Spatial, seasonal and diurnal patterns in physicochemical characteristics and sources of PM2.5 in both inland and coastal regions within a megacity in China

Journal of Hazardous Materials 342 (2018) 139–149

Contents lists available at ScienceDirect

Journal of Hazardous Materials journal homepage: www.elsevier.com/locate/jhazmat

Research paper

Spatial, seasonal and diurnal patterns in physicochemical characteristics and sources of PM2.5 in both inland and coastal regions within a megacity in China Yingze Tian a , Jiayuan Liu a , Suqin Han b , Xurong Shi a , Guoliang Shi a,∗ , Hong Xu a , Haofei Yu c , Yufen Zhang a , Yinchang Feng a , Armistead G. Russell d a State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China b Research Institute of Meteorological Science, Tianjin, 300074, China c Department of civil environmental and construction engineering, University of Central Florida, United States d Georgia Institute of Technology, Atlanta, GA, 30332, United States

h i g h l i g h t s • Spatial, seasonal, day-night and transport variations of PM2.5 compositions were investigated. • Sources’ spatio-temporal variations were studied through developing a three-way receptor model. • Physicochemical feature and mechanism of PM2.5 pollutions in coastal and inland were compared.

a r t i c l e

i n f o

Article history: Received 18 April 2017 Received in revised form 26 July 2017 Accepted 7 August 2017 Available online 10 August 2017 Keywords: PM2.5 Chemical composition Source ME2 Three-way factor analysis model

a b s t r a c t Day and night PM2.5 samples were collected at coastal and inland stations in a megacity in China. Temporal, spatial, and directional characteristics of PM2.5 concentrations and compositions were investigated. Average PM2.5 concentration was higher at inland (153.28 ␮g/m3 ) than at coastal (114.46 ␮g/m3 ). PM2.5 were significantly influenced by season and site but insignificantly by diurnal pattern. Sources were quantified by a two-way and a newly developed three-way receptor models conducted using ME2. Secondary sulfate and SOC (SS&SOC, 25% and 23%), coal and biomass burning (CC&BB, 20% and 21%), crustal and cement dust (CRD&CED, 19% and 21%), secondary nitrate (SN, 13% and 18%), vehicular exhaust (VE, 14% and 17%), and sea salt (SEA, 6% and 2%) were major sources for coastal and inland. Different mechanisms of heavy pollution were observed: heavy PM2.5 caused by primary sources and secondary sources showed similar frequency at coast, while most of heavy pollutions at inland site might be associated with the elevation of secondary particles. For spatial characteristics, SS&SOC, CRD&CED contributions were higher at coastal; SN and VE presented higher fractions at inland. Higher SS&SOC, SN and CC&BB in winter might be attributed to intensive coal combustion for residential warming and to stable meteorological conditions. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Due to the negative effects of particulate matter (PM) on air quality and human health, governments and researchers have focused on the levels of ambient PM [43,31]. Along with rapid economic development, China is experiencing serious PM pollu-

∗ Corresponding author at: State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China. E-mail address: [email protected] (G. Shi). http://dx.doi.org/10.1016/j.jhazmat.2017.08.015 0304-3894/© 2017 Elsevier B.V. All rights reserved.

tion. The particulate pollution in China has been receiving a great deal of attention by international researchers, because the high PM level can provide specific opportunities to understand the PM formation mechanisms and to develop control strategies [11,16,14]. According to the published works, it has been found that locations of cities can influence the concentrations of urban PM [10]. Generally, the PM concentrations in coastal urban areas are usually lower than those in inland urban areas. Therefore, understanding why the coastal urban environment can exhibit lower concentrations can help us better understand the PM formation mechanisms and

140

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

can help produce some useful strategies for reducing particulate pollution. According to the published works, atmospheric circulation, dispersion processes, and deposition can result in the different PM levels between inland and coastal regions [18,37,6,20], which have been studied extensively. Commonly, coastal regions have good dispersion conditions, which can help disperse the particulate matter. Furthermore, PM deposition into the sea can largely alleviate the particulate pollution in coastal areas because the resuspension of dust is a major contributor of PM in inland areas [42]. Compared with the factors mentioned above, the differences of sources, transport pathways, pollution mechanisms between inland and coastal regions have received relatively little research attention. There were several studies investigating the sources in inland and coastal urban areas; however, the long-term observations of simultaneous ambient PM at two sites are extremely limited in developing countries, such as China. Studies on the sources of PM in two regions based on a long-term simultaneous dataset can provide useful information regarding the spatiotemporal characteristics and distinct pattern of the particulate pollution, which can be used to clarify the picture of PM cycling between inland and coastal areas. To that end, daily PM2.5 samples were simultaneously collected from two sites: at an inland station and at a coastal station. The sampling sites were located in the Jing-Jin-Ji area, which has some of the highest PM pollution in China [11]. Source apportionment methods were employed to explore the sources of PM in the two regions, to further investigate the PM fate. In most of the source apportionment works, two-way source apportionment methods such as PMF2 (a version of Positive Matrix Factorization) were applied to address the ambient datasets from single sites [21,19]; however, it might lose the key information supplied by simultaneous samples. PMF2 was proposed by [23], which attempted to apportion the source profile and the contribution based on receptor data [28,12]. Three-way factor analysis models were developed and applied to solve problems associated with three-way blocks, which could be implemented by the Multilinear Engine 2 (ME2) [24,26]. Three-way models could account for the spatial, temporal and chemical variability, such that it permits the extraction of the maximal amount of information from the three-way block. The experience of threeway source apportionment techniques is still very limited and more efforts should be paid. Moreover, the comparison of the outcomes from two- and three- way models is meritorious, which is useful to provide information about the model application. In addition, in the present work, we used a new three-way source apportionment method (AAB three-way factory analysis model) to analyze the multi-sites dataset. The AAB three-way model can estimate the same source profile matrix but independent emission pattern matrixes for two sites and consider inner information of chemical species, temporal visibility and sampling sites. The three-way receptor dataset was constructed by the simultaneous PM concentrations from two sites, which would be introduced into the three-way model in order to best mine the information of the sources. In summary, in order to compare the sources and factors influencing the observed PM concentrations and variability between inland and coastal areas and describe the picture of PM cycling, a simultaneous three-way dataset of PM2.5 was sampled at coastal and inland stations. To better capture variability and processes, 12-h samples for day and night were collected. On the basis of the three-way dataset, the purposes of the present paper were as following:

