , 45 hm north of the LG site) atop of a graduate building at a height of $16\,\mathrm{m}$ . The samplers' heights provided well-mixed air masses at the point of sample collection. Detailed descriptions for both sampling site and sampling techniques are provided elsewhere (Dewan et al., 2016). Wind and visibility data during this study were obtained from the weather forecast at Shenzhen Bao'an International Airport (SGSZ), approximately 18 and $48\,\mathrm{km}$ east of PU and LG sampling sites, respectively. The difference in the altitudes of the two sites could have affected PM collected, so throughout this report we emphasize differences between controlled and uncontrolled periods at each site, rather than comparisons across the two sites.
2.2. Chemical analysis of organic species
Filter extraction and gas chromatography-mass spectrometry (GC-MS) followed established methods (Al-Naiema et al., 2015; Stone et al., 2012) and are summarized in the supplementary information (SI).
2.3. Chemical mass balance source apportionment modeling
Chemical mass balance (CMB v8.2) modeling (EPA, 2004) was used to estimate source contributions to organic carbon in $\operatorname{PM}_{2.5},$ using effective variance weighted least squares (Watson et al., 1984). The CMB model relies on prior knowledge of emission profiles and assumes that those profiles are representative of the investigated samples. Source profiles were selected to represent emission sources and conditions in China when possible, i.e. for biomass burning (Zhang et al., 2007) and coal combustion (Zhang et al., 2008). When such profiles were not available, profiles developed elsewhere, but applied previously in source apportionment in China (Guo et al., 2013; Zheng et al., 2005), were used. Input source profiles and chemical species for the model are reported in the SI. The model fit was considered acceptable when the correlation coefficient $(\mathbb{R}^{2})$ was greater than 0.8, and chi-squared $(\chi^{2})$ was less than 7.
2.4. Radiocarbon $(^{I4}C)$ measurements
Four composites and one lab blank were prepared for $^{14}\!C$ analysis. The composites included equal mass fractions of filter samples collected during the controlled and uncontrolled periods for both LG and PU sites. This compositing scheme ensured that composites impartially represented source contributions from each sample regardless of varying daily mass concentration.
Filter punches for each composite and blank were collected in baked petri dishes, acid-fumigated in a desiccator over $^{1\,\mathrm{N}}$ hydrochloric acid for $12\,\mathrm{h}$ , and then dried at $60\,^{\circ}\mathrm{C}$ for $1\,\mathrm{h}$ . Each petri dish was then wrapped in baked aluminum foil, bagged in individual Ziploc bags, and shipped on ice to the National Ocean Science Accelerator Mass Spectrometry (NOSAMS) facility for $^{14}\!C$ analysis. At NOSAMS, samples were analyzed using an accelerator mass spectrometry (AMS) to determine the fraction of modern $\left(\mathrm{F_{m}}\right)$ carbon. $\mathsf{F}_{\mathrm{m}}$ is the deviation of the $^{14}\mathrm{C}/^{12}\mathrm{C}$ ratio in a sample from $95\%$ of the reference “Modern”, NBS Oxalic Acid I, which is normalized to $\mathtt{\delta}\delta13\mathsf{C}_{\mathrm{VPDB}}=-19\%_{0}$ (Olsson, 1970). Apportionment for contemporary (or non-fossil) to fossil fuel sources can be calculated using a mixing model ratio for $\Delta^{14}C_{\mathrm{TOC}}$ :
$$
\Delta^{14}C_{T O C}=(\Delta^{14}C_{c o n t e m p o r a r y})\:(f_{M})\:+(\Delta^{14}C_{f o s s i l})\:(1\!-\!f_{M})
$$
In the calculation, known end-member $\Delta^{14}C$ values were included for radiocarbon-dead fossil fuel $(-1000\%_{00})$ (Gustafsson et al., 2009) and contemporary $(+67.5\%)$ . The end-member value for contemporary sources was an average of wood burning $(+107.5\%)$ (Zotter et al., 2014) and fresh biogenic $(+28.1\%_{0})$ (Widory, 2006) sources. The $f_{M}$ corrected for knownend-members is multiplied by ambient concentration to calculate fossil and contemporary carbon concentrations.
2.5. Statistical analysis
Statistical analyses were used to evaluate significant differences in $\mathsf{P M}_{2.5}$ composition and sources during the controlled and uncontrolled periods. A nonparametric t-test (Wilcoxon) was used to assess the statistical differences at the $95\%$ confidence interval using Statistical Package for the Social Sciences (SPSS) software.
3. Results and discussion
3.1. $P M_{2.5}$ mass concentrations
$\mathsf{P M}_{2.5}$ concentrations measured at Longgang (LG) and Peking University (PU) sites in Shenzhen were significantly lower during Universiade games when strict emission controls were implemented (12e23 August) than during the uncontrolled period (24 August e 4 September; Table 1). At LG, the average $\mathsf{P M}_{2.5}$ concentration during the controlled period was $24.9\pm5.0\ensuremath{\,\upmu\mathrm{g\,m}^{-3}}$ versus $53.8\pm6.1~\upmu\mathrm{g}\,\mathrm{\bar{m}}^{-3}$ during the uncontrolled period. At PU, the average $\mathsf{P M}_{2.5}$ concentrations were $12.8\pm3.5\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ during the controlled period and $48.0\pm8.1\;\upmu\mathrm{g}\,\mathrm{m}^{-3}$ during the uncontrolled period.
Pollution controls were not the only influence on $\mathsf{P M}_{2.5}$ concentrations over these time periods: wind directions shifted between the controlled and uncontrolled periods. Southerly winds that bring relatively clean ocean air to Shenzhen were predominant during the controlled period, while northwesterly winds transporting relatively polluted air from continental areas were more prevalent during the uncontrolled period (Fig. 1a). Thus, changes in wind direction added to the effects of emissions control to lower $\mathsf{P M}_{2.5}$ by $54\%$ at LG and $73\%$ for PU, on average, during the controlled period. Lower $\mathsf{P M}_{2.5}$ concentrations corresponded to doubling of visibility (Fig. 1b).
The average $\mathsf{P M}_{2.5}$ concentrations during the uncontrolled period are comparable to those reported in summer time in Shenzhen (Dai et al., 2013; Niu et al., 2006) and are approximately half of $\mathsf{P M}_{2.5}$ levels reported in winter $(99.0\pm17.6)\$ (Niu et al., 2006). Thus, the uncontrolled period is considered to be representative of typical summertime concentrations in the absence of emission controls.
On all of the study days, the measured $\mathsf{P M}_{2.5}$ concentrations in Shenzhen were below the $24\,\mathrm{h}$ average $\mathsf{P M}_{2.5}$ Class-II standard for urban and industrial cities of $75\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ (Chinese Ambient Air Quality Standard, GB 3095e2012) (GB3095, 2012).
3.2. Elemental and organic carbon
EC and OC were significant contributors to $\mathsf{P M}_{2.5}$ mass, with average contributions of $24.2\pm4.6\%$ and $9.5\pm4.2\%$ at LG and $31.4\pm6.7\%$ and $15.5\pm9.2\%$ at PU, respectively. OC concentrations at both sites were significantly increased after the controlled period (Table 1; Fig. 1). Meanwhile, EC increased significantly at the PU site, but only slightly (not significantly) at the LG site (Table 1; Fig. 1). Further discussion of OC and EC levels are provided elsewhere (Wang et al., 2014). OC:EC ratios across both sites increased from an average of 1.7 during the controlled period to 3.6 during the uncontrolled period (Fig. 1), indicating a shift in the sources of carbonaceous aerosol between the controlled and uncontrolled periods. OC and EC sources are discussed in section 3.3.
The OC and EC concentrations during the uncontrolled period were comparable to prior studies in the PRD during late summer. Prior studies have reported OC in Shenzhen in the summer of 2004 ranging $4.0{-}20.6\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC ranging $1.7{-}3.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ , with a mean OC:EC ratio of 3.4 (Niu et al., 2006). Slightly lower concentrations were reported for Shenzhen in 2002, with mean OC levels of $7.6\pm4.9\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC levels of $4.2\pm3.1\ \upmu\mathrm{g}\,\mathrm{m}^{-3}$ (Cao et al., 2004). In nearby Guangzhou in the summer of 2008, OC ranged from 1.92 to $13.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ and EC ranged $0.69{-}5.07\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ with a mean OC:EC of 3.41 (Ding et al., 2012). These comparisons indicate that the uncontrolled OC and EC levels in Shenzhen found in this study are typical for this region.
3.3. Source apportionment by CMB modeling
The CMB model apportioned $\mathsf{P M}_{2.5}$ OC to five primary sources (vegetative detritus, gasoline vehicles, diesel engines, coal combustion, and biomass burning) and four secondary sources (SOC from isoprene, a-pinene, b-caryophyllene, and aromatic precursors). The observed concentrations of molecular markers used in source apportionment are summarized in Table 1 and Table S1. CMB results are summarized in Fig. 2, Fig. S1, and Table S2 for controlled and uncontrolled conditions at each site. CMB results were not reported for several days that had an unacceptable model fit (August 12, 22, 26 and September 1 for LG, and August 24, 29, and 31 for PU), as indicated by $\mathrm{R}^{2}<0.8$ and/or $\chi^{2}$ values $>7$ (section 2.3), indicating that the selected profiles poorly fit the ambient data. The difference between the concentrations of apportioned sources and total OC mass is represented as “other OC”. On average, during controlled and uncontrolled periods, respectively the model apportioned $90\%$ and $67\%$ of OC in LG, and $88\%$ and $75\%$ of OC in PU.
During the controlled period at LG, the average OC mass of $5.9\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ was apportioned $80\%$ to primary sources and $10\%$ to secondary sources; $10\%$ was not apportioned. The major primary OC sources at LG, on average, were gasoline vehicles $(38\%)$ , diesel engines $(20\%)$ , and coal combustion $(12\%)$ .
At PU during the controlled period, the average OC mass of $4.3\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ was apportioned $80\%$ to primary sources and $8\%$ to secondary sources; $12\%$ was not apportioned. The major primary OC sources were gasoline vehicles $(30\%)$ , diesel engines $(26\%)$ , and biomass burning $(21\%)$ (Table S2). With regard to the modelresolved secondary OC, aromatic precursors were more significant contributors than biogenic precursors by a factor of 4.5 at LG and 2.5 at PU.
After the controls were lifted, OC at LG was apportioned $48\%$ to primary sources and $19\%$ to secondary sources; $33\%$ was not apportioned. At PU, OC was apportioned $44\%$ to primary sources and $31\%$ to secondary sources; $25\%$ was not apportioned. The major primary OC sources at both sites after the controls were lifted were gasoline vehicles, diesel engines, and biomass burning (Table S2). In regard to secondary OC, precursors were determined to be aromatic, $\mathfrak{a}$ -pinene, and isoprene, and SOC was dominated by aromatics for both sites.
The OC fraction not attributed to primary and secondary sources is expected to derive from sources that were not included in the model. Based on prior studies in the PRD (Li et al., 2012; Zheng et al., 2011), these are expected to include cigarette smoke, cooking emissions, and road dust. In addition, SOA is likely to be underestimated (Zheng et al., 2002), as SOC formed from VOC emitted by biomass burning, semi-VOCs like long-chain alkanes, and other precursors were not included in this model (Huang et al., 2011).
3.3.1. Gasoline and diesel engines
The combined vehicular emissions from gasoline and diesel engines are the largest source of OC in Shenzhen. Together, gasoline and diesel engines contributed $3.33\ensuremath{~\upmu\mathrm{g}\mathrm{c}\ \mathrm{m}^{-3}}$ and $3.03\ensuremath{\,\upmu\mathrm{g}\mathrm{C}\,\:\mathrm{m}^{-3}}$ during the controlled and uncontrolled periods at LG, while they contributed $2.41\;\upmu\mathrm{g}\mathrm{C}\;\mathrm{m}^{-3}$ and $3.34\,\upmu\mathrm{gC}\,\textrm{m}^{-3}$ at PU, respectively. These results indicate a slight decrease in vehicle-derived OC at LG after the controls were lifted, which may be attributed to the drop in post-Universiade transportation demands near the main stadium that was closer to this site. Alternatively, the control on motor vehicles (alternating odd-even license plate vehicle operation) did not likely influence the change in OC at LG. Meanwhile, there was a substantially larger increase in vehicle-derived OC at PU after the controls were lifted.
The stability of the CMB model, with respect to its estimate of vehicular contributions to OC, was evaluated in a sensitivity test in which the non-catalyzed gasoline profile used in the “base-case” model results was replaced with a catalyzed profile (Lough et al., 2007), as described in section 2.3 (Lough and Schauer, 2007). The summed gasoline and diesel engine contributions to OC for the base-case scenario, relative to the sensitivity test, are shown in Fig. S2 for each site. The results show a good agreement $(\mathbb{R}^{2}\geq0.994)$ between the base case and sensitivity test. The slopes of these regressions indicate a minor underestimation ( $.11\substack{-13\%}$ of the vehicle contribution) of the base case relative to the sensitivity test, which is within the standard error of the estimate. Thus, the selection of the non-gasoline engine profile has only a minor influence on the estimated vehicular contribution to OC, indicating that this is a robust estimate.
