Review on recent progress in observations, source ...

Environment International 86 (2016) 150–170

Contents lists available at ScienceDirect

Environment International journal homepage: www.elsevier.com/locate/envint

Review on recent progress in observations, source identifications and countermeasures of PM2.5 Chun-Sheng Liang a,c, Feng-Kui Duan a,⁎⁎, Ke-Bin He a,b,⁎, Yong-Liang Ma a,b a b c

State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing 100084, China Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

a r t i c l e

i n f o

Article history: Received 12 July 2015 Received in revised form 27 October 2015 Accepted 29 October 2015 Available online xxxx Keywords: PM2.5 Observations Temporal variability Source identifications Countermeasures Pollution control

a b s t r a c t Recently, PM2.5 (atmospheric fine particulate matter with aerodynamic diameter ≤2.5 μm) have received so much attention that the observations, source appointment and countermeasures of it have been widely studied due to its harmful impacts on visibility, mood (mental health), physical health, traffic safety, construction, economy and nature, as well as its complex interaction with climate. A review on the PM2.5 related research is necessary. We start with summary of chemical composition and characteristics of PM2.5 that contains both macro and micro observation results and analysis, wherein the temporal variability of concentrations of PM2.5 and major components in many recent reports is embraced. This is closely followed by an overview of source appointment, including the composition and sources of PM2.5 in different countries in the six inhabitable continents based on the best available results. Besides summarizing PM2.5 pollution countermeasures by policy, planning, technology and ideology, the World Air Day is proposed to be established to inspire and promote the crucial social action in energy-saving and emission-reduction. Some updated knowledge of the important topics (such as formation and evolution mechanisms of hazes, secondary aerosols, aerosol mass spectrometer, organic tracers, radiocarbon, emissions, solutions for air pollution problems, etc.) is also included in the present review by logically synthesizing the studies. In addition, the key research challenges and future directions are put forward. Despite our efforts, our understanding of the recent reported observations, source identifications and countermeasures of PM2.5 is limited, and subsequent efforts both of the authors and readers are needed. © 2015 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . Chemical composition and characteristics . . 2.1. Sampling and observation methods . 2.2. Inorganic elements . . . . . . . . . 2.3. Water soluble ions . . . . . . . . . 2.4. Carbonaceous aerosols . . . . . . . 2.5. Organic compounds . . . . . . . . 2.6. Radicals and atmospheric oxidation . 2.7. Water, nucleation, and climate . . . 2.8. Inhalable microorganisms . . . . . . 2.9. Summary . . . . . . . . . . . . . Source apportionment . . . . . . . . . . 3.1. Receptor models . . . . . . . . . . 3.1.1. Positive matrix factorization 3.1.2. Principal component analysis

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . .

⁎ Corresponding author at: State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (K.-B. He), [email protected] (F.-K. Duan).

http://dx.doi.org/10.1016/j.envint.2015.10.016 0160-4120/© 2015 Elsevier Ltd. All rights reserved.

. . . . . . . . . . . . . . .

151 151 151 152 152 152 153 153 153 154 154 156 156 156 159

C.-S. Liang et al. / Environment International 86 (2016) 150–170

3.2. Other source appointment methods and summary 4. PM2.5 control by policy, planning, technology and ideology 5. Future perspectives . . . . . . . . . . . . . . . . . 6. Conclusions . . . . . . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

1. Introduction Among many environmental issues, air pollution (especially particulate air pollution) is most easily felt by the public because of its obvious impact on visibility. However, its invisible or indirect influences on mood (mental health), physical health, traffic safety, construction, economy, nature and climate may be more serious or complex. Today many Asian cities, especially in China and India, and sometimes even some European and American cities, have been experiencing severe particulate matter (PM) pollution associated with the rapid industrialization, energy consumption, urbanization, globalization and population growth (Han et al., 2014; Hu et al., 2014b; Huang et al., 2014a; Lary et al., 2014; Lin et al., 2014; Liu et al., 2013c; Song et al., 2015; Zhang et al., 2012a). The most mentioned event is the record heavy PM2.5 air pollution in January 2013 in Beijing (Zheng et al., 2015), with a maximum PM2.5 hourly concentration of 996 μg m−3 (Uno et al., 2014). Ambient (outdoor) air pollution kills more than 3.7 million people a year worldwide in 2012 (WHO, 2014a), costing OECD countries, China and India about 1.7 trillion, 1.4 trillion and 0.5 trillion dollars (including deaths and illness), respectively, in 2010 (OECD, 2014). Because of the complex relationship between emissions and PM2.5 concentrations, which interact with climate, the maximums and minimums of PM2.5 concentrations appear in different cities and temporally change. However, the particle capacity of the atmosphere cannot go beyond what it can bear and is subject to the conservation of mass in the dynamic emission-deposition equilibrium. In other words, the particles formed from emitted air pollutants (SO2, NOx, smog & dust, carbon, organics, CO, O3 and NH3) and dynamically suspended in the atmosphere over a relatively isolated region would eventually deposit on the earth, although they may enter a next aerosol cycle. In this sense, the formation of PM2.5 particles from toxic gases (SO2, NOx, organic vapors, CO, O3, and NH3) heterogeneously reacted with and condensed on the surfaces of other aerosols in the atmosphere is a process that speeds up the deposition of these toxic gases. Without this speeded transformation and deposition of the accumulated toxic gases, we human beings might be unable to survive because of the high concentrations of toxic gases in the atmosphere. Although PM2.5, resulting from the force of nature (natural metabolism) that turns the toxic gases to less toxic ions (sulfate, nitrate and ammonium, namely SNA, from SO2, NOx and NH3) or small molecules (CO2 and H2O from CO and organic vapors) with water vapors and mixture nuclei via atmospheric chemical reductions, protects us from being extinct through the gas-to-particle conversion processes and the deposition pathway, its harmful effects on visibility, mood (mental health), physical health, traffic safety, construction, economy and ecosystem as well as its complex interaction with climate are so overwhelming that we cannot stop controlling it. PM2.5 was first publicly discussed, mainly about its composition and health impact, in the late 1980s and early 1990s by Douglas W Dockery et al., Judith C. Chow et al. and Chih-Shan Li et al. (Chow et al., 1993; Dockery et al., 1992; Dockery et al., 1989; Li et al., 1993). As the growing anthropogenic activities are carried out unprecedentedly, aerosol particles such as PM2.5 and its precursors can greatly influence the air quality, ozone layer, and climate (precipitation and radiation) on scales ranging from local to regional and global (Edwards et al., 2014; Rastogi et al., 2014; Riccobono et al., 2014; Savee et al., 2015; Zhang et al., 2014e).

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

151

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

. . . . . . . .

159 160 161 163 163 163 163 163

Effects from fine particulate matter are very wide, severe or complex that PM2.5 has gradually become a global concern and its related research is increasingly conducted. Web of Science results show that almost all countries are involved in PM2.5 research, and USA, China, Italy, Canada, Germany, Spain, England, South Korea, India, Japan, France, Netherlands, Greece, Finland, Switzerland, Sweden, Australia, Mexico, Brazil and Belgium are most actively involved in terms of their respective total of published papers. In virtually all published literature wherein “PM2.5” is included, several research areas are extensively developed, namely composition and characteristics (He et al., 2001), source apportionment (Zheng et al., 2002), health impact (Monn and Becker, 1999), modeling and estimating & predicting (Chow and Watson, 2002; Turpin and Lim, 2001), measurement (Hering and Cass, 1999), emissions inventory (Reff et al., 2009), light extinction (Kunzli et al., 2006), and PM2.5 reduction (control) (Hanninen et al., 2005). Publications on PM2.5 have been so many in recent three years that even the number of those containing “PM2.5” in titles is more than 750 (Web of Science: SCI-EXPANDED, Timespan = 2013–2015, last updated on 2015-10-22), and about 230 PM2.5-in-title publications (~ 220/230 of them were published in 2013–2015) are cited in this PM2.5-in-title review. Therefore, some publications containing “fine particulate matter” or “fine particles” in titles might have been missed. The rest 130 references are mainly about some hot topics (nucleation, atmospheric chemical reactions, secondary aerosols, aerosol mass spectrometer, emissions, solutions, organic tracers, radiocarbon, etc.) in the field. The recent research advances in composition and characteristics, source apportionment and control measures are summarized in the present review, based on the abovementioned ~ 360 publications mainly published from 2013 to 2015. The key research challenges and future directions, and possible solutions of air (including PM2.5) pollution, probably of great importance and interest, are also discussed. 2. Chemical composition and characteristics 2.1. Sampling and observation methods Chemical composition such as elements, ions, carbon and organics of PM2.5 should be known before identifying the sources, process and impact of the particulate air pollution. To identify the composition and physicochemical characteristics of PM2.5, many kinds of instruments were applied in this field (Table S1). Several kinds of filters such as quartz (glass) and Teflon (PTFE) filters were most commonly used. High, medium, low and mini volume samplers were respectively used in different studies. Electronic microbalance with a sensitivity of 1 μg was used by many researchers. To determine the chemical composition, various analytical instruments, such as ion chromatograph, carbonaceous analyzer, inductively coupled plasma and mass spectrometry were applied. More useful information of aerosol mass spectrometry instrumentation can be found in a review (Suess and Prather, 1999). Various real-time instruments such as Aerosol Mass Spectrometer (AMS) were implied by many researchers for PM measurements (Canagaratna et al., 2015; Huang et al., 2014d; Mueller et al., 2015). AMS was widely used in recent years to determine the PM sources (Brown et al., 2013; Dallmann et al., 2014; Elsasser et al., 2013;

152


Fountoukis et al., 2014; Irei et al., 2014; McGuire et al., 2014; Mueller et al., 2015; Petit et al., 2014; Stevanovic et al., 2013; Timko et al., 2014), organic aerosol composition (Brown et al., 2013; Collier and Zhang, 2013; Fountoukis et al., 2014; May et al., 2015; Stevanovic et al., 2013; Verma et al., 2015), black carbon (Canagaratna et al., 2015; Willis et al., 2014), primary and secondary particulate matter (Diab et al., 2015), and aerosol chemical properties (Petit et al., 2015). For the highly time-resolved measurements of PM2.5, OC & EC, ionic species, and elemental species, commercial semi-continuous instruments were also used (Park et al., 2013). Besides, some researchers even organized or assembled different analyzing tools to conduct their novel experiments.

Control measures and regional transport besides meteorological factors could largely influence the secondary ions in PM2.5 (Gao et al., 2013b). SNA in the atmosphere can be (NH4)2SO4 and NH4NO3 (Singh et al., 2013), and acid components such as (NH4)2SO4, NH4HSO4, H2SO4 and HNO3 can co-exist when ammonium is far from being enough to neutralize sulfate and nitrate (Hu et al., 2014a). Total ammonia (NH3 and ammonium) concentrations declined 1–4% per year in the Southeast US during 2004–2012, but the fraction of NH3 has simultaneously risen steadily (1–3% per year) because of the declining emissions of SO2 and NOx under air quality regulations (Saylor et al., 2015).

2.4. Carbonaceous aerosols 2.2. Inorganic elements Elemental composition of particulate matter often contains heavy metal elements such as Cd, Pb, As, and Sb, which are toxic to human health even with very low concentrations (Boman et al., 2013; Onat et al., 2013). There are various elements in the particulate matter, and the mass concentrations and species of these elements differ among different districts. Besides their strong toxicity to human health, metal elements can be used as tracers in identifying sources (such as crustal elements mainly from soil dust, and trace elements mainly from industry), so measuring metals is of great importance. Fig. S1 shows typical elemental characterization of PM2.5. S, Si, K, Cl, Ca, Na, Fe, Al, Mg, Mn, Ba Br, Ba, Sb Ni and Cr could be dominated in PM2.5 (Fig. S1a and S1b) (Avino et al., 2013; Bozlaker et al., 2014; Ezeh et al., 2014; Lu et al., 2015; Shaltout et al., 2013; Shaltout et al., 2015; Zhang et al., 2013b). Pt and Ti could also exist in PM2.5 (Chen et al., 2012; Morton-Bermea et al., 2014). More sensitive analytical techniques such as ICPMS, ICP-OES, TOFMS, EDXRF, XRD, SEM–EDS and HR-CSGFAAS to determine the levels of toxicological elements (As, Cd, Hg and Pb) are needed (Boman et al., 2013; Manousakas et al., 2014; Paulino et al., 2014; Satsangi and Yadav, 2014; Shaltout et al., 2014; Zhang et al., 2014d). Properties such as water solubility of metals might be useful in determining the adverse impact of metals on health (Jiang et al., 2014). 2.3. Water soluble ions Characteristics, sources and formation mechanisms of major watersoluble ions are necessary to be understood because they influence not only the acidity, formation and fate of aerosols directly, but also the ecosystem and environmental materials through deposition (Hu et al., 2014a; Yuan et al., 2014). For example, low Cl−/Na+ ratio can indicate several sources beyond sea salt (Souza et al., 2014). Similarly, in addition to the ion ratio indicator, ratios of other PM2.5 composition such as elements, carbon and organics can be used in source appointment and formation process of PM2.5. Among all WSIIs (water-soluble inorganic ions) in PM2.5, the three secondary ions (sulfate–nitrate–ammonium, SNA) are the most abundant (Chang et al., 2013; Tao et al., 2013a; Yin et al., 2014). The recent reported mean concentrations of WSIIs in PM2.5, in Paris (Bressi et al., 2013), Raipur (Deshmukh et al., 2013), Western Taiwan Straits Region (Yin et al., 2014), Guangzhou (Chang et al., 2013), urban area of Beijing (Hu et al., 2014a), Thessaloniki (Voutsa et al., 2014), and Dongying, Shandong (Yuan et al., 2014), showed that SNA are generally the largest contributor to WSIIs. Sulfate and nitrate are formed from their precursors SO2 and NOx respectively (Voutsa et al., 2014). Besides SNA ions, there are many other watersoluble ions such as Na+, K+, Mg2 +, Ca2 + and Cl− in PM2.5 (Bressi et al., 2013; Deshmukh et al., 2013). Ambient ion monitors (in-situ) were increasingly used to conduct continuous online measurements of the WSIIs of PM2.5 (Hu et al., 2014a; Yuan et al., 2014). Ions (such as nitrate) and the atmospheric mixing and aging processes have great influence on the occurrence of PM2.5 pollution episodes (Salameh et al., 2015).

Carbonaceous aerosols, including organic carbon (OC, primarily or secondarily produced by gas-to-particle conversion from volatile organic compounds) and elemental carbon (EC, primarily from incomplete combustion), are common species in PM2.5 and can greatly influence environments, health and climate systems (Niu et al., 2013b; Zhu et al., 2014). EC is also known as light absorbing carbon or black carbon (BC) (Hand et al., 2013), which is the dominant absorber of visible solar radiation in the atmosphere, and vehicle emission is its main source (Feng et al., 2014; Song et al., 2014). The global warming effect of EC and cooling effect of OC make the carbonaceous aerosols with a lower OC/ EC ratio have a greater warming effect (Zhu et al., 2014). OC/EC analyzer was generally employed to determine the OC and EC of PM2.5 (Bressi et al., 2013; Samara et al., 2014). EC can be used as an indicator for primary combustion emissions (Huang et al., 2014c). OC/EC ratios and the correlation coefficients can indicate secondary organic aerosol (SOA) formation (Cheng et al., 2014; Souza et al., 2014). For instance, OC/TC ratios are highest for the dusts in areas with lots of oil exploited and consumed (Kong et al., 2014). Recent reported concentrations of OC, EC and PM2.5, in Paris (Bressi et al., 2013), Thessaloniki (Samara et al., 2014), and São Paulo & Piracicaba (Souza et al., 2014), showed that carbon content usually accounts for one third of the total PM2.5 concentration. Besides EC and the ratios of OC/EC or OC/TC that have the indicative function for sources, radiocarbon (14C) is even more important in that radiocarbon measurements can be used to determine PM sources (Beekmann et al., 2015; Buchholz et al., 2013; Heal, 2014; Ikemori et al., 2015; Kirillova et al., 2014; Liu et al., 2013a; Szidat et al., 2013), especially for identifying sources of fossil carbon and non-fossil carbon in conjunction with mass spectrometry (Heal, 2014; Szidat et al., 2013). Total carbon in PM2.5 usually presented annual mean concentrations of 20–30 μg m−3 in Beijing (Wang et al., 2014d; Zhao et al., 2013a). The annually averaged BC concentrations in 2010 and 2011 in Shanghai were 3.8 μg m−3 and 3.3 μg m−3 (Feng et al., 2014). The average BC and PM2.5 concentration were about 2.2 μg m−3 and 41.8 μg m−3 over the Northwestern Himalayan Region of India during 12–22 March 2013 (Sharma et al., 2014). BC soot and PM levels in subways would be much higher than street levels (Vilcassim et al., 2014). POC (primary organic carbon) made up half the TC in rural, urban and tunnel environments, while the lowest level percentage could be found in remote samples (Zhu et al., 2014). Condensation or adsorption of semivolatile organic species onto existing particles instead of photochemical activity might greatly influence secondary OC concentrations (Samara et al., 2014). OC could be converted to organic matter (OM) using the chemical mass closure methodology, and the conversion factors (fOC − OM) were 1.95 for the suburban and urban sites, and 2.05 for the rural ones in the region of Paris (Bressi et al., 2013). The organic multiplier across the United States was 1.8, namely POM (particulate organic matter) = 1.8 ∗ OC, and values for urban OC may be lower (Hand et al., 2013). The organic multipliers (conversion factors) generally range from 1.2 to 2.2 in different areas. Brown carbon (BrC)'s light absorption is very important to interpret the aerosol optical depth (AOD), and the molecular structures of BrC compounds have been fully summarized in a review (Laskin et al., 2015).


2.5. Organic compounds Organic compounds (organic matter, OM), composing a substantial fraction of the atmospheric aerosols (especially fine PM), are generally toxic and can be the markers for source identification (Li et al., 2013c). Annually, about 2000 Tg of organic compounds are emitted into the atmosphere as gases, another 300 Tg as particulate matter (aerosols), and a smaller fraction is dissolved in cloud or fog droplets (Nozière et al., 2015). There are 104–105 distinct compounds of them (Goldstein and Galbally, 2007), which play essential roles in many atmospheric processes (Nozière et al., 2015). A large number of organic aerosols remain as unresolved complex mixture (UCM) (Nallathamby et al., 2014). OC measurements can be multiplied by 1.6 for urban aerosols and 2.1 for nonurban aerosols to obtain the mass of particulate organic compounds (Turpin and Lim, 2001). Secondary organic aerosols (SOAs) would constitute about 40% (32–45%) of PM2.5 (Mancilla et al., 2015). Detailed information of the formation, properties, transformation and impact (on atmospheric processes, climate and human health) of SOA can be found in a respected review (Hallquist et al., 2009). The widely studied organic compounds are polycyclic aromatic hydrocarbons (PAHs) (Pongpiachan et al., 2015). PAHs are formed by incomplete combustion or pyrolysis of fossil fuels and biomass, and the release of petroleum products and natural sources, which include benzo(a)pyrene that can cause cancers (Kong et al., 2013a; Limu et al., 2013; Villar-Vidal et al., 2014). Gas-phase PAHs generally exist for few hours or less, and particle-bound PAHs may also be chemically and photochemically reactive (Oliveira et al., 2014). PAH concentrations in the coal-chemical base site can be comparatively very high (Wu et al., 2014a). Annual average concentrations of PAHs are generally in the range of dozens to hundreds of ng m−3 (Li et al., 2013c; Limu et al., 2013; Ma et al., 2014; Wang et al., 2014c), and the lifetime carcinogenic risks would be about several 10−7 (Wang et al., 2014c), or equal to BaP equivalent toxicity (dozens of ng m−3) and individual carcinogenicity index (several 10−5) (Li et al., 2014b). Benzo[b]fluoranthene, benzo[e] pyrene and phenanthrene dominated the PAHs, while C19–C25 compounds were the most abundant n-alkanes (Li et al., 2013c). The PAH ratios may be used to reflect sources from light-duty vehicles fueled by gasohol and ethanol (Oliveira et al., 2014). Atmospheric aerosol formation involves nucleating vapors produced from photo-oxidation of atmospheric gases, such as SO2 and volatile organic compounds (VOCs) (saturated, unsaturated, or aromatic hydrocarbons) (Zhang et al., 2012b). Atmospheric clusters containing H2SO4 and oxidized organic vapors can promote new-particle formation that affects the climate in the lower atmosphere (Riccobono et al., 2014). SOA can be generated by irradiating 1,3-butadiene (13BD) with H2O2 or NOx (Jaoui et al., 2014). OOCs can be formed as atmospheric oxidation products of all hydrocarbons (Mellouki et al., 2015). Oxygenated volatile organic compounds (OVOCs) are generally more reactive than their precursor alkanes, and OVOCs are responsible for the formation of tropospheric ozone (Mellouki et al., 2015). CH3OH, CH3C(O)CH3, CH3OOH, HCHO, and CH3CHO are the five most abundant OVOCs in the atmosphere, having lifetimes of about 13 days, 18 days, 2 days, 11 h, and 7.3 h, respectively (Mellouki et al., 2015). ClPAHs widely exist in inhalable fine particles by atmospheric mixing and photochemically degradation during daylight hours (Ma et al., 2013). Other organic chemicals such as phthalate acid esters (PAEs), organic nitrogen were also investigated by researchers (Kong et al., 2013b; Samy and Hays, 2013). 2.6. Radicals and atmospheric oxidation In the lower atmosphere, ~ 30% of the primary OH radical is produced by the photolysis of nitrous acid (HONO) released from soil nitrite and microbes (Su et al., 2011). There are very high OH concentrations over the pristine Amazon forest, and the natural VOC oxidation efficiently recycles OH in low-NOx air via reactions of organic