1. Investigation on the temporal, spatial and directional characteristics of PM2.5 concentrations.

2. Characterization of the variation in chemical compositions between inland and coastal stations, between day and night, among four seasons and among transport patterns. 3. Apportionment and characterization of the contributions of individual source categories based on the results of a two-way ME2 and a newly developed three-way factor analysis model. 4. Comparison of the physicochemical characteristics and factors influencing the observed PM concentrations between coastal and inland areas. The three-way model, which was newly developed in the present work, could account for the variation in the source contributions in different sites and permit the extraction of maximal information from a three-way block. It could provide sufficient contributions to permit a comprehensive application of the novel methods and help the effective PM control. In addition, the spatiotemporal characteristics and distinct patterns of sources at two sites are important for a description of PM cycling between inland and coastal areas. The high PM levels in China can provide a specific opportunity to understand the fate and mechanisms of PM, which are of great significance for developing countries that may experience a similar economic trajectory. 2. Methods and materials 2.1. Sampling The PM2.5 filter samples were simultaneously collected at two different sites in Tianjin, China, a megacity with a population of over 14 million and an area of 11947 km2 . To investigate the differences in sources and emission patterns between inland and coastal regions, two sampling sites were selected for the present study, one in an inland region and the other in a coastal region. The map of samplings sites is presented in Fig. S1. The inland site (39◦ 06’ N, 117◦ 09’E) is located in the urban district of Tianjin and set on the rooftop of a four-story building in a mixed residential and commercial area. Surrounding the inland site, there are few direct industrial sources, but other human activities, such as vehicular emissions, are intensive. The coastal site (39◦ 02’ N, 117◦ 43’E) is located in the Tianjin Binhai New Area (TBNA), near the Bohai Sea. The TBNA has been experiencing rapid development recently, leading to more construction and more industrial sources, while the vehicular emissions are less than those in the urban district. The simultaneous sampling campaigns of PM2.5 at both coastal and inland sites were conducted from May 2013 to January 2014. The seasons were defined as spring (May), summer (June, July and Aug.), autumn (Sep., Oct. and Nov.) and winter (Dec. and Jan.). Further detail regarding the sampling is available in the Supporting Information. The average meteorological data which are available at http://rp5.byhttp://rp5.by are exhibited in Fig. S1(b). 2.2. Chemical analyses Chemical analyses were performed for various elements (Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Cd, Zn, Hg and Pb), watersoluble ions (Cl− , NO3 − , SO4 2− , NH4 + , Na+ , K+ , Mg2+ and Ca2+ ) and carbonaceous species. Inductively coupled plasma-atomic emission spectroscopy (ICP-AES) (IRIS Intrepid II, Thermo Electron) was used to determine the elemental compositions of the samples on polypropylene fiber filters. Standard reference materials were analyzed with the same procedure and the recovered values for all the target elements were in the range or within 5% of certified values. Levels of carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were detected by DRI/OGC carbon analyzers based on quartz-fiber filters. The DRI/OGC carbon

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

141

analyzers is a technique based on the IMPROVE thermal/optical reflectance (TOR) protocol. The calibration of the carbon analyzer was performed before and after sample analysis every day; the first sample was analyzed again every ten samples with the precision within 2%. The water soluble ions on quartz-fiber were quantified by ion chromatography (DX-120, DIONEX). Standard solutions of ions were prepared and analyzed in triplicate with low relative standard deviations. All extractions were conducted at least three times so that the samples were extracted adequately into the solution. Enough blank tests were conducted and used to validate and correct the corresponding data. The sampling, chemical analyses and QA/QC refer to our previous works [15,44,40]. The PM samples at two sites were collected by same types of samplers and filters, and chemical analysis was conducted by same instruments and methods, using same QA/QC process. Characterizing the compositions of major species is essential to examine PM2.5 total mass concentration (Fig. S2). Methods for data screening were provided in Supporting Information. 2.3. Models In the present work, two kinds of receptor models, including a two-way receptor model and a newly advanced three-way receptor model, were used to identify sources and quantify contributions. Both two-way and three-way source apportionment (defined as 2WSA and 3WSA) were conducted using Multilinear Engine 2 (ME2). In this work, three steps were included in the ME2 solutions: (1) ME2 base run, which was similar to the base PMF solution; (2) ME2 pulling run, which uses the “pulling equation” to make some source markers in the extracted factors more prominent; (3) ME2 ratios pulling run, which applies the ratios of OC/EC to pull the source profiles, in order to avoid the underestimation of secondary organic carbon (SOC). The principles underpinning two-way ME2 are provided in Supporting Information. With the increasing number of monitoring sites in a city, three-way blocks would be produced which assess chemical species, temporal variability and spatial variability, as in the present work. The three-way factor analysis model, PARAFAC (Parallel Factor Analysis), was developed to solve problems related to three-way blocks [25,8,1]. For the traditional PARAFAC solution, the algorithm can be described as the following [26]: xijk =

p 

aih bjh ckh + eijk

h=1

xijk =

h=1

where xijk is the concentration of the jth species in the ith sample at the kth site; aihk is the estimated contribution of the hth source to the ith sample at the kth site; bjh is the jth species mass fraction in the hth source profile; eijk is the residual. This advanced threeway model is based on ME2 and evolved from a two-way model but is organized as a three-way array [29,33]. This new model is named the AAB three-way model. The ME2 has been described in detail in related literature (Paratero, 1999; [2] and in our previous works [33]. Because of the rotations of the matrices, there are multiple possible solutions for factor analysis methods [29]. Similar with two-way PMF, the AAB model also uses a solution that minimizes the Q(E), based upon uncertainties for each observation uijk (Paratero, 1999): Q (E) =

