Abstract

Based on a network of field stations belonging to the Chinese Academy of Sciences (CAS), the Campaign on Atmospheric Aerosol Research network of China (CARE-China) was recently established as the country’s first monitoring network for the study of the spatiotemporal distribution of aerosol physical characteristics, chemical components, and optical properties, as well as aerosol gaseous precursors. The network comprises 36 stations in total and adopts a unified approach in terms of the instrumentation, experimental standards, and data specifications. This ongoing project is intended to provide an integrated research platform to monitor online PM2.5 concentrations, nine-size aerosol concentrations and chemical component distributions, nine-size secondary organic aerosol (SOA) component distributions, gaseous precursor concentrations (including SO2, NOx, CO, O3, and VOCs), and aerosol optical properties. The data will be used to identify the sources of regional aerosols, the relative contributions from nature and anthropogenic emissions, the formation of secondary aerosols, and the effects of aerosol component distributions on aerosol optical properties. The results will reduce the levels of uncertainty involved in the quantitative assessment of aerosol effects on regional climate and environmental changes and ultimately provide insight into how to mitigate anthropogenic aerosol emissions in China. The present paper provides a detailed description of the instrumentation, methodologies, and experimental procedures used across the network, as well as a case study of observations taken from one station and the distribution of main components of aerosol over China during 2012.

CARE-China is the first comprehensive attempt to assess the physical, chemical, and optical properties of atmospheric aerosols across China and their impact on climate change.

Aerosols represent an important component of Earth's atmosphere and are composed of solid and liquid particles of varying chemical complexity, size, and phase. The main components of anthropogenic aerosols are sulfate, nitrate, ammonium salt, black carbon (EC), and organic carbon (OC) (Prather et al. 2008). Aerosols play an important role in global and regional climate change through direct and indirect effects. The direct effects influence the radiation and energy budget of Earth, mainly by absorbing and scattering solar and terrestrial radiation (Dubovik et al. 2002; Menon 2004). The indirect effects, however, are more complicated. Aerosols can act as cloud condensation nuclei (CCN), thereby participating in the process of cloud formation, evolution, and dissipation, which changes the microphysical structure of clouds, their lifespan and optical properties. Furthermore, they can influence cloud droplet size and precipitation, thus having a significant influence on climate (Jones et al. 1994; Buseck 2010; Gantt et al. 2012). In addition, the impacts of nonnatural levels of aerosols on the atmospheric environment and human health are generally quite negative.

The effects of atmospheric aerosols on climate and the environment depend on their physical properties, chemical composition, and optical characteristics (Charlson et al. 1992; Cwiertny et al. 2008; Buseck 2010). China is one of the world’s major sources of aerosols, with large spatiotemporal differences across the country (Xin et al. 2007; Lee et al. 2007; Y. Wang et al. 2011), and one can begin to understand the aforementioned level of uncertainty in terms of the climatic effects of aerosols. During the past 30 years, dramatic development has taken place in China, which in turn may aggravate further the degree of uncertainty in the impact of aerosols on the radiation budget of the region (Penner et al. 1992; Streets and Aunan 2005; Galloway et al. 2008). China has become a hot-topic area for studying the uncertainty of aerosol radiative forcing and related regional climate effects (Huebert et al. 2003; Li 2004; Solomon et al. 2007; Seinfeld et al. 2004).

Comprehensive observational networks for atmospheric aerosols exist in Europe and the United States and other developed countries, such as the Maritime Aerosol Network (Smirnov et al. 2011), Aerosol Robotic Network (AERONET) (Holben et al. 2001), North American Research Strategy for Tropospheric Ozone (NARSTO) particulate matter (PM) assessment (McMurry et al. 2004), Global Atmosphere Watch: Aerosols (GAW 2008), the European Supersites for Atmospheric Aerosol Research (EUSAAR) (Cavalli et al. 2010), and multisource satellite observations and research plans (Kaufman et al. 2002). The success of these networks indicates the importance of building similar facilities in other areas of the world in order to monitor the physical, chemical, and optical properties of aerosols not only at other national scales but also contributing to the global effort. In China, the implementation of such a network would aim to supply significant amounts of data for studying the effects of aerosols on climate and environmental change, which in turn would be key for providing breakthroughs in localizing and developing climate models throughout the country. Accordingly, the Chinese Academy of Sciences (CAS) launched the Campaign on Atmospheric Aerosol Research network of China (CARE-China) in 2011, which is the first comprehensive research platform for atmospheric aerosols in the country. The network aims to provide a platform for valuable scientific guidance and research for the gradual reduction of anthropogenic aerosol emissions, with an overall aim to help combat climate change in China.