153

peroxy radicals (OPRs) (Lelieveld et al., 2008). OA is an intermediate state of OM between primary reduced species and highly oxidized volatile products including CO and CO2 (Jimenez et al., 2009). Oxidation of unsaturated organics in combustion or Earth's troposphere can produce very long-lived hydroperoxyalkyl radical (QOOH) intermediates (Savee et al., 2015). A stabilized Criegee intermediate (a carbonyl oxide with two freeradical sites) or its derivative can oxidize SO2 (to produce H2SO4) and other trace gases (Mauldin Iii et al., 2012). Anthropogenic emissions (SO2 and NOx) can interact with extremely lowvolatility organic compounds (ELVOCs) directly or alter their formation pathways after reacting with OH, O3 (organic peroxy radical (RO2) formed from ozonolysis) and Criegee intermediate (Ehn et al., 2014). The reactive oxygen species (ROS) generation capacity of ambient PM can be a good predictor for particle induced adverse health effects (Hellack et al., 2014), but the exact nature of ROS formation and its source are still unresolved (Gehling and Dellinger, 2013). High levels of hydroxyl radicals (a ROS) can be generated from an aqueous suspension of ambient PM2.5 and environmentally persistent free radicals (EPFRs) in PM2.5 without the addition of H2O2 (Gehling et al., 2014), because atmospheric pollutants (phenol, ozone, NOx, SO2, etc.) can promote radical formation and the decay rate of EPFRs through photochemical processes (Gehling and Dellinger, 2013). The atmospheric lifetime of OH is ≤1 s, thus its concentration is generally determined by the longer-lived species such as O3, VOCs, and NOx (Heard and Pilling, 2003). 2.7. Water, nucleation, and climate H2O is both an important component of PM2.5 and a reactant that can speed up the formation and accumulation of PM2.5 aerosols from toxic gases (SO2, NOx, organic vapors, CO, O3, and NH3) via heterogeneous and oxidation-reduction reactions, making the transformation and deposition of the accumulated toxic gases become quick processes under high relative humility (RH) conditions. Liquid water content (LWC, the amount of liquid water on aerosols) would increase by more than 2 times because of limited (NH4)3H(SO4)2 crystallization at RH = 40% when half the SO24 − is replaced by NO− 3 (Xue et al., 2014). The phase of NaCl arising from ocean spray and anthropogenic (NH4)2SO4 is solid crystalline at low RH, which will combine with vapors to form aqueous electrolyte solutions at high RH (Martin, 2000). Atmospheric phase transitions under low-temperature (b 200 K) can impact heterogeneous chemistry, cloud microphysics and chemistry, lightning, air visibility, polar ozone depletion (by H2SO4·4H2O or HNO3·3H2O ices), and global radiative forcing (Martin, 2000). Atmospheric NH3 can greatly speed up the nucleation of H2SO4 particles via a base-stabilization mechanism, while the ion-induced binary nucleation of H2SO4–H2O is negligible in the boundary layer because of too low concentrations of NH3 and H2SO4 (Kirkby et al., 2011). Cloud condensation nuclei (CCN) can become cloud droplets via heterogeneous nucleation of liquid water (Farmer et al., 2015). Biological K+–rich particles can initiate organic aerosol growth by acting as seeds for the condensation of low- or semi-VOCs, so the primary emission of biogenic salt particles can directly influence the number concentration of CCN and the microphysics (Pöhlker et al., 2012). Feldspar minerals dominate ice nucleation that accounts for a large part of the ice nuclei in Earth's atmosphere, making freezing temperatures below about −15 °C (Atkinson et al., 2013). Organic compounds can greatly influence nucleation (Kulmala et al., 2013), while most aerosol particles are formed by condensation of gaseous molecules (Andreae, 2013). Therefore, both amines and SO2 can impact the atmospheric particle formation caused by anthropogenic activities where amine sources are available (Almeida et al., 2013). Aerosols can partly counteract the warming effects of greenhouse gas (GHG) by increasing the amount of sunlight reflected back to space (Rosenfeld et al., 2014). Anthropogenic (natural) aerosols lead to large uncertainties, namely 34% (45%) in the radiative forcing of

154


climate over the industrial period since about 1750 by influencing cloud droplet concentrations and radiative properties (Carslaw et al., 2013). A transition from pristine to slightly polluted atmosphere would yield a negative forcing of ~15 watts per m2 (cooling) (Koren et al., 2014). 2.8. Inhalable microorganisms Atmospheric biological material include living organisms (pollens, fungal spores, bacteria, viruses, any fragments from plants, and animals) (Raisi et al., 2013), and dead microbes. Inhalable microorganisms in PM2.5 and PM10 can cause various allergies and the spread of respiratory diseases (Cao et al., 2014). Although fungal spore levels are generally higher at rural areas, they may also be abundant in urban ones (Liang et al., 2013c). The microbes of Beijing's PM pollutants during Jan. 10– 14, 2013 mainly were soil-associated and nonpathogenic to human (Cao et al., 2014). Flow cytometry (FCM) combined with Calcofluor White stain can be used to quantify fungal spores in the atmosphere (Liang et al., 2013b). The variation in fungi concentrations (proportional to that of heterotrophic bacteria) depends on the variation in PM concentrations, meteorological conditions and solar radiation (Liang et al., 2013b; Raisi et al., 2013). 2.9. Summary Some recent reported temporal variability of concentrations of PM2.5 and major components (Fig. 1) as well as the related ratios is shown in Table S2. Annual average (time inconsistencies are ignored here for the purposes of comparison) concentration of PM2.5 among the above

regions is 92.46 μg m−3, ranging from 28.1 μg m−3 to 191.19 μg m−3. Concentrations (μg m−3) in spring (92.29) and fall (94.3) are close to the average, while in summer (61.33) is lowest and winter (102.36) is highest. Increasing sequence of the annual average concentrations of major components is sulfate (15.53 μg m−3, 2.8–35.66 μg m−3) N OC (15.25 μg m−3, 5.62–37.73 μg m−3) N nitrate (10.44 μg m−3, 0.85– 30.68 μg m−3) N ammonium (6.19 μg m−3, 1.75–15.6 μg m−3) N EC (4.86 μg m−3, 0.7–9.84 μg m−3) among the above regions. Monthly data are not included in the seasonal averages of the above cities, namely only the already given seasonal data in the tables of the cited papers are used to calculate the seasonal averages of the above regions. Annual average ratios SNA/PM2.5 and OM/PM2.5 among the above regions are 35.15% (15.58–54.64%) and 28.28% (18.38–73.94%) respectively. The annual average OC/EC ratio is 3.97, ranging from 0.62 to 10.43. In winter, the concentration of OM and the ratio OM/PM2.5 are concurrently highest, whereas the ratio SNA/PM2.5 is lowest (Table S2 and Graphic Abstract). The concentrations of both OM and PM2.5 are lowest in summer when sulfate is dominated. The close relationships between OM and PM2.5 suggest the importance of controlling VOCs (precursors of OM). Based on the above literature review of the total Section 2 (2.1 to 2.9), one can find that the components in PM2.5 are i, water-soluble ions (acids, alkalis and salts) including SNA, K+, Mg2+, Ca2+, F−, Cl−; ii, carbonaceous compounds including OC and EC; iii, crustal or trace elements (Al, Fe, Si, Ca, K, Mg, Mn, Pb, Cu, Zn, Ni, Ti, Mn, V, Bi, Cd, Co, Cr, Ga, Li, Mo, Pt, Rb, Sb, Sn, Te, Tl, U, Ba, P, Sr and so on, these elements are usually in the form of oxidates or salts); iv, organic compounds such as PAHs and OVOCs; v, reactive oxygen species (ROS) such as hydroxyl radicals; vi, water; vii, inhalable microorganisms. The characteristics

Fig. 1. Recent reported temporal variability of concentrations of PM2.5 and major components. Data taken from references: Beijing (2008/2009) (Hu et al., 2015), Beijing (2009/2010) (Zhang et al., 2013c), Beijing (2009/2010) (Zhao et al., 2013b), Tianjin (2009/2010) (Zhao et al., 2013b), Shijiazhuang (2009/2010) (Zhao et al., 2013b), Chengde (2009/2010) (Zhao et al., 2013b), Shangdianzi (2009/2010) (Zhao et al., 2013b), Zhengzhou (2010) (Geng et al., 2013), Shanghai (2011–2013) (Wang et al., 2015b), Shanghai (2011/2012) (Zhao et al., 2015), Jinsha, Hubei (2012/2013) (Zhang et al., 2014a), Fuzhou (2007/2008) (Zhang et al., 2013a), Guangzhou (2009/2010) (Tao et al., 2014b), Qingyuan (2009/2010) (Huang et al., 2013), Chengdu (2011) (Tao et al., 2014a), Mong Duong, Vietnam (2009/2010) (Hang and Oanh, 2014), Delhi (2012) (Bisht et al., 2015), Baghdad, Iraq (2012/2013) (Hamad et al., 2015), Po Valley, Italy (2010/2012) (Perrino et al., 2014), Katowice, Poland (2010) (Rogula-Kozlowska et al., 2014). Details can be found in Table S2.


155

Table 1 Summary of PM2.5 composition characteristics and determinations. Content

Main component & often studied

Toxicity

Determination

Gist

Ref.

PM2.5

Primary/secondary aerosol (ratios or relations of distinct composition can help source appointment)

Yes

Sampler, filters, microbalance, continuous monitors/sensors (monitoring net)

Avino et al. (2013), Boman et al. (2013), Bressi et al. (2013)

Element (mainly metals)

Al, Fe, Si, Ca, K, Mg, Mn, Pb, Cu, Zn, Ni, Ti, Mn, V, Bi, Strong Cd, Co, Cr, Ga, Li, Mo, Pt, Rb, Sb, Sn, Te, Tl, U, Ba, P, (heavy Sr and so on metals)

ICP, MS, OES, EDXRF, AAS

Ion

+ 2+ − + , Ca2+, F−, Cl−, SIA SO2− 4 , NO3 , NH4 , K , Mg (secondary inorganic aerosols)

Yes

IC, XRF, AAS, UV–Visible, In-situ method

Carbon

OC/EC

Yes

OC/EC analyzer

Organics

PAHs, VOCs, OVOCs, SOA

Yes

TD, GC, MS, NMR

Radical

Yes OH radicals, QOOH intermediates, atmospheric oxidation (of organics, SO2 and other trace gases)

EPR, LIF, CIMS

Water, CCN

Liquid water content, humility, absorption & adsorption, gas-particle phase partitioning, new particle formation

No

API-MS, CI API TOF MS,TD CI MS

Microbe

Bacterial and archaeal species

No

Metagenomic methods, monitor

- Anthropogenic (energy quality & consumption, industry) & meteorologic - Hurt health, visibility and nature - Many kinds but low concentrations - Very toxic to human health - Unstable and hard to measure - Influence the acidity, formation and fate of PM2.5 aerosols - Impact the ecosystem - Warming effect of EC (BC) and cooling effect of OC (climate) - Ubiquitous and influence health - Exist especially in fine PM - Source identification makers - 2000 Tg/yr. as gases, 300 Tg/yr. as aerosols, 104–105 species - Promising predictor for particle toxicity - A central role in tropospheric chemistry as atmospheric oxidants - Impact atmosphere via solvent and phase transition - CCN become cloud droplets via heterogeneous nucleation of water - N1300 species (also high in city) - Generally soil- & climate-associated and nonpathogenic to human

Boman et al. (2013), Bozlaker et al. (2014), Onat et al. (2013) Hu et al. (2014a), Singh et al. (2013), Yuan et al. (2014) Bressi et al. (2013), Samara et al. (2014), Zhu et al. (2014) Li et al. (2013c), Limu et al. (2013), Nozière et al. (2015) Gehling et al. (2014), Lelieveld et al. (2008), Savee et al. (2015)

Andreae (2013), Kulmala et al. (2013), Xue et al. (2014)

Cao et al. (2014), Liang et al. (2013c), Raisi et al. (2013)

Note: “Yes” means the toxic level of the composition is generally medium and up to its concentration and specific species.

and determination methods of the composition associated to PM2.5 are summarized in Table 1. PM2.5 has both anthropogenic and natural sources, and different components of PM2.5 differ in their numbers of species. The species numbers of currently already identified water, carbon, elements, ions, microbes and organics are approximately 1, 2, 100, hundreds, thousands and dozens of thousands, respectively. The unidentified microbes and organics may be more. Generally speaking, SNA, OM (including OC) and EC, besides H2O, can promote haze formation and are the dominant chemical components during pollution events (Wang et al., 2013b; Zhang et al., 2013a). The pollution sources are often season dependent; for instance, carbon concentrations in the spring and summer might be lower than those in the autumn and winter because of fuel combustions for heating (Zhao et al., 2013b). In urban areas, the whole mass of PM2.5 is about equivalent to that of organics, SNA, EC, and geological material (Al, Si, Ca, Ti, and Fe) (Rogula-Kozlowska et al., 2014). Geographical and meteorological conditions, size and population, traffic density and industrial activities all have influence on the concentration and the chemical composition of airborne particles (Barker, 2013; Pateraki et al., 2014; Szigeti et al., 2013; Tiwari et al., 2013), so the PM2.5 characteristics differ a lot among different areas or in different time (Li et al., 2013a). For instances, wind speed N 2 m/s could make exposures to PM2.5 decrease (Onat and Stakeeva, 2013), enhanced PM2.5 concentrations might be associated with dust transport events (Remoundaki et al., 2013a; Remoundaki et al., 2013b), and the PM2.5 levels might increase due to oil field activities and long-range transmission of smoke plume into the residual layer (Devi et al., 2014; Sun et al., 2013). The deposition of PM2.5 was significantly higher (lower) during the daytime (nighttime) over Deciduous and Coniferous Forests in Beijing (Sun et al., 2014). This phenomenon might be explained by the air entropy. The

air is heated by the sun during daytime and the entropy is increased, while on the contrary during nighttime. An increased entropy speeds the deposition (and departure in a dynamic equilibrium) of PM2.5 up, and a decreased one slows it down. Biomass burning and secondary reactions are important topics for their contributions to aerosols and the consequent influence on human health and climate (Cheng et al., 2013; Okoshi et al., 2014). Complex photochemistry (Li et al., 2014c), such as hydroxyl radical oxidation and triplet excited states of organic compounds (3C*) (Smith et al., 2014; Yu et al., 2014b), in biomass burning smokes could largely change the concentrations of O3 and organic aerosol (Alvarado et al., 2015). Multiple parameters, such as strongly absorbing brown carbon compounds (Slikboer et al., 2015), precursors (gases, VOCs and intermediates) (He et al., 2014; Kitanovski et al., 2014; Kudo et al., 2014; Ortega et al., 2013; Shrivastava et al., 2015; Sinha et al., 2014; Stockwell et al., 2015; Tsimpidi et al., 2014; Yee et al., 2013; Ying et al., 2014a; Yokelson et al., 2013; Yu et al., 2014b), tracers and markers (Lauraguais et al., 2014; Pietrogrande et al., 2014; Piletic et al., 2013; Tsai et al., 2013; Yokelson et al., 2013), nitrogen isotope ratios (Pavuluri et al., 2015), mass-to-charge (m/z) ratios (Diab et al., 2015; Dzepina et al., 2015), calculated emission factors (EFs) (Hatch et al., 2015; Yokelson et al., 2013), and composition changes of aerosol particles (Cayetano et al., 2014), from biomass burning are extremely helpful in explaining secondary organic aerosols. Studies on secondary aerosols (SOA and SIA), especially SOA, and their underlying reactions (including between organic and inorganic precursors and aerosols) are challenging and will remain as hot topics even in future research. Advanced instruments such as AMS (including its improved forms) and methods (such as using radiocarbon and organic tracers) are very helpful in these studies and the source apportionment of PM2.5.

156


3. Source apportionment The above part discussed what the PM2.5 is made of, and this section is going to review where it comes from, also mainly based on the PM2.5in-title papers published in 2013–2015. Source apportionment (SA) is very important in making control policies of PM2.5 pollution. SA involves one or more techniques that are used to quantify how individual sources contribute to PM2.5 concentrations (Balachandran et al., 2013), which is an important task in air pollution management, control and policy options (Chelani, 2013). SA techniques that rely on statistical analysis of observations at monitor sites are receptor models, including positive matrix factorization (PMF), principal component analysis (PCA), chemical mass balance (CMB), factor analysis with multiple regression (FA-MR), Unmix, back-trajectory analysis, conditional probability function, nonparametric wind regression (Balachandran et al., 2013; Chelani, 2013; Zhao et al., 2014). CMB model requires the input of unique profile for each major source, while PMF is a multivariate model that identifies factors without needing source information of a receptor site (Gao et al., 2013a). The convenient and practicable performance of PMF makes it a most popular method for PM2.5 source apportionment. However, SA results are often rough estimations since such models generally do not utilize features in highly time-resolved data (Vedantham et al., 2014). To deal with this, source apportionment results should be checked by the spatial and temporal patterns of local PM2.5 characteristics, regional emissions, meteorology, atmospheric transmission, etc. Controlling ambient PM2.5 concentrations needs controlling PM2.5 sources, but the sources are difficult to quantify because they emit a mixture of pollutants (gases and particles) that can undergo atmospheric chemical transformations before impacting a specific receptor location (Balachandran et al., 2013). The newly emitted reactants, namely gases, aqueous and organic vapors (volatiles), as well as aerosols react with each other, either indirectly in the sites of preexisting aerosols (secondary aerosols, dusts, sea salts and primary aerosols) to form products in these old aerosols or directly to form new (primary) aerosols as products. Eventually, the emitted pollutants become PM2.5 dominated particles by both the indirect and the direct formation processes. PM2.5 sources include all types of combustion activities (motor vehicles, marine traffic, power plants, biomass burning) and certain industrial processes (metal smelters, chemical plants, paper and pulp industries, refineries) (Engelbrecht et al., 2014), which may also include various emissions from anthropogenic construction (cement industry) dust, road dust, cooking, etc. 3.1. Receptor models 3.1.1. Positive matrix factorization Positive matrix factorization (PMF) is developed by Pentti Paatero and Unto Tapper (Paatero and Tapper, 1994); and it has been most widely applied for the source apportionment using PM data even in recent years (Table 2) (Bove et al., 2014; Choi et al., 2013; Geng et al., 2013; Huang et al., 2014b; Molnar et al., 2014). Fig. S2 shows that different areas have different air pollution sources (Choi et al., 2013; Geng et al., 2013; Gibson et al., 2013; Huang et al., 2014d; Jorquera and Barraza, 2013; Yu et al., 2013), which lead to their different proportions of PM2.5 composition that consist of sulfate, nitrate, ammonium, organics, carbon, heavy metals, etc. To achieve a direct view of the recent reported sources and basic information of PM2.5, a comparative description is made in Fig. 2 (Table S3). Timeliness of the data is not good enough despite the newest literature (2013–2015), but some of the observed results are based on observations for one or more whole years, which might be useful for understanding the general characteristics and sources of PM2.5. In terms of the 17 cities (time inconsistencies are ignored here for calculating the averages), OM (30%), sulfate (18%), nitrate (8%), ammonium (8%), EC (7%), and Cl (2%) are the most abundant species in PM2.5; others include H2O (abundant but usually not mentioned), and other ions, organics,

metals and unidentified species. For the 17 cities, secondary aerosols (SIA: 35%, SOA 19%), coal (25%), traffic (20%) are generally the largest PM2.5 sources. The sum of average source percentages is greater than 100%, due to the diversity of sources among different cities and the average is calculated based on the number of cities that have the corresponding sources of N0%. In China, Asia, coal burning is an important source, especially in North China, demonstrating China's heavy dependence of electricity and cooking on coal. Oil is generally used in nontraffic activities in cities of Europe, North America and South America. Traffic is a common source among different cities in all the six continents. PM2.5 aerosols cannot deposit in a second after their formation, which need several days or more days to subside, therefore, secondary aerosols (both inorganic and organic ones) are the most abundant source. However, most secondary aerosols are essentially and originally caused by anthropogenic emissions. The more traffic and industries there are, the more anthropogenic emissions there will be. The average of the PM2.5 concentrations, ranging from 4.5 μg m−3 to 175 μg m−3, of the 17 cities is 64 μg m−3. Notably, the world's average PM10 concentration is 71 μg m−3 during 2008 to 2013, ranging from 26 to 208 μg m−3, based on the results of about 1600 cities of 91 countries (WHO, 2015). The corresponding PM2.5 concentration may be 28.4–56.8 μg m−3 as the conversion factor PM2.5/PM10 is generally within the range of 0.4 to 0.8 (WHO, 2014b). However, the result of 71 μg m−3 may underestimate the present value, namely world's average PM10 concentration in 2014/2015, as showed by the website http://aqicn.org/map/world/, which has the newest PM2.5 & PM10 database and can (is expected to) give timely annual average PM2.5 concentration of the world based on it. Therefore, although the average PM2.5 concentration of 64 μg m−3 in cities of the world based on the data of the 17 cities of the 6 continents may overestimate the whole average, it can be useful in some senses because the data here (Table S3 and Fig. 2) may be more close to the present (2014/2015) observed values (http://aqicn.org/map/world/) than the WHO reported ones (WHO, 2014b, 2015). PMF using radiocarbon and AMS in PM is important for source apportionment and the chemistry of atmospheric oxidation. Results from 14 C, organic markers, and PMF analysis of AMS data showed a high correlation of fossil OC with semivolatile oxygenated organic carbon (SVOOC) (r = 0.84) and oxygenated-PAHs (r = 0.87) from May to June, 2010 in Pasadena, CA (Zotter et al., 2014). Low-volatility oxygenated organic carbon (LV-OOC) mainly (by N 69%) stemmed from non-fossil sources, while SV-OOC was largely (68%–74%) due to fossil sources (Zotter et al., 2014). Combined 14C and PMF analysis of AMS data indicated that non-fossil fuel origin of secondary organic aerosol could be from biogenic VOC precursors and wood burning emissions in the summer and winter of 2009–2010 in Paris (Beekmann et al., 2015). 14Cincorporated PMF analysis identified that mobile sources (28%), sulfur containing coal combustion (3%), biomass combustion (17%), other combustion (30%) and SOA (22%) were the 5 sources of the OC of PM2.5 in downtown Cleveland, Ohio during 2009–2010 (Piletic et al., 2013). PMF of the AMS full particle-phase mass spectrum revealed that variability in the non-refractory aerosols in Detroit, Michigan was caused by six factors: amine-containing (Amine), ammonium sulfate- and oxygenated organic aerosol-containing (Sulfate-OA), ammonium nitrateand oxygenated organic aerosol-containing (Nitrate-OA), ammonium chloride-containing (Chloride), hydrocarbon-like organic aerosol (HOA) and moderately oxygenated organic aerosol (OOA) (McGuire et al., 2014). Results of double positive matrix factorization (PMF2) using an aerosol chemical speciation monitor (ACSM) demonstrated that oxidized organic aerosols (OOA) can contribute up to 78% to organic matter during wintertime pollution in Paris (Petit et al., 2014). AMSPMF results showed that most (56%) organic aerosols in Mountain Tai, Shandong during 2010–2012 were oxidized in summer due to strong photochemical reactions (Zhang et al., 2014f). SOA could have much higher mass fractions (5 times) than did POA (mainly cooking organic aerosol (COA) and HOA) in non-refractory components in PM at a


157

Table 2 A summary of recent PM2.5 (μg m−3) source appointment results by PMF. Place

PM2.5

Sources (contribution percentage, %)

Time

Ref.