J K I   



Xijk −

i=1 j=1 k=1

where p is the number of factors; aih is the element of emission pattern matrix, linked with the source contributions; bjh is the element of source profile matrix, associated with source profiles; ckh is the fractional contribution of each of n planes. The same emission pattern matrix is extracted for different sites by traditional PARAFAC. However, due to the different local sources and transport, the emission pattern of one source category at two sites might be changed, which is not in agreement with the assumptions of PARAFAC. According to the general constraints to PMF solutions from multiple sites [7,2,34], an advanced three-way factor analysis model was newly developed in this work and was named the multi-sites three-way factor analysis model. The idea of this model has already been referred to in the literature [29,33], but unlike those works, the source profile matrix is the same and the emission pattern matrices are independent for two sites. The main principle can be expressed by: H 

Fig. 1. The levels, seasonal patterns and diurnal patterns of PM2.5 concentrations at inland and coastal sites. The annual concentration of PM2.5 was higher in the inland site and the maximum concentrations occurred in winter.

aihk bjh + eijk

H



a b h=1 ihk jh

uijk

The source directional apportionment (SDA) developed in our previous work [41] was employed to quantify the contributions of each source category from diverse directions. In this work, first, the source categories and daily contributions are determined by both 2WSA and 3WSA; second, trajectory cluster analysis based on back trajectories conducted by NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) [22] is used to determine atmospheric transport patterns [17,4]; third, the percentage source directional contributions are quantified by combining the contributions and transport patterns. The details, including the equations and steps of SDA, parameters for computing back trajectories, etc., are provided in Supporting Information. 3. Results and discussion 3.1. PM2.5 concentrations To evaluate the similarities and differences between the inland and coastal sites, the concentrations, seasonal patterns and diurnal patterns of PM2.5 were investigated (Fig. 1). The average concentra-

142

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

Table 1 ANOVA table to explore the influence from variations of sites, seasons and day-night patterns on the PM2.5 concentrations. Source

Sum of Squares

df

Mean Square

F

Sig.

Seasons Day-night Sites

207602.20 147.71 134125.90

3.00 1.00 1.00

69200.72 147.71 134125.90

15.36 0.03 29.77

0.00 0.86 0.00

tion of PM2.5 was higher at the inland site (153.28 ␮g/m3 ) than at the coastal site (114.46 ␮g/m3 ), which might be due to the diffusion conditions caused by sea wind [13]. Moderately strong correlations between concentrations at the two sites were found, with the Pearson’s correlation coefficients being 0.68 for day and 0.47 for night. That the two sites were less similar at night might be due to the weaker winds at night which led to the larger influence of local sources. On several days and nights, the mass concentrations at the coastal site were higher than those at the inland site, which might indicate that PM2.5 was influenced by both regional and local factors, including regional pollution and weather system as well as local sources and meteorological factors. For a further understanding of the influence factors on the PM2.5 concentrations, multi-way variance analysis (ANOVA) was conducted, in which PM2.5 concentrations were considered to be dependent variables and sites, seasons, diurnal were considered to be independent. According to the ANOVA table (Table 1), seasonal and site variations exerted statically significant influences on concentrations at the 99% confidence level, but the diurnal pattern exerted insignificant effects. 3.2. Chemical compositions and variations Chemical compositions and their variations are important to understand the chemical characteristics and fate of ambient PM2.5 (Fig. 2 and Fig. S3). The chemical compositions, including both absolute and relative concentrations, varied slightly with different sites. For most species, the absolute mass concentrations were higher at inland site, while interesting variations can be observed in the abundances of species. As reflected in Fig. 2, for coastal PM, the relatively higher fractions of Na and Cl− might indicate the influence of sea salt. The fraction of Si was higher inland, while Ca and Fe showed relatively higher abundances at the coastal site. Si is one of the most important marker elements of crustal dust [45] and its higher fraction indicates the relatively stronger contributions from soil dust at the inland site. The coastal area is experiencing rapid development and is concentrated with industrial activities, which can explain the high abundances of Ca and Fe. The higher SO4 2− abundance in the coastal area might reflect the stronger influence of coal combustion for industry. The fractions of OC and NO3 − were slightly higher in the inland area, which might be explained by the larger contribution from vehicular exhaust at the inland site. In Fig. S3, it is notable to observe that the spatial difference in absolute mass concentrations for SO4 2− was lower than those for OC and NO3 − , which indicate more regional influence of sulfate. It is interesting to notice the seasonal variations of chemical compositions, according to Fig. 2. Most crustal elements (especially for Al, Si, Ca, Fe) showed obviously higher fractions in spring at both inland and coastal sites, which might be caused by the stronger winds. Lower PM concentrations but higher crustal abundances suggest that although the transport and re-suspension was enhanced in spring, the dispersion function of wind was stronger for PM2.5 pollution. The seasonal variations of carbonaceous species were relatively complex due to a variety of sources, including coal combustion, straw burning and SOC formation. The higher OC/EC ratios (Fig. S4) were observed in summer at both sites, implying the stronger SOC formation. Furthermore, it is noticeable that the higher NO3 − mainly occurred in winter and the SO4 2−