3.3.2. Coal combustion
Coal combustion contributions were lower at both sites during the controlled period, though more significantly reduced at PU.
During the controlled period, the contribution from coal combustion was slightly reduced to $0.65\pm0.08\;\upmu\mathrm{g}\mathrm{C}\;\mathrm{m}^{-3}$ in LG and significantly reduced to $0.13\pm0.03\mathrm{\,\mugC\;m}^{-3}$ in PU (Table S2). During the uncontrolled period, coal contributed $0.93\pm0.13\mathrm{\,\upmugC\,m^{-3}}$ at LG and $0.88\pm0.14\,\upmu\mathrm{g}\mathrm{{C}\,m}^{-3}$ at PU, which are comparable to annual average contributions of coal combustion sources in Shenzhen of $1.10\pm0.12\:\upmu\mathrm{gC}\:\:\mathrm{m}^{-3}$ , reported elsewhere (Zheng et al., 2011). The more significant change observed at PU is likely due to the fact that there were more coal-operated power plants near PU compared to LG (Dewan et al., 2016).
3.3.3. Biomass burning
OC from biomass burning was lower at both sites during the controlled period. The average biomass burning contribution to $\mathsf{P M}_{2.5}$ OC during the controlled period was $0.47\pm0.12\,\upmu\mathrm{g}\mathrm{C}\ \mathrm{m}^{-3}$ at LG and $0.77\pm0.06\,\upmu\mathrm{g}\mathrm{C}\,\mathrm{m}^{-3}$ at PU. After the controls were lifted, the average contribution of biomass burning to OC (in $\upmu\mathrm{g}(\mathrm{~m}^{-3})$ increased by a factor of 4.9 at LG and by 2.3 at PU. The increase shows the influence of the restriction on emissions from woodfired industrial boilers and biomass burning. These results indicate that reducing biomass combustion in the PRD can improve air quality in Shenzhen through reductions in the associated $\mathrm{PM}_{2.5}\,0C.$
It is potentially an important policy measure, as previous studies have found that biomass burning contributed $20.8\%$ to $\mathsf{P M}_{2.5}$ OC in Shenzhen during October, when households may have used biofuels for heating (Zheng et al., 2011).
3.3.4. Vegetative detritus
Vegetative detritus made a minor contribution to OC in Shenzhen, with its average contribution less than $2.5\%$ of OC at both sites (Table S2). Apportioning vegetative detritus using the CMB model is largely dependent on the concentrations of $\mathbf{n}$ -alkanes, which can have either biogenic or anthropogenic origins. The carbon preference index (CPI) is a diagnostic parameter that provides a means of identifying the sources of n-alkanes, where a CPI greater than three is an indication of biological origins, while a CPI of approximately one suggests anthropogenic origin (Simoneit, 1989). Average CPI value for $\mathbf{n}$ -alkane $\left(\mathsf{C}_{25}\mathrm{-}\mathsf{C}_{34}\right)$ in this study was 1.5 in LG and 1.7 in PU, with no significant change in CPI values for controlled versus uncontrolled periods. The CPI result indicates very little odd-carbon preference, as n-alkanes are primarily from evaporation and combustion of fossil fuels. Consistent with this is the small contribution from vegetative detritus to OC in Shenzhen.
3.3.5. Secondary organic aerosol
SOC in Shenzhen was largely dominated by aromatic precursors, which contributed more than $70\%$ of the apportioned SOC. The high level of SOC from aromatic relative to biogenic precursors had been previously observed in the PRD region (Ding et al., 2012), and attributed to the elevated levels of aromatic VOCs such as toluene in this industrial region (Barletta et al., 2008), compared to those quantified in other cities (Mohamed et al., 2002; von Schneidemesser et al., 2010). The contribution of SOC from aromatic, isoprene, and $\mathfrak{a}$ -pinene precursors to OC is discussed in detail in section 3.4.
3.3.6. Source apportionment of EC
Elemental carbon (EC) was simultaneously apportioned with OC by the CMB model. During the controlled period at PU, EC was primarily attributed to diesel engines $(96\pm2\%$ ; $\pm$ standard deviation), with minor contributions from non-catalyzed gasoline vehicles $(1\pm1\%)$ , coal combustion $(1\pm1\%)$ , and biomass burning $(2\pm1\%)$ . During the uncontrolled period at PU absolute concentrations of EC were higher, as were the absolute contributions from diesel engines, coal combustion, and biomass burning, with $97\%$ of EC attributed to fossil sources.
At LG during the controlled period, EC was attributed largely to diesel engines $(92\pm6\%)$ , with minor contributions from coal combustion $(6\pm4\%)$ , non-catalyzed gasoline engines $(1\pm1\%)$ and biomass burning $(1\pm1\%)$ . During the uncontrolled period, absolute concentrations of EC at LG increased, as did absolute contributions to EC from diesel engines, coal combustion, and biomass burning; the EC contribution from fossil sources was estimated to be $96\%$ .
CMB results indicated that diesel engines were largely responsible for the observed increase in EC from the controlled to uncontrolled periods at LG (from 3.3 to $2.8\,\upmu\mathrm{g}\mathrm{C}\,\mathrm{m}^{-3}$ ) and PU $(3.8{-}2.8\ensuremath{\,\upmu\mathrm{g}}\ensuremath{\mathrm{C}}\ensuremath{\,\mathrm{m}}^{-3})$ and that other fossil fuel and biomass emissions were minor contributors to EC throughout this study. However, previous studies in the PRD that conducted radiocarbon source apportionment of EC reported a greater biomass burning influence than the current study does. In Guangzhou, PRD, Zhang et al. (2015) reported EC was $57\pm5\%$ fossil during a less-polluted event and $80\pm2\%$ fossil during a heavily polluted event in 2013, while Liu et al. (2014) reported that EC was $60{-}91\%$ fossil EC for eight samples spanning 2012e13. Although these studies represent a different location in the PRD, these studies suggest a relatively larger contribution from contemporary carbon to EC than was found in this study. Potential reasons for this are discussed in section 3.6.
3.4. Secondary organic aerosol
3.4.1. Isoprene-derived SOC
Three isoprene tracers, 2-methylglyceric acid (MGA) and 2- methyltetrols (2-methylthreitol and 2-methylerythritol; MTLs) were observed in Shenzhen (Table 1, Fig. 3a). During the controlled period, the estimated contribution of isoprene SOC to OC was significantly lower $\langle{\mathsf{p}}<0.05\rangle$ , by a factor of 5 in LG and 4.5 in PU. The overall isoprene SOC contributed less than $2\%$ to the OC mass in both sampling sites, indicating that isoprene SOC is not a major source of OC in Shenzhen.
On average, the concentrations of the sum of the three tracers increased significantly during the uncontrolled period from $5.6\pm4.5\,\mathrm{ng\,m}^{-3}$ to $30.9\pm27.9\,\mathrm{ng\,m}^{-3}$ in LG, and from $2.7\pm2.0\:\mathrm{ng}\:\mathrm{m}^{-3}$ to $46.6\pm29.9\,\mathrm{ng\,m}^{-3}$ at PU, respectively (Table 1). The MGA/MTL ratio remained steady throughout: the average $\pm$ standard deviation) MGA/MTL ratio was 0.6 $\left(\pm0.6\right)$ in LG, and 0.5 $(\pm0.4)$ in PU. Since MGA forms through the high- $\cdot\mathrm{NO}_{\tt x}$ isoprene SOA formation pathway (Surratt et al., 2010), this result suggests that there was no substantive shift in the effect of $\mathsf{N O}_{\mathtt{X}}$ on forming isoprene SOA.
It is likely that controlled period reductions in emissions of anthropogenic pollutants (e.g., sulfur dioxide that contribute to aerosol acidity when oxidized to sulfuric acid), as indicated by the lower sulfate levels (Dewan et al., 2016), decreased the extent of SOA formation at both sites. In the Southeastern United States, sulfate and isoprene SOA positively correlate $\mathrm{\Deltaxu}$ et al., 2015), indicating acid-enhanced isoprene SOA formation. Similarly, in this dataset, there is a statistically significant positive correlation between sulfate and isoprene tracer concentrations $\mathrm{\bfr}\!>\!0.58$ , $\mathsf{p}<0.004;$ , with trends shown in Fig. S3. Thus, reductions in biogenic SOA may be accessible by decreasing anthropogenic sulfate levels.
3.4.2. $\alpha$ -Pinene-derived SOC
$\mathfrak{a}$ -Pinene SOC contributed up to $3.5\%$ of OC in Shenzhen (Table S2), and that contribution was significantly increased $(\mathsf{p}<0.05)$ by an average factor of 2.6 in LG and 8.8 in PU during the uncontrolled period. Four $\pmb{\alpha}$ -pinene tracers (3-hydroxyglutaric acid, pinic acid, 2-hydroxy-4,4-dimethylglutaric acid, and 3-acetyl hexanedioic acid) were consistently detected in the samples collected from both sampling sites (Fig. 3b, Table 1). In general, the concentrations of these tracers in PU are higher than those in LG, likely because the PU site is surrounded by forest, unlike LG which is more urbanized with fewer green spaces. Like isoprene tracers, the sum of $\pmb{\alpha}$ -pinene tracers was significantly higher during the uncontrolled period $(38.5.\pm40.2\mathrm{\,ng\,m^{-3}})$ than in the controlled period $(6.8\pm8.7\,\mathrm{ng\,m}^{-3})$ ) at LG and PU $(103.0\pm76.7\,\mathrm{ng\,m}^{-3}$ and $2.5\pm2.5\:\mathrm{ng}\:\mathrm{m}^{-3}$ , respectively).
$\pmb{\alpha}$ -Pinene SOA tracer levels showed consistent temporal trends to isoprene SOA tracers (Fig. 3). It has been previously reported that $\pmb{\alpha}$ -pinene SOA tracer concentrations correlate with gas phase concentrations of $\Nu0_{\mathtt{x}}$ and $S0_{2}$ $\mathrm{{Xu}}$ et al., 2015). Monoterpene SOA reduction during the controlled period, then, is likely due to emission controls’ reduction of the ambient concentrations of these species. Although there was no significant correlation between $\pmb{\alpha}$ pinene SOA tracers and nitrate ions, there is a significant moderate correlation between the sum of $\mathfrak{a}$ -pinene SOA tracers and sulfate at PU $\operatorname{\mathrm{\ddot{r}}}\operatorname{=}0.56$ , ${\tt p}=0.005)$ and a significant strong correlation at LG $\mathrm{\bf~r}\!=\!0.79$ , ${\tt p}<0.001$ , Fig. S3). The ratios of biogenic SOA tracers-tosulfate were consistently greater during the uncontrolled period, suggesting that SOA formation was enhanced during the uncontrolled period. Thus, $\pmb{\alpha}.$ -pinene SOA may also be reduced by controlling primary emissions.
Shenzhen, with an average contribution of less than $1.8\%$ (Table S2). Upon emission control, the concentrations of $\upbeta{\mathrm{.}}$ -caryophyllene SOC decreased significantly at both sites (Table 1).
3.4.4. Aromatic VOC-derived SOC
The SOC from aromatic VOCs was estimated based on the ambient concentrations of 2,3-dihydroxy-4-oxopentanoic acid (DHOPA). The average ambient concentrations of DHOPA rose from $5.1\pm4.7\mathrm{\,ng\,m^{-3}}$ in LG and $2.0\pm1.4\,\mathrm{ng\,m}^{-3}$ in PU to $12.3\pm9.5\mathrm{\,ng\,m}^{-3}$ and $26.8\pm9.2\mathrm{\,ng\,m}^{-3}$ , respectively, after the pollution controls were lifted. The increase in DHOPA is statistically significant at both sites: LG $\left(\mathbf{p}=0.06\right)$ and PU $\left\langle\mathbf{p}=0.002\right\rangle$ . SOC from aromatic VOCs was the most abundant contributor to OC among the quantified sources and its contribution to OC during the uncontrolled period was as high as $23\%$ at the PU site.
Aromatic SOC contributed much more to $\mathsf{P M}_{2.5}$ OC than did biogenic SOC from isoprene and $\pmb{\alpha}$ -pinene. These observations can be explained by prior observations that the rates of aromatic VOC emissions, such as toluene and xylenes, are higher than emission rates of biogenic VOC in industrial megacities in the PRD region (Wang et al., 2013). Several studies of megacities such as Mexico City (Stone et al., 2010) and the PRD (Ding et al., 2012; Wang et al., 2013), have also reported higher contributions from anthropogenic precursors to SOC than biogenic precursors. This result illustrates that anthropogenic SOC contributes more than biogenic sources to the OC fraction of $\mathsf{P M}_{2.5}$ in megacities, despite global SOA budgets largely dominated by biogenic sources (Henze et al., 2008).