The present paper provides a detailed description of the instrumentation, methodologies, and experimental procedures used across CARE-China, as well as a case study of observations taken from one station during 2012.

METHODOLOGY.

Introduction to the network.

Supported by a CAS Strategic Priority Research Program grant (category A), the Institute of Atmospheric Physics has organized and completed the first phase of construction of CARE-China. The work was jointly organized by several institutes, including the Institute of Tibetan Plateau Research (ITPR), the Institute of Earth Environment (IEE), the Guangzhou Institute of Geochemistry (GIG), the Capital Normal University (CNU), and the Beijing University of Chemical Technology (BUCT). The network is composed of 36 field stations, which will carry out a comprehensive 3-year observational network campaign. Figure 1 and Table 1 show the geographic distribution and details of the network stations, respectively, which include 20 first-level background stations and 16 second-level stations with unified experimental instruments that meet international standards. The network’s first task was realized at the national scale: the synchronous observation of aerosol physical characteristics, chemical components, and optical properties, as well as gaseous precursors. Table 2 presents the observational elements, experimental methods, and temporal resolution of the network.

Fig. 1.

Geographic distribution of CARE-China.

Fig. 1.

Geographic distribution of CARE-China.

Table 1.

Geographic information of CARE-China. The classification of the stations is based on the standards of the Ministry of Environmental Protection and the China Meteorological Administration.

Geographic information of CARE-China. The classification of the stations is based on the standards of the Ministry of Environmental Protection and the China Meteorological Administration.
Geographic information of CARE-China. The classification of the stations is based on the standards of the Ministry of Environmental Protection and the China Meteorological Administration.
Table 2.

Observational elements, experimental methods, and temporal resolutions across the network.

Observational elements, experimental methods, and temporal resolutions across the network.
Observational elements, experimental methods, and temporal resolutions across the network.

Experimental methods.

Online PM2.5 concentration and bypass sampling

Thirty sites of the network are equipped with RP1400-PM2.5 or RP1405-PM2.5 (Thermo Scientific: www.thermoscientific.com) instruments. The tapered element oscillating microbalance (TEOM) operates by drawing air through a filter attached at the tip of a quartz tube (Patashnick and Rupprecht 1991). The measurement range of the TEOM is 0–5 g m–3, with a 0.1 µg m–3 resolution, precisions of ±1.5 (1-h average) and ±0.5 µg m–3 (24-h average), accuracy of ±7.5%, and a 0.06 µg m–3 (1-h average) minimum detectable limit (Xin et al. 2012). The other six sites of the network are equipped with beta gauge instruments (EBAM, Met One Instruments Inc., Oregon). The measurement range of EBAM is 0–1000 µg m–3, with a precision of 0.1 µg m–3 and a 0.1 µg m–3 resolution. The filters are exchanged every week and the inlet is cleaned every month. The flow rates are also monitored and calibrated at the same time.

The Nine-Stage Anderson Samplers And Weight Samples

Atmospheric particulate matter is sampled at 36 sites across China, collected using nine-stage Anderson samplers (Anderson Series 20–800, United States) at an airflow rate of 28.3 L min–1 with cutoff points as 0.4, 0.7, 1.1, 2.1, 3.3, 4.7, 5.8, and 9.0 µm. The mass concentration and chemical composition spectral distribution are determined in order to examine the mass closure of atmospheric particulate matter (Sun et al. 2011). Each set of the size-segregated samples is continuously collected for 48 h on the same days each week, except at sites on the Tibetan Plateau, where samples are collected for 72 h. Cellulose membrane and quartz fiber filters are used for different purposes of chemical composition analysis. After sampling, the filters are individually placed in plastic boxes and then placed in a freezer (–20°C) prior to transport and subsequent analysis.

The quartz fiber filters are wrapped with aluminum foil and preheated at 800°C for 2 h to remove all organic material and are then conditioned in a dryer (temperature: 25°C; humidity: 10%) for 72 h before weighing, while the cellulose membranes are conditioned in constant-humidity desiccators (temperature: 25°C; humidity: 50%) for 48 h before weighing. The filters are weighed before and after sampling by a microbalance with balance sensitivity of ±0.01 mg. After reweighing, the exposed filters are divided into different parts with clean tools and prepared for chemical analysis (water soluble salt ions, metal elements, EC/OC, and organic matter species).