presented higher abundances in both summer and winter. The increased emissions of precursors (stronger emission of NOx and SO2 caused by more coal consumption for heating in winter) and lower boundary layer height in winter would increase the sulfate and nitrate concentrations. Additionally, the higher SO4 2− abundances and lower NO3 − in summer might be explained by the more efficient photochemical process of sulfate and the thermal instability of ammonium nitrate in summer [10]. Comparing the two sites, the concentrations of SO4 2− in summer is obviously higher at the coastal site than at the inland site. This might be due to the stronger sunlight, higher humidity (Fig. S1) and higher emissions of SO2 at coastal area. Slightly lower fractions of NO3 − and OC in inland area were observed in spring, which may be explained by relatively weaker chemical process caused by lower humidity (Fig.S1(b)) and precursor concentrations. The chemical compositions of PM2.5 during day and night at both sites are shown in Fig. 2 as well. It can be observed that the higher fractions of secondary ions, including NO3 − , SO4 2− and NH4 + , occurred during the daytime at both coastal and inland sites. The higher OC/EC ratios during daytime in Fig. S4 indicate the higher SOC levels. All these results may be attributed to the stronger formation of secondary particles due to the more intense illumination during the daytime. To investigate the chemical variations with the transport patterns and directions, the chemical composition of each back trajectory cluster was calculated (Fig. 3 and Fig. S7). As shown in Fig. 3 and Fig. S5, during days at both coastal and inland sites, the higher fractions of crustal elements (sum of Al, Si and Ca) occurred during the trajectories from IMAR-Hebei-Beijing-Tianjin (Clustercoastal,day 5 and Clusterinland,day 4), demonstrating that the northwest might be a potential origin of crustal dust. The higher Na+ and Cl− abundances were unsurprisingly found from the Bohai Sea direction at the coastal site, especially for days when the sea breeze was strong. The higher Na+ /Na, K+ /K and Mg2+ /Mg in the coastal area might imply an influence of marine aerosol that consists of more watersoluble ions or a stronger conversion of elements into ions. Lower nss-ions at the coastal site (Table S1) demonstrated the stronger influence of marine aerosol in the coastal area. More coal combustion for heating in winter and autumn explained their higher nss-Cl− [36], while marine aerosol reflected the strongest influence on Cl− in summer. Furthermore, it is interesting that we find more nss-Cl− during the night than during the day in the coastal area, which might be due to the sea-land breeze circulation in which the surface wind is from sea to land during day and from land to sea during night. The higher correlations between Na+ and Cl− in the coastal area than in the inland area, as shown in Fig. S6, could also imply the stronger influence of marine aerosol in the coastal area.

3.3. Source apportionment and characterization To quantitatively evaluate the source contributions to PM2.5 and further understand how their distribution varies with sites, seasons, diurnal patterns and transport patterns, the ambient PM2.5 speciation datasets for coastal and inland sites were analyzed by two-way and three-way source apportionment techniques. Two dependent matrixes for each site in size 178 rows (number of samples) × 21 columns (number of species) were individually introduced into the two-way ME2. For 3WSA, the combination of two sites, which was a 178 (number of PM samples for each site) × 21 (number of chemical species) × 2 (number of sites) dataset, was introduced. The multi-sites three-way model could analyze the sources through three dimensions (chemical species, temporal variability and spatial variability). The detailed description of modeling is provided in Supporting Information, including

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

143

Fig. 2. The annual, seasonal and diurnal patterns of chemical compositions for the ambient PM2.5 in inland and coastal areas, in the form of abundances (the fractions of species accounting for the PM2.5 ). The chemical compositions slightly varied with sites, seasons and diurnal.

parameter selection, Q values, fitting plot between the measured and estimated PM2.5 (Fig. S7), and comparison of 2WSA and 3WSA.

3.3.1. Secondary particles The secondary source categories were mainly characterized by the presence of ammonium, sulfate, nitrate and OC (Fig. 4 and Fig. S8), which were split into two factors. One is a nitrate-rich factor and can be identified as the secondary nitrate, in spite of a small contribution of sulfate included in the factor estimated by 3WSA. The other factor was linked with SO4 2− and OC. Sulfate, nitrate, and ammonium are commonly identified as markers of secondary particles [43]. The OC was extracted together with secondary sulfate might imply the secondary OC. Thus, this factor might be a mixed source of secondary sulfate & SOC. Factor analysis models are based on internal correlations among species that have similar time series [43]. The unification of secondary sulfate and SOC indicates that they might be influenced by similar factors. As reflected in the Figures, it can be found that secondary aerosols were very important contributors to PM2.5 at both coastal and inland sites. The percentage contributions of secondary sulfate & SOC were 26%2WSA and 25%3WSA in the coastal area and were 26%2WSA and 23%3WSA in the inland area. The secondary nitrate presented higher percentage contributions to PM2.5 at the inland site (15%2WSA and 18%3WSA ) than at the coastal site (11%2WSA and 13%3WSA ). Although the formation of secondary particles is complex, the larger number of vehicles might be one of the reasons for the higher secondary nitrate in the inland area.

To illustrate the role of primary and secondary source contributions in different PM2.5 levels, the distribution of primary and secondary contributions along with PM2.5 concentrations are shown in Fig. 5 as three-way scatter plots with a regression line (blue line) and y = x line (dotted line). Different distributions were found at the two sites. For the coastal site, the red scatters who indicates high PM2.5 levels distributed around the regression line, suggesting that the heavy PM2.5 episodes caused by the primary sources and secondary sources showed the similar frequency. However, for the inland site, most red scatters were above the regression line, even above the y = x line, i.e., most of the heavy pollution during sampling periods in the inland area might be associated with the elevation of secondary particles. [11] also indicated the high secondary aerosol contribution to particulate pollution during haze events in China. In the present work, although the sampling sites were both in Tianjin, different mechanisms of heavy particulate pollution were observed at coastal and inland areas, which might be linked with their different meteorological conditions, surface, industrial layout, etc. It is of great significance to understand how the different pollutions mechanisms vary between areas and further work is needed. Furthermore, for two sites, the percentage source contributions during heavily-polluted days with PM2.5 concentrations higher than 200 ␮g/m3 and common days were calculated and shown in Fig. S9. For the coastal site, the primary contributions (␮g/m3 ) increased by approximately 2.1 units, and the secondary contributions increased by 1.2 units; meanwhile, for the inland site, the primary contributions increased by approxi-

144

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

Fig. 3. The chemical composition and percentage source contributions (estimated by 3WSA) of each back trajectory cluster in day at two sites, to investigate the chemical and source variations with the transport patterns and directions. During days of both sites, higher fractions of crustal elements occurred during the trajectories from IMAR-Hebei-Beijing-Tianjin; and higher Na and Cl− abundances were found from the Bohai Sea direction.