3.5. Isotope analysis
3.5.1. Radiocarbon $(^{I4}C)$
For the radiocarbon results, the total carbon was determined to be predominantly fossil for the entire period; these total carbon results would include both OC and EC contributions. By following ambient levels and percent contributions of both fossil and contemporary carbon, it is possible to determine whether any relative or absolute change in source contributions occurred over the study period. Ambient concentrations indicated that fossil and contemporary carbon increased at both sites during the uncontrolled period, but the concentration of contemporary carbon increased to a greater degree. PU had more fossil signature than LG, in both controlled and uncontrolled periods.
3.5.2. Stable isotopes: carbon $(^{13}C)$
Stable carbon and nitrogen $\hat{\textrm{\textcent}}^{13}\hat{\textrm{C}}$ and $\S~^{15}\mathrm{{N}}^{\cdot}$ ) for both sampling locations are shown in Fig. S4. At LG, the average $\eth\,^{13}\!C$ during the controlled period was $-27.2\pm0.5\%_{0}$ and increased significantly to $-26.6\pm0.2\%_{0}$ after the emission control $\mathbf{\nabla}_{\cdot}\mathbf{p}=0.003)$ . At PU the average $\eth\ ^{13}C$ was constant in both periods $(-26.5)$ . The stable carbon fraction of total carbon can be affected by the end members, or isotope signature of the primary emission sources; however, it can also be affected by kinetic isotope effects during reaction of gas or particle phase species. More investigation would be needed to determine what is driving the small change in the $\eth\nobreakspace\nobreakspace13_{C}$ at LG. However, since the difference is only apparent at LG during the controlled period, this does further stress the difference between the sites during the controlled period. The sites were significantly different in $\mathfrak{d}^{\ 1\bar{3}}\mathfrak{C}$ during the controlled period $\mathbf{\nabla}.\mathbf{p}<0.001^{\prime}$ ); during the uncontrolled period, the $^{13}C$ data from the two sites were not significantly different $\mathbf{\check{p}}=0.440$ using student two population (two-tailed) t-tests). There was no difference in the $^{15}\mathrm{N}$ by site or by the presence or absence of controls.
3.6. Comparison of CMB and radiocarbon source apportionment results
Prior to comparing CMB and $^{14}\!C$ results, the CMB-estimated source contributions to EC were summed with those for OC, so that fossil and contemporary contributions to total carbon could be compared across the two methods, as seen in Fig. 4. CMB results were excluded from source apportionment results for days with unacceptable model fit, as described in section 2.3, whereas $^{14}\!C$ measurements were performed as a composite and included all sampling days. Because CMB and $^{14}\!C$ are distinct ways of quantifying the average difference between controlled and uncontrolled periods, $^{14}\!C$ can indicate the overall bias in the CMB results and potential bias introduced by excluding non-fit days.
For the uncontrolled period, the radiocarbon measurements and CMB estimates of fossil carbon agree within $2\%$ of their total carbon contribution (Table S3) for both sampling locations. Thus, the unapportioned OC unresolved by the CMB model $.25.6\%$ at LG and $20.1\%$ at PU) is contemporary in origin. For the controlled period, the radiocarbon measurements indicated a smaller fossil carbon fraction than was estimated by the CMB model, by an average of $19\%$ of total C at LG and $8\%$ at PU (Table S3). However, the radiocarbon measurements of fossil and contemporary carbon are either within or near to one standard deviation of the mean CMB estimates. While the radiocarbon was composited by control status, the tracer-based CMB apportionment was daily and demonstrated large day-to-day variability in source contributions. The overestimate of fossil C in the CMB model may result from an underestimation of biomass burning contributions to EC, because the cereal straw burning profile (Zhang et al., 2007) has a low EC-to-OC ratio. As discussed in section 3.3, EC was attributed almost entirely to fossil sources during the controlled period, which does not match previous measurements of EC radiocarbon in the PRD. In addition, if levoglucosan the primary biomass burning tracer degrades during transport, CMB would under-estimate the contribution of biomass burning to OC (Lai et al., 2014). Similar to the uncontrolled period, we conclude that the OC unapportioned by the CMB model is derived from contemporary sources (Table S3). The underestimate of contemporary OC by CMB modeling relative to $^{14}\!C$ measurements was also observed in Bakersfield, California (Sheesley et al., 2017).
In sum, while the CMB model provides source specificity in the apportionment of $\operatorname{PM}_{2.5}\!\operatorname{C}$ to its sources, the $^{14}\!C$ measurements provide constraint in interpreting the unapportioned OC fraction. Here, the unapportioned OC is shown to be contemporary in origin; biomass burning and/or biogenic SOC are the likely origins. This suggests that improved CMB model representation is needed for a more complete apportionment of OC by this approach. This could include improved source characterization and improved handling of degradation or atmospheric lifetime of tracers in the model.
4. Conclusions
Key findings from this study include:
$\mathsf{P M}_{2.5}$ concentrations in Shenzhen were significantly lower during the period of emission controls by $54\%$ at LG and $73\%$ at PU. However, because the wind blew from different directions during these two periods, it is not evident what extent of these reductions were due to stricter emission controls versus changes in wind direction.
OC was significantly lower during the controlled period, and CMB source apportionment modeling indicated significant reductions in OC contributions from coal combustion, biomass burning, and SOC from isoprene, $\pmb{\alpha}.$ -pinene, $\upbeta$ -caryophyllene, and aromatic VOCs.
The correlation of biogenic SOA tracers with sulfate suggested that anthropogenic emissions via acidic PM enhanced SOA formation during the uncontrolled period.
Aromatic SOC contributed up to $8\%$ of OC during the controlled period and up to $23\%$ during the uncontrolled period, indicating that anthropogenic VOC strongly influence SOA formation.
Measurements of $^{14}\!C$ content indicated the importance of fossil and contemporary sources of OC and EC, with both decreasing in their ambient concentrations during the controlled period.
Radiocarbon estimates for the fossil contribution to carbon agree with CMB source apportionment within the uncertainty of the CMB estimates. Together, these data indicate that the unapportioned OC fraction in CMB is mainly from contemporary sources (i.e. biomass burning and biogenic SOA).
Acknowledgments
I.M.A. and E.A.S. were supported by the University of Iowa. R.J.S. and S.Y. were supported by Baylor University. We thank James J. Schauer from the University of Wisconsin-Madison for leadership in coordinating this research study.
Appendix A. Supplementary data
Supplementary data related to this article can be found at https://doi.org/10.1016/j.envpol.2018.04.071.
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 1. $\mathrm{PM}_{2.5}$ samples were taken in seven southern China cities: Chongqing (CQ), Guangzhou (GZ), Hong Kong (HK), Hangzhou (HZ), Shanghai (SH), Wuhan (WH), and Xiamen (XM); and seven northern China cities: Beijing (BJ), Changchun (CC), Jinchang (JC), Qingdao (QD), Tianjin (TJ), Xi’an (XA), and Yulin (YL). Filter samples were obtained from 0900 to 0900 LST the next morning over 2-week periods during winter (January 6–20) and summer (June $3-$ July 30) of 2003. Cities are classified as representing northern and southern China since: (1) precipitation events are more frequent and intense in southern China, and (2) northern China cities have lower wintertime temperatures, resulting in a greater amount of domestic heating, often using coal, along with shallower and more prolonged surface inversions at night and early morning. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 2. Average (square), median (central horizontal bar), 25th and 75th percentiles (lower and upper bars), 1st and 99th percentiles (lower and upper x), and minimum and maximum $(-)$ concentrations for each chemical component across all cities and seasons. Average chemical components are ordered by abundance, with OC $(24.5~\upmu\mathrm{g}\,\textrm{m}^{-3})$ ), $\mathrm{SO}_{4}^{\ 2-}$ $(19.9\ \upmu\mathrm{g}\ \mathrm{m}^{-3})$ ), $\mathrm{NO}_{3}^{\mathrm{~-~}}$ $(9.9~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}$ , $\mathrm{NH_{4}}^{+}$ $(9.2~\upmu\mathrm{g}\textrm{m}^{-3})$ ), EC $(6.5~\upmu\mathrm{g}\textrm{m}^{-3})$ , $\mathrm{Cl^{-}}$ $(3.1~\upmu\mathrm{g}\textrm{m}^{-3})$ ), $\mathrm{K}^{+}\,(1.\,9~\upmu\mathrm{g}$ $\mathfrak{m}^{-3},$ , and $\mathrm{Na}^{+}\,(1.5~\upmu\mathrm{g}\,\mathrm{m}^{-3})$ all being at important levels. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 1. Arithmetic averages standard deviations (mg m3) for PM2.5 mass and chemical components by city and season. See Figure 1 for city codes. Each average contains 14 values |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 2. Comparison of $\mathrm{PM}_{2.5}$ chemical component ratios for the 14 Chinese cities with ratios from selected cities in Europe, Canada, Mexico, and the United States |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 3. Relationships between $\mathrm{PM}_{2.5}$ As, $\mathrm{Pb}$ , and $\mathrm{SO}_{4}{}^{2-}$ concentrations from the 14 cities during winter and summer, 2003. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 4. Wintertime material balance of $\mathrm{PM}_{2.5}$ for the 14 Chinese cities. Organic matter (OM) is estimated as $1.6\times\mathrm{OC}$ (Chen and Yu, 2007; El-Zanan et al., 2005; ElZanan et al., 2009) to account for unmeasured hydrogen and oxygen. Geological material is estimated as $25\times\mathrm{Fe}$ (Cao et al., 2008; Wu et al., 2011) to account for unmeasured oxygen and non-iron minerals. “Others” is the remaining unaccounted-for mass after subtracting the sum of measured components from the $\mathrm{PM}_{2.5}$ mass. Unaccounted-for mass can be potentially composed of unmeasured geological material (e.g., calcium carbonate), a higher fraction of oxygen in OM, and liquid water associated with $\mathrm{NH_{4}}^{+}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{SO}_{4}^{\ 2-}$ at the $35\%$ to $45\%$ relative humidity filter weighing conditions. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | image | Figure 5. Summertime material balance of $\mathrm{PM}_{2.5}$ for the 14 Chinese cities. Organic matter, geological material, and others are explained in the Figure 4 caption. |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | table | Table 3. Comparison of PM2.5 and major chemical concentrations (mg m3) from this study with measurements from other PM2.5 studies in Beijing (BJ), Xi’an (XA), Shanghai (SH), and Guangzhou (GZ) |
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atmosphere | 0001 | 10.1080/10962247.2012.701193 | text | Not supported with pagination yet | Journal of the Air & Waste Management Association
Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uawm20
Winter and Summer $\mathbf{PM}_{2.5}$ Chemical Compositions in Fourteen Chinese Cities
Jun-Ji Cao a , Zhen-Xing Shen b , Judith C. Chow a c , John G. Watson a c , Shun-Cheng Lee d Xue-Xi Tie a e , Kin-Fai Ho a , Ge-Hui Wang a & Yong-Ming Han a
a Key Lab of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences , Xi'an , China
b Department of Environmental Sciences and Engineering , Xi'an Jiaotong University , Xi'an , China
c Division of Atmospheric Sciences , Desert Research Institute , Reno , Nevada , USA d The Hong Kong Polytechnic University , Hong Kong
e National Center for Atmospheric Research , Boulder , Colorado , USA
Accepted author version posted online: 24 Jul 2012.Published online: 24 Sep 2012.
To cite this article: Jun-Ji Cao , Zhen-Xing Shen , Judith C. Chow , John G. Watson , Shun-Cheng Lee , Xue-Xi Tie , KinFai Ho , Ge-Hui Wang & Yong-Ming Han (2012) Winter and Summer $\mathrm{PM}_{2.5}$ Chemical Compositions in Fourteen Chinese Cities, Journal of the Air & Waste Management Association, 62:10, 1214-1226, DOI: 10.1080/10962247.2012.701193
To link to this article: http://dx.doi.org/10.1080/10962247.2012.701193
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TECHNICAL PAPER
Winter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities
Jun-Ji Cao,1,⁄ Zhen-Xing Shen,2 Judith C. Chow,1,3 John G. Watson,1,3 Shun-Cheng Lee,4
Xue-Xi Tie,1,5 Kin-Fai Ho,1 Ge-Hui Wang,1 and Yong-Ming Han1
1Key Lab of Aerosol, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China
2Department of Environmental Sciences and Engineering, Xi’an Jiaotong University, Xi’an, China
3Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA
4The Hong Kong Polytechnic University, Hong Kong
5National Center for Atmospheric Research, Boulder, Colorado, USA
⁄Please address correspondence to: Jun-Ji Cao, Institute of Earth Environment, Chinese Academy of Sciences (CAS), No. 10 Fenghui South Road,
High-Tech Zone, Xi’an 710075, China; e-mail: cao@loess.llqg.ac.cn
$P M_{2.5}$ in 14 of China’s large cities achieves high concentrations in both winter and summer with averages $>\!I O O\;\mu g\;m^{-3}$ being common occurrences. A grand average of $I l5\,\mu g\,m^{-3}$ was found for all cities, with a minimum of $27\,\mu g\,m^{-3}$ measured at Qingdao during summerand a maximum of $\!\!\!\!\!\operatorname{\mathrm{356}}\mu g\,m^{-3}$ at $X i$ ’an during winter. Both primary and secondary $P M_{2.5}$ are important contributors at all of the cities and during both winter and summer. While ammonium sulfate is a large contributor during both seasons, ammonium nitrate contributions are much larger during winter. Lead levels are still high in several cities, reaching an average of $I.68\;\mu g\;m^{-3}$ in $X i$ ’an. High correlations of lead with arsenic and sulfate concentrations indicate that much of it derives from coal combustion, rather than leaded fuels, which were phased out by calendar year 2000. Although limited fugitive dust markers were available, scaling of iron by its ratios in source profiles shows ${\sim}20\%$ of $P M_{2.5}$ deriving from fugitive dust in most of the cities. Multipollutant control strategies will be needed that address incomplete combustion of coal and biomass, engine exhaust, and fugitive dust, as well as sulfur dioxide, oxides of nitrogen, and ammonia gaseous precursors for ammonium sulfate and ammonium nitrate.