Asia Seoul

42.6

Jun. 2009 to May 2010

Choi et al. (2013)

Beijing

55.4

Secondary nitrate (25.4), secondary sulfate (19), motor vehicle (23), industry (8.5), biomass burning (6.1), soil (6.1), combustion & copper production emissions (6.1), and sea salt (5.9) Secondary sulfate (26.5), vehicle (17.1), fossil combustion (16), dust (23.1), biomass burning (11.2) and metal processing (6) Traffic (14.7), coal (25.9), SIA (22.5), metallurgical emission (0.3), dust/soil (11.7), industry (7.5), and SOA (10.9) Biomass burning (40) to WSOC Crust, combustion, and traffic and steel industrial to trace elements in PM2.5 Coal (25.9), secondary (21.8), industry (16.2), Ba, Mn and Zn (12.7), road dust (10.9), motor vehicle (7.7), K+, As and V (6.3) and fuel oil (2.5) Secondary (55.15), coal (20.98), soil dust (9.30), vehicles (6.06), biomass burning (4.55), and industry (2.87) Coal and biomass burning (42), secondary sulfate (36), secondary nitrate and vehicle emissions (17), and soil dust (14) Secondary sulfate (23.0), dust (20.5), coal (19.9), secondary nitrate (15.5), biomass burning (14.3) and vehicle (6.8) in during Coal (17.6), SIA (30.3), vehicle (14.2), geological (10.6), biomass burning (10.4), industrial (9.5) and construction dust (7.4) Dust (26), secondary (24), coal combustion (23), biomass/oil combustion & incineration (13), vehicles (10) and industry (4) Wheat residue burning (51.3) SIA (24.6), vehicle (18.8), dust (23.6), biomass burning (33.0) SIA (37), coal (20), biomass burning (11), iron and steel manufacturing (11), Mo-related industries (11), and soil dust (10) SOC largely associated with nitrate and sulfate Coal (30.5), gasoline engine (29.0), diesel engine (17.5), air-surface exchange (11.9) and biomass burning (11.1) to the PM2.5-bound 16 USEPA priority PAHs (16.9 ng m−3) Vehicle (27.0), coal (24.5), biomass burning (16.5), petroleum residue (16.3) and air-surface exchange (15.7) to PAHs (5.24 ng m−3) in PM2.5 Iron/steel manufacturing and secondary aerosol, motor vehicle and road resuspension dust. Secondary (39.1), biomass burning (15.4), regional industries (13.3), coal combustion (13.4) and crustal (8.2) Combustion, fresh & aged sea-salt, secondary, and airborne dust SIA (39.3), vehicle (26.9) and biomass burning (9.8) Vehicle (29), secondary (27), waste incinerator/biomass burning (23), residual oil (10), marine (6), industrial (4), and road dust (1) SIA (38.8), biomass burning (19.5), residual oil combustion (12.4), sea salt (16.8), crustal dust (7.4), and vehicle (5.1)

2010

Yu et al. (2013)

2010

Wu et al. (2014b)

2010 to 2011 2011 to 2012

Du et al. (2014) Gao et al. (2014)

Dec. 2012 to Jan. 2013

Wei et al. (2014)

Dec. 2007 to Oct. 2008

Yang et al. (2013)

2010

Gu et al. (2014)

Mar. 2012 to Feb. 2013

Xiao et al. (2014a)

2010

Wang et al. (2015c)

2010

Geng et al. (2013)

Jun. 2013 Spring, 2009–2010 2011

Li et al. (2014a) Tao et al. (2013b) Tao et al. (2014a)

Jan. 2010 to Jan. 2011 Oct. 2011 to Aug. 2012

Feng et al. (2013) Wang et al. (2015a)

Oct. 23, 2011 to Aug. 20, 2012

Wang et al. (2014b)

Apr. 2004 to Mar. 2005

Liu et al. (2015b)

Aug. 2009 and Jan. 24 to Feb. 4, 2010 Summer & winter, 2009/10 2009 Oct. 2004 to Sept. 2005

Huang et al. (2013)

Mar. 2011 to Feb. 2012

Huang et al. (2014d)

Coal (25), vehicle (24.2), stationary (29.3) and evaporative/unburned fuel (21.5) to the total PM2.5-bound PAH Secondary sulfate (27), sea salt (17), shipping oil combustion (15) and dust (25) to elements in PM2.5 Long-range transported pollution Mineral (African, regional, urban and harbor dusts), natural sources (African dust, regional dust and sea spray) (11), aged polluted air masses Ammonium sulfate (27), ammonium nitrate (24), heavy oil combustion (17), traffic (14), biomass burning (12), marine aerosols (6) and metal industry (1) Ammonium sulfate (27.3), crustal (16.4), traffic (16.4), oil combustion–industrial including ship emissions in harbor (15.3), biomass burning (11.7), crustal carbonates (7.7), aged marine (2.6) and industrial (0.4) Indoor sources (wood burning, resuspended soil and chalk) (60)

2011–2012

Callen et al. (2014)

20 Sept. to 20 Oct., 2010

Dall'Osto et al. (2013)

2008 to 2010 Jan. 2004 and Jul. 2005

Perrone et al. (2013) Pey et al. (2013)

Sept. 2009 to 10 Sept. 2010

Bressi et al. (2013), Bressi et al. (2014)

Jun. to Oct. 2012

Cesari et al. (2014)

2009/2010

Canha et al. (2014)

Secondary ammonium nitrate (35) and ammonium sulfate (15), vehicle (20) Vehicle (17–18), biomass burning, aged (0.5–13) and fresh (2–27) sea salt and soil (1–19) SIA, tire wear, oil combustion, vehicle/biogenic/atmospheric processing, wood burning, medium alkane/alkanoic acid Long-range transport (47), marine mixture (27.9), vehicle (13.2), fugitive dust (6.3), ship emissions (3.4) and refinery (2.2) SIA (54.28), mobile source (8.11), iron/steel manufacturing (2.30), sludge incinerator (1.53), oil combustion (3.58), slag/sinter processing (4.73), coal (0.38) and dust/crustal (12.71) Vehicle (12–25), secondary oxidation processes and biomass burning

2002 to 2013

Jan. 2003 to Oct. 2005

Hasheminassab et al. (2014a) Hasheminassab et al. (2014b) Xie et al. (2013b)

Jul. 11 to Aug. 26, 2011

Gibson et al. (2013)

Jul.–Aug. 2007

Pancras et al. (2013)

Jun. 2012 to Sept. 2013

Verma et al. (2014)

69

Handan, China

160.1

Jinan, China 169 Baoji, Shaanxi Xi'an

142. 6

Zhengzhou, China

175

Suixi, Anhui Chengdu, China

110.7 165.1 119

Shanghai

Huaniao Island (~66 km to Shanghai) Hangzhou, China

108.2

Qingyuan, South China

83.4

Shenzhen

67.2 42.2 55.5

Hong Kong

55.4

Europe Zaragoza, Spain

13.1

Barcelona Mediterranean Western Mediterranean

25 20

Paris

14.7

Brindisi (Italy)

15.1

A primary school in Portugal

70.5

North America Los Angeles and Rubidoux Southern California

8.2 to 36.6

Denver, CO Halifax, Nova Scotia, Canada Dearborn, Michigan

Southeastern United States

4.5 15.7

2002 to 2007

Dai et al. (2013) Huang et al. (2014b) Cheng et al. (2015)

(continued on next page)

158


Table 2 (continued) Place

PM2.5

South America Metropolitan, Costa Rica Belen, Costa Rica

36

Northern Chile

42

Santiago, Chile (indoor)

21.6

Recife, Brazil

7.3

Africa Nairobi, Kenya

17

Traffic (39), dust (35), mixed factors (13), industry (7) and combustion (6) 2008 to 2010

Gaita et al. (2014)

Oceania Newcastle NSW, Australia

8.11

Vehicle (27), biomass burning (23) and secondary sulfate from industry (20)

Stelcer et al. (2014)


Time

Ref.

Vehicle (30), wood smoke (5) and industrial combustion (9) SIA (28.2), SOA (26.7), oil combustion (11.8), industrial (10.1), crustal material (9.8), traffic (7.8), sea salt (5.6) Cement plant (33.7), dust (22.4), sulfates (17.8), minerals stockpiles/brine plant (12.4), Antofagasta (8.5) and copper smelter (5.3). Cooking and environmental tobacco smoke (28), vehicle (26), secondary sulfate (16), street dust (13), cleaning and cooking (11), and indoor dust (7) Vehicles + sea spray (44.6), biomass burning (27.5), soil (9.2), Na/S/Ca/Br (9.1), chlorine (5.7), metallurgy (3.8)

2010–2011 Jun. 2010 to Jul. 2011

Murillo et al. (2013a) Murillo et al. (2013b)

Dec. 17, 2007 to Jan. 27, 2008 Spring, 2012

Jorquera and Barraza (2013) Barraza et al. (2014)

Jun. 2007 to Jul. 2008

dos Santos et al. (2014)

Feb. 1998 to Dec. 2013

- Coal: coal combustion; vehicle: vehicular emissions.

suburban site in Hong Kong (Li et al., 2015a). For most source categories at two southern England sites in Jan.–Feb., 2012, the CMB and AMS-PMF results were highly correlated (r2 = 0.69–0.91), but the AMS may

overestimate the biomass burning/coal and food cooking sources and meanwhile underestimate secondary component (Yin et al., 2015). The highest contributors, among different burning phases of wood

Fig. 2. Concentrations, composition and sources of PM2.5 in different continents according to the recently reported results. Results were based on PMF, except in New Delhi (pragmatic mass closure). Data taken from references: Seoul (Choi et al., 2013), Beijing (Wu et al., 2014b), Jinan (Gu et al., 2014), Zhengzhou (Geng et al., 2013), Xi'an (Wang et al., 2015c), Chengdu (Tao et al., 2014a), Shenzhen (Huang et al., 2014b), New Delhi (Pant et al., 2015), Paris (Bressi et al., 2013; Bressi et al., 2014), Brindisi (Cesari et al., 2014), Halifax (Gibson et al., 2013), Dearborn (Pancras et al., 2013), Costa Rica (Murillo et al., 2013b), North Chile (Jorquera and Barraza, 2013), Recife (dos Santos et al., 2014), Nairobi (Gaita et al., 2014), Newcastle (Stelcer et al., 2014). See Table S3 for details.


combustion, to the total organic fraction might be ignition (44%), ember (30%) and combustion (26%) (Elsasser et al., 2013). To study SOA, combining AMS and organic tracer technique is probably the important future direction (Guo et al., 2014). Much work was conducted, using organic tracers as input for the PMF model, to address the challenge of source apportionment of OC and EC (Wang et al., 2015d), PAHs (Gao et al., 2015), organic aerosols (Budisulistiorini et al., 2013; Budisulistiorini et al., 2015; Kostenidou et al., 2013; Peng et al., 2013), and PM (Qadir et al., 2014). Biomass burning and coal combustion contributed significantly to OC and EC of PM2.5 in Dongguan, Guangdong from 2010 to 2012, accounting for 15–17% of OC and 24–30% and 34–35% of EC, respectively (Wang et al., 2015d). Among the three common sources, namely vehicular emissions (VE), biomass burning (BB) and coal combustion (CC), CC contributed the most to both particle-phase (58%) and total (gas + particle) PAHs (40%) of PM2.5 at six sites in Guangzhou during Nov.–Dec., 2009 (Gao et al., 2015). Isoprene-epoxydiol (IEPOX)-derived SOA tracer mass was about 25% (up to 47%) of the IEPOX-OA mass at Look Rock, Tennessee during June–July, 2013, and accounting for 32% of the total OA (Budisulistiorini et al., 2015). The SOC in PM2.5 in Shanghai during 2010–2011 was largely associated with sulfate and nitrate rather than the SOA tracers (Peng et al., 2013). The IEPOX-OA in the summer of 2011 in Atlanta accounts for about 33 ± 10% of total OA, higher than LV-OOA (23 ± 15%), SV-OOA (26 ± 15%), or HOA (18 ± 10%) (Budisulistiorini et al., 2013). OOA (55%), HOA (11.3%) and olive tree branches burning (otBB)-OA (33.7%) contributed the most to the OA in the Mediterranean during the burning of olive tree branches (Kostenidou et al., 2013). Solid fuel combustion, traffic emissionrelated, secondary sulfate & nitrate, and mixed sources (cooking, tobacco smoke and traffic) contributed the most to PM10 in Augsburg, Germany during Feb.–Mar., 2008 (Qadir et al., 2014). The above review in this section shows that the PMF method has been used to identify the PM2.5 sources in all inhabited continents including Asia, Europe, America (north and south), Africa and Oceania, excluding Antarctica without permanent human residents. To overcome the disadvantage in particle only-based PMF analysis, more source marker data are needed to remove the impacts of G/P partitioning, pre-assumptions comparing with field measurements (Xie et al., 2013a). The PMF method with factor selection would improve the analysis of multiple microenvironments (Molnar et al., 2014). Although combining PMF and chemical transport CAMx model is rare, they might be integrated in the future by using bottom-up emission inventories (Bove et al., 2014). Combined PMF results can

159

not only reflect main contributions but also underline the key role of atmospheric dynamics and aerosol aging processes (Mantas et al., 2014). Cluster analysis, coupled with PMF, can reveal the meteorological (besides fossil fuel, traffic and industry) influence on PM2.5 sources (Masiol et al., 2014). 3.1.2. Principal component analysis The recent reported PM2.5 source appointment results by principal component analysis (PCA) are summarized in Table 3. Coal: coal combustion; vehicle: vehicular emissions. 3.2. Other source appointment methods and summary Receptor models, source models, tracer (source marker) methods, portable and on-site emission measurement systems, and other source apportionment methods of PM2.5 are briefly summarized in Table 4. From the discussions above, it is can be concluded that various PM2.5 source appointment methods, including different models (receptor and dispersion), tracer methods, and direct emission-based measurements have been used in recent PM2.5 research. Receptor models such as PMF, CMB and UNMIX can obtain similar results from mobile sources and biomass burning (Heo et al., 2013b). However, the CMB model did not directly quantify primary biogenic emissions, and the two multivariate receptor models (PMF and UNMIX) could not separate sources from diesel and gasoline engines (Heo et al., 2013b). Therefore, non-model tools such as radiocarbon analysis, ionic & elemental tracers, and direct emission-based measurements are both very objective and direct, which may become more and more popular. Meanwhile, PMF using radiocarbon, organic tracers and AMS in PM has shown many advantages and it would be further developed and more widely used in future. Besides, since the anthropogenic sources are mainly existing in industrial and populated areas, robust direct monitoring, investigation and statistic of the emissions from fossil combustion in power stations, transports and factories, as well as organic volatilization in factories and gasoline stations are supposed to be authoritative source apportionment method, especially in heavy air pollution regions such as many Chinese and Indian cities. Sources will be obvious if the total consumption of energy, resources and other air-pollutant-producing industrial raw materials, as well as the individual emission per ton is given. Nowadays, fully sharing and publishing actual consumption & emission data without concealing through the Internet can be of great help in achieving such source

Table 3 A summary of recent reported PM2.5 (μg m−3) source appointment results by PCA. Place

PM2.5


Time

Ref.

Chuncheon, Korea (a residential area) Yeongwol (acement industrial area) Beijing (an urban site) Beijing

23.0 19.7 72.3

2012 to 2013 2012 to 2013 2004 to 2012 2006 to 2007

Han et al. (2015) Han et al. (2015) Liu et al. (2015c) Li et al. (2013b)

Shanghai Taipei Western Taiwan Strait region, China

83

Soil re-suspension, traffic, asphalt concrete production Cement & Ni–Cr plating and other industries Traffic and combustion (35.5–75.1) Biogenic sources and fossil fuel combustion to alkane; vehicular emission and coal combustion to PAHs in PM2.5 Industrial activities, coal combustion, and traffic sources Vehicle (64), dust storm (24) Traffic emissions, coal combustion, pyrometallurgical processes, and crust to the elements in PM2.5 Anthropogenic sources to C14–C18 fatty acids (70), natural sources to of the C20–C32 fatty acids (50–85), secondary to dicarboxylic acids and 1,2-phthalic acid (80–95), while primary to 1,4-phthalic acid (81) Industrial activities, traffic, and soil dust Diesel fueled vehicle to PM2.5; gasoline fueled vehicle to BTEX (benzene, toluene, ethylbenzene and xylenes) Biomass burning Crustal sources Soil (61), coal (17), industrial (11), and sea salt (5) Diesel, gasoline and alcohol engines to carboxylic acids

2009 to 2010 Apr. 25/26, 09 Apr. 2011

Wang et al. (2013a) Liang et al. (2013a) Xu et al. (2013)

winter 2009

Zhao et al. (2014)

2012 to 2013 Dec. 2007 to Jan. 2008

Xiao et al. (2014b) Giang and Oanh (2014)

Mar. 2013 2007 Spring/fall 09, and fall 2010 Apr. to May 2010

Pongpiachan et al. (2013) Revuelta et al. (2014) Gonzalez-Maddux et al. (2014) Mkoma et al. (2014)

Pearl River Delta region

Dafushan forest park at Guangzhou Ho Chi Minh City, Vietnam Northern Thailand Barcelona Shiprock, New Mexico Lapa bus terminal, Salvador, Bahia, Northeastern Brazil

97

160


Table 4 A summary of recently used source appointment methods of PM2.5. Source appointment methods Receptor models

Positive matrix factorization

Principal component analysis

Chemical mass balance

Transport and dispersion simulation (source model)

Multi-linear engine (regression) Hybrid single particle Lagrangian integrated trajectory Community multiscale air quality

Tracer (source marker) methods

Gist

Ref.

- Needs no prior knowledge of receptor sites - Mostly used - Various sources can be quantified - Improved by tracers, other models (CMB, back trajectory, PCA), micro-environmental datasets and field measurements (including 14 C and AMS) - Widely used - Generally focus on a few specific sources - Associate with physicochemistry parameters - Improved by SEM, other models

Beekmann et al. (2015), Choi et al. (2013), Elsasser et al. (2013) Gao et al. (2013a), Geng et al. (2013), Guo et al. (2014), Huang et al. (2014a), Li et al. (2015a), Mantas et al. (2014), Masiol et al. (2014), McGuire et al. (2014), Petit et al. (2014), Piletic et al. (2013), Yin et al. (2015,, Zhang et al. (2014f), Zotter et al. (2014) Giang and Oanh (2014), Han et al. (2015) Li et al. (2013b), Liang et al. (2013a), Liu et al. (2015c), Pongpiachan et al. (2013), Revuelta et al. (2014), Wang et al. (2013a), Xiao et al. (2014b), Xu et al. (2013), Zhao et al. (2014)Gonzalez-Maddux et al. (2014), Mkoma et al. (2014) Gao et al. (2013a), Zhang et al. (2014b), Hasheminassab et al. (2014c), Pipalatkar et al. (2014), Subramoney et al. (2013) Crawford et al. (2013) Rogula-Kozlowska et al. (2013) Li et al. (2015b), Molnar and Sallsten (2013), Moreda-Pineiro et al. (2015), Pachauri et al. (2013), Pipal and Satsangi (2015), Sandeep et al. (2013) Kwok et al. (2013), Wang et al. (2014a), Ying et al. (2014a), Ying et al. (2014b), Squizzato et al. (2014), Zhang et al. (2014a), Zhang et al. (2013c),Dimitriou and Kassomenos (2014), Heo et al. (2013a) Tian et al. (2015), Oanh et al. (2013),

- Needs input of unique profile of sources - Improved by molecular marker, source profile database - Improves source apportionment methods - Can be an alternative; results similar to PMF's - Widely used - Regional (continental scale) - Source-oriented; improved by other methods

Potential source contribution function

- Regional - Generally no quantification

Source directional apportionment Statistical package for the social sciences Radiocarbon analysis

- Sources from different directions are quantified - Identify emission-pollution multivariate relationships - Identify fossil fuel, biomass burning, and biogenic emissions - Sources of carbonaceous aerosols - Ionic/elemental ratios - Markers for various sources - Primary OC and EC have common sources - Improved by receptor modeling - Improve emission inventory models - Flexible and direct, but time-consuming and costly - Combine with land use, monitoring station data; hybrid receptor models

Ions (elements) as tracers EC as a tracer Portable and on-site emission measurement systems

Other source apportionment methods

apportionment information for environmental investigation, management and research. Air pollution is caused by emissions. The accuracy of emissions inventory is of great importance for sources identification and policy making. Studies of aerosol loading (mass burden of atmosphere) showed that, minus preindustrial (year 1860) annual emission, 141.2 Tg SO2 (i.e. 211.8 Tg sulfate), 117.6 Tg NO2 (i.e. 158.5 Tg nitrate), 36.8 Tg NH3, 5.4 Tg BC (normalized to the mass of carbon), 154.4 Tg VOCs (based on the mass of carbon) and 44 Tg POAs are annually emitted into the atmosphere by human (Kokhanovsky, 2008; Tsigaridis et al., 2006). Based on the results, annual anthropogenic emission of aerosols (complete gas-particle conversion is assumed for SO2-sulfate and NO2-nitrate mentioned here) might be more than 600 Tg (0.6 billion tons), i.e. exceeding 234.8 g per capita per day (based on 7 billion people), demonstrating the transformative effect of industry on nature. Consequently, the earth is connected by the undesired trans-border air pollution that is mainly attributed to the anthropogenic emissions of SO2, NOx, smog & dust, carbon, organics, CO, O3 and NH3. If the per capita emission value is associated with the average PM2.5 concentration (64 μg m−3) in cities of the world, an emission-concentration coefficient (3.66875 ≈ 3.67) can be figured out. Based on this hypothesis, a mean PM2.5 concentration of ≤ 15 μg m−3 can be achieved if the per capita per day emission is controlled and below 55 g. Although the per capita emissions are generally low in China, its total emissions are high (Chen et al., 2014a; Fu et al., 2013; Guan et al., 2014b; Takahashi et al., 2014; Yang et al., 2015b; Zhang et al., 2013d). Fig. 3, Tables S4 and S5 show the emissions of air pollutants in

Buchholz et al. (2013), Keuken et al. (2013), Liu et al. (2013b), Liu et al. (2014b), Niu et al. (2013a) Matawle et al. (2014), Pachon et al. (2013), Yang et al. (2014), Yu et al. (2014a) Huang et al. (2014c), Zhang et al. (2014b) Shen et al. (2015), Shen et al. (2014b), Yao et al. (2015a), Yao et al. (2015b), Bonifacio et al. (2015) Donateo et al. (2014) Sofowote et al. (2014), Xu et al. (2014)

different countries & regions. Emissions of SO2 and NOx in Europe and US (China) generally decrease (increase and then reduce) (Fig. 3a and b), which might be caused by the market-decided transferred configurations of high-pollution production chains and technological disparities of desulfurization and denitration. The internal factor (Fig. 3c and d), namely the intensive emissions in North China can essentially explain the severe air pollution in Jan. 2013 when the external factor, namely undesired and diffusion-unfavorable (moist and stable) meteorological conditions added fuel to the fire. The emission distribution in Fig. 3d is also very similar with the current (2014/2015) air pollution status in China, demonstrating the decisive effect of main pollutant emissions in waste gas on PM2.5 (air) pollution or hazes. There might be problems in thus kind of statistics since they are not based on direct measurements covering all pollution sources, but they could reflect the major emissions of air pollutants. Non-methane volatile organic compounds (NMVOC) are very important air pollutants and supposed to be included in the China's National Environmental Statistics Bulletin and China Statistical Yearbook. 4. PM2.5 control by policy, planning, technology and ideology Air pollution (including PM2.5 pollution), identical with catastrophes and great wars, has been causing substantial damage (millions of deaths and trillions of dollars each year) in modern society. Consequently, PM2.5 pollution receives more attentions in recent years than ever before. To improve public health protection, the United States Environmental Protection Agency in Dec. 2006 lowered the 24-hour PM2.5


161

Fig. 3. Emissions of air pollutants in different countries: a and b based on OECD's data from Emissions of air pollutants, Environment Database, http://stats.oecd.org/, accessed on 24 May 2015; c and d based on China's data from http://zls.mep.gov.cn/hjtj/qghjtjgb/ (1995\ \1998, industry only) and http://www.stats.gov.cn/tjsj/ndsj/; Canada's SO2 data (2013) in a from http://www.ec.gc.ca/inrp-npri/default.asp?lang=En&n=386BAB5A-1&offset=1&toc=show. Notes: Europe: OECD-Europe; Sulfur oxides: expressed as sulfur dioxide (SO2); S&D: smoke and dust.

standard from 65 μg m− 3 to 35 μg m− 3 (Mwaniki et al., 2014), and further strengthened the annual PM2.5 standard by raising the level from 15.0 μg m− 3 (set in 1997) to 12.0 μg m− 3 on Dec. 14, 2012 (USEPA, 2012). The newest PM2.5 standards in typical areas excerpted from the official websites are listed in Table 5. China succeeded in setting up a very wide PM2.5 monitoring network that covers all the 338 prefecture-level cities of China (367 cities are currently included) (MEPPRC, 2015), which has 1436 monitoring sites (stations) and may have cost the government billions of renminbi (RMB) to establish and run in recent years; and the US PM2.5 monitoring network has about 1500 monitoring sites (USEPA, 2014). Noticeably and usefully, hundreds of free PM2.5 APPs (mobile phone applications) and many Internet AQI-containing websites make the real-time monitoring data readily accessible to the public. Environmental protection is led by governments that are promoted by academia and corporations; hence PM2.5 control by policy, planning and technology is mainly the combined efforts of these three parts.