mately 0.9 units, and the secondary contributions increased by 1.1 units. The contributions of secondary sulfate and SOC showed distinct seasonal variability (Fig. 6), with the highest levels observed during summer and the lowest levels observed during spring at both sites. The high contributions in summer might be attributed to the increased photochemical activity due to higher temperatures and

more intense sunshine. For percentage contributions of secondary nitrate, less obvious seasonality was found at the coastal site as estimated by both models. At the inland site, secondary nitrate contributions varied with seasons, displaying springtime minima and peaks in wintertime, which might be due to the enhanced partitioning of ammonium nitrate into the particle phase [10]. To investigate the variations of source contributions with the transport patterns,

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

145

Fig. 4. The source profiles and contributions estimated by 3WSA for coastal and inland sites. Secondary particles, coal combustion & biomass burning, crustal dust & cement dust, vehicular exhaust, and sea salt were identified.

the averaged percentage contributions of each back trajectory cluster were calculated and shown in Fig. 3, Fig. S7 (estimated by 3WSA) and Fig. S10 (estimated by 2WSA). Similar with chemical compositions, the influence of the transport patterns on source contributions can be observed. To quantitatively assess the source contributions from individual directions, the SDA was performed and is presented in Fig. 7. During daytime, secondary particles from the west (W) contributed the highest (17% at coastal site and18% inland site) to PM2.5 ; at night, secondary particles from west-northwest (WNW) contributed the most (12% at the coastal site and 12% at the inland site).

autumn and winter. The strong influence of this factor in autumn can also be inferred that several biomass burning events of wildfires might be captured during the sampling period. As exhibited in Fig. 3 and S7, the maximum percentage contributions of coal combustion & biomass burning were during Clustercoastal,day 4 (Hebei-Beijing-Tianjin), Clustercoastal,night 5 (Beijing-Tianjin), Clusterinland,day 5 (Hebei-Tianjin) and Clusterinland,night 4 (Beijing-Tianjin). According to Fig. 7, the coal combustion and biomass burning attributed 9% (coastal, day) and 9% (inland, day) from W and 7% (coastal, night) and 9% (inland, night) from WNW to PM2.5 .

3.3.2. Coal combustion & biomass burning The identified chemical profiles of the coal combustion factor consisted of high proportions of OC, EC, Al, Si and Cl− [41]. Additionally, it can be seen that K also showed moderately higher weight, which is a tracer for biomass burning along with OC. Biomass burning is an accidental source and can be a significant contributor only in severe episodes when wildfires occur. Thus, it is difficult to identify biomass burning as a specific source category in urban areas of China. In the present work, a mixed factor of coal combustion and biomass burning was determined by both two-way and three-way models, partly due to their collinearity of source profiles. The percentage contributions of coal combustion and biomass burning were very similar at the two sites, contributing 22%2WSA and 20%3WSA in the coastal area and 22%2WSA and 21%3WSA in the inland area (as shown in Fig. 4 and S8). Seasonally, it is not surprising to observe the larger contributions of this factor in autumn and winter at both the coastal and inland sites, associated with intensive coal combustion for residential warming during the late

3.3.3. Crustal dust & cement dust The source category of crustal dust was distinguished because its resolved source profiles were dominated by crustal elements such as Si, Al, Ca, and Fe (Figs. 4 and S10) [27]. Ca is also a marker of cement dust, which is usually prevalent in China due to rapid development [45]. Additionally, the relatively higher V and Ni, especially at the coastal site, might imply the influence of ships and the influence in this factor might be related with re-suspension. This factor may be attributed to both natural (such as windblown of soil dust from unpaved lands) and anthropogenic sources (such as repair or construction of buildings and roads). The huge construction efforts in Tianjin might be the major cause for the high contribution from crustal dust and cement dust. The average source contributions of crustal dust and cement dust were 22%2WSA and 21%3WSA , respectively, at the coastal site and 21%2WSA and 19%3WSA , respectively, in the inland area. The relatively higher contribution at the coastal site might be due to the occurrence of more construction in the Tianjin Economic-Technological Development Area

146

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

Fig. 5. The distribution of primary and secondary contributions along with PM2.5 concentrations to investigate the role of primary and secondary source contributions in different PM2.5 levels. For coastal site, the heavy PM2.5 episodes caused by the primary sources and secondary sources showed the similar frequency; however, most heavy pollution during sampling periods in inland area might be associated with the elevation of secondary particles.

because it is developing quickly. For investigation on seasonal variations (Fig. 6), crustal dust and cement dust contributions peaked in the spring, as reported in previous studies [39,21]. In this season, the percentage contributions reached 34%2WSA and 36%3WSA at the coastal site and 33%2WSA and 36%3WSA at the inland site. The stronger wind could increase the dispersion of PM2.5 but could also contribute to the transport and re-suspension of crustal and cement dust. The two-way and three-way solutions for the crustal dust and cement dust source were highly consistent with maximum contribution observed in spring. The peaks of crustal dust and cement dust percentage contributions were during Clustercoastal,day 5 (IMAR-Hebei-Beijing-Tianjin), Clustercoastal,night 2 (Shandong-Hebei-Tianjin), Clusterinland,day 4 (IMAR-Hebei-Beijing-Tianjin) and Clusterinland,night 1 (HebeiTianjin). IMAR is an important origin of storm dust, which might demonstrate the higher crustal dust and cement dust fractions, especially during daytime. As listed in Fig. 7, the contributions of crustal dust and cement dust from W during daytime were 7% for both sites, and this was the direction with the highest contribution. During nighttime, the source contributions were more directionally homogeneous. 3.3.4. Vehicular exhaust Vehicular exhaust was determined by the high weights of carbonaceous species (EC and OC) in chemical profiles by the 2WSA and 3WSA. To avoid the underestimation of SOC, the ratios of OC/EC