Implications: $\mathrm{PM}_{2.5}$ mass and chemical composition show large contributions from carbon, sulfate, nitrate, ammonium, and fugitive dust during winter and summer and across fourteen large cities. Multipollutant control strategies will be needed that address both primary $\mathrm{PM}_{2.5}$ emissions and gaseous precursors to attain China’s recently adopted $\mathrm{PM}_{2.5}$ national air quality standards.
Introduction
gases to secondary sulfate $(\mathrm{SO}_{4}^{\ 2-})$ , nitrate $\left(\mathrm{NO}_{3}^{\mathrm{~-}}\right)$ , ammonium $\mathrm{(NH_{4}}^{+})$ , and organic carbon (OC).
Suspended particulate matter (PM) is the major pollutant in many Chinese cities (Chan and Yao, 2008; Tie and Cao, 2009). Coal combustion to generate electricity and for domestic cooking and heating constitutes $\sim\!70\%$ of the national energy budget (NAE et al., 2008). Total biomass burning in China, which includes domestic cooking and residential heating, field burning of crop residue, forest fires, and grassland fires, is estimated at $511.3~\mathrm{{Tg}~\mathrm{{yr}^{-1}}}$ (Yan et al., 2006). Improved engines and tighter emission standards are being offset by rapid growth in the motor vehicle fleet (Han and Hayashi, 2008). Paved and unpaved roads, construction, agricultural operations, and wind-blown soil eject geological material into the atmosphere (Du et al., 2008; Xuan et al., 2004). These and other emitters are contributing to high PM levels in Chinese cities, both through direct PM emissions and through conversion of sulfur dioxide $(\mathrm{SO}_{2})$ , nitrogen oxides $\mathrm{(NO_{x})}$ , ammonia $\left(\mathrm{NH}_{3}\right)$ , and volatile organic compound (VOC)
The Chinese government issued a national $\mathrm{PM}_{2.5}$ standard on February 29, 2012, that requires cities to have concentrations below $\dot{3}5~\upmu\mathrm{g}~\mathrm{m}^{-3}$ annual average and $<\!75\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ for $24\ \mathrm{hr}.$ , beginning in 2016 (http://cleanairinitiative.org/portal/node/ 8163). These standards were adopted owing to recognized adverse effects of $\mathrm{PM}_{2.5}$ chemical components on human health, visibility, and materials (Hu et al., 2009; Mauderly and Chow, 2008; Pope and Dockery, 2006; Watson, 2002). Elements, ions, and carbon fractions are often measured in $\mathrm{PM}_{2.5}$ to better evaluate the adverse effects and to indicate contributing sources. Several studies have reported these measurements in China (Cao et al., 2011; Chow et al., 2006; Deng et al., 2011; Duan et al., 2006; Gu et al., 2011; Guinot et al., 2007; He et al., 2001; Ho et al., 2006; Hu et al., 2010; Louie et al., 2005; Louie et al., 2005; Shen et al., 2007; So et al., 2007; Song et al., 2007; Sun et al., 2004; Wang et al., 2006; Wang et al., 2007; Wu et al., 2003; Xu et al., 2004; Yang et al., 2011; Zhang et al., 2010; Zhang and Friedlander, 2000; Zhao et al., 2010), but the areas studied, sampling site zones of representation, sampling periods, variables measured, and analysis methods are of insufficient consistency to evaluate similarities and differences. Reported here are consistently characterized simultaneous winter and summer $\mathrm{PM}_{2.5}$ mass and chemical concentrations obtained during 2003 at receptors with neighborhood and urban scale (Chow et al., 2002) in 14 of China’s major cities. These measurements are used to compare and contrast the situation across a broad range of emissions and meteorology, examine seasonal changes, and assess contributions from coal combustion using elemental concentration ratios. These measurements from nearly a decade ago provide a baseline against which to evaluate future speciated $\mathrm{PM}_{2.5}$ measurements that will be needed to create and evaluate the multipollutant (Chow and Watson, 2011) control strategies required to attain the national standards.
Materials and Methods
As shown in Figure 1, measurement sites were located in 14 economically developed and developing cities across China. The neighborhood- and urban-scale sites were located on the campuses of schools and research institutes, as previously described (Cao et al., 2007; Cao et al., 2011; Han et al., 2009; Ho et al., 2007; Wang et al., 2006). Filter samplers were located on rooftops at 6 to $^{20\textrm{m}}$ above ground level for around 2 weeks of sampling during winter (January 6–20) and summer (June $3-$ July 30) of 2003.
$\mathrm{PM}_{2.5}$ samples were obtained on prefired $(900^{\circ}\mathrm{C},3\,\mathrm{h})$ ) 47-mm Whatman QM-A quartz-fiber filters by mini-volume air samplers (Airmetrics, Eugene, OR) at $5\ \mathrm{L\min}^{-1}$ flow rates. The exposed filters were stored at ${\sim}4^{\circ}\mathrm{C}$ after sampling, including shipping to the Xi’an laboratory, to minimize evaporation of volatile components. Filters were weighed before and after sampling with a $\pm1-\upmu\mathrm{g}$ sensitivity Sartorius MC5 electronic microbalance (Sartorius, Göttingen, Germany) after 24-hr equilibration at 20 to $23{}^{\circ}\mathbf{C}$ and 35 to $45\%$ relative humidity (RH). Each filter was weighed at least three times before and after sampling. The maximum differences among the three repeated weights were less than $10~\upmu\mathrm{g}$ for blank filters and less than $20~\upmu\mathrm{g}$ for exposed filters. The collected PM was the difference between the average of exposed weights and the average of unexposed weights. Field blanks were also collected at each sampling site every seventh day by exposing filters in the sampler without drawing air through them; these were used to account for passive deposition or artefacts introduced between sample changing.
Elemental concentrations of Fe, Ti, Mn, Zn, As, Br, and Pb in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry (PANalytical Epsilon 5, Almelo, The Netherlands) (Chow and Watson, 2012; Watson et al., 2012). Other elements, such as Si, Ca, Al, and Mg, were not quantified owing to high and variable blank values on quartzfiber filters and potential biases caused by absorption of lowenergy x-rays from particles penetrating into the filter. XRF measurements on nine collocated Teflon-membrane and quartzfiber filters from Xi’an were comparable for these elements, with correlations $(r)$ ranging from 0.982 for Fe and $Z\mathfrak{n}$ (with slopes of 1.054 and 1.062, respectively) to 0.915 for As (with slope of 1.204). Measurement precision was determined as the standard deviation of several analyses of the same samples, yielding $\pm7.6\%$ for Fe, $\pm8.6\%$ for Ti, $\pm12.5\%$ for Mn, $\pm7.6\%$ for $Z\mathfrak{n}$ , $\pm23.5\%$ for As, $\pm33.3\%$ for Br, and $\pm7.9\%$ for $\mathrm{Pb}$ at typical concentration levels. Instrumental detection limits are $24.0~\mathrm{ng}$ $\mathfrak{m}^{-3}$ for Fe, $14.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Ti, $25.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Mn, $24.0\,\mathrm{ng}\,\mathrm{m}^{-3}$ for Zn, $26.0\,\mathrm{ng}\,\mathrm{m}^{\bar{-}3}$ for As, $9.0\,\mathrm{ng~m}^{-3}$ for Br, and $21.0\,\mathrm{n}\bar{\mathrm{g}}\,\mathrm{m}^{-3}$ for $\mathrm{Pb}$ based on the uncertainties of blank filter counts. Replicate measurements were taken for every eight samples, and no differences were found that exceeded the precision intervals.
Following XRF analysis, the filter was sectioned with a precision cutter and one-fourth was extracted in $10\;\mathrm{mL}$ of distilled deionized water; the extract was submitted to ion chromatographic (IC) analysis (Shen et al., 2008; Shen et al., 2009) for cations $\mathrm{Na}^{+}$ , $\mathrm{NH_{4}}^{+}$ , and $\ K^{+}$ and anions $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{Cl}^{-}$ . Detection limits were $4.6\,\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{Na}^{+}$ , $4.0\,\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{NH_{4}}^{+}$ , $10.0~\upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\ K^{+}$ , $0.5~{\upmu\mathrm{g}}~\mathrm{L}^{-1}$ for $\mathrm{Cl}^{-}$ , $15\ \upmu\mathrm{g}\,\mathrm{L}^{-1}$ for $\mathrm{NO}{_3}^{-}$ , and $\overline{{20}}\ \upmu\mathrm{g}\ \mathrm{L}^{-1}$ for $\mathrm{SO}_{4}^{\bar{2}-}$ . Reference materials (National Research Center for Certified Reference Materials, China) agreed with analyses values within $\pm~4\%$ . One in 10 extracts was reanalyzed and none of the differences between these replicates exceeded precision intervals. Blank values were also subtracted from sample concentrations.
Organic carbon (OC) and elemental carbon (EC) were determined on a $0.5{\mathrm{-cm}}^{2}$ punch from each filter by a DRI model 2001 carbon analyzer (Atmoslytic, Inc., Calabasas, CA) following the IMPROVE thermal/optical reflectance (TOR) protocol (Cao et al., 2003; Chow et al., 1993; Chow et al., 2007; Chow et al., 2011). This produced four OC fractions (OC1, OC2, OC3, and OC4 at 120, 250, 450, and $550^{\circ}\mathrm{C}$ , respectively, in a helium [He] atmosphere); OP (a pyrolyzed carbon fraction determined when reflected laser light attained its original intensity after oxygen $[\mathrm{O}_{2}]$ was added to the analysis atmosphere); and three EC fractions (EC1, EC2, and EC3 at 550, 700, and $800^{\circ}\mathrm{C}$ , respectively, in a $2\%\,\mathrm{O}_{2}/98\%$ He atmosphere). OC is defined as ${\mathrm{OC}}1+{\mathrm{OC}}2+{\mathrm{OC}}3+{\mathrm{OC}}4+{\mathrm{OP}}_{}$ and EC is defined as $\mathrm{EC}1+$ $\mathrm{EC}2+\mathrm{EC}3-\mathrm{OP}$ .
Results and Discussion
$\mathrm{PM}_{2.5}$ mass concentrations
Figure 2 shows the wide distribution of concentrations observed across seasons and cities. The grand average of 115 $\upmu\mathrm{g}\,\mathrm{m}^{-3}$ is more than 3 times the annual standard, and the highest 24-hr value of $543.9~\upmu\mathrm{g}\mathrm{~m}^{-3}$ , found in Xi’an during winter, is more than 7 times the 24-hr standard. OC, $\mathrm{SO}_{4}^{\,\,2-}\,\mathrm{N}\bar{\mathrm{O}_{3}}^{-}$ , $\mathrm{NH_{4}}^{+}$ , and EC are the most abundant species, all with averages exceeding $5~{\upmu\mathrm{g}}~\mathrm{m}^{-3}$ . Elemental averages are less than the averages for carbon and ions, with Fe having the highest average of $2.4~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}$ at Chongqing during winter. Concentrations ranged over several orders of magnitude, with the range increasing as the average concentration decreased. This variability indicates large spatial and temporal differences across the network.
Table 1 summarizes winter and summer $\mathrm{PM}_{2.5}$ averages for each city. Standard deviations are typically $25\%$ to $50\%$ of the averages, indicating that these averages are not highly influenced by extreme events. Standard errors (standard deviation divided by the square root of the number of samples) of the averages are in the range of $6\%$ to $13\%$ .
In every city except Beijing and Xiamen (no summer data), wintertime $\mathrm{PM}_{2.5}$ exceeded those of summertime, in many cases by a factor of 2 or more. Seasonal averages for $\mathrm{PM}_{2.5}$ mass were similar in Beijing, with a winter/summer ratio of 0.88, in contrast to the highest ratio of 4.9 at Qingdao, a coastal city in northern China. The lack of difference in the mass ratio for Beijing is partially due to the lack of change in the OC concentrations, which were $23.9\pm12.4\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in winter, only $20\%$ higher than summer. The winter/summer mass ratios for other cities are reflected in the major chemical component averages, which are 2 to 3 for OC, EC, and $\mathrm{SO}_{4}{}^{2-}$ in most cities, with $\mathrm{NO}{_3}^{-}$ and $\mathrm{NH_{4}}^{+}$ showing even higher winter/summer differences.