Specific PM2.5 control measures reported recently including policy (laws and standards), city planning (tree-planting) and technologies (additive materials, particle filtration devices, electrostatic precipitator) are summarized in Table 6 (For details, see supplementary material). To improve the ecological ideology that is essential for reducing PM2.5 pollution in the long run, World Air Day is proposed to be established and other positive ideas (environmental awareness) should be more frequently shared in practice, education and propaganda. 5. Future perspectives Some key challenges and future directions of PM2.5 research and possible solutions of air pollution are put forward, which may include: 1) More accurate measurements and methods to identify the formation and composition of PM2.5 should be designed, developed and used. It is challenging, but accuracy is the first and most important thing in

Table 5 Current PM2.5 standards in different places of the world. PM2.5 (μg m−3) International Developed countries

Developing countries

WHO EU US Japan Australia Canada China 1 China 2 India Mexico

24 h

Annual

Reference

25 – 35 35 25 30 35 75 40 15

10 25 12 15 8 – 15 35 60 65

http://whqlibdoc.who.int/hq/2006/WHO_SDE_PHE_OEH_06.02_eng.pdf?ua=1 http://ec.europa.eu/environment/air/quality/standards.htm http://www.epa.gov/airquality/particlepollution/2012/decfsstandards.pdf http://www.env.go.jp/en/air/aq/aq.html http://www.environment.gov.au/protection/air-quality/air-quality-standards http://www.ec.gc.ca/rnspa-naps/default.asp?lang=En&n=07BC2AC0-1 http://hbj.new.cqcs.gov.cn/upfiles/2013-3/2013327153015207.pdf http://dmc.kar.nic.in/Pollution.pdf http://www.salud.gob.mx/unidades/cdi/nom/025ssa193.html

162


Table 6 A summary of PM2.5 control measures. PM2.5 reduction (control)

Key points

Effective

Reference

Policies & laws and standards

- PM2.5 concentration as evaluation index of government officials

Very

You (2014)

- Act & Regulation

Very

- Multiple emission control measure - Reinforce measures - Street sweeping/washing - Mechanical ventilation heat recovery systems - Cleaner fuels (low-sulfur fuel) for ship - Consumption-based accounts - Not negligible - Up to wood species - Inspire the public to transform social traditions - Stricter standards - The limits of metals should be included - Compare with standards of other countries - Should be optimized

Yes Yes Yes Yes Very Yes Yes

- Mg-based additives - Reduce PM2.5 formation during combustion - Sodium aluminosilicate (AlNaO6Si2), kaolin (H4Al2Si2O9) - Particle filtration units (PFUs) for dwellings and buildings - Oxidative particle filters for diesel vehicles reduce particles but produce NO2

Yes

General strategies

Control fireworks and meat smoking

Improving standards

City planning

Tree-planting

Technologies

Additives

Particle filtration devices

Electrostatic precipitator Ideology

tiān rén hé yī (天人合一), a supreme harmony between nature and man that one treats nature as herself or himself

Anthropocene

World Air Day

Very Very

Yes

(Jin et al. (2014), Nguyen et al. (2015), Nowak et al. (2013) (Si et al., (2014), Wei et al. (2013)

Yes

Si et al. (2014)

Very

Hanninen et al. ((2005), Spilak et al. (2014)

Yes (side effect)

Durant et al. (2014)

- Bio-mimic anti PM2.5 mask

Very

- Polyacrylonitrile (PAN) transparent air filters - High magnetic field, low working voltage and high gas velocity - Learn from the London smog in December 1952

Very Yes

- Natural resources and environment are exploited and consumed in unprecedented speed - European and American cities sometimes have PM10 AQIs of about 900 - Comfort, convenience and consumption (3C) and PM2.5 concentration (C) are a 3C-C seesaw - Moderate 3C is best - Change our producing & consuming lifestyles and ideology by reducing waste, consciousness, compassion and care, cap-and-trade programs (CTPs) etc. - Humans have replaced nature as the ruling environmental force on Earth - North America interacts with other regions including Asia, Europe, Africa, and the Middle East in air pollution - A worldwide joint effort is urgently needed - There are many environment Days, but World Air Day is absent (Table S6) - WAD has been proposed to be established

Kleeman et al. (2013, Kotchenruther (2015), Zapata et al. (2013) Molders (2013) Chen et al. (2014b) Kassomenos et al. (2014) Taylor et al. (2015) Contini et al. (2015) Guan et al. (2014a) Liu et al. (2014a), Motorykin et al. (2015) This review Tao et al. (2015), Yang et al. (2015a)

Essential

Shareefdeen et al., (2005), Shen et al. (2014a) Liu et al. (2015a Miller (2011), Zhang et al. (2014c) Davis et al. (2002), Dooley (2002), Stone (2002) This review http://aqicn.org/map/world/ This review Armaroli and Balzani (2007), Foley et al. (2011), Taylor (2012)

Inspiring

Crutzen (2002), Crutzen and Stoermer (2000), Ruddiman et al. (2015) Yu et al. (2012)

Inspiring

This review This review

Note: “Yes” means the efficiency of the control measure is medium and depends on the status of its implementation.

PM2.5 observations. Only by achieving true and accurate results of the concentrations, spatial and temporal distribution, formation (including gas-particle transformation) & evolution processes of PM2.5, can other related research and policy-making be successfully conducted. 2) Organic aerosols and microbes in PM2.5 and their roles in climate and human health should be better understood. The species number of organic aerosols and microorganisms are very large, but many of them have yet to be identified. 3) Tracer methods using radiocarbon, ions, elements and EC, organics as tracers, as well as portable and on-site emission measurement systems (mobile laboratories) need to be further improved. These methods will improve source appointment and predicting results via more direct associations and inventories by figuring out more rational emission factors. 4) PM2.5 research is more and more interdisciplinary, and the understanding of the composition, sources, impacts and solutions of it

needs comprehensive ability. For example, one needs to widely go through the knowledge and information of geography information, earth system, meteorology, biology, economics and politics in addition to chemistry and physics to be able to handle the complexity of the effect PM2.5 pollution on climate and ecosystem and eventually find control strategies. 5) Secondary aerosols in PM2.5 generally refer to ‘chemical secondary aerosols’ that are named in terms of chemical reactions in the atmosphere, while changes of physical morphology of PM (physical secondary aerosols) are expected to be exclusively pointed out in the future. Dust storms are not well associated with PM2.5 concentration, but very heavy dust storms bring slightly heavy PM2.5 pollution. These phenomena can be explained by the changes of physical morphology of PM during dust storms, namely bigger particles become many more relatively smaller particles because of detachment and desorption especially after the sandstorm peaks. Consequently, the currently used chemical secondary aerosols (SOA and SIA) may not


6)

7)

8)

9)

be enough to cover all the secondary aerosols, and physical secondary aerosols should be added into the category. The factors of frozen soils and plant pollens (catkins) should be taken into consideration when explaining the saw-toothed patterns (sawtooths) of PM2.5 concentrations. For example, the soils are frozen and the plant pollens (catkins) are fewest in Inner Mongolia, North China from November to March, so the sawtooths caused by north wind are generally more obvious (∕| or ↗↓) in Beijing than those in other months. While in April, the soils are unfrozen and the plant pollens (catkins) are most both in Inner Mongolia and Beijing, the north wind may bring dusts and the plant pollens (catkins) also interfere with the concentration decline during north winds, leading to slow declines and unobvious sawtooths (∧ or↗↘). Better filtration devices (including baghouse filtration), electrostatic precipitator (ESP) and degassing (end-of-pipe abatement) technologies need to be developed. Both area (stationary) and point (mobile) emissions can be controlled by more advanced filtration (precipitator) devices and degassing technologies, and outdoor PM2.5 concentrations or exposures can be reduced. More novel materials (catalytic, biomimetic, nano, etc.) and devices (filter membrane, dust remover, air purifier, etc.) will be designed to fight against PM2.5 pollution. Indoor PM2.5 is very important and can be more feasibly controlled by many of these strategies. Governments should support companies and research institutes to develop these useful technologies. Market methods such as cap-and-trade will also become increasingly popular. Monitoring networks and standards of PM2.5 have yet to be widely established in the world, and real-time data should be more easily accessible to the public. China has shown great determination and executive capability in setting up PM2.5 monitoring networks. European and North American countries are expected to strengthen and update their networks. Other countries in such as India and countries in Africa, South America and Oceania are supposed to establish PM2.5 standards and monitoring networks. Standards and concentration limits of PM1, and many other toxic organics and heavy metals besides BaP and Pb will be gradually established. People's ideology on PM2.5 pollution would be more rational with increasing propaganda of knowledge, education and information from environmentalists. When citizen's environmental awareness improves, some unhealthy traditional activities (such as meat smoking, celebration with fireworks/sparklers and field burning of straw) and resource wasting behaviors (such as excessive automobile dependency and leaving the lights on when not needed) will be astringed, especially in heavily polluted areas.

6. Conclusions Due to its obvious direct harm to visibility and mood, potential harm to physical health and other parts (traffic, construction especially the ancient one that is irretrievable once it is destroyed, economy and ecosystem), complex interaction with climate, and the worldwide conducted research, discussion and monitoring of it, PM2.5 has become a well-known technical term. This article reviews the recent advances in PM2.5 research (primarily concerning observations, sources and control) mainly based on the papers that contain “PM2.5” in their titles and have been published from 2013 to 2015. Basic observations such as temporal-spatial variations of PM2.5 and its composition are worldwide conducted, especially in Asian cities where PM2.5 concentrations are comparatively high. As for the composition and evolution of PM2.5, besides the ordinary measurement of SNA and OC&EC, the new particle formation, atmospheric oxidation (radicals and excited states), secondary pollutants (and the underlying formation and evolution mechanisms), climatic (meteorological) effect and microbes are of great research interest and may be given more focus in future. To better understand and control PM2.5 pollution, coupled with the PM2.5 standards and control policies issued by the

163

governments, more cost-effective monitors and control technologies (devices and materials) are needed. Source appointment and emissions inventory are very important in formulating the policy strategies of PM2.5 control. Countries in different inhabitable continents (Africa, Oceania, South America, Asia, Europe and North America) have conducted source appointment of PM2.5, and the results show that different energy structures, geographies, urbanization levels and populations mainly decided their PM2.5 characteristics in cities. Identifying the total social consumption and emissions per unit consumption is an accurate way to quantify different PM2.5 sources and might be very useful in conducting source appointment and emissions inventories. It will be more feasible in the era of big data. Air (including PM2.5) pollution control is becoming a hot topic, especially under the current conditions of rapid economy development, energy consumption and robust population growth. Reducing PM2.5 pollution is an urgent task in many countries such as China and India. To control PM2.5 pollution is to control the emissions and to get along well with the natural environment. Importantly, identifying the most economical and ecological ways (including laws, policy, management, planning and technology) to control the emission thus the air pollution in this world needs the combined and thoughtful efforts of people from all countries, organizations and departments (government, corporation and academia). Much has been learned over the past years of PM2.5 research, but there is so much more yet to be discovered by using the multidisciplinary knowledge of physics (optics, electricity, dynamics, and mechanics), chemistry, meteorology, geography, economic data, emissions, information technology, statistic methods, biomedicine and materials. Conflict of interest The authors declare no competing financial interest. Acknowledgments This work was supported by the National Science and Technology Support Program of China (2014BAC22B01), the National Natural Science Foundation of China (21107061, 21190054), and the Fund of Japan International Cooperation Agency (JICA). Appendix A. Supplementary data Supplementary data (Tables S1 to S6, Figs. S1 to S2, the full content of Section 4, etc.) to this article can be found online at http://dx.doi. org/10.1016/j.envint.2015.10.016. References Almeida, J., Schobesberger, S., Kurten, A., Ortega, I.K., Kupiainen-Maatta, O., Praplan, A.P., Adamov, A., Amorim, A., Bianchi, F., Breitenlechner, M., David, A., Dommen, J., Donahue, N.M., Downard, A., Dunne, E., Duplissy, J., Ehrhart, S., Flagan, R.C., Franchin, A., Guida, R., Hakala, J., Hansel, A., Heinritzi, M., Henschel, H., Jokinen, T., Junninen, H., Kajos, M., Kangasluoma, J., Keskinen, H., Kupc, A., Kurten, T., Kvashin, A.N., Laaksonen, A., Lehtipalo, K., Leiminger, M., Leppa, J., Loukonen, V., Makhmutov, V., Mathot, S., McGrath, M.J., Nieminen, T., Olenius, T., Onnela, A., Petaja, T., Riccobono, F., Riipinen, I., Rissanen, M., Rondo, L., Ruuskanen, T., Santos, F.D., Sarnela, N., Schallhart, S., Schnitzhofer, R., Seinfeld, J.H., Simon, M., Sipila, M., Stozhkov, Y., Stratmann, F., Tome, A., Trostl, J., Tsagkogeorgas, G., Vaattovaara, P., Viisanen, Y., Virtanen, A., Vrtala, A., Wagner, P.E., Weingartner, E., Wex, H., Williamson, C., Wimmer, D., Ye, P., Yli-Juuti, T., Carslaw, K.S., Kulmala, M., Curtius, J., Baltensperger, U., Worsnop, D.R., Vehkamaki, H., Kirkby, J., 2013. Molecular understanding of sulphuric acid-amine particle nucleation in the atmosphere. Nature 502, 359–363. Alvarado, M.J., Lonsdale, C.R., Yokelson, R.J., Akagi, S.K., Coe, H., Craven, J.S., Fischer, E.V., McMeeking, G.R., Seinfeld, J.H., Soni, T., Taylor, J.W., Weise, D.R., Wold, C.E., 2015. Investigating the links between ozone and organic aerosol chemistry in a biomass burning plume from a prescribed fire in California chaparral. Atmos. Chem. Phys. 15, 6667–6688. Andreae, M.O., 2013. The aerosol nucleation puzzle. Science 339, 911–912. Armaroli, N., Balzani, V., 2007. The future of energy supply: challenges and opportunities. Angew. Chem. Int. Ed. 46, 52–66.

164


Atkinson, J.D., Murray, B.J., Woodhouse, M.T., Whale, T.F., Baustian, K.J., Carslaw, K.S., Dobbie, S., O/’Sullivan, D., Malkin, T.L., 2013. The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds. Nature 498, 355–358. Avino, P., Capannesi, G., Lopez, F., Rosada, A., 2013. Determination of interesting toxicological elements in PM2.5 by neutron and photon activation analysis. Sci. World J. Balachandran, S., Chang, H.H., Pachon, J.E., Holmes, H.A., Mulholland, J.A., Russell, A.G., 2013. Bayesian-based ensemble source apportionment of PM2.5. Environ. Sci. Technol. 47, 13511–13518. Barker, H.W., 2013. Isolating the industrial contribution of PM2.5 in Hamilton and Burlington, Ontario. J. Appl. Meteorol. Climatol. 52, 660–667. Barraza, F., Jorquera, H., Valdivia, G., Montoya, L.D., 2014. Indoor PM2.5 in Santiago, Chile, spring 2012: source apportionment and outdoor contributions. Atmos. Environ. 94, 692–700. Beekmann, M., Prevot, A.S.H., Drewnick, F., Sciare, J., Pandis, S.N., van der Gon, H.A.C.D., Crippa, M., Freutel, F., Poulain, L., Ghersi, V., Rodriguez, E., Beirle, S., Zotter, P., von der Weiden-Reinmueller, S.L., Bressi, M., Fountoukis, C., Petetin, H., Szidat, S., Schneider, J., Rosso, A., El Haddad, I., Megaritis, A., Zhang, Q.J., Michoud, V., Slowik, J.G., Moukhtar, S., Kolmonen, P., Stohl, A., Eckhardt, S., Borbon, A., Gros, V., Marchand, N., Jaffrezo, J.L., Schwarzenboeck, A., Colomb, A., Wiedensohler, A., Borrmann, S., Lawrence, M., Baklanov, A., Baltensperger, U., 2015. In situ, satellite measurement and model evidence on the dominant regional contribution to fine particulate matter levels in the Paris megacity. Atmos. Chem. Phys. 15, 9577–9591. Bisht, D.S., Dumka, U.C., Kaskaoutis, D.G., Pipal, A.S., Srivastava, A.K., Soni, V.K., Attri, S.D., Sateesh, M., Tiwari, S., 2015. Carbonaceous aerosols and pollutants over Delhi urban environment: temporal evolution, source apportionment and radiative forcing. Sci. Total Environ. 521, 431–445. Boman, J., Shaltout, A.A., Abozied, A.M., Hassan, S.K., 2013. On the elemental composition of PM2.5 in central Cairo, Egypt. X-Ray Spectrom. 42, 276–283. Bonifacio, H.F., Maghirang, R.G., Trabue, S.L., McConnell, L.L., Prueger, J.H., Bonifacio, E.R., 2015. TSP, PM10, and PM2.5 emissions from a beef cattle feedlot using the fluxgradient technique. Atmos. Environ. 101, 49–57. Bove, M.C., Brotto, P., Cassola, F., Cuccia, E., Massabo, D., Mazzino, A., Piazzalunga, A., Prati, P., 2014. An integrated PM2.5 source apportionment study: positive matrix factorisation vs. the chemical transport model CAMx. Atmos. Environ. 94, 274–286. Bozlaker, A., Spada, N.J., Fraser, M.P., Chellam, S., 2014. Elemental characterization of PM2.5 and PM10 emitted from light duty vehicles in the washburn tunnel of Houston, Texas: release of rhodium, palladium, and platinum. Environ. Sci. Technol. 48, 54–62. Bressi, M., Sciare, J., Ghersi, V., Bonnaire, N., Nicolas, J.B., Petit, J.E., Moukhtar, S., Rosso, A., Mihalopoulos, N., Feron, A., 2013. A one-year comprehensive chemical characterisation of fine aerosol (PM2.5) at urban, suburban and rural background sites in the region of Paris (France). Atmos. Chem. Phys. 13, 7825–7844. Bressi, M., Sciare, J., Ghersi, V., Mihalopoulos, N., Petit, J.E., Nicolas, J.B., Moukhtar, S., Rosso, A., Féron, A., Bonnaire, N., Poulakis, E., Theodosi, C., 2014. Sources and geographical origins of fine aerosols in Paris (France). Atmos. Chem. Phys. 14, 8813–8839. Brown, S.G., Lee, T., Roberts, P.T., Collett, J.L., 2013. Variations in the OM/OC ratio of urban organic aerosol next to a major roadway. J. Air Waste Manage. Assoc. 63, 1422–1433. Buchholz, B.A., Fallon, S.J., Zermeno, P., Bench, G., Schichtel, B.A., 2013. Anomalous elevated radiocarbon measurements of PM2.5. Nucl. Instrum. Methods B 294, 631–635. Budisulistiorini, S.H., Canagaratna, M.R., Croteau, P.L., Marth, W.J., Baumann, K., Edgerton, E.S., Shaw, S.L., Knipping, E.M., Worsnop, D.R., Jayne, J.T., Gold, A., Surratt, J.D., 2013. Real-time continuous characterization of secondary organic aerosol derived from isoprene epoxydiols in downtown Atlanta, Georgia, using the aerodyne aerosol chemical speciation monitor. Environ. Sci. Technol. 47, 5686–5694. Budisulistiorini, S.H., Li, X., Bairai, S.T., Renfro, J., Liu, Y., Liu, Y.J., McKinney, K.A., Martin, S.T., McNeill, V.F., Pye, H.O.T., Nenes, A., Neff, M.E., Stone, E.A., Mueller, S., Knote, C., Shaw, S.L., Zhang, Z., Gold, A., Surratt, J.D., 2015. Examining the effects of anthropogenic emissions on isoprene-derived secondary organic aerosol formation during the 2013 Southern Oxidant and Aerosol Study (SOAS) at the Look Rock, Tennessee ground site. Atmos. Chem. Phys. 15, 8871–8888. Callen, M.S., Iturmendi, A., Lopez, J.M., 2014. Source apportionment of atmospheric PM2.5bound polycyclic aromatic hydrocarbons by a PMF receptor model. Assessment of potential risk for human health. Environ. Pollut. 195, 167–177. Canagaratna, M.R., Massoli, P., Browne, E.C., Franklin, J.P., Wilson, K.R., Onasch, T.B., Kirchstetter, T.W., Fortner, E.C., Kolb, C.E., Jayne, J.T., Kroll, J.H., Worsnop, D.R., 2015. Chemical compositions of black carbon particle cores and coatings via soot particle aerosol mass spectrometry with photoionization and electron ionization. J. Phys. Chem. A 119, 4589–4599. Canha, N., Almeida, S.M., Freitas, M.D., Wolterbeek, H.T., Cardoso, J., Pio, C., Caseiro, A., 2014. Impact of wood burning on indoor PM2.5 in a primary school in rural Portugal. Atmos. Environ. 94, 663–670. Cao, C., Jiang, W.J., Wang, B.Y., Fang, J.H., Lang, J.D., Tian, G., Jiang, J.K., Zhu, T.F., 2014. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event. Environ. Sci. Technol. 48, 1499–1507. Carslaw, K.S., Lee, L.A., Reddington, C.L., Pringle, K.J., Rap, A., Forster, P.M., Mann, G.W., Spracklen, D.V., Woodhouse, M.T., Regayre, L.A., Pierce, J.R., 2013. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature 503, 67–71. Cayetano, M.G., Hopke, P.K., Lee, K.H., Jung, J., Batmunkh, T., Lee, K., Kim, Y.J., 2014. Investigations of the particle compositions of transported and local emissions in Korea. Aerosol Air Qual. Res. 14, 793–805. Cesari, D., Genga, A., Ielpo, P., Siciliano, M., Mascolo, G., Grasso, F.M., Contini, D., 2014. Source apportionment of PM2.5 in the harbour-industrial area of Brindisi (Italy): identification and estimation of the contribution of in-port ship emissions. Sci. Total Environ. 497, 392–400. Chang, S.Y., Chou, C.C.K., Liu, S., Zhang, Y.H., 2013. The characteristics of PM2.5 and its chemical compositions between different prevailing wind patterns in Guangzhou. Aerosol Air Qual. Res. 13, 1373–1383.