in vehicular profiles were pulled when pulling run. In the final results, the OC/EC ratios were close to 2 for two sites by two models. These ratios are within the ranges presented in related works, and the OC/EC in the present work might indicate that this factor contained both diesel and gasoline vehicles [10]. As shown in Figs. 4 and S8, the vehicular exhaust source contributed 14%2WSA and 14%3WSA to PM2.5 in the coastal area and 16%2WSA and 17%3WSA in the inland area. Usually, vehicular exhaust is considered to be a local source [10]. It is reasonable to observe this spatial pattern of PM2.5 emissions from mobile sources, as the number of vehicles is larger in the inland area. The higher percentage contributions of vehicular exhaust were during Clustercoastal,day 3 (Tianjin), Clustercoastal,night 5 (Beijing-Tianjin), Clusterinland,day 2 (Shandong-Hebei-Tianjin) and Clusterinland,night 3 (Hebei-Tianjin). The influence of local vehicular exhaust should warrant attention. The contributions of this source from W for day and WNW for night also exhibited higher fractions with 6% (coastal, day), 6% (coastal, night), 8% (inland, day) and 5% (inland night). 3.3.5. Sea salt According to Figs. S8 and 4, in the results of 2WSA for coastal and 3WSA for both sites, a factor was distinguished with high loadings of Na, Mg, and Cl− and moderate loadings of NO3 − and SO4 2− . The tracers, such as Na, Cl− and Mg, are clearly associated with fresh sea salt, sea spray and marine aerosols sources, as discussed in the literature [30]. Aged sea salt presents high loadings of Na, NO3 −

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

147

only 2%3WSA percentage contribution was obtained by the advanced three-way ME2 due to the algorithm of this advanced three-way model. Temporal variations of this factor (Fig. 6) indicate slight evolutions during the summer and winter seasons at the coastal site. The increased influence of sea salt in the summer has been investigated in previous studies [38,21]. The influence of natural sea salt was underestimated for days with less influence of aging [3]. The aforementioned results of nss-Cl− also demonstrated the phenomenon. In the winter, the higher Cl− caused by coal combustion might lead to the overestimation of sea salt in this season; the ability of industrial NaCl to melt snow might also be an explanation for the higher sea salt contribution in winter [5]. It is reasonable to find the relatively higher fractions of sea salt contributions during the back trajectories from the Bohai Sea direction (Clustercoastal,day 1 and Clustercoastal,night 1). The sea salt from the Bohai Sea direction contributed 2% in day and 1% in night to PM2.5 at the coastal site as estimated by the SDA method. Although the results of 2WSA and 3WSA were similar, difference can be observed. The sea salt was not extracted by 2WSA at inland site, but it was quantified by the novel multi-sites three-way model and the results demonstrated the slight contribution of sea salt at inland site. The difference between results of 2WSA and 3WSA can be explained by the reason that three-way model can consider inner information of chemical species, temporal visibility and sites; while two-way model computes datasets of single site independently, leading to the missing of information between sites. That is to say, the 3WSA could make full use of the information from the threeway blocks to better capture the spatiotemporal characteristics of sources.

4. Conclusion

Fig. 6. The seasonal variations in the percentage contributions of modeled source categories. Secondary sulphate & SOC showed the highest levels during summer and the lowest during spring at both sites. Crustal dust & cement dust contributions peaked in spring. The increased influence of sea salt was in summer.

and SO4 2− and a negligible share of Cl− [35]. Thus, this source is recognized as sea salt, including fresh and aged sea salt. The sea salt factor was also a contributor to PM2.5 , especially for the coastal area, contributing 5%2WSA and 6%3WSA at the coastal site. At the inland site, the sea salt was not extracted by 2WSA, and

To investigate variations of physicochemical characteristics, sources and factors influencing the observed PM concentrations and variability in inland and coastal regions, PM2.5 were simultaneously collected at coastal and inland stations in a megacity in China. The average PM2.5 concentration was higher in the inland site. Correlations between concentrations at two sites were lower during night due to the weaker wind and stronger influence of local source. Seasonal and site variations reflected statistically significant influence on concentrations, but insignificant affected by diurnal pattern. To compare the composition of two areas, higher fractions of Na, Cl− , Ca, Fe and SO4 2− at coastal site and Si, OC and NO3 − were slightly higher in inland area. The lower PM2.5 concentrations and higher fractions of crustal elements in spring might be explained by better dispersion and stronger re-suspension. During days, higher fractions of crustal elements occurred during the trajectories from IMAR-Hebei-Beijing-Tianjin. The sources were apportioned by two-way ME2 and new multi-sites three-way ME2 and results were consistent. It’s noticeable to find different mechanisms of heavy pollution in two areas. For coastal site, heavy PM2.5 episodes caused by primary sources and secondary sources showed similar frequency; while for inland site, most heavy pollution during sampling periods in inland area might be associated with elevation of secondary particles. The coal combustion & biomass burning were comparable at two sites and showed higher fractions in autumn and winter, associated with intensive coal combustion for residential warming. The crustal dust & cement dust contributed relatively higher at coastal site because of more construction in the fast-developing areas; and the maximum contribution were observed in spring due to stronger transport and re-suspension. The percentage contribution of vehicular exhaust was higher in inland area than in coastal area. The sea salt was not extracted by 2WSA at inland site, but it was quantified by the novel multisites three-way model and demonstrated its slight contribution.

148

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149

Fig. 7. Results of SDA for day and night, to quantitatively assess the source contributions from individual directions. During daytime, secondary particles from west (W) contributed the highest; during nighttime, secondary particles from west-north-west (WNW) contributed the highest. During nighttime, the source directional contributions were more directionally homogeneous.

Furthermore, the SDA showed that for most sources, west during daytime and west-north-west during nighttime were the higher contributed directions. The multi-sites three-way factor analysis model was newly developed and applied in this work and the results demonstrated that it could make full use of the information from the three-way blocks to better capture the spatiotemporal characteristics of sources. The high PM in China provided us a specific opportunity to explore the physicochemical characteristics and mechanisms of heavy pollution in coastal and inland areas.