Average wintertime $\mathrm{PM}_{2.5}$ was lowest in Xiamen $(74.2~\upmu\mathrm{g}$ $\mathfrak{m}^{-3}.$ ) and highest in Xi’an $(356.3\;\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ). $\mathrm{PM}_{2.5}$ was higher at inland cities (e.g., Xi’an, Wuhan, and Chongqing), and lower at the coastal (e.g., Xiamen and Hong Kong) and desert (i.e., Jinchang) cities. For the summer samples, average $\mathrm{PM}_{2.5}$ was lowest in Qingdao $(27.3\;\;\upmu\mathrm{g}\;\;\mathrm{m}^{-3})$ , and highest in Beijing $\left(131.6~\upmu\mathrm{g}~\mathrm{m}^{-3}\right)$ ).
$\mathrm{PM}_{2.5}$ composition
OC and EC exhibited winter maxima and summer minima. OC was the most abundant wintertime $\mathrm{PM}_{2.5}$ constituent in all cities except Hangzhou and Hong Kong, ranging from 13.3 (Hong Kong) to $\bar{9}5.8~\upmu\mathrm{g}~\mathrm{m}^{-3}$ (Xi’an). Wintertime EC levels vary with OC concentrations, which ranged from 4.6 (Jinchang) to $\dot{2}1.5~\upmu\mathrm{g}\;\mathrm{m}^{-3}$ (Xi’an). This co-occurrence is expected, as OC and EC typically result from incomplete combustion of solid and liquid fuels (Lighty et al., 2000). OC and EC concentrations were highest in the inland cities, such as Changchun, Xi’an, Wuhan, and Chongqing, and lower in the coastal cities, such as Qingdao, Xiamen, and Hong Kong.
Wintertime $\mathrm{SO}_{4}^{-2-}$ was the second most abundant component of $\mathrm{PM}_{2.5}$ for all the cities except Hong Kong, varying from 11.5 $\upmu\mathrm{g}\textrm{m}^{-3}$ in Jinchang to $60.9~\mathrm{\dot{\mu}g~m}^{-\overline{{3}}}$ in Chongqing. This was followed by $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , ranging from $2.1~\upmu\mathrm{g}\mathrm{~m}^{-3}$ (Jinchang) to 29 $\upmu\mathrm{g}\:\mathrm{m}^{-3}$ (Xi’an), and $\mathrm{NH_{4}}^{+}$ ranged from $6.6\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ (Jinchang) to $29.8~\,\upmu\mathrm{g}\,\,\mathrm{m}^{-3}$ (Xi’an). These high secondary ammonium sulfate $((\mathrm{NH}_{4})_{2}\mathrm{SO}_{4})$ and ammonium nitrate $\left(\mathrm{NH}_{4}\mathrm{NO}_{3}\right)$ levels imply the need for precursor gas, as well as primary PM, emission reductions to reduce $\mathrm{PM}_{2.5}$ mass. The higher $\mathrm{NH}_{4}\mathrm{NO}_{3}$ values in winter than summer are consistent with a shift in equilibrium from the gas to particle phase with lower temperatures and higher RH (Stelson et al., 1979).
$\ K^{+}$ is considered a marker for biomass burning (Andreae, 1983; Duan et al., 2004), although it is also a component of certain soils and sea spray (Pytkowicz and Kester, 1971). Wintertime $\ K^{+}$ levels exceeded $3~\upmu\mathrm{g}\,\textrm{m}^{-3}$ at Xi’an, Wuhan, Chongqing, and Hangzhou. The inland cities experience cold temperatures during winter and have abundant biomass available for residential heating.
Fe is a marker for fugitive dust, although it also originates from heavy industry. The wintertime Fe concentration was highest at $2.4\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ in Chongqing, followed by Xi’an $(1.8~\upmu\mathrm{g}\,\mathrm{m}^{-\overline{{3}}})$ , with the lowest wintertime average of $0.6\,\upmu\mathrm{g}\,\mathrm{m}^{-3}$ at Xiamen. The two arid-region cities had low Fe concentrations, $1.2\ \upmu\mathrm{g}\ \mathrm{m}^{-3}$ at Jinchang and $0.7~\upmu\mathrm{g}\textrm{m}^{-3}$ at Yulin. The wintertime Fe averages did not correlate well with other soil components such as Ti and Mn across the sites, which may indicate additional Fe sources or variability in the fugitive dust compositions.
The highest wintertime As $(0.11\;\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ) and $\mathrm{Pb}\,(1.7\,\upmu\mathrm{g}\,\mathrm{m}^{-3})$ ) concentrations were found at Xi’an. As and $\mathrm{Pb}$ are found in Chinese coal (Tian et al., 2011; Want et al., 2006), while $\mathrm{Pb}$ gasoline additives were discontinued in 2000 (Xu et al., 2012). The highest Br average was found at a coastal city, Qingdao $(0.17\ \upmu\mathrm{g}\:\textrm{m}^{-3})$ , consistent with a potential marine aerosol contribution.
Summertime averages were lower than those for winter for nearly all chemical components. In most cases, this can be attributed to warmer weather that improved dispersion and shifted the $\mathrm{NH}_{4}\mathrm{NO}_{3}$ from the particle to gas phase. Lower OC and EC averages are probably less related to domestic biomass and coal combustion, which is consistent with lower $\Chi^{+}$ and As averages. Engine exhaust and agricultural burning emissions are expected to contribute larger portions of OC and EC during summer.
$\mathrm{NO}_{3}^{\mathrm{~-~}}$ and $\mathrm{NH_{4}}^{+}$ show the biggest contrast between winter and summer, consistent with the change in equilibrium. $\mathrm{SO}_{4}^{\ 2-}$ levels were also much lower during summer than winter. This would be consistent with more nearby $\mathrm{SO}_{2}$ to $\mathrm{SO}_{4}{}^{2-}$ conversion during winter, possibly in conjunction with reactive fogs and clouds (Pandis et al., 1992) and with local accumulation under stagnant conditions. The summer values could be more influenced by standard photochemical mechanisms occurring during long-range transport (Qian et al., 2001).
The Fe and Ti fugitive dust markers do not show a clear winter/summer pattern, being higher in some cities during summer and lower in others. The sampling periods did not include the April/May Asian dust storms (Gong and Zhang, 2008; Li et al., 2008) that are causes of high $\mathrm{PM}_{2.5}$ during these periods. The other elements do not show major or consistent differences between winter and summer, except that the summertime averages are generally lower. The summer $\mathrm{Pb}$ average in Xi’an decreased by more than a factor of two $(0.75~\upmu\mathrm{g}\;\mathrm{m}^{-\frac{\tt3}{\tt4}})$ ).
Chemical ratios as source indicators
Several potential sources of different chemical components were mentioned earlier. These can be better understood by examining some of the elemental ratios available from the data set that might correspond to similar ratios in the source profiles. OC/EC ratios across the 14 cities are compared in Cao et al. (2007). Given the large role of domestic and industrial coal use, the $\mathrm{SO}_{4}^{\;\;2-}/\mathrm{OC}$ , $\mathrm{SO}_{4}^{\frac{\gamma}{2}-}/\mathrm{EC}$ , $\mathrm{NO}_{3}^{\mathrm{~-}}/\mathrm{SO}_{4}^{\mathrm{~2-}}$ , $\mathrm{As/Fe}_{\mathrm{,}}$ , and $\mathrm{Pb/Fe}$ ratios are compared with ratios from other cities in Table 2. The 2003 $\mathrm{SO}_{4}^{\;\;\hat{2}-}/\mathrm{OC}$ ratio found in this study $(0.90\pm0.43)$ is much higher than that for the other cities, as is the $\mathrm{SO}_{4}{}^{2-}/\mathrm{EC}$ ratio $(3.42\pm2.06)\$ . Only Toronto had a higher $\mathrm{SO}_{4}^{~2-}/\mathrm{EC}$ ratio (i.e., 4.93), mostly due to low EC levels. As noted earlier, there are spatial and seasonal variations in these ratios that reflect local and regional contributions.
$\mathrm{NO}_{3}^{\bar{-}}/\mathrm{SO}_{4}^{\;2-}$ ratios have been used to evaluate relative contributions from coal-burning emissions, which abound in $\mathrm{NO_{x}}$ and $\mathrm{SO}_{2}$ , and engine exhaust, which is a major $\mathrm{NO_{x}}$ emitter but contains little $\mathrm{SO}_{2}$ (Hu etal.,2002; Wang etal.,2005; Yaoetal.,2002). Average $\mathrm{NO}_{3}^{\scriptscriptstyle-}/\mathrm{SO}_{4}^{\;2-}$ ratios were 0.61 in winter and 0.30 in summer. The $\mathrm{NO}_{3}^{\mathrm{~-}}/\mathrm{SO}_{4}^{\mathrm{~2-}}$ ratio for Toronto (0.81) was ${>}75\%$ higher than the value found in this study (i.e., $0.46\,\pm\,0.27)$ , while the ratios in Seattle, WA (0.43), and Mexico City (0.45) were comparable. $\mathrm{As/Fe}$ and $\mathrm{Pb/Fe}$ ratios were $0.04\pm\:0.03$ and $0.39\pm\:0.32$ , respectively, much higher than those for the other cities and indicative of the ash in uncontrolled coal combustion. Figure 3 shows a reasonably good association of $\mathrm{Pb}$ and $\bar{\mathrm{SO}_{4}}^{2-}$ concentrations with the As marker for coal ash. The scatter (e.g., Figure 3d) is typical of different ash composition and $\mathrm{SO}_{2}$ to $\bar{\mathrm{SO}_{4}}^{2-}$ transformation rates. The $\mathrm{Pb/As}$ correlation indicates that the Pb more probably derives from the coal ash than from the remnants from leaded gasoline, as also indicated by differences in abundances for $\mathrm{Pb}$ isotopic ratios (Xu et al., 2012; Widory et al., 2010; Zheng et al., 2004).
Material balance
Material balances estimating organic matter and soil from their marker species are shown in Figures 4 and 5 for the winter and summer seasons. Consistent with the previous discussion, organic material (OM), $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\bar{\mathrm{NH}_{4}}^{+}$ are large components. OC takes on an even larger rolewhen its unmeasured hydrogen and oxygen components are taken into account as OM. The role of geological material is also enhanced when the Fe marker is leveraged by reasonable assumptions about its abundance in Chinese soils. Approximately 0 to $15\%$ of the measured mass is not quantified by the chemical analysis, which is potentially due to unmeasured species, underestimations for weighting factors for OM and geological material, and uncertainties in filter equilibration and gravimetric analysis (Malm et al., 2011; Kajino et al., 2006).
For winter samples, contributions in order of importance were $\mathrm{OM}>$ geological material $>$ sulfate $>$ nitrate> ammonium $>$ elemental carbon at major cities such as Beijing, Tianjin, Wuhan, Chongqing, Hangzhou, and Xiamen. Compositions differed for Yulin, Xi’an, and Hong Kong, where the wintertime $\mathrm{SO}_{4}^{\ 2-}$ contribution exceeded that from geological material. At arid Jinchang, the geological material contribution exceeded the $\mathrm{SO}_{4}^{\ 2-}$ and OM contributions.
During summer, most cities follow the general trend of $\mathrm{OM}>$ geological material $>$ sulfate $>$ nitrate, with elemental carbon contributions higher than ammonium contributions at all cities but Beijing and Tianjin. The contribution from $\mathrm{SO}_{4}^{\ 2-}$ in Hong Kong differed between winter $(25\%)$ and summer $(14\%)$ . At Shanghai and Hangzhou, $\mathrm{SO}_{4}{}^{2-}$ contributions exceeded those of geological material.
Comparison with other $\mathrm{PM}_{2.5}$ speciation studies in Chinese cities
Table 3 compares city-specific results from this study with chemical concentrations from other major cities (i.e., Beijing, Xi’an, Shanghai, and Guangzhou). Although there are differences in magnitude owing to the differences in measurement periods, zones of representation, and measurement methods, the major components are similar in magnitude and order of importance for nearly all of the studies. There is no evidence of major upward or downward trends in mass and chemical composition from 1999 to 2006, but this is expected, given the short durations of the measurement programs and the large variability in emissions and meteorology expected over this time period. Trends in the United States have only been associated with emission reductions over long periods of a decade or more using chemically speciated measurements that are specific to those emissions.
$\mathrm{PM}_{2.5}$ in Chinese cities versus non-Chinese cities
Table 4 compares the major components from Chinese cities with $\mathrm{PM}_{2.5}$ compositions in other countries. The geological material contribution is on the order of $10\%$ in $\mathrm{PM}_{2.5}$ from the non-Chinese cities, about half of that estimated from this study $(19.5\%)$ . Roadside sites in St. Louis, MO, and Barcelona, Spain, showed more comparable geological material contributions (15.4 and $15.2\%)$ ). The OM fractions in the Chinese cities are similar to most of the other cities, although the absolute OM concentrations are much higher in China. $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{NH_{4}}^{+}$ are important in $\mathrm{PM}_{2.5}$ in all of the cities, but their fractions are more variable and their absolute values are generally lower than those found in the 14 Chinese cities.