Chelani, A.B., 2013. Statistical characteristics of ambient PM2.5 concentration at a traffic site in Delhi: source identification using persistence analysis and nonparametric wind regression. Aerosol Air Qual. Res. 13, 1768–1778. Chen, H., Nanayakkara, C.E., Grassian, V.H., 2012. Titanium dioxide photocatalysis in atmospheric chemistry. Chem. Rev. 112, 5919–5948. Chen, L.H., Sun, Y.Y., Wu, X.C., Zhang, Y.X., Zheng, C.H., Gao, X., Cen, K., 2014a. Unit-based emission inventory and uncertainty assessment of coal-fired power plants. Atmos. Environ. 99, 527–535. Chen, Y., Schleicher, N., Chen, Y.Z., Chai, F.H., Norra, S., 2014b. The influence of governmental mitigation measures on contamination characteristics of PM2.5 in Beijing. Sci. Total Environ. 490, 647–658. Cheng, Y., Engling, G., He, K.B., Duan, F.K., Ma, Y.L., Du, Z.Y., Liu, J.M., Zheng, M., Weber, R.J., 2013. Biomass burning contribution to Beijing aerosol. Atmos. Chem. Phys. 13, 7765–7781. Cheng, Y., He, K.-B., Duan, F.-K., Du, Z.-Y., Zheng, M., Ma, Y.-L., 2014. Ambient organic carbon to elemental carbon ratios: influence of the thermal–optical temperature protocol and implications. Sci. Total Environ. 468–469, 1103–1111. Cheng, Y., Lee, S.C., Gu, Z.L., Ho, K.F., Zhang, Y.W., Huang, Y., Chow, J.C., Watson, J.G., Cao, J.J., Zhang, R.J., 2015. PM2.5 and PM10–2.5 chemical composition and source apportionment near a Hong Kong roadway. Particuology 18, 96–104. Choi, J.K., Heo, J.B., Ban, S.J., Yi, S.M., Zoh, K.D., 2013. Source apportionment of PM2.5 at the coastal area in Korea. Sci. Total Environ. 447, 370–380. Chow, J.C., Watson, J.G., 2002. Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model. Energy Fuel 16, 222–260. Chow, J.C., Watson, J.G., Lowenthal, D.H., Solomon, P.A., Magliano, K.L., Ziman, S.D., Richards, L.W., 1993. PM10 and PM2.5 compositions in California's San Joaquin Valley. Aerosol Sci. Technol. 18, 105–128. Collier, S., Zhang, Q., 2013. Gas-phase CO2 subtraction for improved measurements of the organic aerosol mass concentration and oxidation degree by an aerosol mass spectrometer. Environ. Sci. Technol. 47, 14324–14331. Contini, D., Gambaro, A., Donateo, A., Cescon, P., Cesari, D., Merico, E., Belosi, F., Citron, M., 2015. Inter-annual trend of the primary contribution of ship emissions to PM2.5 concentrations in Venice (Italy): efficiency of emissions mitigation strategies. Atmos. Environ. 102, 183–190. Crawford, J., Cohen, D.D., Chambers, S., Williams, A., Stelcer, E., 2013. Incorporation of Radon-222 as a parameter in ME-2 to improve apportionment of PM2.5 sources in the Sydney region. Atmos. Environ. 80, 378–388. Crutzen, P.J., 2002. Geology of mankind. Nature 415, 23-–223. Crutzen, P.J., Stoermer, E.F., 2000. The “anthropocene”. IGBP Newsl. 41, 17–18. Dai, W., Gao, J.Q., Cao, G., Ouyang, F., 2013. Chemical composition and source identification of PM2.5 in the suburb of Shenzhen, China. Atmos. Res. 122, 391–400. Dall'Osto, M., Querol, X., Amato, F., Karanasiou, A., Lucarelli, F., Nava, S., Calzolai, G., Chiari, M., 2013. Hourly elemental concentrations in PM2.5 aerosols sampled simultaneously at urban background and road site during SAPUSS — diurnal variations and PMF receptor modelling. Atmos. Chem. Phys. 13, 4375–4392. Dallmann, T.R., Onasch, T.B., Kirchstetter, T.W., Worton, D.R., Fortner, E.C., Herndon, S.C., Wood, E.C., Franklin, J.P., Worsnop, D.R., Goldstein, A.H., Harley, R.A., 2014. Characterization of particulate matter emissions from on-road gasoline and diesel vehicles using a soot particle aerosol mass spectrometer. Atmos. Chem. Phys. 14, 7585–7599. Davis, D.L., Bell, M.L., Fletcher, T., 2002. A look back at the London smog of 1952 and the half century since. Environ. Health Perspect. 110, A734–A735. Deshmukh, D.K., Deb, M.K., Suzuki, Y., Kouvarakis, G.N., 2013. Water-soluble ionic composition of PM2.5–10 and PM2.5 aerosols in the lower troposphere of an industrial city Raipur, the eastern central India. Air Qual. Atmos. Health 6, 95–110. Devi, G., Bhattacharyya, K.G., Mahanta, L.B., Devi, A., 2014. Trace metal composition of PM2.5, soil, and Machilus bombycina leaves and the effects on Antheraea assama silk worm rearing in the oil field area of Northeastern India. Water Air Soil Pollut. 225. Diab, J., Streibel, T., Cavalli, F., Lee, S.C., Saathoff, H., Mamakos, A., Chow, J.C., Chen, L.W.A., Watson, J.G., Sippula, O., Zimmermann, R., 2015. Hyphenation of a EC/OC thermaloptical carbon analyzer to photo-ionization time-of-flight mass spectrometry: an off-line aerosol mass spectrometric approach for characterization of primary and secondary particulate matter. Atmos. Meas. Tech. 8, 3337–3353. Dimitriou, K., Kassomenos, P., 2014. Indicators reflecting local and transboundary sources of PM2.5 and PMCOARSE in Rome — impacts in air quality. Atmos. Environ. 96, 154–162. Dockery, D.W., Schwartz, J., Spengler, J.D., 1992. Air-pollution and daily mortality — associations with particulates and acid aerosols. Environ. Res. 59, 362–373. Dockery, D.W., Speizer, F.E., Stram, D.O., Ware, J.H., Spengler, J.D., Ferris, B.G., 1989. Effects of inhalable particles on respiratory health of children. Am. Rev. Respir. Dis. 139, 587–594. Donateo, A., Gregoris, E., Gambaro, A., Merico, E., Giua, R., Nocioni, A., Contini, D., 2014. Contribution of harbour activities and ship traffic to PM2.5, particle number concentrations and PAHs in a port city of the Mediterranean Sea (Italy). Environ. Sci. Pollut. Res. 21, 9415–9429. Dooley, E.E., 2002. Fifty years later: clearing the air over the London smog. Environ. Health Perspect. 110, A748. dos Santos, L.H.M., Kerr, A.A.F.S., Verissimo, T.G., Andrade, M.D., de Miranda, R.M., Fornaro, A., Saldiva, P., 2014. Analysis of atmospheric aerosol (PM2.5) in Recife city, Brazil. J. Air Waste Manage. Assoc. 64, 519–528. Du, Z.Y., He, K.B., Cheng, Y., Duan, F.K., Ma, Y.L., Liu, J.M., Zhang, X.L., Zheng, M., Weber, R., 2014. A yearlong study of water-soluble organic carbon in Beijing I: sources and its primary vs. secondary nature. Atmos. Environ. 92, 514–521. Durant, J.L., Beelen, R., Eeftens, M., Meliefste, K., Cyrys, J., Heinrich, J., Bellander, T., Lewne, M., Brunekreef, B., Hoek, G., 2014. Comparison of ambient airborne PM2.5, PM2.5 absorbance and nitrogen dioxide ratios measured in 1999 and 2009 in three areas in Europe. Sci. Total Environ. 487, 290–298.

C.-S. Liang et al. / Environment International 86 (2016) 150–170 Dzepina, K., Mazzoleni, C., Fialho, P., China, S., Zhang, B., Owen, R.C., Helmig, D., Hueber, J., Kumar, S., Perlinger, J.A., Kramer, L.J., Dziobak, M.P., Ampadu, M.T., Olsen, S., Wuebbles, D.J., Mazzoleni, L.R., 2015. Molecular characterization of free tropospheric aerosol collected at the Pico Mountain Observatory: a case study with a long-range transported biomass burning plume. Atmos. Chem. Phys. 15, 5047–5068. Edwards, P.M., Brown, S.S., Roberts, J.M., Ahmadov, R., Banta, R.M., deGouw, J.A., Dube, W.P., Field, R.A., Flynn, J.H., Gilman, J.B., Graus, M., Helmig, D., Koss, A., Langford, A.O., Lefer, B.L., Lerner, B.M., Li, R., Li, S.M., McKeen, S.A., Murphy, S.M., Parrish, D.D., Senff, C.J., Soltis, J., Stutz, J., Sweeney, C., Thompson, C.R., Trainer, M.K., Tsai, C., Veres, P.R., Washenfelder, R.A., Warneke, C., Wild, R.J., Young, C.J., Yuan, B., Zamora, R., 2014. High winter ozone pollution from carbonyl photolysis in an oil and gas basin. Nature 514, 351-+. Ehn, M., Thornton, J.A., Kleist, E., Sipila, M., Junninen, H., Pullinen, I., Springer, M., Rubach, F., Tillmann, R., Lee, B., Lopez-Hilfiker, F., Andres, S., Acir, I.-H., Rissanen, M., Jokinen, T., Schobesberger, S., Kangasluoma, J., Kontkanen, J., Nieminen, T., Kurten, T., Nielsen, L.B., Jorgensen, S., Kjaergaard, H.G., Canagaratna, M., Maso, M.D., Berndt, T., Petaja, T., Wahner, A., Kerminen, V.-M., Kulmala, M., Worsnop, D.R., Wildt, J., Mentel, T.F., 2014. A large source of low-volatility secondary organic aerosol. Nature 506, 476–479. Elsasser, M., Busch, C., Orasche, J., Schon, C., Hartmann, H., Schnelle-Kreis, J., Zimmermann, R., 2013. Dynamic changes of the aerosol composition and concentration during different burning phases of wood combustion. Energy Fuel 27, 4959–4968. Engelbrecht, J.P., Menendez, I., Derbyshire, E., 2014. Sources of PM2.5 impacting on Gran Canaria, Spain. Catena 117, 119–132. Ezeh, G.C., Obioh, I.B., Asubiojo, O.I., Chiari, M., Nava, S., Calzolai, G., Lucarelli, F., Nuviadenu, C.K., 2014. Elemental compositions of PM10–2.5 and PM2.5 aerosols of a Nigerian urban city using ion beam analytical techniques. Nucl. Instrum. Methods B 334, 28–33. Farmer, D.K., Cappa, C.D., Kreidenweis, S.M., 2015. Atmospheric processes and their controlling influence on cloud condensation nuclei activity. Chem. Rev. 115, 4199–4217. Feng, J., Li, M., Zhang, P., Gong, S., Zhong, M., Wu, M., Zheng, M., Chen, C., Wang, H., Lou, S., 2013. Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers. Atmos. Environ. 79, 614–622. Feng, J.L., Zhong, M., Xu, B.H., Du, Y., Wu, M.H., Wang, H.L., Chen, C.H., 2014. Concentrations, seasonal and diurnal variations of black carbon in PM2.5 in Shanghai, China. Atmos. Res. 147, 1–9. Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O'Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., Monfreda, C., Polasky, S., Rockstrom, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M., 2011. Solutions for a cultivated planet. Nature 478, 337–342. Fountoukis, C., Megaritis, A.G., Skyllakou, K., Charalampidis, P.E., Pilinis, C., van der Gon, H., Crippa, M., Canonaco, F., Mohr, C., Prevot, A.S.H., Allan, J.D., Poulain, L., Petaja, T., Tiitta, P., Carbone, S., Kiendler-Scharr, A., Nemitz, E., O'Dowd, C., Swietlicki, E., Pandis, S.N., 2014. Organic aerosol concentration and composition over Europe: insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis. Atmos. Chem. Phys. 14, 9061–9076. Fu, X., Wang, S.X., Zhao, B., Xing, J., Cheng, Z., Liu, H., Hao, J.M., 2013. Emission inventory of primary pollutants and chemical speciation in 2010 for the Yangtze River Delta region, China. Atmos. Environ. 70, 39–50. Gaita, S.M., Boman, J., Gatari, M.J., Pettersson, J.B.C., Janhall, S., 2014. Source apportionment and seasonal variation of PM2.5 in a Sub-Saharan African city: Nairobi, Kenya. Atmos. Chem. Phys. 14, 9977–9991. Gao, B., Guo, H., Wang, X.M., Zhao, X.Y., Ling, Z.H., Zhang, Z., Liu, T.Y., 2013a. Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF). Environ. Sci. Pollut. Res. 20, 2398–2409. Gao, B., Wang, X.M., Zhao, X.Y., Ding, X., Fu, X.X., Zhang, Y.L., He, Q.F., Zhang, Z., Liu, T.Y., Huang, Z.Z., Chen, L.G., Peng, Y., Guo, H., 2015. Source apportionment of atmospheric PAHs and their toxicity using PMF: impact of gas/particle partitioning. Atmos. Environ. 103, 114–120. Gao, J.J., Tian, H.Z., Cheng, K., Lu, L., Wang, Y.X., Wu, Y., Zhu, C.Y., Liu, K.Y., Zhou, J.R., Liu, X.G., Chen, J., Hao, J.M., 2014. Seasonal and spatial variation of trace elements in multi-size airborne particulate matters of Beijing, China: mass concentration, enrichment characteristics, source apportionment, chemical speciation and bioavailability. Atmos. Environ. 99, 257–265. Gao, X.M., Nie, W., Xue, L.K., Wang, T., Wang, X.F., Gao, R., Wang, W.X., Yuan, C., Gao, J., Ravi, K.P., Wang, J., Zhang, Q.Z., 2013b. Highly time-resolved measurements of secondary ions in PM2.5 during the 2008 Beijing Olympics: the impacts of control measures and regional transport. Aerosol Air Qual. Res. 13, 367–376. Gehling, W., Dellinger, B., 2013. Environmentally persistent free radicals and their lifetimes in PM2.5. Environ. Sci. Technol. 47, 8172–8178. Gehling, W., Khachatryan, L., Dellinger, B., 2014. Hydroxyl radical generation from environmentally persistent free radicals (EPFRs) in PM2.5. Environ. Sci. Technol. 48, 4266–4272. Geng, N.B., Wang, J., Xu, Y.F., Zhang, W.D., Chen, C., Zhang, R.Q., 2013. PM2.5 in an industrial district of Zhengzhou, China: chemical composition and source apportionment. Particuology 11, 99–109. Giang, N.T.H., Oanh, N.T.K., 2014. Roadside levels and traffic emission rates of PM2.5 and BTEX in Ho Chi Minh City, Vietnam. Atmos. Environ. 94, 806–816. Gibson, M.D., Pierce, J.R., Waugh, D., Kuchta, J.S., Chisholm, L., Duck, T.J., Hopper, J.T., Beauchamp, S., King, G.H., Franklin, J.E., Leaitch, W.R., Wheeler, A.J., Li, Z., Gagnon, G.A., Palmer, P.I., 2013. Identifying the sources driving observed PM2.5 temporal variability over Halifax, Nova Scotia, during BORTAS-B. Atmos. Chem. Phys. 13, 7199–7213. Goldstein, A.H., Galbally, I.E., 2007. Known and unexplored organic constituents in the earth's atmosphere. Environ. Sci. Technol. 41, 1514–1521.

165

Gonzalez-Maddux, C., Marcotte, A., Upadhyay, N., Herckes, P., Williams, Y., Haxel, G., Robinson, M., 2014. Elemental composition of PM2.5 in Shiprock, New Mexico, a rural community located near coal-burning power plants and abandoned uranium mine tailings sites. Atmos. Pollut. Res. 5, 511–519. Gu, J.X., Du, S.Y., Han, D.W., Hou, L.J., Yi, J., Xu, J., Liu, G.H., Han, B., Yang, G.W., Bai, Z.P., 2014. Major chemical compositions, possible sources, and mass closure analysis of PM2.5 in Jinan, China. Air Qual. Atmos. Health 7, 251–262. Guan, D.B., Lin, J.T., Davis, S.J., Pan, D., He, K.B., Wang, C., Wuebbles, D.J., Streets, D.G. and Zhang, Q., Reply to Lopez et al.: consumption-based accounting helps mitigate global air pollution, Proc. Natl. Acad. Sci. U. S. A. 111, 2014a, E2631-E2631. Guan, D.B., Su, X., Zhang, Q., Peters, G.P., Liu, Z., Lei, Y., He, K.B., 2014b. The socioeconomic drivers of China's primary PM2.5 emissions. Environ. Res. Lett. 9. Guo, S., Hu, M., Shang, D.J., Guo, Q.F., Hu, W.W., 2014. Research on secondary organic aerosols basing on field measurement. Acta Chim. Sin. 72, 145–157. Hallquist, M., Wenger, J.C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N.M., George, C., Goldstein, A.H., Hamilton, J.F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.E., Jimenez, J.L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, T.F., Monod, A., Prevot, A.S.H., Seinfeld, J.H., Surratt, J.D., Szmigielski, R., Wildt, J., 2009. The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmos. Chem. Phys. 9, 5155–5236. Hamad, S.H., Shafer, M.M., Kadhim, A.K.H., Al-Omran, S.M., Schauer, J.J., 2015. Seasonal trends in the composition and ROS activity of fine particulate matter in Baghdad, Iraq. Atmos. Environ. 100, 102–110. Han, L.J., Zhou, W.Q., Li, W.F., Li, L., 2014. Impact of urbanization level on urban air quality: a case of fine particles (PM2.5) in Chinese cities. Environ. Pollut. 194, 163–170. Han, Y.J., Kim, H.W., Cho, S.H., Kim, P.R., Kim, W.J., 2015. Metallic elements in PM2.5 in different functional areas of Korea: concentrations and source identification. Atmos. Res. 153, 416–428. Hand, J.L., Schichtel, B.A., Malm, W.C., Frank, N.H., 2013. Spatial and temporal trends in PM2.5 organic and elemental carbon across the United States. Adv. Meteorol. Hang, N.T., Oanh, N.T.K., 2014. Chemical characterization and sources apportionment of fine particulate pollution in a mining town of Vietnam. Atmos. Res. 145, 214–225. Hanninen, O.O., Palonen, J., Tuomisto, J.T., Yli-Tuomi, T., Seppanen, O., Jantunen, M.J., 2005. Reduction potential of urban PM2.5 mortality risk using modern ventilation systems in buildings. Indoor Air 15, 246–256. Hasheminassab, S., Daher, N., Ostro, B.D., Sioutas, C., 2014a. Long-term source apportionment of ambient fine particulate matter (PM2.5) in the Los Angeles Basin: a focus on emissions reduction from vehicular sources. Environ. Pollut. 193, 54–64. Hasheminassab, S., Daher, N., Saffari, A., Wang, D., Ostro, B.D., Sioutas, C., 2014b. Spatial and temporal variability of sources of ambient fine particulate matter (PM2.5) in California. Atmos. Chem. Phys. 14, 12085–12097. Hasheminassab, S., Daher, N., Shafer, M.M., Schauer, J.J., Delfino, R.J., Sioutas, C., 2014c. Chemical characterization and source apportionment of indoor and outdoor fine particulatematter (PM2.5) in retirement communities of the Los Angeles Basin. Sci. Total Environ. 490, 528–537. Hatch, L.E., Luo, W., Pankow, J.F., Yokelson, R.J., Stockwell, C.E., Barsanti, K.C., 2015. Identification and quantification of gaseous organic compounds emitted from biomass burning using two-dimensional gas chromatography-time-of-flight mass spectrometry. Atmos. Chem. Phys. 15, 1865–1899. He, K.B., Yang, F.M., Ma, Y.L., Zhang, Q., Yao, X.H., Chan, C.K., Cadle, S., Chan, T., Mulawa, P., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35, 4959–4970. He, Q.F., Ding, X., Wang, X.M., Yu, J.Z., Fu, X.X., Liu, T.Y., Zhang, Z., Xue, J., Chen, D.H., Zhong, L.J., Donahue, N.M., 2014. Organosulfates from pinene and isoprene over the Pearl River Delta, South China: seasonal variation and implication in formation mechanisms. Environ. Sci. Technol. 48, 9236–9245. Heal, M.R., 2014. The application of carbon-14 analyses to the source apportionment of atmospheric carbonaceous particulate matter: a review. Anal. Bioanal. Chem. 406, 81–98. Heard, D.E., Pilling, M.J., 2003. Measurement of OH and HO2 in the troposphere. Chem. Rev. 103, 5163–5198. Hellack, B., Yang, A., Cassee, F.R., Janssen, N.A.H., Schins, R.P.F., Kuhlbusch, T.A.J., 2014. Intrinsic hydroxyl radical generation measurements directly from sampled filters as a metric for the oxidative potential of ambient particulate matter. J. Aerosol Sci. 72, 47–55. Heo, J., McGinnis, J.E., de Foy, B., Schauer, J.J., 2013a. Identification of potential source areas for elevated PM2.5, nitrate and sulfate concentrations. Atmos. Environ. 71, 187–197. Heo, J.B., Dulger, M., Olson, M.R., McGinnis, J.E., Shelton, B.R., Matsunaga, A., Sioutas, C., Schauer, J.J., 2013b. Source apportionments of PM2.5 organic carbon using molecular marker positive matrix factorization and comparison of results from different receptor models. Atmos. Environ. 73, 51–61. Hering, S., Cass, G., 1999. The magnitude of bias in the measurement of PM2.5 arising from volatilization of particulate nitrate from teflon filters. J. Air Waste Manage. Assoc. 49, 725–733. Hu, G.Y., Sun, J.Y., Zhang, Y.M., Shen, X.J., Yang, Y., 2015. Chemical composition of PM2.5 based on two-year measurements at an urban site in Beijing. Aerosol Air Qual. Res. 15, 1748–1759. Hu, G.Y., Zhang, Y.M., Sun, J.Y., Zhang, L.M., Shen, X.J., Lin, W.L., Yang, Y., 2014a. Variability, formation and acidity of water-soluble ions in PM2.5 in Beijing based on the semicontinuous observations. Atmos. Res. 145, 1–11. Hu, J.L., Wang, Y.G., Ying, Q., Zhang, H.L., 2014b. Spatial and temporal variability of PM2.5 and PM10 over the North China plain and the Yangtze River Delta, China. Atmos. Environ. 95, 598–609. Huang, B., Liu, M., Ren, Z.F., Bi, X.H., Zhang, G.H., Sheng, G.Y., Fu, J.M., 2013. Chemical composition, diurnal variation and sources of PM2.5 at two industrial sites of South China. Atmos. Pollut. Res. 4, 298–305.