Acknowledgments This study is supported by the National Key Research and Development Program of China (2016YFC0208500 and 2016YFC0208505), Tianjin Natural Science Foundation (16JCQNJC08700), Tianjin Science and Technology Project (16YFZCSF00260) and Fundamental Research Funds for the Central Universities. Additionally, the meteorological data were obtained at http://rp5.byhttp://rp5.by. The PM Datasets can be found through email: [email protected].

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jhazmat.2017.08. 015.

Reference: [1] H. Abdollahi, S.M. Sajjadi, On rotational ambiguity in parallel factor Analysis, Chemom, Intell. Lab. Syst. 103 (2010) 144–151. [2] F. Amato, P.K. Hopke, Source apportionmentof the ambient PM2.5 acrossSt. Louis using constrained positive matrix factorization, Atmos. Environ. 46 (2012) 329–337. [3] H. Beuck, U. Quass, O. Klemm, T.A.J. Kuhlbusch, Assessment of sea salt and mineral dust contributions to PM10 in NW Germany using tracer models and positive matrix factorization, Atmos. Environ. 45 (2011) 5813–5821. [4] M. Bressi, J. Sciare, V. Ghersi, N. Mihalopoulos, J.E. Petit, J.B. Nicolas, S. Moukhtar, A. Rosso, A. Féron, N. Bonnaire, E. Poulakis, C. Theodosi, Sources and geographical origins of fine aerosols in Paris (France), Atmos. Chem. Phys. 14 (2014) 8813–8839. [5] J.H. Chen, W. Wang, H.J. Liu, L.H. Ren, Determination of road dust loadings and chemical characteristics using resuspension, Environ. Monit. Assess. 184 (2012) 1693–1709. [6] M.A. Elangasinghe, N. Singhal, K.N. Dirks, J.A. Salmond, S. Samarasinghe, Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering, Atmos. Environ. 94 (2014) 106–116. [7] A. Escrig, E. Monfort, I. Celades, X. Querol, F. Amato, M.C. Minguillon, P.K. Hopke, Application of optimally scaled target factor analysis for assessing source contribution of ambient PM10 , J. Air Waste Manage. Assoc. 59 (2009) 1296–1307. [8] N.M. Faber, R. Bro, P.K. Hopke, Recent developments in CANDECOMP/PARAFAC algorithms: a critical review, Chemom. Intell. Lab. Syst. 65 (2003) 119–137. [10] S. Hasheminassab, N. Daher, A. Saffari, D. Wang, B.D. Ostro, C. Sioutas, Spatial and temporal variability of sources of ambient fine particulate matter (PM2.5 ) in California, Atmos. Chem. Phys. 14 (2014) 12085–12097. [11] R.J. Huang, Y.L. Zhang, C. Bozzetti, K.F. Ho, J.J. Cao, Y.M. Han, K.R. Daellenbach, J.G. Slowik, S.M. Platt, F. Canonaco, P. Zotter, R. Wolf, S.M. Pieber, E.A. Bruns, M. Crippa, G. Ciarelli, A. Piazzalunga, M. Schwikowski, G. Abbaszade, J. Schnelle-Kreis, R. Zimmermann, Z.-S. An, S. Szidat, U. Baltensperger, I.E. Haddad, A.S. Pre´ıvoˆt, High secondary aerosol contribution to particulate pollution during haze events in China, Nature 514 (2014) 218.

Y. Tian et al. / Journal of Hazardous Materials 342 (2018) 139–149 [12] J.H. Jeong, Z.H. Shon, M. Kang, S.K. Song, Y.K. Kim, J. Park, H. Kim, Comparison of source apportionment of PM2.5 using receptor models in the main hub port city of East Asia, Busan. Atmos. Environ. 148 (2017) 115–127. [13] M.F. Khan, Y. Shirasuna, K. Hirano, S. Masunaga, Characterization of PM2.5 , PM2. 5–10 and PM >10 in ambient air yokohama, Japan. Atmos. Res. 96 (2010) 159–172. [14] S.F. Kong, L. Li, X.X. Li, Y. Yin, K. Chen, D.T. Liu, L. Yuan, Y.Z. Zhang, Y.P. Shan, Y.Q. Ji, The impacts of firework burning at the Chinese Spring Festival on air quality: insights of tracers, source evolution and aging processes, Atmos. Chem. Phys. 15 (2015) 2167–2184. [15] S.F. Kong, B. Han, Z.P. Bai, L. Chen, J.W. Shi, J.W. Xu, Receptor modeling of PM2.5 , PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China. Sci. Total. Environ. 408 (2010) 4681–4694. [16] J.W. Liu, J. Li, Y.L. Zhang, D. Liu, P. Ding, C.D. Shen, K.J. Shen, Q.F. He, X. Ding, X.M. Wang, D.H. Chen, S. Szidat, G. Zhang, Source apportionment using radiocarbon and organic tracers for PM 2.5 carbonaceous aerosols in guangzhou, south China: contrasting local- and regional-Scale haze events, Environ. Sci. Technol. 48 (20) (2014) 12002–12011. [17] M.T. Markou, P. Kassomenos, Cluster analysis of five years of back trajectories arriving in Athens, Greece. Atmos. Res. 98 (2010) 438–457. [18] M. Masiol, G. Rampazzo, D. Ceccato, S. Squizzato, B. Pavoni, Characterization of PM10 sources in a coastal area near Venice (Italy): An application of factor-cluster analysis, Chemosphere 80 (2010) 771–778. [19] M. Masiol, P.K. Hopke, H.D. Felton, B.P. Frank, O.V. Rattigan, M.J. Wurth, G.H. LaDuke, Source apportionment of PM2. 5 chemically speciated mass and particle number concentrations in New York City, Atmos. Environ. 148 (2017) 215–229. [20] A.G. Megaritis, C. Fountoukis, P.E. Charalampidis, H.A.C. Denier van der Gon, C. Pilinis, S.N. Pandis, Linking climate and air quality over Europe: effects of meteorology on PM2. 5 concentrations, Atmos. Chem. Phys. 14 (2014) 10283–10298. [21] Q.T. Nguyen, H. Skov, L.L. Sørensen, B.J. Jensen, A.G. Grube, A. Massling, M. Glasius, J.K. Nøjgaard, Source apportionment of particles at station nord, north east Greenland during 2008–2010 using COPREM and PMF analysis, Atmos. Chem. Phys. 13 (2013) 35–49. [22] NOAA’s, Air Resources Laboratory, Hysplit4 User’s Guide, Version 4.9. Last Revision: January, 2009, http://www.arl.noaa.gov/HYSPLIT pubs.ph. [23] P. Paatero, U. Tapper, Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics 5 (1994) 111–126. [24] P. Paatero, Least squares formulation of robust non-negative factor analysis, Chemom. Intell. Lab. Syst. 37 (1997) 23–35. [25] P. Paatero, The multilinear engine – a table-driven least squares program for solving multilinear problems, including the n-way parallel factor analysis model, J. Comput. Graph. Stat. 8 (1999) 854–888. [26] P. Paatero, User’s Guide for Positive Matrix Factorization Programs PMF2 and PMF3, Part 1–2: Tutorial, University of Helsinki, Helsinki, Finland, 2007. [27] P. Pant, R.M. Harrison, Critical review of receptor modelling for particulate matter: a case study of India, Atmos. Environ. 49 (2012) 1–12. [28] C. Parworth, J. Fast, F. Mei, Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol chemical Speciation Monitor (ACSM), Atmos. Environ. 106 (2015) 43–55. [29] E. Pere´ı-Trepat, E. Kim, P. Paatero, P.K. Hopke, Source apportionment of time and size resolved ambient particulate matter measured with a rotating DRUM impactor, Atmos. Environ. 41 (2007) 5921–5933.