Average $\mathrm{PM}_{2.5}$ concentrations for this study ranged from 3 to 9 times higher than the values in Seoul, Yokohama, St. Louis, Indianapolis, Toronto, Mexico City, Barcelona, and Milan, with corresponding 2 to 10 times higher levels of OM. The average Chinese secondary aerosol concentrations for $\mathrm{SO}_{4}^{\ 2-}$ , $\mathrm{NO}_{3}^{\mathrm{~-~}}$ , and $\mathrm{NH_{4}}^{+}$ were 2–5, 1–10, and 2–7 times higher, respectively, than those in other cities in the world. As and $\mathrm{Pb}$ were 10 times and average geological material was 5–43 times those found in other cities.
Conclusions
$\mathrm{PM}_{2.5}$ in 14 of China’s large cities achieved high concentrations in both winter and summer of 2003 with averages ${>}100~\upmu\mathrm{g}\,\mathsf{m}^{-3}$ being common occurrences. A grand average of $115\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ was found for all cities, with a minimum of $27.3\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ measured at coastal Qingdao during summer and a maximum of $356.3\ensuremath{~\upmu\mathrm{g}\,\mathrm{m}^{-3}}$ at inland Xi’an during winter. Both primary and secondary $\mathrm{PM}_{2.5}$ are important contributors at all of the cities during both winter and summer. While ammonium sulfate is a large contributor during both seasons, ammonium nitrate contributions are much larger during winter. Lead levels are still high in several cities, reaching an average of $1.68\;\upmu\mathrm{g}\;\mathrm{m}^{-3}$ in Xi’an during winter. High correlations of lead with arsenic and sulfate concentrations indicate that much of it derives from coal combustion rather than leaded fuels that were phased out by calendar year 2000. Although limited fugitive dust markers were available, scaling of iron by its ratios in source profiles shows ${\sim}20\%$ of $\mathrm{PM}_{2.5}$ deriving from fugitive dust in most of the cities. Multipollutant control strategies will be needed that address incomplete combustion of coal and biomass, engine exhaust, and fugitive dust, as well as sulfur dioxide, oxide of nitrogen, and ammonia gaseous precursors for ammonium sulfate and ammonium nitrate.
Acknowledgments
This work was supported by the Natural Science Foundation of China (NSFC40925009), projects from Chinese Academy of Sciences (KZCX2-YW-BR-10, O929011018, and KZCX2-YW148). The authors thank Jo Gerrard of the Desert Research Institute for her assistance in assembling and editing the paper.
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About the Authors
Jun-Ji Cao, Kin-Fai Ho, Ge-Hui Wang, and Yong-Ming Han are professors in the Division of Aerosol & Environment, Institute of Earth Environment, Chinese Academy of Sciences.
Xue-Xi Tie is a scientist at National Center for Atmospheric Research, USA.
Zhen-Xing Shen is an associate professor at Xi’an Jiaotong University, China.
Judith C. Chow and John G. Watson are research professors in Desert Research Institute, USA.
Shun-Cheng Lee is a professor in the Hong Kong Polytechnic University, Hong Kong.
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 1. Location of the sampling site at Xi’an, China. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 1. Average of OC and EC concentrations during September 2003 to February 2004 at Xi’an, China. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 2. Time series of $\mathrm{PM}_{2.5}$ mass, organic carbon (OC), elemental carbon (EC), fraction of $\mathrm{PM}_{2.5}$ composed of $\mathrm{OC}\!\times\!1.6\!+\!\mathrm{EC}$ $(\mathrm{TCA}\%)$ , and OC/EC ratios at Xi’an from 13 September 2003 to 29 February 2004. OC is multiplied by 1.6 for the $\mathrm{TCA}\%$ calculation to account for unmeasured hydrogen and oxygen in organic material (Turpin and Lim, 2001). |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 3. Relationships between OC and EC concentrations in $\mathrm{PM}_{2.5}$ and $\mathrm{PM_{10}}$ . |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 4. Distribution of $\mathrm{PM}_{2.5}$ and $\mathrm{PM_{10}}$ mass concentrations during fall and winter. The valid paired samples were 17 in fall and 36 in winter. The box plots indicate the mean $24\mathrm{-h}$ concentration and the min, 1st, 25th, 50th, 75th, 99th and max percentiles. A normal curve is fitted to the measurements. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 2. Statistical summary of the percentage of OC, EC, and $\mathrm{TCA}\%$ in $\mathrm{PM}_{2.5}$ and $\mathrm{PM}_{10}^{\mathrm{a}}$ |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 5. Abundances (mass fraction of total carbon) of eight thermally-derived carbon fractions in ambient and source samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 3. Comparison of $\operatorname{PM}_{2.5}$ OC, EC at Xi’an with other Asian cities. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 6. Periodicity of $\mathrm{PM}_{2.5}$ OC, EC, mass, and daily average wind speed. (PSD TISA on the $\mathrm{Y}$ axis refers to Power as Time-Integral Squared Amplitude.) |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 4. APCA results of fall samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | table | Table 5. APCA results of winter samples. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | image | Fig. 7. Relative contributions of major sources to $\mathrm{PM}_{2.5}$ TC during fall and winter 2003. |
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atmosphere | 0002 | 10.5194/acp-5-3127-2005 | text | Not supported with pagination yet | Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi’an, China
J. J. $\mathbf{Cao}^{1}$ , F. $\mathbf{W}\mathbf{u}^{1,2}$ , J. C. Chow3, S. C. Lee4, Y. Li1, S. W. Chen5, Z. S. $\mathbf{A}\mathbf{n}^{1}$ , K. K. Fung6, J. G. Watson3, C. S. $\mathbf{Zhu}^{1}$ , and S. X. Liu1
1SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710075, China
2The Graduate School of Chinese academy of Sciences, Beijing 100049, China
3Desert Research Institute, Reno, Nevada, USA
4The Hong Kong Polytechnic University, Hong Kong, China
5Tongji University, Shanghai 200092, China
6Atmoslytic, Inc., Calabasas, CA, USA
Received: 14 March 2005 – Published in Atmos. Chem. Phys. Discuss.: 1 June 2005
Revised: 1 September 2005 – Accepted: 9 November 2005 – Published: 22 November 2005
Abstract. Continuous measurements of atmospheric organic and elemental carbon (OC and EC) were taken during the high-pollution fall and winter seasons at Xi’an, Shaanxi Province, China from September 2003 through February 2004. Battery-powered mini-volume samplers collected $\mathrm{PM}_{2.5}$ samples daily and $\mathrm{\bfPM}_{10}$ samples every third day. Samples were also obtained from the plumes of residential coal combustion, motor-vehicle exhaust, and biomass burning sources. These samples were analyzed for OC/EC by thermal/optical reflectance (TOR) following the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol. OC and EC levels at Xi’an are higher than most urban cities in Asia. Average $\mathrm{PM}_{2.5}$ OC concentrations in fall and winter were $34.1{\pm}18.0\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $61.9{\pm}33.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively; while EC concentrations were $11.3{\pm}6.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $12.3{\pm}5.3\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. Most of the OC and EC were in the $\mathrm{PM}_{2.5}$ fraction. OC was strongly correlated $(\mathbf{R}{>}0.95)$ with EC in the autumn and moderately correlated $(\mathsf{R}{=}0.81)$ ) with EC during winter. Carbonaceous aerosol $(\mathrm{OC}\!\times\!1.6\!+\!\mathrm{EC})$ accounted for $48.8\%{\pm}10.1\%$ of the $\mathrm{PM}_{2.5}$ mass during fall and $45.9{\pm}7.5\%$ during winter. The average OC/EC ratio was 3.3 in fall and 5.1 in winter, with individual OC/EC ratios nearly always exceeding 2.0. The higher wintertime OC/EC corresponded to increased residential coal combustion for heating. Total carbon (TC) was associated with source contributions using absolute principal component analysis (APCA) with eight thermally-derived carbon fractions. During fall, $73\%$ of TC was attributed to gasoline engine exhaust, $23\%$ to diesel exhaust, and $4\%$ to biomass burning. During winter, $44\%$ of TC was attributed to gasoline engine exhaust, $44\%$ to coal burning, $9\%$ to biomass burning, and $3\%$ to diesel engine exhaust.
1 Introduction
This study examines temporal variations of $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ concentrations of organic and elemental carbon (OC and EC) in Xi’an, China. ( $\mathrm{PM}_{2.5}$ is particulate matter with an aerodynamic diameter smaller than 2.5 micrograms $[\mu\mathrm{m}]$ , $\mathrm{{PM}_{10}}$ is particulate matter with an aerodynamic diameter smaller than $10\,\mu\mathrm{m})$ . This study also quantifies contributions of organic and elemental carbon in Xi’an from coal combustion, vehicle exhaust, fugitive dust and dust storms (Cao et al., 2005; Gao et al., 1997; Zhang et al., 1993; Zhuang et al., 1992).
With a population of five million, Xi’an, in Shaanxi Province, is the largest city in northwestern China. It has served as the capital city of 13 Chinese dynasties for more than a millennium. Since the discovery in 1974 of hundreds of buried life-size terra-cotta figures of warriors and horses, the city has been a major tourist attraction. Xi’an also experiences some of the worst air pollution among China’s cities (Zhang et al., 2001, 2002), where elevated carbonaceous aerosol components contribute to high PM levels. Several studies have been conducted in China’s well-developed coastal cities, such as Beijing, Shanghai, Guangzhou, and Hong Kong (Cao et al., 2003, 2004; He et al., 2001; Louie et al., 2005a, b; Ye et al., 2003), but few measurements are available from inland cities, such as Xi’an.
OC and EC in suspended particulate matter (PM) play important roles in health, visibility, and climate effects (ACEAsia, 1999; Cooke et al., 1999; IPCC, 2001; UNEP and NOAA, 2003; Vedal, 1997; Watson, 2002). EC, which is often equated with optically-derived, light-absorbing black carbon (BC), is known to cause heating in the air on a regional scale, thus altering atmospheric stability and vertical mixing, and affecting large-scale circulation and the hydrologic cycle (Menon et al., 2002). Since about one fourth of global BC emissions are believed to originate from China (Cooke et al., 1999), a reduction of BC emissions in China could produce positive consequences for global warming (Jacobson, 2002).
2 Sampling and analysis
2.1 Sampling site
Xi’an is located on the Guanzhong Plain at the south edge of the Loess Plateau $400\,\mathrm{m}$ above sea level at $33^{\circ}29^{\prime}–34^{\circ}44^{\prime}\,\mathrm{N}$ , $107^{\circ}40^{\prime}–109^{\circ}49^{\prime}\,\mathrm{E}$ (Fig. 1). The monitoring site was located in an urban-scale zone of representation (Chow et al., 2002) surrounded by a residential area $\mathord{\sim}15\,\mathrm{km}$ south of downtown Xi’an, where there are no major industrial activities, nor local fugitive dust sources. $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ samples were obtained from 13 September 2003 to 29 February 2004 from the rooftop of the Chinese Academy of Sciences’ Institute of Earth Environment building at $10\,\mathrm{m}$ above ground. Based on local meteorological characteristics and the residential heating season (mid-November through February), the period from 13 September 2003 to 31 October 2003 was designated as fall, and the period from 1 November 2003 to 29 February 2004 was designated as winter.
2.2 Sample collection
Daily $\mathrm{PM}_{2.5}$ and every-third-day $\mathrm{\bfPM}_{10}$ samples were collected using two battery-powered mini-volume samplers (Airmetrics, Oregon, USA) operating at flow rates of 5 liters per minute $(\mathrm{L}\,\mathrm{min}^{-1}$ ; Cao, 2003). Prior to field operations, calibrated MiniVol samplers were collocated with low volume $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ Partisol samplers (model 2000, Rupprecht & Patashnick, Albany, New York, USA) at the Hong Kong Polytechnic University. The difference between the two types of samplers was less than $5\%$ for the $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ mass.