166


Huang, R.J., Zhang, Y., Bozzetti, C., Ho, K.F., Cao, J.J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., El Haddad, I., Prevot, A.S., 2014a. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222. Huang, X.F., Yun, H., Gong, Z.H., Li, X., He, L., Zhang, Y.H., Hu, M., 2014b. Source apportionment and secondary organic aerosol estimation of PM2.5 in an urban atmosphere in China. Sci. China Earth Sci. 57, 1352–1362. Huang, X.H.H., Bian, Q.J., Louie, P.K.K., Yu, J.Z., 2014c. Contributions of vehicular carbonaceous aerosols to PM2.5 in a roadside environment in Hong Kong. Atmos. Chem. Phys. 14, 9279–9293. Huang, X.H.H., Bian, Q.J., Ng, W.M., Louie, P.K.K., Yu, J.Z., 2014d. Characterization of PM2.5 major components and source investigation in Suburban Hong Kong: a one year monitoring study. Aerosol Air Qual. Res. 14, 237–250. Ikemori, F., Honjyo, K., Yamagami, M., Nakamura, T., 2015. Influence of contemporary carbon originating from the 2003 Siberian forest fire on organic carbon in PM2.5 in Nagoya, Japan. Sci. Total Environ. 530, 403–410. Irei, S., Shimono, A., Hikida, T., Kuramoto, K., Suzuki, Y., Takami, A., 2014. Qualitative evaluation of m/z 85, 87, and 133 signals in organic aerosol mass spectra of fly ash produced by coal combustion. Aerosol Air Qual. Res. 14, 406–412. Jaoui, M., Lewandowski, M., Docherty, K., Offenberg, J.H., Kleindienst, T.E., 2014. Atmospheric oxidation of 1,3-butadiene: characterization of gas and aerosol reaction products and implications for PM2.5. Atmos. Chem. Phys. 14, 13681–13704. Jiang, S.Y.N., Yang, F.H., Chan, K.L., Ning, Z., 2014. Water solubility of metals in coarse PM and PM2.5 in typical urban environment in Hong Kong. Atmos. Pollut. Res. 5, 236–244. Jimenez, J.L., Canagaratna, M.R., Donahue, N.M., Prevot, A.S.H., Zhang, Q., Kroll, J.H., DeCarlo, P.F., Allan, J.D., Coe, H., Ng, N.L., Aiken, A.C., Docherty, K.S., Ulbrich, I.M., Grieshop, A.P., Robinson, A.L., Duplissy, J., Smith, J.D., Wilson, K.R., Lanz, V.A., Hueglin, C., Sun, Y.L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J.M., Collins, D.R., Cubison, M.J., Dunlea, E.J., Huffman, J.A., Onasch, T.B., Alfarra, M.R., Williams, P.I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S., Shimono, A., Sun, J.Y., Zhang, Y.M., Dzepina, K., Kimmel, J.R., Sueper, D., Jayne, J.T., Herndon, S.C., Trimborn, A.M., Williams, L.R., Wood, E.C., Middlebrook, A.M., Kolb, C.E., Baltensperger, U., Worsnop, D.R., 2009. Evolution of organic aerosols in the atmosphere. Science 326, 1525–1529. Jin, S.J., Guo, J.K., Wheeler, S., Kan, L.Y., Che, S.Q., 2014. Evaluation of impacts of trees on PM2.5 dispersion in urban streets. Atmos. Environ. 99, 277–287. Jorquera, H., Barraza, F., 2013. Source apportionment of PM10 and PM2.5 in a desert region in northern Chile. Sci. Total Environ. 444, 327–335. Kassomenos, P.A., Vardoulakis, S., Chaloulakou, A., Paschalidou, A.K., Grivas, G., Borge, R., Lumbreras, J., 2014. Study of PM10 and PM2.5 levels in three European cities: analysis of intra and inter urban variations. Atmos. Environ. 87, 153–163. Keuken, M.P., Moerman, M., Voogt, M., Blom, M., Weijers, E.P., Rockmann, T., Dusek, U., 2013. Source contributions to PM2.5 and PM10 at an urban background and a street location. Atmos. Environ. 71, 26–35. Kirillova, E.N., Andersson, A., Tiwari, S., Srivastava, A.K., Bisht, D.S., Gustafsson, O., 2014. Water-soluble organic carbon aerosols during a full New Delhi winter: isotope-based source apportionment and optical properties. J. Geophys. Res.-Atmos. 119, 3476–3485. Kirkby, J., Curtius, J., Almeida, J., Dunne, E., Duplissy, J., Ehrhart, S., Franchin, A., Gagne, S., Ickes, L., Kurten, A., Kupc, A., Metzger, A., Riccobono, F., Rondo, L., Schobesberger, S., Tsagkogeorgas, G., Wimmer, D., Amorim, A., Bianchi, F., Breitenlechner, M., David, A., Dommen, J., Downard, A., Ehn, M., Flagan, R.C., Haider, S., Hansel, A., Hauser, D., Jud, W., Junninen, H., Kreissl, F., Kvashin, A., Laaksonen, A., Lehtipalo, K., Lima, J., Lovejoy, E.R., Makhmutov, V., Mathot, S., Mikkila, J., Minginette, P., Mogo, S., Nieminen, T., Onnela, A., Pereira, P., Petaja, T., Schnitzhofer, R., Seinfeld, J.H., Sipila, M., Stozhkov, Y., Stratmann, F., Tome, A., Vanhanen, J., Viisanen, Y., Vrtala, A., Wagner, P.E., Walther, H., Weingartner, E., Wex, H., Winkler, P.M., Carslaw, K.S., Worsnop, D.R., Baltensperger, U., Kulmala, M., 2011. Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation. Nature 476, 429–433. Kitanovski, Z., Cusak, A., Grgic, I., Claeys, M., 2014. Chemical characterization of the main products formed through aqueous-phase photonitration of guaiacol. Atmos. Meas. Tech. 7, 2457–2470. Kleeman, M.J., Zapata, C., Stilley, J., Hixson, M., 2013. PM2.5 co-benefits of climate change legislation part 2: California governor's executive order S-3-05 applied to the transportation sector. Clim. Chang. 117, 399–414. Kokhanovsky, A.A., 2008. Microphysical Parameters and Chemical Composition of Atmospheric Aerosol Aerosol Optics. Springer, Berlin Heidelberg, pp. 1–15. Kong, S.F., Ji, Y.Q., Li, Z.Y., Lu, B., Bai, Z.P., 2013a. Emission and profile characteristic of polycyclic aromatic hydrocarbons in PM2.5 and PM10 from stationary sources based on dilution sampling. Atmos. Environ. 77, 155–165. Kong, S.F., Ji, Y.Q., Liu, L.L., Chen, L., Zhao, X.Y., Wang, J.J., Bai, Z.P., Sun, Z.R., 2013b. Spatial and temporal variation of phthalic acid esters (PAEs) in atmospheric PM10 and PM2.5 and the influence of ambient temperature in Tianjin, China. Atmos. Environ. 74, 199–208. Kong, S.F., Ji, Y.Q., Lu, B., Zhao, X.Y., Han, B., Bai, Z.P., 2014. Similarities and differences in PM2.5, PM10 and TSP chemical profiles of fugitive dust sources in a coastal oilfield city in China. Aerosol Air Qual. Res. 14, 2017–2028. Koren, I., Dagan, G., Altaratz, O., 2014. From aerosol-limited to invigoration of warm convective clouds. Science 344, 1143–1146. Kostenidou, E., Kaltsonoudis, C., Tsiflikiotou, M., Louvaris, E., Russell, L.M., Pandis, S.N., 2013. Burning of olive tree branches: a major organic aerosol source in the Mediterranean. Atmos. Chem. Phys. 13, 8797–8811. Kotchenruther, R.A., 2015. The effects of marine vessel fuel sulfur regulations on ambient PM2.5 along the West Coast of the US. Atmos. Environ. 103, 121–128.

Kudo, S., Tanimoto, H., Inomata, S., Saito, S., Pan, X.L., Kanaya, Y., Taketani, F., Wang, Z.F., Chen, H.Y., Dong, H.B., Zhang, M.G., Yamaji, K., 2014. Emissions of nonmethane volatile organic compounds from open crop residue burning in the Yangtze River Delta region, China. J. Geophys. Res.-Atmos. 119, 7684–7698. Kulmala, M., Kontkanen, J., Junninen, H., Lehtipalo, K., Manninen, H.E., Nieminen, T., Petäjä, T., Sipilä, M., Schobesberger, S., Rantala, P., Franchin, A., Jokinen, T., Järvinen, E., Äijälä, M., Kangasluoma, J., Hakala, J., Aalto, P.P., Paasonen, P., Mikkilä, J., Vanhanen, J., Aalto, J., Hakola, H., Makkonen, U., Ruuskanen, T., Mauldin, R.L., Duplissy, J., Vehkamäki, H., Bäck, J., Kortelainen, A., Riipinen, I., Kurtén, T., Johnston, M.V., Smith, J.N., Ehn, M., Mentel, T.F., Lehtinen, K.E.J., Laaksonen, A., Kerminen, V.-M., Worsnop, D.R., 2013. Direct observations of atmospheric aerosol nucleation. Science 339, 943–946. Kunzli, N., Mudway, I.S., Gotschi, T., Shi, T.M., Kelly, F.J., Cook, S., Burney, P., Forsberg, B., Gauderman, J.W., Hazenkamp, M.E., Heinrich, J., Jarvis, D., Norback, D., Payo-Losa, F., Poli, A., Sunyer, J., Borm, P.J.A., 2006. Comparison of oxidative properties, light absorbance, and total and elemental mass concentration of ambient PM2.5 collected at 20 European sites. Environ. Health Perspect. 114, 684–690. Kwok, R.H.F., Napelenok, S.L., Baker, K.R., 2013. Implementation and evaluation of PM2.5 source contribution analysis in a photochemical model. Atmos. Environ. 80, 398–407. Lary, D.J., Faruque, F.S., Malakar, N., Moore, A., Roscoe, B., Adams, Z.L., Eggelston, Y., 2014. Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospat. Health 8, S611–S630. Laskin, A., Laskin, J., Nizkorodov, S.A., 2015. Chemistry of atmospheric brown carbon. Chem. Rev. 115, 4335–4382. Lauraguais, A., Coeur-Tourneur, C., Cassez, A., Deboudt, K., Fourmentin, M., Choel, M., 2014. Atmospheric reactivity of hydroxyl radicals with guaiacol (2-methoxyphenol), a biomass burning emitted compound: secondary organic aerosol formation and gasphase oxidation products. Atmos. Environ. 86, 155–163. Lelieveld, J., Butler, T.M., Crowley, J.N., Dillon, T.J., Fischer, H., Ganzeveld, L., Harder, H., Lawrence, M.G., Martinez, M., Taraborrelli, D., Williams, J., 2008. Atmospheric oxidation capacity sustained by a tropical forest. Nature 452, 737–740. Li, C.S., Hsu, L.Y., Chuang, Y.Y.T., 1993. Elemental profiles of indoor and outdoor particulate matter less than 10 μm (PM10) and 2.5 μm (PM2.5) in Taipei. Chemosphere 27, 2143–2154. Li, J.F., Song, Y., Mao, Y., Mao, Z.C., Wu, Y.S., Li, M.M., Huang, X., He, Q.C., Hu, M., 2014a. Chemical characteristics and source apportionment of PM2.5 during the harvest season in eastern China's agricultural regions. Atmos. Environ. 92, 442–448. Li, J.J., Wang, G.H., Wang, X.M., Cao, J.J., Sun, T., Cheng, C.L., Meng, J.J., Hu, T.F., Liu, S.X., 2013a. Abundance, composition and source of atmospheric PM2.5 at a remote site in the Tibetan Plateau, China. Tellus B 65. Li, R.J., Kou, X.J., Geng, H., Dong, C., Cai, Z.W., 2014b. Pollution characteristics of ambient PM2.5-bound PAHs and NPAHs in a typical winter time period in Taiyuan. Chin. Chem. Lett. 25, 663–666. Li, X.R., Wang, Y.S., Guo, X.Q., Wang, Y.F., 2013b. Seasonal variation and source apportionment of organic and inorganic compounds in PM2.5 and PM10 particulates in Beijing, China. J. Environ. Sci. (China) 25, 741–750. Li, Y.J., Huang, D.D., Cheung, H.Y., Lee, A.K.Y., Chan, C.K., 2014c. Aqueous-phase photochemical oxidation and direct photolysis of vanillin — a model compound of methoxy phenols from biomass burning. Atmos. Chem. Phys. 14, 2871–2885. Li, Y.J., Lee, B.P., Su, L., Fung, J.C.H., Chan, C.K., 2015a. Seasonal characteristics of fine particulate matter (PM) based on high-resolution time-of-flight aerosol mass spectrometric (HR-ToF-AMS) measurements at the HKUST Supersite in Hong Kong. Atmos. Chem. Phys. 15, 37–53. Li, Y.S., Cao, J.J., Li, J.J., Zhou, J.M., Xu, H.M., Zhang, R.J., Ouyang, Z.Y., 2013c. Molecular distribution and seasonal variation of hydrocarbons in PM2.5 from Beijing during 2006. Particuology 11, 78–85. Li, Y.Y., Lin, T., Wang, F.W., Ji, T.Y., Guo, Z.G., 2015b. Seasonal variation of polybrominated diphenyl ethers in PM2.5 aerosols over the East China Sea. Chemosphere 119, 675–681. Liang, C.S., Yu, T.Y., Chang, Y.Y., Syu, J.Y., Lin, W.Y., 2013a. Source apportionment of PM2.5 particle composition and submicrometer size distribution during an Asian dust storm and Non-dust storm in Taipei. Aerosol Air Qual. Res. 13, 545–554. Liang, L., Engling, G., Cheng, Y., Duan, F., Du, Z., He, K., 2013b. Rapid detection and quantification of fungal spores in the urban atmosphere by flow cytometry. J. Aerosol Sci. 66, 179–186. Liang, L., Engling, G., He, K., Du, Z., Cheng, Y., Duan, F., 2013c. Evaluation of fungal spore characteristics in Beijing, China, based on molecular tracer measurements. Environ. Res. Lett. 8, 014005. Limu, Y.L.M.A.B.D., Lifu, D.L.N.T., Miti, A.B.L.Y., Wang, X.M., Ding, X., 2013. Autumn and wintertime polycyclic aromatic hydrocarbons in PM2.5 and PM2.5–10 from Urumqi, China. Aerosol Air Qual. Res. 13, 407–414. Lin, J.T., Pan, D., Davis, S.J., Zhang, Q., He, K.B., Wang, C., Streets, D.G., Wuebbles, D.J., Guan, D.B., 2014. China's international trade and air pollution in the United States. Proc. Natl. Acad. Sci. U. S. A. 111, 1736–1741. Liu, C., Hsu, P.C., Lee, H.W., Ye, M., Zheng, G.Y., Liu, N.A., Li, W.Y., Cui, Y., 2015a. Transparent air filter for high-efficiency PM2.5 capture. Nat. Commun. 6, 9. Liu, D., Li, J., Zhang, Y., Xu, Y., Liu, X., Ding, P., Shen, C., Chen, Y., Tian, C., Zhang, G., 2013a. The use of levoglucosan and radiocarbon for source apportionment of PM2.5 carbonaceous aerosols at a background site in East China. Environ. Sci. Technol. 47, 10454–10461. Liu, D., Li, J., Zhang, Y.L., Xu, Y., Liu, X., Ding, P., Shen, C.D., Chen, Y.J., Tian, C.G., Zhang, G., 2013b. The use of levoglucosan and radiocarbon for source apportionment of PM2.5 carbonaceous aerosols at a background site in East China. Environ. Sci. Technol. 47, 10454–10461. Liu, G., Li, J.H., Wu, D., Xu, H., 2015b. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology 18, 135–143.

C.-S. Liang et al. / Environment International 86 (2016) 150–170 Liu, J., Man, Y., Liu, Y., 2014a. Temporal variability of PM10 and PM2.5 inside and outside a residential home during 2014 Chinese Spring Festival in Zhengzhou, China. Nat. Hazards 73, 2149–2154. Liu, J.W., Li, J., Zhang, Y.L., Liu, D., Ding, P., Shen, C.D., Shen, K.J., He, Q.F., Ding, X., Wang, X.M., Chen, D.H., Szidat, S., Zhang, G., 2014b. Source apportionment using radiocarbon and organic tracers for PM2.5 carbonaceous aerosols in Guangzhou, South China: contrasting local- and regional-scale haze events. Environ. Sci. Technol. 48, 12002–12011. Liu, Z., Guan, D.B., Crawford-Brown, D., Zhang, Q., He, K.B., Liu, J.G., 2013c. A low-carbon road map for China. Nature 500, 143–145. Liu, Z.R., Hu, B., Wang, L.L., Wu, F.K., Gao, W.K., Wang, Y.S., 2015c. Seasonal and diurnal variation in particulate matter (PM10 and PM2.5) at an urban site of Beijing: analyses from a 9-year study. Environ. Sci. Pollut. Res. 22, 627–642. Lu, S.L., Liu, D.Y., Zhang, W.C., Liu, P.W., Fei, Y., Gu, Y., Wu, M.H., Yu, S., Yonemochi, S., Wang, X.J., Wang, Q.Y., 2015. Physico-chemical characterization of PM2.5 in the microenvironment of Shanghai subway. Atmos. Res. 153, 543–552. Ma, J., Chen, L.L., Guo, Y., Wu, Q., Yang, M., Wu, M.H., Kannan, K., 2014. Phthalate diesters in airborne PM2.5 and PM10 in a suburban area of Shanghai: seasonal distribution and risk assessment. Sci. Total Environ. 497, 467–474. Ma, J., Chen, Z.Y., Wu, M.H., Feng, J.L., Horii, Y., Ohura, T., Kannan, K., 2013. Airborne PM2.5/ PM10-associated chlorinated polycyclic aromatic hydrocarbons and their parent compounds in a suburban area in Shanghai, China. Environ. Sci. Technol. 47, 7615–7623. Mancilla, Y., Herckes, P., Fraser, M.P., Mendoza, A., 2015. Secondary organic aerosol contributions to PM2.5 in Monterrey, Mexico: temporal and seasonal variation. Atmos. Res. 153, 348–359. Manousakas, M., Papaefthymiou, H., Eleftheriadis, K., Katsanou, K., 2014. Determination of water-soluble and insoluble elements in PM2.5 by ICP-MS. Sci. Total Environ. 493, 694–700. Mantas, E., Remoundaki, E., Halari, I., Kassomenos, P., Theodosi, C., Hatzikioseyian, A., Mihalopoulos, N., 2014. Mass closure and source apportionment of PM2.5 by positive matrix factorization analysis in urban Mediterranean environment. Atmos. Environ. 94, 154–163. Martin, S.T., 2000. Phase transitions of aqueous atmospheric particles. Chem. Rev. 100, 3403–3454. Masiol, M., Squizzato, S., Rampazzo, G., Pavoni, B., 2014. Source apportionment of PM2.5 at multiple sites in Venice (Italy): spatial variability and the role of weather. Atmos. Environ. 98, 78–88. Matawle, J., Pervez, S., Dewangan, S., Tiwari, S., Bisht, D.S., Pervez, Y.F., 2014. PM2.5 chemical source profiles of emissions resulting from industrial and domestic burning activities in India. Aerosol Air Qual. Res. 14, 2051–2066. Mauldin Iii, R.L., Berndt, T., Sipila, M., Paasonen, P., Petaja, T., Kim, S., Kurten, T., Stratmann, F., Kerminen, V.M., Kulmala, M., 2012. A new atmospherically relevant oxidant of sulphur dioxide. Nature 488, 193–196. May, A.A., Lee, T., McMeeking, G.R., Akagi, S., Sullivan, A.P., Urbanski, S., Yokelson, R.J., Kreidenweis, S.M., 2015. Observations and analysis of organic aerosol evolution in some prescribed fire smoke plumes. Atmos. Chem. Phys. 15, 6323–6335. McGuire, M.L., Chang, R.Y.W., Slowik, J.G., Jeong, C.H., Healy, R.M., Lu, G., Mihele, C., Abbatt, J.P.D., Brook, J.R., Evans, G.J., 2014. Enhancing non-refractory aerosol apportionment from an urban industrial site through receptor modeling of complete high timeresolution aerosol mass spectra. Atmos. Chem. Phys. 14, 8017–8042. Mellouki, A., Wallington, T.J., Chen, J., 2015. Atmospheric chemistry of oxygenated volatile organic compounds: impacts on air quality and climate. Chem. Rev. 115, 3984–4014. MEPPRC, 2015. National Urban Ambient Air Quality Daily Report (Ministry of Environmental Protection of the People's Republic of China). (http://datacenter.mep.gov. cn/; http://www.gov.cn/xinwen/2015-01/16/content_2805618.htm). Miller, C.A., 2011. Source Emissions in Multipollutant Air Quality Management. In: Hidy, G.M., Brook, J.R., Demerjian, K.L., Molina, L.T., Pennell, W.T., Scheffe, R.D. (Eds.), Technical Challenges of Multipollutant Air Quality Management. Springer, Netherlands, pp. 261–298. Mkoma, S.L., da Rocha, G.O., Regis, A.C.D., Domingos, J.S.S., Santos, J.V.S., de Andrade, S.J., Carvalho, L.S., de Andrade, J.B., 2014. Major ions in PM2.5 and PM10 released from buses: the use of diesel/biodiesel fuels under real conditions. Fuel 115, 109–117. Molders, N., 2013. Investigations on the impact of single direct and indirect, and multiple emission-control measures on cold-season near-surface PM2.5 concentrations in Fairbanks, Alaska. Atmos. Pollut. Res. 4, 87–100. Molnar, P., Johannesson, S., Quass, U., 2014. Source apportionment of PM2.5 using positive matrix factorization (PMF) and PMF with factor selection. Aerosol Air Qual. Res. 14, 725–733. Molnar, P., Sallsten, G., 2013. Contribution to PM2.5 from domestic wood burning in a small community in Sweden. Environ. Sci.: Processes Impacts 15, 833–838. Monn, C., Becker, S., 1999. Cytotoxicity and induction of proinflammatory cytokines from human monocytes exposed to fine (PM2.5) and coarse particles (PM10–2.5) in outdoor and indoor air. Toxicol. Appl. Pharmacol. 155, 245–252. Moreda-Pineiro, J., Turnes-Carou, I., Alonso-Rodriguez, E., Moscoso-Perez, C., BlancoHeras, G., Lopez-Mahia, P., Muniategui-Lorenzo, S., Prada-Rodriguez, D., 2015. The influence of oceanic air masses on concentration of major ions and trace metals in PM2.5 fraction at a coastal European suburban site. Water Air Soil Pollut. 226, 15. Morton-Bermea, O., Amador-Munoz, O., Martinez-Trejo, L., Hernandez-Alvarez, E., Beramendi-Orosco, L., Garcia-Arreola, M.E., 2014. Platinum in PM2.5 of the metropolitan area of Mexico City. Environ. Geochem. Health 36, 987–994. Motorykin, O., Schrlau, J., Jia, Y.L., Harper, B., Harris, S., Harding, A., Stone, D., Kile, M., Sudakin, D., Simonich, S.L.M., 2015. Determination of parent and hydroxy PAHs in personal PM2.5 and urine samples collected during Native American fish smoking activities. Sci. Total Environ. 505, 694–703. Mueller, L., Jakobi, G., Czech, H., Stengel, B., Orasche, J., Arteaga-Salas, J.M., Karg, E., Elsasser, M., Sippula, O., Streibel, T., Slowik, J.G., Prevot, A.S.H., Jokiniemi, J., Rabe, R.,