Heavy metals

The microwave digestion method is used for the pretreatment of the atmospheric particulate samples. After digestion, the metallic elements in the samples are determined by using inductively coupled plasma mass spectroscopy (ICP-MS) technology. After repeated testing, CARE-China adopted a unified determination method for metallic elements of atmospheric particulates (Pan et al. 2013). Specifically, one-quarter of each cellulose filter is digested in a mixture of 6 mL of HNO3, 2 mL of HCl, and 0.2 mL of HF using a microwave accelerated reaction system (MARS, CEM Corporation, United States). In addition, a blank filter is used in each batch sample to control the analysis quality. After digestion, the dissolved sample in the digestion tank is transferred to a PET bottle with deionized water used repeatedly to wash the digestion tank (Pan et al. 2010). The concentrations of 25 metallic elements, including Be, Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Ba, Tl, Pb, Th, and U, are determined with ICP-MS (7500ce, Agilent Corporation, United States). Quantitative analysis is carried out by external calibration standards at concentration levels close to those of the samples, and internal standard elements (45Sc, 73Ge, 115In, and 209Bi) are added online during the metallic element analysis. The concentration of each metallic element in the air is calculated according to the sampling volume.

Soluble inorganic salts

One-quarter of each filter sample is transferred into a polyethylene terephthalate (PET) vial with 25 mL of distilled deionized water (resistivity of 18 MΩ cm) and then extracted ultrasonically for 30 min in a thermostatic condition. Five cations (Na+, NH4+, K+, Mg2+, and Ca2+) and three anions (Cl, NO3, and SO42–) are analyzed by ion chromatography (IC) after the extracts have been passed through microporous membranes (0.22 µm). For the ion analyses, the IC is equipped with a separation column (Ionpac CS12A 4 × 250 mm for cations and Ionpac AS14A 4 × 250 mm for anions) and a suppressor (CSRS 300–4 mm for cations and ASRS 300–4 mm for anions); the eluent for cations and anions is 22 mmol L–1 MSA and 3.5 mmol L–1 Na2CO3 per 1 mmol L–1 NaHCO3, respectively. Ions are quantified by external standard curves every week, and one trace calibration standard solution is to check the curve every day. The ions’ limits of detection are less than 0.02 µg m–3 when the injection volume is 100 µL.

Elemental carbon and organic carbon

A thermal/optical carbon analyzer (DRI Model 2001A, Desert Research Institute, United States) is used to analyze EC and OC compositions (Chow et al. 1993, 2004). The temperature program for the thermal analysis follows the Interagency Monitoring of Protected Visual Environments (IMPROVE_A) protocol (Chow et al. 2007). A punch aliquot (0.50 cm2) of a quartz fiber filter sample is heated stepwise in an oven at 140° (OC1), 280° (OC2), 480° (OC3), and 580°C (OC4) in a pure helium atmosphere for OC volatilization and 580° (EC1), 740° (EC2), and 840°C (EC3) in a 2% oxygen-contained helium atmosphere for EC oxidation. At each stage, the CO2 formed is catalytically converted to CH4 by a methanator (hydrogen-enriched nickel catalyst), and the resulting CH4 is then measured using a flame ionization detector (FID). The analyzer is calibrated using a 5% nominal CH4 in He as an internal standard at the end of each analysis. The pyrolyzed or charred OC is monitored by reflectance at λ= 633 nm. The portion of EC1 until the laser signal returns to its initial value is assigned to pyrolyzed organic carbon (OP). OC is defined by the sum of OC1 + OC2 + OC3 + OC4 + OP, while EC is defined by EC1 + EC2 + EC3 – OP. One sample is selected at random from every 10 samples to carry out a duplicate sample analysis. The errors in the measurements were less than 30% for total carbon (TC). Field blank filters are also analyzed, and the average blank concentrations are subtracted from the sample results, which were 0.9 and 0.1 µg m–3 for OC and EC, respectively. Sucrose was used to establish the calibration curve of the analyzer semiannually. The errors in the measurement presented here were less than 30% for both OC and EC (Chow et al. 2004, 2007).

Organic compound components

The analytical procedure used for organic compounds (OCs) has been described previously (Zhang et al. 2007; Agarwal et al. 2010; G. Wang et al. 2011). However, briefly, one-quarter of each filter is cut into small pieces directly into a glass vessel and ultrasonically extracted for 20 min with 25 mL of dichloromethane (DCM) [high-performance liquid chromatography (HPLC) grade, Supelco, United States] and methanol (HPLC grade, Supelco, United States) mixture (2:1, v/v). The extraction procedure is repeated three times, and the combined solution is filtered through a glass fiber filter and then evaporated down to approximately 1.5 mL under reduced pressure at 35°C by a rotary evaporator (Buchi, Sweden), before being dried in a gentle nitrogen stream. For derivatization, 100 µL of N,O-Bis-(trimethylsilyl)trifluoroacetamide (BSTFA) (99%) mixture containing trimethylsilylchloride (TMCS) (1%) and 20 µL of pyfidine is added to the silylation vial and the reaction is carried out at 70°C for 1 h. After derivatization, the final volume is adjusted to 1 mL using dichloromethane for gas chromatography to mass spectrometry (GC–MS) analysis.