PM samples were collected on $47\,\mathrm{mm}$ Whatman quartz microfiber filters (QM/A) that were pre-heated at $900^{\circ}\mathrm{C}$ for three hours before sampling. The exposed filters were stored in a refrigerator at ${\sim}4^{\circ}\mathbf{C}$ before chemical analysis to minimize the evaporation of volatile components. Quartz-fiber filters were analyzed gravimetrically for mass concentrations using a Sartorius MC5 electronic microbalance with a $\pm1\,\mu\mathrm{g}$ sensitivity (Sartorius, G¨ottingen, Germany) after 24-h equilibration at a temperature between $20^{\circ}\mathbf{C}$ and $23^{\circ}\mathbf{C}$ and a relative humidity $\left(R H\right)$ between $35\%$ and $45\%$ . Each filter was weighed at least three times before and after sampling, and the net mass was obtained by subtracting the average of pre-sampling weights from the average of postsampling weights. Differences among replicate weighings were $<\!10\,\mu\mathrm{g}$ for blanks and $<\!20\,\mu\mathrm{g}$ for samples. Sixteen field blanks were collected to correct for adsorbed gas-phase organic components. Volatilization of particle-phase organics during and immediately after sampling was not quantified. A total of $165\,\,\,\mathrm{PM}_{2.5}$ and $53\,\mathrm{\PM_{10}}$ samples were collected during the ambient sampling period. Five $\mathrm{PM}_{2.5}$ source samples were collected from residential stoves burning coal, six from alongside a major highway with heavy traffic, and five from smoke plumes when maize residue was burned after harvest.
Meteorological data were monitored continuously with a HFY-IA Wind Speed/Wind Direction Instrument (Changchun Institute of Metrological Instruments, Changchun, Jilin Province, China).
2.3 Thermal/optical carbon analysis
A $0.5\,\mathrm{cm}^{2}$ punch from each samples was analyzed for OC and EC with a Desert Research Institute (DRI) Model 2001 Thermal/Optical Carbon Analyzer (Atmoslytic Inc., Calabasas, CA, USA) for eight carbon fractions following the IMPROVE (Interagency Monitoring of Protected Visual Environments) thermal/optical reflectance (TOR) protocol (Chow et al., 1993, 2001, 2004a, 2005; Fung et al., 2002). This produces four OC fractions (OC1, OC2, OC3, and OC4 at $120^{\circ}\mathbf{C}.$ , $250^{\circ}\mathrm{C}$ , $450^{\circ}\mathrm{C}$ , and $550^{\circ}\mathrm{C}.$ , respectively, in a He atmosphere); a pyrolyzed carbon fraction (OP, determined when a reflected laser light attained its original intensity after ${\bf O}_{2}$ was added to the analysis atmosphere); and three EC fractions (EC1, EC2, and EC3 at $550^{\circ}\mathrm{C}$ , $700^{\circ}\mathrm{C}$ , and $800^{\circ}\mathrm{C}$ , respectively, in a $2\%$ $\mathrm{O}_{2}/98\%$ He atmosphere). IMPROVE OC is defined as $\mathrm{OC1+OC2+OC3+OC4+OP}$ and EC is defined as $\mathtt{E C l+E C2+E C3-O P}$ . Inter-laboratory comparisons of samples between IMPROVE protocol with the DRI Model 2001 instrument and the TMO (thermal manganese dioxide oxidation) method (done by AtmAA, Inc., Calabasas, CA) has shown a difference of ${<}5\%$ for total carbon (TC) and $10\%$ for OC/EC (Fung et al., 2002). Comparisons with other OC/EC methods (Watson et al., 2005) show that IMPROVE TOR OC and EC are near the middle of the distribution of differences for the average of all methods. Average field blanks were 1.56 and $0.4\bar{2}\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ for OC and EC, respectively. Quality Assurance/Quality Control (QA/QC) procedures have been described in Cao et al. (2003).
3 Results and discussion
3.1 Temporal variations of OC and EC
Monthly and seasonally averaged OC/EC concentrations are summarized in Table 1. $\mathrm{PM}_{2.5}$ OC and EC during winter were 1.8 and 1.1 times, respectively, of those during fall; while. $\mathrm{\bfPM}_{10}$ OC and EC during winter were 2.2 and 1.5 times, respectively, of those during fall. Monthly average OC and EC were highest during December and lowest during September. In December, OC in $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ were $81.7{\pm}36.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $124.8{\pm}54.8\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively; and EC in $\mathrm{PM}_{2.5}$ and $\mathsf{P M}_{10}$ were $15.2{\pm}4.6\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $28.9{\pm}8.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. The maximum-to-minimum ratios for $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ were 3.3 and 4.2 for OC and 1.8 and 2.6 for EC, respectively. Higher variability for OC concentrations may be due to the contributions of different emission sources.
Figure 2 shows that temporal variations of $\mathrm{PM}_{2.5}$ OC coincided with mass and, to a lesser extent, with EC. OC was highly correlated with $\mathrm{PM}_{2.5}$ $(\mathrm{r}{=}0.96$ , significance level $99\%$ ) and EC was moderately correlated with $\mathrm{PM}_{2.5}$ $_{\mathrm{(r=0.72}}$ , significance level $99\%$ ). $\operatorname{PM}_{2.5}$ OC increased gradually from September to November, and reached a maximum on 14 December 2003 $(189.6\,\mu\mathrm{g}\,\mathrm{m}^{-3})$ ). Major emission sources of OC and EC in China include coal combustion (mostly residential), motor-vehicle exhaust, and biomass burning (Streets et al., 2001; Zhang et al., 2001), all of which are also evident in Xi’an. During the fall harvest season in midOctober, the residues of diverse crops like corn and rice are burned. Biofuels are also used by farmers for residential heating and cooking for both fall and winter. Zhang et al. (2001) showed that total suspended particle (TSP) in Xi’an reaches maximum levels in winter and minimum levels in summer. After the Chinese Spring Festival (22 January 2004 to 29 January 2004), OC decreases rapidly initially, then decreases further as February progresses. A similar trend was found for EC, but while EC concentration was lowest during the festival, it fluctuated at low values from 22 January 2004 to 5 February 2004.
3.2 Relationship between OC and EC
OC/EC ratios give some indication of the origins of carbonaceous $\mathrm{PM}_{2.5}$ (Chow et al., 1996; Gray et al., 1986; Turpin and Huntzicker, 1991). As shown in Fig. 3, strong OC/EC correlations (0.95–0.97) in fall suggest impacts from a combination of common source contributions (i.e. residential and commercial coal combustion, biomass burning, motorvehicle exhaust). OC/EC correlations (0.81) were lower in winter, consistent with a changing mixture of source contributions. Residential coal combustion was estimated to contribute more than $50\%$ of TSP in 1997 (Zhang, 2001). Even though many residents in Xi’an have replaced coal with natural gas, a large number of low-income families still use coal for cooking and heating. Coal-fired boilers have been banned within the second beltway in downtown Xi’an since 1998, but due to the low cost of coal, many middle- and small-scale boilers are still in use.
The slopes of OC versus EC in winter were 5.12 for $\mathrm{PM}_{2.5}$ and 3.83 for $\mathrm{\bfPM}_{10}$ , compared to those in fall (2.46) (Fig. 3), implying that OC emissions in winter increased relative to EC emissions. The difference may be ascribed to the change of emission sources between the two seasons, primarily due to the completion of burning in corn and rice fields.
3.3 Variability of OC/EC ratios
OC/EC ratios are influenced by: 1) emission sources; 2) secondary organic aerosol (SOA) formation; and 3) different OC/EC removal rates by deposition (Cachier et al., 1996). Atmospheric EC is directly emitted, while OC can be both directly emitted and formed in the atmosphere from the low vapor pressure products of chemical reactions involving emissions of volatile organic compounds (VOCs).
As shown in Table 1, monthly averaged OC/EC ratios in $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ ranged from 3.0 to 3.4 in fall, and 3.6 to 6.4 in winter. The highest ratios were recorded in January, with 6.4 in $\mathrm{PM}_{2.5}$ and 5.1 in $\mathsf{P M}_{10}$ . Daily variations of $\mathrm{PM}_{2.5}$ OC/EC ratios in Fig. 2 show lower ratios and variability in fall and higher ratios and variability in winter.
Regarding source samples, the average OC/EC ratio was 12.0 for coal-combustion, 4.1 for vehicle exhaust, and 60.3 for biomass burning. These ratios are much higher than reported values elsewhere of 2.7 for coal-combustion and 1.1 for motor vehicles (Watson et al., 2001), and 9.0 for biomass burning (Cachier et al., 1989). The individual OC/EC ratios for this study exceeded 2.0 for both $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ fractions (Fig. 2), which might reflect the combined contributions from coal combustion, motor-vehicle exhaust, and biomass burning sources. Elevated OC/EC ratios (8.0) during mid-December can be attributed to biomass burning and coal combustion. High OC/EC ratios (6.0–9.0) during the Chinese Spring Festival may be due to lower contributions from motor-vehicle exhaust and biomass burning during the holiday, and higher contributions from residential coal combustion.
3.4 Contributions to $\mathrm{PM}_{2.5}$ and $\mathrm{\bfPM}_{10}$ mass
Figure 4 shows a larger $\mathrm{\bfPM}_{10}$ scatter than $\mathbf{PM}_{2.5}$ in both seasons. Daily $\mathrm{{PM}_{10}}$ in winter varied by a factor of 5.7, ranging from $155\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (06 November 2003) to $885\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (14 December 2003), and averaging $450.6\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ . The average for fall was $261.9\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ . The $\mathrm{PM}_{2.5}$ average was $140.1\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ in fall and $258.7\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ in winter. $\mathrm{PM}_{2.5}$ accounted for $55.6\%$ of the $\mathrm{\bfPM}_{10}$ in fall, ranging between $44.3\%$ and $77.4\%$ . In winter, $\mathrm{PM}_{2.5}$ accounted for $60.4\%$ of the $\mathsf{P M}_{10}$ , with a wide range from $33.0\%$ and $97.6\%$ .
Compare to Xi’an, the percentage of $\mathrm{PM}_{2.5}$ in $\mathrm{\bfPM}_{10}$ in other Chinese cities was: Shenzhen in $2001-73.3\%$ (Cao et al., 2003); Zhuhai, $2001-70.8\%$ (Cao et al., 2003); Chongqing, $1997-65.1\%$ (Wei et al., 1999); Wuhan, 1997 — $60.5\%$ (Wei et al., 1999); Xi’an, 2003 — $60.4\%$ ; Lanzhou, 1997 — $51.9\%$ (Wei et al., 1999). In Xi’an, only five of the $17\;\mathrm{PM}_{10}$ sampling days in fall and none of the 36 sampling days in winter were in compliance with China’s legislated Class $2\,\mathrm{PM}_{10}$ standard of $150\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ (GB 3905-1996). The data depict extreme PM pollution in Xi’an despite the current substantial local government pollution control efforts.
As shown in Table 2, total carbonaceous aerosol $(\mathrm{TCA}{=}\mathrm{OC}\!\times\!1.6{+}\mathrm{EC})$ contributed $48.8\%$ of $\operatorname{PM}_{2.5}$ in fall and $45.9\%$ in winter. The percentage of TCA in $\mathrm{\bfPM}_{10}$ was lower than in $\mathbf{PM}_{2.5}$ , with an average of $34.5\%$ in fall and $37.0\%$ in winter. This may be due to higher contributions of geological matter in coarse particles. The material balance also confirmed that TCA is the dominant component of $\mathrm{PM}_{2.5}$ (Li, 2004). As shown in the time series in Fig. 2, $\mathrm{TCA}\%$ varied around the $45\%$ level during the study and did not correlate with changes of $\mathrm{PM}_{2.5}$ mass or OC/EC concentrations.
$\mathrm{PM}_{2.5}$ OC accounted for $81.8\%$ and $72.8\%$ of $\mathrm{\bfPM}_{10}$ OC during fall and winter, respectively, whereas $\mathrm{PM}_{2.5}$ EC accounted for $75.0\%$ and $59.6\%$ of $\mathrm{{PM}_{10}}$ EC in fall and winter (Table 1). Less than $60\%$ of $\mathrm{\bfPM}_{10}$ EC resided in $\mathrm{PM}_{2.5}$ in winter, possibly due to coarse soot particles in the emissions of incomplete coal combustion, or from fugitive coal dust.
3.5 The characterization of eight carbon fractions
The IMPROVE TOR protocol does not advance from one temperature to the next until a well-defined carbon peak has evolved (Chow et al., 1993, 2004a). Carbon abundances in each of these fractions differ by carbon source (Chow et al., 2004b; Watson et al., 1994). Eight carbon fractions have been used before for the source apportionment of carbonaceous aerosol (Kim et al., 2003a, b; Kim and Hopke, 2004).
The average percentages of eight carbon fractions in ambient and source samples are shown in Fig. 5. Distinct differences in carbon fractions are evident among samples from the three source types tested in this study. OC1 contributed $36.8\%$ to TC in biomass-burning samples, $2.0\%$ in coal-combustion samples, and $2.8\%$ in motor-vehicle exhaust samples. OC2 accounted for $46.9\%$ of TC in coalcombustion samples, $29.2\%$ in biomass-burning samples and $30.5\%$ in motor-vehicle samples. EC1 constituted $15.4\%$ to
TC, $5.6\%$ in coal-combustion samples and $0.4\%$ in biomassburning samples.