167

Harndorf, H., Michalke, B., Schnelle-Kreis, J., Zimmermann, R., 2015. Characteristics and temporal evolution of particulate emissions from a ship diesel engine. Appl. Energy 155, 204–217. Murillo, J.H., Marin, J.F.R., Roman, S.R., Guerrero, V.H.B., Arias, D.S., Ramos, A.C., Gonzalez, B.C., Baumgardner, D.G., 2013a. Temporal and spatial variations in organic and elemental carbon concentrations in PM10/PM2.5 in the metropolitan area of Costa Rica, Central America. Atmos. Pollut. Res. 4, 53–63. Murillo, J.H., Roman, S.R., Marin, J.F.R., Ramos, A.C., Jimenez, S.B., Gonzalez, B.C., Baumgardner, D.G., 2013b. Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America. Atmos. Pollut. Res. 4, 181–190. Mwaniki, G.R., Rosenkrance, C., Wallace, H.W., Jobson, B.T., Erickson, M.H., Lamb, B.K., Hardy, R.J., Zalakeviciute, R., VanReken, T.M., 2014. Factors contributing to elevated concentrations of PM2.5 during wintertime near Boise, Idaho. Atmos. Pollut. Res. 5, 96–103. Nallathamby, P.D., Lewandowski, M., Jaoui, M., Offenberg, J.H., Kleindienst, T.E., Rubitschun, C., Surratt, J.D., Usenko, S., Sheesley, R.J., 2014. Qualitative and quantitative assessment of unresolved complex mixture in PM2.5 of Bakersfield, CA. Atmos. Environ. 98, 368–375. Nguyen, T., Yu, X.X., Zhang, Z.M., Liu, M.M., Liu, X.H., 2015. Relationship between types of urban forest and PM2.5 capture at three growth stages of leaves. J. Environ. Sci. (China) 27, 33–41. Niu, Z.C., Wang, S., Chen, J.S., Zhang, F.W., Chen, X.Q., He, C., Lin, L.F., Yin, L.Q., Xu, L.L., 2013a. Source contributions to carbonaceous species in PM2.5 and their uncertainty analysis at typical urban, peri-urban and background sites in southeast China. Environ. Pollut. 181, 107–114. Niu, Z.C., Zhang, F.W., Chen, J.S., Yin, L.Q., Wang, S., Xu, L.L., 2013b. Carbonaceous species in PM2.5 in the coastal urban agglomeration in the Western Taiwan Strait Region, China. Atmos. Res. 122, 102–110. Nowak, D.J., Hirabayashi, S., Bodine, A., Hoehn, R., 2013. Modeled PM2.5 removal by trees in ten US cities and associated health effects. Environ. Pollut. 178, 395–402. Nozière, B., Kalberer, M., Claeys, M., Allan, J., D'Anna, B., Decesari, S., Finessi, E., Glasius, M., Grgić, I., Hamilton, J.F., Hoffmann, T., Iinuma, Y., Jaoui, M., Kahnt, A., Kampf, C.J., Kourtchev, I., Maenhaut, W., Marsden, N., Saarikoski, S., Schnelle-Kreis, J., Surratt, J.D., Szidat, S., Szmigielski, R., Wisthaler, A., 2015. The molecular identification of organic compounds in the atmosphere: state of the art and challenges. Chem. Rev. 115, 3919–3983. Oanh, N.T.K., Kongpran, J., Hang, N.T., Parkpian, P., Hung, N.T.Q., Lee, S.B., Bae, G.N., 2013. Characterization of gaseous pollutants and PM2.5 at fixed roadsides and along vehicle traveling routes in Bangkok Metropolitan Region. Atmos. Environ. 77, 674–685. OECD, 2014. The cost of air pollution. http://www.oecd.org/regional/the-cost-of-airpollution-9789264210448-en.htm (May 21). Okoshi, R., Rasheed, A., Reddy, G.C., McCrowey, C.J., Curtis, D.B., 2014. Size and mass distributions of ground-level sub-micrometer biomass burning aerosol from small wildfires. Atmos. Environ. 89, 392–402. Oliveira, R.L., Loyola, J., Minho, A.S., Quiterio, S.L., Azevedo, D.D., Arbilla, G., 2014. PM2.5bound polycyclic aromatic hydrocarbons in an area of Rio de Janeiro, Brazil impacted by emissions of light-duty vehicles fueled by ethanol-blended gasoline. Bull. Environ. Contam. Toxicol. 93, 781–786. Onat, B., Sahin, U.A., Akyuz, T., 2013. Elemental characterization of PM2.5 and PM1 in dense traffic area in Istanbul, Turkey. Atmos. Pollut. Res. 4, 101–105. Onat, B., Stakeeva, B., 2013. Personal exposure of commuters in public transport to PM2.5 and fine particle counts. Atmos. Pollut. Res. 4, 329–335. Ortega, A.M., Day, D.A., Cubison, M.J., Brune, W.H., Bon, D., de Gouw, J.A., Jimenez, J.L., 2013. Secondary organic aerosol formation and primary organic aerosol oxidation from biomass-burning smoke in a flow reactor during FLAME-3. Atmos. Chem. Phys. 13, 11551–11571. Pöhlker, C., Wiedemann, K.T., Sinha, B., Shiraiwa, M., Gunthe, S.S., Smith, M., Su, H., Artaxo, P., Chen, Q., Cheng, Y., Elbert, W., Gilles, M.K., Kilcoyne, A.L.D., Moffet, R.C., Weigand, M., Martin, S.T., Pöschl, U., Andreae, M.O., 2012. Biogenic potassium salt particles as seeds for secondary organic aerosol in the Amazon. Science 337, 1075–1078. Paatero, P., Tapper, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126. Pachauri, T., Satsangi, A., Singla, V., Lakhani, A., Kumari, K.M., 2013. Characteristics and sources of carbonaceous aerosols in PM2.5 during wintertime in Agra, India. Aerosol Air Qual. Res. 13, 977–991. Pachon, J.E., Weber, R.J., Zhang, X.L., Mulholland, J.A., Russell, A.G., 2013. Revising the use of potassium (K) in the source apportionment of PM2.5. Atmos. Pollut. Res. 4, 14–21. Pancras, J.P., Landis, M.S., Norris, G.A., Vedantham, R., Dvonch, J.T., 2013. Source apportionment of ambient fine particulate matter in Dearborn, Michigan, using hourly resolved PM chemical composition data. Sci. Total Environ. 448, 2–13. Pant, P., Shukla, A., Kohl, S.D., Chow, J.C., Watson, J.G., Harrison, R.M., 2015. Characterization of ambient PM2.5 at a pollution hotspot in New Delhi, India and inference of sources. Atmos. Environ. 109, 178–189. Park, S.S., Jung, S.A., Gong, B.J., Cho, S.Y., Lee, S.J., 2013. Characteristics of PM2.5 haze episodes revealed by highly time-resolved measurements at an air pollution monitoring supersite in Korea. Aerosol Air Qual. Res. 13, 957–976. Pateraki, S., Asimakopoulos, D.N., Bougiatioti, A., Maggos, T., Vasilakos, C., Mihalopoulos, N., 2014. Assessment of PM2.5 and PM1 chemical profile in a multiple-impacted Mediterranean urban area: origin, sources and meteorological dependence. Sci. Total Environ. 479, 210–220. Paulino, S.D., Oliveira, R., Loyola, J., Minho, A.S., Arbilla, G., Quiterio, S.L., Escaleira, V., 2014. Trace metals in PM10 and PM2.5 samples collected in a highly industrialized chemical/ petrochemical area and its urbanized surroundings. Bull. Environ. Contam. Toxicol. 92, 590–595.

168


Pavuluri, C.M., Kawamura, K., Fu, P.Q., 2015. Atmospheric chemistry of nitrogenous aerosols in northeastern Asia: biological sources and secondary formation. Atmos. Chem. Phys. 15, 9883–9896. Peng, J.L., Li, M., Zhang, P., Gong, S.Y., Zhong, M.A., Wu, M.H., Zheng, M., Chen, C.H., Wang, H.L., Lou, S.R., 2013. Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers. Atmos. Environ. 79, 614–622. Perrino, C., Catrambone, M., Dalla Torre, S., Rantica, E., Sargolini, T., Canepari, S., 2014. Seasonal variations in the chemical composition of particulate matter: a case study in the Po Valley. Part I: macro-components and mass closure. Environ. Sci. Pollut. Res. 21, 3999–4009. Perrone, M.R., Becagli, S., Orza, J.A.G., Vecchi, R., Dinoi, A., Udisti, R., Cabello, M., 2013. The impact of long-range-transport on PM1 and PM2.5 at a central mediterranean site. Atmos. Environ. 71, 176–186. Petit, J.E., Favez, O., Sciare, J., Canonaco, F., Croteau, P., Mocnik, G., Jayne, J., Worsnop, D., Leoz-Garziandia, E., 2014. Submicron aerosol source apportionment of wintertime pollution in Paris, France by double positive matrix factorization (PMF2) using an aerosol chemical speciation monitor (ACSM) and a multi-wavelength aethalometer. Atmos. Chem. Phys. 14, 13773–13787. Petit, J.E., Favez, O., Sciare, J., Crenn, V., Sarda-Esteve, R., Bonnaire, N., Mocnik, G., Dupont, J.C., Haeffelin, M., Leoz-Garziandia, E., 2015. Two years of near real-time chemical composition of submicron aerosols in the region of Paris using an Aerosol Chemical Speciation Monitor (ACSM) and a multi-wavelength aethalometer. Atmos. Chem. Phys. 15, 2985–3005. Pey, J., Alastuey, A., Querol, X., 2013. PM10 and PM2.5 sources at an insular location in the western Mediterranean by using source apportionment techniques. Sci. Total Environ. 456, 267–277. Pietrogrande, M.C., Bacco, D., Visentin, M., Ferrari, S., Poluzzi, V., 2014. Polar organic marker compounds in atmospheric aerosol in the Po Valley during the Supersito campaigns — part 1: low molecular weight carboxylic acids in cold seasons. Atmos. Environ. 86, 164–175. Piletic, I.R., Offenberg, J.H., Olson, D.A., Jaoui, M., Krug, J., Lewandowski, M., Turlington, J.M., Kleindienst, T.E., 2013. Constraining carbonaceous aerosol sources in a receptor model by including 14C data with redox species, organic tracers, and elemental/organic carbon measurements. Atmos. Environ. 80, 216–225. Pipal, A.S., Satsangi, P.G., 2015. Study of carbonaceous species, morphology and sources of fine (PM2.5) and coarse (PM10) particles along with their climatic nature in India. Atmos. Res. 154, 103–115. Pipalatkar, P., Khaparde, V.V., Gajghate, D.G., Bawase, M.A., 2014. Source apportionment of PM2.5 using a CMB model for a centrally located Indian City. Aerosol Air Qual. Res. 14, 1089–1099. Pongpiachan, S., Choochuay, C., Chonchalar, J., Kanchai, P., Phonpiboon, T., Wongsuesat, S., Chomkhae, K., Kittikoon, I., Hiranyatrakul, P., Cao, J., Thamrongthanyawong, S., 2013. Chemical characterisation of organic functional group compositions in PM2.5 collected at nine administrative provinces in Northern Thailand during the haze episode in 2013. Asian Pac. J. Cancer Prev. 14, 3653–3661. Pongpiachan, S., Tipmanee, D., Khumsup, C., Kittikoon, I., Hirunyatrakul, P., 2015. Assessing risks to adults and preschool children posed by PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) during a biomass burning episode in Northern Thailand. Sci. Total Environ. 508, 435–444. Qadir, R.M., Schnelle-Kreis, J., Abbaszade, G., Arteaga-Salas, J.M., Diemer, J., Zimmermann, R., 2014. Spatial and temporal variability of source contributions to ambient PM10 during winter in Augsburg, Germany using organic and inorganic tracers. Chemosphere 103, 263–273. Raisi, L., Aleksandropoulou, V., Lazaridis, M., Katsivela, E., 2013. Size distribution of viable, cultivable, airborne microbes and their relationship to particulate matter concentrations and meteorological conditions in a Mediterranean site. Aerobiologia 29, 233–248. Rastogi, N., Singh, A., Singh, D., Sarin, M.M., 2014. Chemical characteristics of PM2.5 at a source region of biomass burning emissions: evidence for secondary aerosol formation. Environ. Pollut. 184, 563–569. Reff, A., Bhave, P.V., Simon, H., Pace, T.G., Pouliot, G.A., Mobley, J.D., Houyoux, M., 2009. Emissions inventory of PM2.5 trace elements across the United States. Environ. Sci. Technol. 43, 5790–5796. Remoundaki, E., Kassomenos, P., Mantas, E., Mihalopoulos, N., Tsezos, M., 2013a. Composition and mass closure of PM2.5 in urban environment (Athens, Greece). Aerosol Air Qual. Res. 13, 72–82. Remoundaki, E., Papayannis, A., Kassomenos, P., Mantas, E., Kokkalis, P., Tsezos, M., 2013b. Influence of Saharan dust transport events on PM2.5 concentrations and composition over Athens. Water Air Soil Pollut. 224. Revuelta, M.A., McIntosh, G., Pey, J., Perez, N., Querol, X., Alastuey, A., 2014. Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain). Environ. Pollut. 188, 109–117. Riccobono, F., Schobesberger, S., Scott, C.E., Dommen, J., Ortega, I.K., Rondo, L., Almeida, J., Amorim, A., Bianchi, F., Breitenlechner, M., David, A., Downard, A., Dunne, E.M., Duplissy, J., Ehrhart, S., Flagan, R.C., Franchin, A., Hansel, A., Junninen, H., Kajos, M., Keskinen, H., Kupc, A., Kürten, A., Kvashin, A.N., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S., Nieminen, T., Onnela, A., Petäjä, T., Praplan, A.P., Santos, F.D., Schallhart, S., Seinfeld, J.H., Sipilä, M., Spracklen, D.V., Stozhkov, Y., Stratmann, F., Tomé, A., Tsagkogeorgas, G., Vaattovaara, P., Viisanen, Y., Vrtala, A., Wagner, P.E., Weingartner, E., Wex, H., Wimmer, D., Carslaw, K.S., Curtius, J., Donahue, N.M., Kirkby, J., Kulmala, M., Worsnop, D.R., Baltensperger, U., 2014. Oxidation products of biogenic emissions contribute to nucleation of atmospheric particles. Science 344, 717–721. Rogula-Kozlowska, W., Blaszczak, B., Szopa, S., Klejnowski, K., Sowka, I., Zwozdziak, A., Jablonska, M., Mathews, B., 2013. PM2.5 in the central part of Upper Silesia, Poland: concentrations, elemental composition, and mobility of components. Environ. Monit. Assess. 185, 581–601.

Rogula-Kozlowska, W., Klejnowski, K., Rogula-Kopiec, P., Osrodka, L., Krajny, E., Blaszczak, B., Mathews, B., 2014. Spatial and seasonal variability of the mass concentration and chemical composition of PM2.5 in Poland. Air Qual. Atmos. Health 7, 41–58. Rosenfeld, D., Sherwood, S., Wood, R., Donner, L., 2014. Climate effects of aerosol–cloud interactions. Science 343, 379–380. Ruddiman, W.F., Ellis, E.C., Kaplan, J.O., Fuller, D.Q., 2015. Defining the epoch we live in. Science 348, 38–39. Salameh, D., Detournay, A., Pey, J., Perez, N., Liguori, F., Saraga, D., Bove, M.C., Brotto, P., Cassola, F., Massabo, D., Latella, A., Pillon, S., Formenton, G., Patti, S., Armengaud, A., Piga, D., Jaffrezo, J.L., Bartzis, J., Tolis, E., Prati, P., Querol, X., Wortham, H., Marchand, N., 2015. PM2.5 chemical composition in five European Mediterranean cities: a 1-year study. Atmos. Res. 155, 102–117. Samara, C., Voutsa, D., Kouras, A., Eleftheriadis, K., Maggos, T., Saraga, D., Petrakakis, M., 2014. Organic and elemental carbon associated to PM10 and PM2.5 at urban sites of northern Greece. Environ. Sci. Pollut. Res. 21, 1769–1785. Samy, S., Hays, M.D., 2013. Quantitative LC–MS for water-soluble heterocyclic amines in fine aerosols (PM2.5) at Duke Forest, USA. Atmos. Environ. 72, 77–80. Sandeep, P., Saradhi, I.V., Pandit, G.G., 2013. Seasonal variation of black carbon in fine particulate matter (PM2.5) at the tropical coastal city of Mumbai, India. Bull. Environ. Contam. Toxicol. 91, 605–610. Satsangi, P.G., Yadav, S., 2014. Characterization of PM2.5 by X-ray diffraction and scanning electron microscopy-energy dispersive spectrometer: its relation with different pollution sources. Int. J. Environ. Sci. Technol. 11, 217–232. Savee, J.D., Papajak, E., Rotavera, B., Huang, H.F., Eskola, A.J., Welz, O., Sheps, L., Taatjes, C.A., Zador, J., Osborn, D.L., 2015. Direct observation and kinetics of a hydroperoxyalkyl radical (QOOH). Science 347, 643–646. Saylor, R., Myles, L., Sibble, D., Caldwell, J., Xing, J., 2015. Recent trends in gas-phase ammonia and PM2.5 ammonium in the Southeast United States. J. Air Waste Manage. Assoc. 65, 347–357. Shaltout, A.A., Boman, J., Al-Malawi, D.A.R., Shehadeh, Z.F., 2013. Elemental composition of PM2.5 particles sampled in industrial and residential areas of Taif, Saudi Arabia. Aerosol Air Qual. Res. 13, 1356–1364. Shaltout, A.A., Boman, J., Shehadeh, Z.F., Al-Malawi, D.A.R., Hemeda, O.M., Morsy, M.M., 2015. Spectroscopic investigation of PM2.5 collected at industrial, residential and traffic sites in Taif, Saudi Arabia. J. Aerosol Sci. 79, 97–108. Shaltout, A.A., Boman, J., Welz, B., Castilho, I.N.B., Al Ashkar, E.A., Gaita, S.M., 2014. Method development for the determination of Cd, Cu, Ni and Pb in PM2.5 particles sampled in industrial and urban areas of Greater Cairo, Egypt, using high-resolution continuum source graphite furnace atomic absorption spectrometry. Microchem. J. 113, 4–9. Shareefdeen, Z., Herner, B., Singh, A., 2005. Biotechnology for air pollution control — an overview. In: Shareefdeen, Z., Singh, A. (Eds.), Biotechnology for Odor and Air Pollution Control. Springer, Berlin Heidelberg, pp. 3–15. Sharma, S.K., Mandal, T.K., Sharma, C., Kuniyal, J.C., Joshi, R., Dhyani, P.P., Rohtash, Sen, A., Ghayas, H., Gupta, N.C., Sharma, P., Saxena, M., Sharma, A., Arya, B.C., Kumar, A., 2014. Measurements of Particulate (PM2.5), BC and trace gases over the Northwestern Himalayan Region of India. Mapan-J. Metrol. Soc. I 29, 243–253. Shen, J., Chen, R.X., He, J.H., 2014a. BIO-mimic design of PM2.5 anti-smog masks. Therm. Sci. 18, 1689–1690. Shen, X., Yao, Z., Zhang, Q., Wagner, D.V., Huo, H., Zhang, Y., Zheng, B., He, K., 2015. Development of database of real-world diesel vehicle emission factors for China. J. Environ. Sci. Shen, X.B., Yao, Z.L., Huo, H., He, K.B., Zhang, Y.Z., Liu, H., Ye, Y., 2014b. PM2.5 emissions from light-duty gasoline vehicles in Beijing, China. Sci. Total Environ. 487, 521–527. Shrivastava, M., Easter, R.C., Liu, X.H., Zelenyuk, A., Singh, B., Zhang, K., Ma, P.L., Chand, D., Ghan, S., Jimenez, J.L., Zhang, Q., Fast, J., Rasch, P.J., Tiitta, P., 2015. Global transformation and fate of SOA: implications of low-volatility SOA and gas-phase fragmentation reactions. J. Geophys. Res.-Atmos. 120, 4169–4195. Si, J.P., Liu, X.W., Xu, M.H., Sheng, L., Zhou, Z.J., Wang, C., Zhang, Y., Seo, Y.C., 2014. Effect of kaolin additive on PM2.5 reduction during pulverized coal combustion: importance of sodium and its occurrence in coal. Appl. Energy 114, 434–444. Singh, K., Tiwari, S., Jha, A.K., Aggarwal, S.G., Bisht, D.S., Murty, B.P., Khan, Z.H., Gupta, P.K., 2013. Mass-size distribution of PM10 and its characterization of ionic species in fine (PM2.5) and coarse (PM10-2.5) mode, New Delhi, India. Nat. Hazards 68, 775–789. Sinha, V., Kumar, V., Sarkar, C., 2014. Chemical composition of pre-monsoon air in the Indo-Gangetic plain measured using a new air quality facility and PTR-MS: high surface ozone and strong influence of biomass burning. Atmos. Chem. Phys. 14, 5921–5941. Slikboer, S., Grandy, L., Blair, S.L., Nizkorodov, S.A., Smith, R.W., Al-Abadleh, H.A., 2015. Formation of light absorbing soluble secondary organics and insoluble polymeric particles from the dark reaction of catechol and guaiacol with Fe(III). Environ. Sci. Technol. 49, 7793–7801. Smith, J.D., Sio, V., Yu, L., Zhang, Q., Anastasio, C., 2014. Secondary organic aerosol production from aqueous reactions of atmospheric phenols with an organic triplet excited state. Environ. Sci. Technol. 48, 1049–1057. Sofowote, U.M., Rastogi, A.K., Debosz, J., Hopke, P.K., 2014. Advanced receptor modeling of near-real-time, ambient PM2.5 and its associated components collected at an urbanindustrial site in Toronto, Ontario. Atmos. Pollut. Res. 5, 13–23. Song, C., Pei, T., Yao, L., 2015. Analysis of the characteristics and evolution modes of PM2.5 pollution episodes in Beijing, China during 2013. Int. J. Environ. Res. Public Health 12, 1099–1111. Song, S.J., Wu, Y., Zheng, X., Wang, Z.S., Yang, L., Li, J.Q., Hao, J.M., 2014. Chemical characterization of roadside PM2.5 and black carbon in Macao during a summer campaign. Atmos. Pollut. Res. 5, 381–387. Souza, D.Z., Vasconcellos, P.C., Lee, H., Aurela, M., Saarnio, K., Teinila, K., Hillamo, R., 2014. Composition of PM2.5 and PM10 collected at urban sites in Brazil. Aerosol Air Qual. Res. 14, 168–176.

C.-S. Liang et al. / Environment International 86 (2016) 150–170 Spilak, M.P., Karottki, G.D., Kolarik, B., Frederiksen, M., Loft, S., Gunnarsen, L., 2014. Evaluation of building characteristics in 27 dwellings in Denmark and the effect of using particle filtration units on PM2.5 concentrations. Build. Environ. 73, 55–63. Squizzato, S., Masiol, M., Visin, F., Canal, A., Rampazzo, G., Pavoni, B., 2014. The PM2.5 chemical composition in an industrial zone included in a large urban settlement: main sources and local background. Environ. Sci.: Processes Impacts 16, 1913–1922. Stelcer, E., Cohen, D.D., Atanacio, A.J., 2014. Long term PM2.5 trends in the Australian industrial city of Newcastle: a 15-year study from 1998 to 2013. Environ. Chem. 11, 644–652. Stevanovic, S., Miljevic, B., Surawski, N.C., Fairfull-Smith, K.E., Bottle, S.E., Brown, R., Ristovski, Z.D., 2013. Influence of oxygenated organic aerosols (OOAs) on the oxidative potential of diesel and biodiesel particulate matter. Environ. Sci. Technol. 47, 7655–7662. Stockwell, C.E., Veres, P.R., Williams, J., Yokelson, R.J., 2015. Characterization of biomass burning emissions from cooking fires, peat, crop residue, and other fuels with highresolution proton-transfer-reaction time-of-flight mass spectrometry. Atmos. Chem. Phys. 15, 845–865. Stone, R., 2002. Air pollution — counting the cost of London's killer smog. Science 298, 2106–2107. Su, H., Cheng, Y., Oswald, R., Behrendt, T., Trebs, I., Meixner, F.X., Andreae, M.O., Cheng, P., Zhang, Y., Pöschl, U., 2011. Soil nitrite as a source of atmospheric HONO and OH radicals. Science 333, 1616–1618. Subramoney, P., Karnae, S., Farooqui, Z., John, K., Gupta, A.K., 2013. Identification of PM2.5 sources affecting a semi-arid coastal region using a chemical mass balance model. Aerosol Air Qual. Res. 13, 60–71. Suess, D.T., Prather, K.A., 1999. Mass spectrometry of aerosols. Chem. Rev. 99, 3007–3036. Sun, F.B., Yin, Z., Lun, X.X., Zhao, Y., Li, R.N., Shi, F.T., Yu, X.X., 2014. Deposition velocity of PM2.5 in the winter and spring above deciduous and coniferous forests in Beijing, China. PLoS One 9, 28. Sun, Y., Song, T., Tang, G.Q., Wang, Y.S., 2013. The vertical distribution of PM2.5 and boundary-layer structure during summer haze in Beijing. Atmos. Environ. 74, 413–421. Szidat, S., Bench, G., Bernardoni, V., Calzolai, G., Czimczik, C.I., Derendorp, L., Dusek, U., Elder, K., Fedi, M.E., Genberg, J., Gustafsson, O., Kirillova, E., Kondo, M., McNichol, A.P., Perron, N., Santos, G.M., Stenstrom, K., Swietlicki, E., Uchida, M., Vecchi, R., Wacker, L., Zhang, Y.L., Prevot, A.S.H., 2013. Intercomparison of 14C analysis of carbonaceous aerosols: exercise 2009. Radiocarbon 55, 1496–1509. Szigeti, T., Mihucz, V.G., Ovari, M., Baysal, A., Atilgan, S., Akman, S., Zaray, G., 2013. Chemical characterization of PM2.5 fractions of urban aerosol collected in Budapest and Istanbul. Microchem. J. 107, 86–94. Takahashi, K., Nansai, K., Tohno, S., Nishizawa, M., Kurokawa, J., Ohara, T., 2014. Production-based emissions, consumption-based emissions and consumption-based health impacts of PM2.5 carbonaceous aerosols in Asia. Atmos. Environ. 97, 406–415. Tao, J., Cheng, T.T., Zhang, R.J., Cao, J.J., Zhu, L.H., Wang, Q.Y., Luo, L., Zhang, L.M., 2013a. Chemical composition of PM2.5 at an urban site of Chengdu in southwestern China. Adv. Atmos. Sci. 30, 1070–1084. Tao, J., Gao, J., Zhang, L., Zhang, R., Che, H., Zhang, Z., Lin, Z., Jing, J., Cao, J., Hsu, S.C., 2014a. PM2.5 pollution in a megacity of southwest China: source apportionment and implication. Atmos. Chem. Phys. 14, 8679–8699. Tao, J., Zhang, L.M., Engling, G., Zhang, R.J., Yang, Y.H., Cao, J.J., Zhu, C.S., Wang, Q.Y., Luo, L., 2013b. Chemical composition of PM2.5 in an urban environment in Chengdu, China: importance of springtime dust storms and biomass burning. Atmos. Res. 122, 270–283. Tao, J., Zhang, L.M., Ho, K.F., Zhang, R.J., Lin, Z.J., Zhang, Z.S., Lin, M., Cao, J.J., Liu, S.X., Wang, G.H., 2014b. Impact of PM2.5 chemical compositions on aerosol light scattering in Guangzhou — the largest megacity in South China. Atmos. Res. 135, 48–58. Tao, J., Zhang, L.M., Zhang, Z.S., Huang, R.J., Wu, Y.F., Zhang, R.J., Cao, J.J., Zhang, Y.H., 2015. Control of PM2.5 in Guangzhou during the 16th Asian Games period: implication for hazy weather prevention. Sci. Total Environ. 508, 57–66. Taylor, J., Mavrogianni, A., Davies, M., Das, P., Shrubsole, C., Biddulph, P., Oikonomou, E., 2015. Understanding and mitigating overheating and indoor PM2.5 risks using coupled temperature and indoor air quality models. Build Serv. Eng. Res. Technol. 36, 275–289. Taylor, M.R., 2012. Innovation under cap-and-trade programs. Proc. Natl. Acad. Sci. 109, 4804–4809. Tian, Y.Z., Shi, G.L., Han, B., Wu, J.H., Zhou, X.Y., Zhou, L.D., Zhang, P., Feng, Y.C., 2015. Using an improved source directional apportionment method to quantify the PM2.5 source contributions from various directions in a megacity in China. Chemosphere 119, 750–756. Timko, M.T., Albo, S.E., Onasch, T.B., Fortner, E.C., Yu, Z.H., Miake-Lye, R.C., Canagaratna, M.R., Ng, N.L., Worsnop, D.R., 2014. Composition and sources of the organic particle emissions from aircraft engines. Aerosol Sci. Technol. 48, 61–73. Tiwari, S., Srivastava, A.K., Bisht, D.S., Parmita, P., Srivastava, M.K., Attri, S.D., 2013. Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: influence of meteorology. Atmos. Res. 125, 50–62. Tsai, Y.I., Sopajaree, K., Chotruksa, A., Wu, H.C., Kuo, S.C., 2013. Source indicators of biomass burning associated with inorganic salts and carboxylates in dry season ambient aerosol in Chiang Mai Basin, Thailand. Atmos. Environ. 78, 93–104. Tsigaridis, K., Krol, M., Dentener, F.J., Balkanski, Y., Lathière, J., Metzger, S., Hauglustaine, D.A., Kanakidou, M., 2006. Change in global aerosol composition since preindustrial times. Atmos. Chem. Phys. 6, 5143–5162. Tsimpidi, A.P., Karydis, V.A., Pozzer, A., Pandis, S.N., Lelieveld, J., 2014. ORACLE (v1.0): module to simulate the organic aerosol composition and evolution in the atmosphere. Geosci. Model Dev. 7, 3153–3172. Turpin, B.J., Lim, H.J., 2001. Species contributions to PM2.5 mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 35, 602–610.