149

[30] C.A. Pio, M.A. Cerqueira, L.M. Castro, M.L. Salgueiro, Sulphur and nitrogen compounds in variable marine/continental air masses at the southwest European coast, Atmos. Environ. 30 (1996) 3115–3127. [31] I. Ricciardelli, D. Bacco, M. Rinaldi, G. Bonafe, F. Scotto, A. Trentini, G. Bertacci, P. Ugolini, C. Zigola, F. Rovere, A three-year investigation of daily PM2.5 main chemical components in four sites: the routine measurement program of the Supersito Project (Po Valley, Italy), Atmos. Environ. 152 (2017) 418–430. [33] G.L. Shi, Y.Z. Tian, S. Ye, X. Peng, J. Xu, W. Wang, B. Han, Y.C. Feng, Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model, Sci. Total Environ. 505 (2015) 1182–1190. [34] U.M. Sofowote, Y.S. Su, E. Dabek-Zlotorzynska, A.K. Rastogi, J. Brook, P.K. Hopke, Constraining the factor analytical solutions obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in Ontario, Atmos. Environ. 108 (2015) 151–157. [35] C.H. Song, G.R. Carmichael, The aging process of naturally emitted aerosol (sea-salt and mineral aerosol) during long range transport, Atmos. Environ. 33 (1999) 2203–2218. [36] Y.L. Sun, Zhuang, G.S. Tang, AH, Y. Wang, Z.S. An, Chemical characteristics of PM2. 5 and PM10 in haze?fog episodes in Beijing, Environ. Sci. Technol. 40 (2006) 3148–3155. [37] S.C. Tan, G.Y. Shi, H. Wang, Long-range transport of spring dust storms in Inner Mongolia and impact on the China seas, Atmos. Environ. 46 (2012) 299–308. [38] I.N. Tang, A.C. Tridico, K.H. Fung, Thermodynamic and optical properties of sea salt aerosols, J. Geophys. Res. Atmos. 102 (1997) 23269–23275. [39] Y.Z. Tian, J.H. Wu, G.L. Shi, J.Y. Wu, Y.F. Zhang, L.D. Zhou, P. Zhang, Y.C. Feng, Long-term variation of the levels: compositions and sources of size-resolved particulate matter in a megacity in China, Sci. Total. Environ. 463–464 (2013) 462–468. [40] Y.Z. Tian, J. Wang, X. Peng, G.L. Shi, Y.C. Feng, Estimation of the direct and indirect impacts of fireworks on the physicochemical characteristics of atmospheric PM10 and PM2.5 , Atmos. Chem. Phys. 14 (2014) 9469–9479. [41] Y.Z. Tian, G.L. Shi, B. Han, J.H. Wu, X.Y. Zhou, L.D. Zhou, P. Zhang, Y.C. Feng, Using an improved Source Directional Apportionment method to quantify the PM2.5 source contributions from various directions in a megacity in China, Chemosphere 119 (2015) 750–756. [42] M. Viana, P. Hammingh, A. Colette, X. Querol, B. Degraeuwe, I.D. Vlieger, J.V. Aardenne, Impact of maritime transport emissions on coastal air quality in Europe, Atmos. Environ. 90 (2014) 96–105. [43] A. Waked, O. Favez, L.Y. Alleman, C. Piot, J.E. Petit, T. Delaunay, E. Verlinden, B. Golly, J.L. Besombes, J.L. Jaffrezo, E. Leoz-Garziandia, Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions, Atmos. Chem. Phys. 14 (2014) (Waked-3346). [44] Y.H. Xue, J.H. Wu, Y.C. Feng, L. Dai, X.H. Bi, X. Li, Source characterization and apportionment of PM10 in panzhihua, China, Aerosol. Air. Qual. Res. 10 (2010) 367–377. [45] A.B. Yuan, A.K.H. Lau, H.Y. Zhang, J.Z. Yu, P.K.K. Louie, J.C.H. Fung, Identification and spatiotemporal variations of dominant PM10 sources over Hong Kong, Atmos. Environ. 40 (2006) 1803–1815.