Monthly variations of the eight carbon fractions were related to the contributions of different emission sources. November experienced the highest contribution from biomass burning, with OC1 attaining $8.7\%$ , which was the highest value in the six months of the study. OC1 decreased to $1.7\%$ in February. OC2 increased during the six months (except for November), possibly reflecting the increased contributions of coal combustion from fall to winter. EC1 reached its lowest values in January, possibly caused by lower motor vehicle activity during the Chinese Spring Festival. OP ranged from $16.0\%$ to $22.1\%$ , with an average of $21.0\%$ . These ratios are higher than the 8.0 to $17.8\%$ OP in TC found during summer for the Pearl River Delta Region in China (Cao et al., 2004).
3.6 Periodic characteristics of OC and EC
The periodic features of emission sources and meteorological conditions can be identified from the OC/EC time series. Hies (2000) showed that domestic heating by coal combustion appears with a 365-day periodicity. In this study, traffic in Berlin, Germany contributes 3.5-, 4.6-, and 7-day peaks in the spectrum, and periodicity for elevated EC can be identified in the 13- to 42-day range.
The comparison of periodicities of OC, EC, $\mathrm{PM}_{2.5}$ mass, and daily average wind speed are illustrated in Fig. 6. These curves were obtained by AutoSignal 1.0 software (SPSS, USA). The common periodicities of OC, EC and $\mathrm{PM}_{2.5}$ were 24, 10, 7, and 5 days. Identical periodicities between $\mathrm{PM}_{2.5}$ mass and OC are consistent as they are controlled by similar processes. In agreement with Hies (2000), the periodicities of motor vehicle variations were five and seven days. Precipitation events had 10-day periodicity from September to November. This periodicity should reflect the impact of precipitation on OC and EC concentrations. Thirteen-day periodicity was a major component in the wind speed spectrum identified by Hies (2000), which also influences EC concentrations. Sixty-day peaks may be related to the change of primary emission sources.
3.7 Comparison of OC and EC with other Asian cities
Table 3 compares TC, OC, and EC concentrations in $\mathrm{PM}_{2.5}$ from 11 Asian cities. Total carbon in fall and winter at Xi’an ranked the highest. While OC and EC concentrations were similar in Beijing and Xi’an in fall, OC in Xi’an was twice that of Beijing in winter, with similar EC levels. This may be due to more motor vehicles and less coal use in Beijing (Yang et al., 2005). Winter OC levels in Xi’an were 2.7, 3.6, 4.7, 5.1, and 6.4 times those in Guangzhou, Shanghai, Shenzhen, Zhuhai, and Hong Kong, respectively (the number of motor vehicles in these coastal cities are 1.1, 0.7, 0.7, 0.3, and 0.5 million, respectively, compared with 0.2 million in Xi’an). Winter EC levels in Xi’an were 1.5, 1.5, 2.0, 2.5, and 2.6 times those of these coastal cities. The lower increment for EC may be attributed to the high emissions of motor-vehicle exhaust in the coastal cities, and the larger increment for OC may be ascribed to the lower use of coal for residential heating (there is almost no use of coal for residential heating in the coastal cities). Winter OC and EC levels in Xi’an were 12.4 and 2.9 times, respectively, those in Chongju, South Korea (Lee and Kang, 2001).
3.8 Source apportionment of carbonaceous PM
Absolute principal component analysis (APCA) (Thurston and Spengler, 1985) was applied to the eight carbon fraction concentrations to identify and quantify source contributions. The first step in APCA is the normalization of all carbon concentrations as $Z_{i k}$ . This is done by adding a zero concentration sample as case 0 (The $Z_{i0}$ is obtained by deriving the ${{Z}}$ -score for absolute zero concentrations).
$$
Z_{i k}=(C_{i k}-C_{i})/S_{i}
$$
where $C_{i k}$ is the concentration of carbon fraction $i$ in sample $k,C_{i}$ is the arithmetic mean concentration of carbon fraction $i$ , and $S_{i}$ is the standard deviation of carbon fraction $i$ for all samples included in the analysis. The normalization process allows any continuous variable, such as wind speed, to be included in future analyses along with the carbon data.
Regressing the TC data on these absolute principal component scores (APCS) gives estimates of the coefficients which convert the APCS into TC contributions from each source for each sample. For each source identified by the APCA, the weighted regression of each carbon fraction’s concentration on the predicted TC contributions yields estimates of the content of that fraction in each source, as follows:
$$
C_{i k}=b+\sum_{j=1}^{n}a_{i j}M_{j k}
$$
where $C_{i k}$ is the concentration of carbon fraction $i$ in sample $k;\,b$ is a constant; $a_{i j}$ is the mean TC fraction of source $j$ ’s particles represented by carbon fraction $i$ , and $M_{j k}$ is the TC concentration of source $j$ for observation $k$ . By repeating this weighted least-square regression for each of the $\ i{=}1$ , $2,\ldots{}\,\mathfrak{n}$ carbon fractions considered in this analysis, one can estimate the mean concentration of the carbon fractions for each factor.
Results for fall and winter are summarized in Tables 4 and 5. Factor 1 (F1) in fall was highly loaded with OC2, OC3, OC4, OP, and EC1. This factor appears to represent gasolinemotor-vehicle exhaust (Chow et al., 2004b). Factor 2 (F2) was highly loaded with high-temperature EC2 and EC3 and appears to represent diesel-vehicle exhaust (Watson et al., 1994). The high loading of OC1 in factor 3 (F3) reflects the contribution of biomass burning. In winter, the highly loaded OC2, OC3, OC4, and EC1 in F1 might represent the mixture of coal-combustion and motor-vehicle exhaust. Similar to the fall results, F2 and F3 in winter represent biomass burning and diesel-vehicle exhaust, respectively.
To simplify the estimation, it is assumed that there is no contribution of coal combustion in fall, and there are equal contributions from gasoline-powered motor vehicles in fall and winter. Coal combustion in winter is assumed to be the difference between winter F1 and fall F1, thus the source attributions can be resolved for the two seasons (Fig. 7). During fall, TC is composed of $73\%$ from gasoline exhaust, $23\%$ from diesel exhaust, and $4\%$ from biomass burning. During winter, TC receives $44\%$ from gasoline exhaust, $3\%$ from diesel exhaust, $9\%$ from biomass burning, and $44\%$ from coal burning.
4 Conclusions
Six months of continuous observations were conducted at Xi’an, Shaanxi Province, China to gain insight into the characterization and source apportionment of organic and elemental carbon (OC/EC). Major findings are as follows.
1. Average $\operatorname{PM}_{2.5}$ OC concentrations during fall and winter were $34.1{\pm}18.0\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ and $61.9{\pm}33.2\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ ; EC concentrations were 11.3±6.9 µg m−3 and
$12.3{\pm}5.3\,\mu\mathrm{g}\,\mathrm{m}^{-3}$ , respectively. Carbonaceous aerosol accounted for $48.8{\pm}10.1\%$ and $45.9{\pm}7.5\%$ of $\mathrm{PM}_{2.5}$ and $34.5{\pm}9.3\%$ and $37{\pm}\:8.9\%$ of $\mathrm{\bfPM}_{10}$ during fall and winter, respectively. This indicates that carbonaceous aerosol is the dominant component of fine particles in Xi’an.
2. All of the OC/EC ratios exceeded 2.0, and average OC/EC ratios were 3.3 in fall and 5.1 in winter. Elevated OC/EC ratios were found during heating seasons with increased primary emissions, such as residential coal combustion. $\mathrm{PM}_{2.5}$ OC and $\mathrm{\bfPM}_{10}$ OC were highly correlated $\mathrm{R}{=}0.95–0.97_{.}$ ) during fall, and moderately correlated $_{\mathrm{R=0.81}}$ ) during winter.
3. $\mathrm{PM}_{2.5}$ total carbon source apportionment by APCA attributed $73\%$ to gasoline engine exhaust, $23\%$ to diesel engine exhaust, and $4\%$ to biomass burning during fall, and $44\%$ to gasoline engine exhaust, $44\%$ to residential coal burning, $9\%$ to biomass burning, and $3\%$ to diesel engine exhaust during winter. Motor-vehicle exhaust and coal combustion were the dominant sources for carbonaceous aerosol in Xi’an.
Acknowledgements. This project was supported by the National Basic Research Program of China (2004CB720203), National Natural Science Foundation of China (40121303, 40205018), and Research Grants Council of Hong Kong (PolyU5038/01E, PolyU5145/03E), Area of Strategic Development on Atmospheric and Urban Air Pollution (A516) funded by The Hong Kong Polytechnic University. T. Richard edited the manuscript and J. Gerard assisted in formatting and corrections.
Edited by: R. Hitzenberger
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Zhuang, G. S., Zhen, Y., Duce, R. A., and Brown, P. R.: Link between iron an sulphur cycles suggested by detection of Fe (II) in remote marine aerosols, Nature, 355(6360), 537–539, 1992.
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 1. Sampling site and its surroundings in a rural area in Lingcheng $37^{\circ}21^{\prime}17^{\prime\prime}\mathrm{N}$ , $116^{\circ}28^{\prime}30^{\prime\prime}\mathrm{E}$ ), a district of Dezhou City in Shandong Province, China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 2. CMAQ modeling domains at a horizontal grid resolution of $27\,\mathrm{km}$ over China (D1, with 180 columns and 150 rows, $\sim\!1.97\times10^{7}\,\mathrm{km}^{2})$ and $9\,\mathrm{km}$ over an area in northern China (D2, with 120 columns and 111 rows, $\sim\!1.08\times10^{6}\,\mathrm{km}^{2};$ . The zoom-in area (D2) shows the regions focused on the analysis of regional contribution. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 1 Descriptive statistics of chemical species in $\mathrm{PM}_{2.5}$ in terms of concentrations $(\upmu\mathrm{g}/\uppi^{3})$ and percentages (in brackets, $\%$ ). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 2 Average concentrations of $\mathrm{PM}_{2.5}$ , $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ in Lingcheng and other areas in China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 3 The mass concentration of secondary organic carbon (SOC) during the sampling period. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 3. Temporal variations in OC and EC abundances $\left(\upmu\mathrm{g}/\uppi^{3}\right)$ and OC/EC ratios at the sampling site in Lingcheng. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 4 Performance statistics for $\mathrm{PM}_{2.5}$ , OC, EC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ concentrations. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 4. Scatter plots of the daily simulated versus observed concentrations of $\mathrm{PM}_{2.5},$ OC, EC, $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ and $\mathrm{NH}_{4}^{+}$ during the winter sampling period in 2010. The daily simulated concentrations were calculated by the averaging the hourly simulated results from the CMAQ model. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 5. Comparison between daily simulated and observed $\mathrm{PM}_{2.5}$ , $S0_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathrm{NH}_{4}^{+}$ , OC and EC at the Lingcheng study site from November 21st to December 20th. Observations are shown with solid line, and simulations are shown with dashed line. The daily simulated concentrations were calculated by averaging the hourly simulated results from the CMAQ model. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | table | Table 5 Average contributions of $\mathrm{PM}_{2.5}$ and main species from local (Lingcheng) and surrounding regions during winter and heavy haze days (in brackets) $(\%)$ . |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 6. Percent contributions of $\mathrm{PM}_{2.5}$ from the four directions (north, east, west, and south; the simulation area is equally divided into four parts centered on the sampling site). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 7. The contribution of $\mathrm{PM}_{2.5}$ per unit area (contribution $/\mathrm{km}^{2}$ ). |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 8. 12-Hour backward trajectories reaching the sampling site for each hour on 21–24 November and 7, 8, 16, 17, and 21 December on a Lambert conformal projection map of North China. |
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atmosphere | 0003 | 10.1016/j.scitotenv.2017.01.066 | image | Fig. 9. Comparison of the $\mathrm{PM}_{2.5}$ contribution rates during the period of relatively clean days $(\mathrm{PM}_{2.5}\leq75\;\upmu\mathrm{g}/\mathrm{m}^{3})$ , haze days $(75~|\mathrm{{ug/m^{3}}<\mathrm{{PM_{2.5}}<200~|\mathrm{{ug/m^{3}}})}}$ and heavy haze days $(\mathrm{PM}_{2.5}\geq200\,\upmu\mathrm{g}/\mathrm{m}^{3})$ in each tagged area. |
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Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System
Overview
Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. This approach not only mitigates limitations inherent in individual studies but also facilitates novel discoveries through integrated data analysis. Traditional meta-analysis involves a complex multi-stage pipeline including literature retrieval, paper screening, and data extraction, which demands substantial human effort and time. However, while LLM-based methods can accelerate certain stages, they still face significant challenges, such as hallucinations in paper screening and data extraction. In this paper, we propose a multi-agent system, Manalyzer, which achieves end-to-end automated meta-analysis through tool calls. The hybrid review, hierarchical extraction, self-proving, and feedback checking strategies implemented in Manalyzer significantly alleviate these two hallucinations. To comprehensively evaluate the performance of meta-analysis, we construct a new benchmark comprising 729 papers across 3 domains, encompassing text, image, and table modalities, with over 10,000 data points. Extensive experiments demonstrate that Manalyzer achieves significant performance improvements over the LLM baseline in multi meta-analysis tasks.
Paper
https://arxiv.org/pdf/2505.20310
Project Page
https://black-yt.github.io/meta-analysis-page/
GitHub Code
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