The derivatives are identified and quantified with a trace GC–MS spectrometer (Thermo DSQ Finnigan, United States) and an HP-5MS capillary column (30 m long, 0.25-mm diameter, and 0.25-µm film thickness). High-purity helium is used as the carrier gas at a constant flow rate of 1.0 mL min–1. A total of 1 µL of sample is injected into the GC in splitless mode. The GC temperature is programmed as follows: started at 50°C (2 min), increased to 120°C at 15°C min–1, then ramped to 300°C at 5°C min–1, and held at 300°C for 10 min. The mass spectrometer is operated on an electron impact (EI) mode at 70 eV and fully scanned ranging from 50 to 550 amu. The identification of organic compounds is performed using m–z mass chromatography, and the mass spectra are compared according to the standards of the National Institute of Standards and Technology (NIST) 2008.

Gaseous precursor concentrations

All monitoring stations were selected and constructed according to U.S. Environmental Protection Agency (EPA) specification. Ozone is measured using a UV photometric O3 analyzer (Model 49C/I, Thermo-Fisher Scientific, United States) with the lowest detection limit of 2.0 ppb, precision of ±1.0 ppb, zero drift of less than 1.0 ppb (24 h)–1, span drift of less than 1% full scale per month, and response time of 10 s. NOx is measured using a chemiluminescence NOx analyzer (Model 42C/I) with the lowest detection limit of 0.4 ppb, precision of ±0.4 ppb, zero drift of less than 0.4 ppb (24 h)–1, span drift of less than 1% full scale per 24 h, and response time of 40 s. SO2 is measured using a pulsed fluorescence SO2 analyzer (Model 43 C/I) with the lowest detection limit of 0.5 ppb, precision of 1% of reading or 1 ppb, zero drift of less than 1 ppb (24 h)–1, span drift of less than 0.5% full scale per 24 h, and response time of less than 20 s.

The Thermo Scientific Model 146i multigas calibrator supplies precise levels (<0.5 ppb) of O3, CO, nonmethane hydrocarbons, SO2, NOx, and NO2. The multigas calibrator includes a 10-L mass flow controller, a 100-mL mass flow controller, a display and control unit, power supply, time controller, mixing chamber, solenoid valve system, and a Model 111 Zero Air Supply. The Model 49 CPS instrument is used to calibrate O3 analyzers, which traces the NIST standard every 2 years. Linear differential processing is carried out on the calibration results and extrapolation of the data is made for the corresponding period. Such an approach ensures the stability and reliability of the data.

VOCs

Analyses of the samples are performed using a preconcentrator instrument (Entench 7100) and a GC–MS system (Finnigan Trace GC/Trace DSQ). With the Entench 7100, samples are concentrated at the glass beads (80–100 mesh) trap (module 1), which is maintained at –165°C with liquid nitrogen. For the GC–MS analysis, 500 mL of air is concentrated when injecting through the instrument at a flow rate of 100 mL min–1. The trapped analyses are then desorbed at 10°C and transferred to a Tenax-TA trap (module 2) maintained at –50°C. The concentrated components are again desorbed at 200°C and then focused on the head of the cold capillary tube, which is cooled down to –150°C by liquid nitrogen, too. The highly focused VOCs are quickly desorbed at 90°C and transferred to the GC column.

The U.S. EPA’s standard TO-14, TO-15, and photochemical assessment monitoring stations (PAMS) are used as the reference standards and reference target analyses of the project. The chromatogram column is a DB-5MS capillary column; the film thickness is 60 m × 0.25 mm × 0.25 µm; and the GC oven temperature program is –35°C (5 min), –35° to 35°C at a rate of 15°C min–1, 35°C (1 min), and 35°–250°C at a rate of 15°C min–1. The carrier gas is helium at a constant pressure of 137 kPa. The mass examination range mz from 29 to 200 amu is used for quantitative determination with the full scan mode. Temperature of the ion source is 230°C. Voltage of the electron multiplier is 1560 eV (auto tune).