169

Uno, I., Sugimoto, N., Shimizu, A., Yumimoto, K., Hara, Y., Wang, Z.F., 2014. Record heavy PM2.5 air pollution over China in January 2013: vertical and horizontal dimensions. Sola 10, 136–140. USEPA, 2012. The national ambient air quality standards for particle pollution. http:// www.epa.gov/airquality/particlepollution/2012/decfsstandards.pdf (Dec. 14). USEPA, 2014. PM2.5 objectives and history. http://www.epa.gov/region4/sesd/pm25/p2. html (August). Vedantham, R., Landis, M.S., Olson, D., Pancras, J.P., 2014. Source identification of PM2.5 in Steubenville, Ohio using a hybrid method for highly time-resolved data. Environ. Sci. Technol. 48, 1718–1726. Verma, V., Fang, T., Guo, H., King, L., Bates, J.T., Peltier, R.E., Edgerton, E., Russell, A.G., Weber, R.J., 2014. Reactive oxygen species associated with water-soluble PM2.5 in the southeastern United States: spatiotemporal trends and source apportionment. Atmos. Chem. Phys. 14, 12915–12930. Verma, V., Fang, T., Xu, L., Peltier, R.E., Russell, A.G., Ng, N.L., Weber, R.J., 2015. Organic aerosols associated with the generation of reactive oxygen species (ROS) by watersoluble PM2.5. Environ. Sci. Technol. 49, 4646–4656. Vilcassim, M.J.R., Thurston, G.D., Peltier, R.E., Gordon, T., 2014. Black carbon and Particulate Matter (PM2.5) concentrations in New York city's subway stations. Environ. Sci. Technol. 48, 14738–14745. Villar-Vidal, M., Lertxundi, A., de Dicastillo, M.D.M.L., Alvarez, J.I., Marina, L.S., Ayerdi, M., Basterrechea, M., Ibarluzea, J., 2014. Air Polycyclic Aromatic Hydrocarbons (PAHs) associated with PM2.5 in a North Cantabric coast urban environment. Chemosphere 99, 233–238. Voutsa, D., Samara, C., Manoli, E., Lazarou, D., Tzoumaka, P., 2014. Ionic composition of PM2.5 at urban sites of northern Greece: secondary inorganic aerosol formation. Environ. Sci. Pollut. Res. 21, 4995–5006. Wang, D.X., Hu, J.L., Xu, Y., Lv, D., Xie, X.Y., Kleeman, M., Xing, J., Zhang, H.L., Ying, Q., 2014a. Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi'an, China. Atmos. Environ. 97, 182–194. Wang, F.W., Lin, T., Feng, J.L., Fu, H.Y., Guo, Z.G., 2015a. Source apportionment of polycyclic aromatic hydrocarbons in PM2.5 using positive matrix factorization modeling in Shanghai, China. Environ. Sci.: Processes Impacts 17, 197–205. Wang, F.W., Lin, T., Li, Y.Y., Ji, T.Y., Ma, C.L., Guo, Z.G., 2014b. Sources of polycyclic aromatic hydrocarbons in PM2.5 over the East China sea, a downwind domain of East Asian continental outflow. Atmos. Environ. 92, 484–492. Wang, H.L., Qiao, L.P., Lou, S.R., Zhou, M., Ding, A.J., Huang, H.Y., Chen, J.M., Wang, Q., Tao, S.K., Chen, C.H., Li, L., Huang, C., 2015b. Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai, China. J. Clean. Prod. Wang, J., Geng, N.B., Xu, Y.F., Zhang, W.D., Tang, X.Y., Zhang, R.Q., 2014c. PAHs in PM2.5 in Zhengzhou: concentration, carcinogenic risk analysis, and source apportionment. Environ. Monit. Assess. 186, 7461–7473. Wang, J., Hu, Z.M., Chen, Y.Y., Chen, Z.L., Xu, S.Y., 2013a. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229. Wang, P., Cao, J.J., Shen, Z.X., Han, Y.M., Lee, S.C., Huang, Y., Zhu, C.S., Wang, Q.Y., Xu, H.M., Huang, R.J., 2015c. Spatial and seasonal variations of PM2.5 mass and species during 2010 in Xi'an, China. Sci. Total Environ. 508, 477–487. Wang, Q.Q., Huang, X.H.H., Zhang, T., Zhang, Q.Y., Feng, Y.M., Yuan, Z.B., Wu, D., Lau, A.K.H., Yu, J.Z., 2015d. Organic tracer-based source analysis of PM2.5 organic and elemental carbon: a case study at Dongguan in the Pearl River Delta, China. Atmos. Environ. 118, 164–175. Wang, Q.Y., Cao, J.J., Shen, Z.X., Tao, J., Xiao, S., Luo, L., He, Q.Y., Tang, X.Y., 2013b. Chemical characteristics of PM2.5 during dust storms and air pollution events in Chengdu, China. Particuology 11, 70–77. Wang, W., Maenhaut, W., Yang, W., Liu, X.D., Bai, Z.P., Zhang, T., Claeys, M., Cachier, H., Dong, S.P., Wang, Y.L., 2014d. One-year aerosol characterization study for PM2.5 and PM10 in Beijing. Atmos. Pollut. Res. 5, 554–562. Wei, Y.J., Wang, Q.Y., Zhang, L., Awaya, I., Ji, M.J., Li, H.X., Yamada, N., Sato, A., Ninomiya, Y., 2013. Effect of magnesium additives on PM2.5 reduction during pulverized coal combustion. Fuel Process. Technol. 105, 188–194. Wei, Z., Wang, L.T., Chen, M.Z., Zheng, Y., 2014. The 2013 severe haze over the Southern Hebei, China: PM2.5 composition and source apportionment. Atmos. Pollut. Res. 5, 759–768. WHO, 2014a. Ambient (outdoor) air quality and health. http://www.who.int/ mediacentre/factsheets/fs313/en/ (March). WHO, 2014b. WHO's ambient air pollution database — Update 2014. (http://www.who.int/ phe/health_topics/outdoorair/databases/AAP_database_methods_2014.pdf?ua=1). WHO, 2015. Ambient (outdoor) air pollution in cities database 2014. http://www.who. int/phe/health_topics/outdoorair/databases/cities/en/. Willis, M.D., Lee, A.K.Y., Onasch, T.B., Fortner, E.C., Williams, L.R., Lambe, A.T., Worsnop, D.R., Abbatt, J.P.D., 2014. Collection efficiency of the soot-particle aerosol mass spectrometer (SP-AMS) for internally mixed particulate black carbon. Atmos. Meas. Tech. 7, 4507–4516. Wu, D., Wang, Z.S., Chen, J.H., Kong, S.F., Fu, X., Deng, H.B., Shao, G.F., Wu, G., 2014a. Polycyclic aromatic hydrocarbons (PAHs) in atmospheric PM2.5 and PM10 at a coal-based industrial city: implication for PAH control at industrial agglomeration regions, China. Atmos. Res. 149, 217–229. Wu, S.W., Deng, F.R., Wei, H.Y., Huang, J., Wang, X., Hao, Y., Zheng, C.J., Qin, Y., Lv, H.B., Shima, M., Guo, X.B., 2014b. Association of cardiopulmonary health effects with source-appointed ambient fine particulate in Beijing, China: a combined analysis from the Healthy Volunteer Natural Relocation (HVNR) Study. Environ. Sci. Technol. 48, 3438–3448. Xiao, S., Wang, Q.Y., Cao, J.J., Huang, R.J., Chen, W.D., Han, Y.M., Xu, H.M., Liu, S.X., Zhou, Y.Q., Wang, P., Zhang, J.Q., Zhan, C.L., 2014a. Long-term trends in visibility and

170


impacts of aerosol composition on visibility impairment in Baoji, China. Atmos. Res. 149, 88–95. Xiao, Y.H., Liu, S.R., Tong, F.C., Kuang, Y.W., Chen, B.F., Guo, Y.D., 2014b. Characteristics and sources of metals in TSP and PM2.5 in an Urban Forest Park at Guangzhou. Atmosphere 5, 775–787. Xie, M., Barsanti, K.C., Hannigan, M.P., Dutton, S.J., Vedal, S., 2013a. Positive matrix factorization of PM2.5 — eliminating the effects of gas/particle partitioning of semivolatile organic compounds. Atmos. Chem. Phys. 13, 7381–7393. Xie, M.J., Piedrahita, R., Dutton, S.J., Milford, J.B., Hemann, J.G., Peel, J.L., Miller, S.L., Kim, S.Y., Vedal, S., Sheppard, L., Hannigan, M.P., 2013b. Positive matrix factorization of a 32-month series of daily PM2.5 speciation data with incorporation of temperature stratification. Atmos. Environ. 65, 11–20. Xu, L.L., Yu, Y.K., Yu, J.S., Chen, J.S., Niu, Z.C., Yin, L.Q., Zhang, F.W., Liao, X., Chen, Y.T., 2013. Spatial distribution and sources identification of elements in PM2.5 among the coastal city group in the Western Taiwan Strait region, China. Sci. Total Environ. 442, 77–85. Xu, P., Wang, W., Ji, J.W., Yao, S.Y., 2014. Analysis of the contribution of the road traffic industry to the PM 2.5 emission for different land-use types. Comput. Intell. Neurosci. 6. Xue, J., Griffith, S.M., Yu, X., Lau, A.K.H., Yu, J.Z., 2014. Effect of nitrate and sulfate relative abundance in PM2.5 on liquid water content explored through half-hourly observations of inorganic soluble aerosols at a polluted receptor site. Atmos. Environ. 99, 24–31. Yang, F., Gu, Z.P., Feng, J.L., Liu, X.H., Yao, X.H., 2014. Biogenic and anthropogenic sources of oxalate in PM2.5 in a mega city, Shanghai. Atmos. Res. 138, 356–363. Yang, J., Fu, Q., Guo, X.S., Chu, B.L., Yao, Y.W., Teng, Y.G., Wang, Y.Y., 2015a. Concentrations and seasonal variation of ambient PM2.5 and associated metals at a typical residential area in Beijing, China. Bull. Environ. Contam. Toxicol. 94, 232–239. Yang, L.X., Cheng, S.H., Wang, X.F., Nie, W., Xu, P.J., Gao, X.M., Yuan, C., Wang, W.X., 2013. Source identification and health impact of PM2.5 in a heavily polluted urban atmosphere in China. Atmos. Environ. 75, 265–269. Yang, X.F., Liu, H., Man, H.Y., He, K.B., 2015b. Characterization of road freight transportation and its impact on the national emission inventory in China. Atmos. Chem. Phys. 15, 2105–2118. Yao, Z., Shen, X., Ye, Y., Cao, X., Jiang, X., Zhang, Y., He, K., 2015a. On-road emission characteristics of VOCs from diesel trucks in Beijing, China. Atmos. Environ. 103, 87–93. Yao, Z., Wu, B., Shen, X., Cao, X., Jiang, X., Ye, Y., He, K., 2015b. On-road emission characteristics of VOCs from rural vehicles and their ozone formation potential in Beijing, China. Atmos. Environ. 105, 91–96. Yee, L.D., Kautzman, K.E., Loza, C.L., Schilling, K.A., Coggon, M.M., Chhabra, P.S., Chan, M.N., Chan, A.W.H., Hersey, S.P., Crounse, J.D., Wennberg, P.O., Flagan, R.C., Seinfeld, J.H., 2013. Secondary organic aerosol formation from biomass burning intermediates: phenol and methoxyphenols. Atmos. Chem. Phys. 13, 8019–8043. Yin, J., Cumberland, S.A., Harrison, R.M., Allan, J., Young, D.E., Williams, P.I., Coe, H., 2015. Receptor modelling of fine particles in southern England using CMB including comparison with AMS-PMF factors. Atmos. Chem. Phys. 15, 2139–2158. Yin, L.Q., Niu, Z.C., Chen, X.Q., Chen, J.S., Zhang, F.W., Xu, L.L., 2014. Characteristics of water-soluble inorganic ions in PM2.5 and PM2.5–10 in the coastal urban agglomeration along the Western Taiwan Strait Region, China. Environ. Sci. Pollut. Res. 21, 5141–5156. Ying, Q., Cureno, I.V., Chen, G., Ali, S., Zhang, H.L., Malloy, M., Bravo, H.A., Sosa, R., 2014a. Impacts of stabilized Criegee intermediates, surface uptake processes and higher aromatic secondary organic aerosol yields on predicted PM2.5 concentrations in the Mexico City Metropolitan Zone. Atmos. Environ. 94, 438–447. Ying, Q., Wu, L., Zhang, H.L., 2014b. Local and inter-regional contributions to PM2.5 nitrate and sulfate in China. Atmos. Environ. 94, 582–592. Yokelson, R.J., Burling, I.R., Gilman, J.B., Warneke, C., Stockwell, C.E., de Gouw, J., Akagi, S.K., Urbanski, S.P., Veres, P., Roberts, J.M., Kuster, W.C., Reardon, J., Griffith, D.W.T., Johnson, T.J., Hosseini, S., Miller, J.W., Cocker, D.R., Jung, H., Weise, D.R., 2013. Coupling field and laboratory measurements to estimate the emission factors of identified and unidentified trace gases for prescribed fires. Atmos. Chem. Phys. 13, 89–116. You, M.Q., 2014. Addition of PM2.5 into the National Ambient Air Quality Standards of China and the contribution to air pollution control: The Case Study of Wuhan, China. Sci. World J. Yu, G.H., Cho, S.Y., Bae, M.S., Park, S.S., 2014a. Difference in production routes of watersoluble organic carbon in PM2.5 observed during non-biomass and biomass burning periods in Gwangju, Korea. Environ. Sci.: Processes Impacts 16, 1726–1736. Yu, H., Remer, L.A., Chin, M., Bian, H., Tan, Q., Yuan, T., Zhang, Y., 2012. Aerosols from overseas rival domestic emissions over North America. Science 337, 566–569. Yu, L., Smith, J., Laskin, A., Anastasio, C., Laskin, J., Zhang, Q., 2014b. Chemical characterization of SOA formed from aqueous-phase reactions of phenols with the triplet excited state of carbonyl and hydroxyl radical. Atmos. Chem. Phys. 14, 13801–13816.

Yu, L.D., Wang, G.F., Zhang, R.J., Zhang, L.M., Song, Y., Wu, B.B., Li, X.F., An, K., Chu, J.H., 2013. Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aerosol Air Qual. Res. 13, 574–583. Yuan, Q., Yang, L.X., Dong, C., Yan, C., Meng, C.P., Sui, X., Wang, W.X., 2014. Temporal variations, acidity, and transport patterns of PM2.5 ionic components at a background site in the Yellow River Delta, China. Air Qual. Atmos. Health 7, 143–153. Zapata, C., Muller, N., Kleeman, M.J., 2013. PM2.5 co-benefits of climate change legislation part 1: California's AB 32. Clim. Chang. 117, 377–397. Zhang, F., Cheng, H.R., Wang, Z.W., Lv, X.P., Zhu, Z.M., Zhang, G., Wang, X.M., 2014a. Fine particles (PM2.5) at a CAWNET background site in Central China: chemical compositions, seasonal variations and regional pollution events. Atmos. Environ. 86, 193–202. Zhang, F.W., Xu, L.L., Chen, J.S., Chen, X.Q., Niu, Z.C., Lei, T., Li, C.M., Zhao, J.P., 2013a. Chemical characteristics of PM2.5 during haze episodes in the urban of Fuzhou, China. Particuology 11, 264–272. Zhang, J.M., Chen, J.M., Yang, L.X., Sui, X., Yao, L., Zheng, L.F., Wen, L., Xu, C.H., Wang, W.X., 2014b. Indoor PM2.5 and its chemical composition during a heavy haze-fog episode at Jinan, China. Atmos. Environ. 99, 641–649. Zhang, J.P., Fang, F., Pan, W.G., Wu, H.L., Ren, J.X., 2014c. PM2.5 removal performance of a wire-pipe ESP under multi-field coupling and applied magnetic field. Int. J. Appl. Electromagn. Mech. 46, 739–750. Zhang, L.Y., Fu, C., Yang, F.M., Yang, J.D., Huang, Y.M., Zhang, Q., Wu, B.Y., 2014d. Determination of metal elements in PM2.5 by ICP-OES with microwave digestion. Spectrosc. Spectr. Anal. 34, 3109–3112. Zhang, N.N., Cao, J.J., Liu, S.X., Zhao, Z.Z., Xu, H.M., Xiao, S., 2014e. Chemical composition and sources of PM2.5 and TSP collected at Qinghai Lake during summertime. Atmos. Res. 138, 213–222. Zhang, N.N., Cao, J.J., Xu, H.M., Zhu, C.S., 2013b. Elemental compositions of PM2.5 and TSP in Lijiang, southeastern edge of Tibetan Plateau during pre-monsoon period. Particuology 11, 63–69. Zhang, Q., He, K.B., Huo, H., 2012a. Cleaning China's air. Nature 484, 161–162. Zhang, R., Jing, J., Tao, J., Hsu, S.C., Wang, G., Cao, J., Lee, C.S.L., Zhu, L., Chen, Z., Zhao, Y., Shen, Z., 2013c. Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective. Atmos. Chem. Phys. 13, 7053–7074. Zhang, R., Khalizov, A., Wang, L., Hu, M., Xu, W., 2012b. Nucleation and growth of nanoparticles in the atmosphere. Chem. Rev. 112, 1957–2011. Zhang, Y.M., Zhang, X.Y., Sun, J.Y., Hu, G.Y., Shen, X.J., Wang, Y.Q., Wang, T.T., Wang, D.Z., Zhao, Y., 2014f. Chemical composition and mass size distribution of PM1 at an elevated site in central east China. Atmos. Chem. Phys. 14, 12237–12249. Zhang, Y.S., Shao, M., Lin, Y., Luan, S.J., Mao, N., Chen, W.T., Wang, M., 2013d. Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region, China. Atmos. Environ. 76, 189–199. Zhao, M.F., Huang, Z.S., Qiao, T., Zhang, Y.K., Xiu, G.L., Yu, J.Z., 2015. Chemical characterization, the transport pathways and potential sources of PM2.5 in Shanghai: seasonal variations. Atmos. Res. 158, 66–78. Zhao, P., Dong, F., Yang, Y., He, D., Zhao, X., Zhang, W., Yao, Q., Liu, H., 2013a. Characteristics of carbonaceous aerosol in the region of Beijing, Tianjin, and Hebei, China. Atmos. Environ. 71, 389–398. Zhao, P.S., Dong, F., He, D., Zhao, X.J., Zhang, X.L., Zhang, W.Z., Yao, Q., Liu, H.Y., 2013b. Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing, Tianjin, and Hebei, China. Atmos. Chem. Phys. 13, 4631–4644. Zhao, X.Y., Wang, X.M., Ding, X., He, Q.F., Zhang, Z., Liu, T.Y., Fu, X.X., Gao, B., Wang, Y.P., Zhang, Y.L., Deng, X.J., Wu, D., 2014. Compositions and sources of organic acids in fine particles (PM2.5) over the Pearl River Delta region, south China. J. Environ. Sci. (China) 26, 110–121. Zheng, G.J., Duan, F.K., Su, H., Ma, Y.L., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., Chang, D., Poschl, U., Cheng, Y.F., He, K.B., 2015. Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions. Atmos. Chem. Phys. 15, 2969–2983. Zheng, M., Cass, G.R., Schauer, J.J., Edgerton, E.S., 2002. Source apportionment of PM2.5 in the southeastern United States using solvent-extractable organic compounds as tracers. Environ. Sci. Technol. 36, 2361–2371. Zhu, C.S., Cao, J.J., Tsai, C.J., Shen, Z.X., Han, Y.M., Liu, S.X., Zhao, Z.Z., 2014. Comparison and implications of PM2.5 carbon fractions in different environments. Sci. Total Environ. 466, 203–209. Zotter, P., El-Haddad, I., Zhang, Y., Hayes, P.L., Zhang, X., Lin, Y.-H., Wacker, L., Schnelle-Kreis, J., Abbaszade, G., Zimmermann, R., Surratt, J.D., Weber, R., Jimenez, J.L., Szidat, S., Baltensperger, U., Prevot, A.S.H., 2014. Diurnal cycle of fossil and nonfossil carbon using radiocarbon analyses during CalNex. J. Geophys. Res.Atmos. 119, 6818–6835.