Aerosol optical properties

A narrowband portable sunphotometer (Microtops II; www.solarlight.com) is used to measure the direct sunlight in order to determine aerosol optical depth (AOD) in CARE-China. The sunphotometer is equipped with five spectral channels at 440-, 500-, 675-, 870-, and 936-nm wavelengths, which is widely used for measuring aerosol optical properties (Morys et al. 2001; Ichoku et al. 2002; Knobelspiesse et al. 2003). Over 20 measurements can be performed each day from 1000 to 1500 local time, depending on the required sky conditions. The observers record and judge the real-time cloud conditions with free cloud or cloud amounting to less than a half during the observation, which can efficiently reduce the cloud pollution (Xin et al. 2007, 2011). A log-linear curve fitting algorithm is applied to AODs at three wavelengths (440, 500, and 675 nm) to estimate the Angström exponent α, a basic parameter related to the aerosol size distribution (Dubovik et al. 2002; Kim et al. 2004). The single scattering albedo (SSA) is retrieved using Mie theory (Dubovik et al. 2002; Lee et al. 2007) and the Santa Barbara Discrete Ordinate Radiative Transfer model (DISORT) Atmospheric Radiative Transfer (SBDART) model with Moderate Resolution Imaging Spectroradiometer (MODIS) data (Ricchiazzi et al. 1998). The aerosol direct radiative forcing (ARF) at the top of the atmosphere (TOA) or at the surface (SRF) in the wavelength range 0.25–4.0 µm is defined as the difference in the net solar fluxes with and without aerosol, using the SBDART model (Ricchiazzi et al. 1998; Levy et al. 2007; Li et al. 2010). The sunphotometers are calibrated using Langley plot calibration (Shaw 1976) and transfer calibration (Brooks and Mims 2001), which is same as the Chinese Sun Hazemeter Network (Xin et al. 2007). In CARE-China, the Xianghe and Xinglong sites belong to AERONET, which uses CE-318 sunphotometers (Xia et al. 2007).

Data collection and the database

CARE-China employs a system of data acquisition, data transmission, data storage, and category queries. The system consists of one piece of hardware and one piece of software. The hardware part adopts the star topology of Internet architecture; it includes data acquisition modules, an industrial control PC, a data transmission unit, and a server. The software are modules in design, including serial communication modules, analog–digital conversion modules, database storage computation modules, network connection modules, and server synchronization modules. The system computes a higher time resolution of the original data for the average data in the industrial PC at the substation and, through the networking that uses the TCP/IP protocol, the average data are sent to the central server. After the central server’s storage structure is optimized, the data are released according to the site at real time on a dynamic website by active server pages (ASP) technology. The data can be queried and downloaded, and figures can be produced.

CASE STUDY: OBSERVATIONAL DATA FROM THE XIANGHE SITE.

Online concentration of PM2.5.

Figure 2 shows the daily variation of PM2.5 at the Xianghe site in 2012. The Xianghe site (XHZ in Fig. 1) is a typical suburban site in the serious pollution region, the Beijing–Tianjin–Hebei large urban region. A total of 7,969 hourly averaged PM2.5 mass concentration data were obtained from Xianghe, which contributed 91% of the entire observation period in 2012. The missing data were attributed to the calibration and maintenance of the instruments, which took less than 3 h on each occasion to complete. Consequently, a total of 362 daily averaged PM2.5 mass concentration data were obtained in 2012, which are shown in Fig. 2. The average PM2.5 daily value was 79.2 ± 55.1 µg m–3 at Xianghe, with the maximum and minimum daily average of PM2.5 being 421.2 and 3.3 µg m–3. Average concentrations of PM2.5 across the four seasons were 72.9 ± 38.9, 70.3 ± 42.4, 79.0 ± 55.7, and 95.3 ± 74.8 µg m–3 in spring, summer, autumn, and winter, respectively. Overall, a lower (higher) value of PM2.5 was found in spring and summer (autumn and winter).

Fig. 2.

Daily variation of PM2.5 at the Xianghe site in 2012.

Fig. 2.

Daily variation of PM2.5 at the Xianghe site in 2012.

Online concentration of gaseous precursors.

Figure 3 shows the seasonal variations of NOx, SO2, CO, and O3 at Xianghe in 2012. In total, 8,275 h of data for NOx, SO2, O3, and CO were recorded, which accounted for 94.5% of all data. Missing data were caused by routine maintenance and calibration. In total, there were 345 days of valid data recorded for NOx, SO2, O3, and CO. The annual average concentrations of NOx, SO2, O3, and CO were 31.3 ± 46.2 ppb, 9.8 ± 13.5 ppb, and 21.3 ± 27.7 ppb, and 1.1 ± 1.6 ppm, respectively. Overall, lower (higher) NOx, SO2, and CO concentrations were observed in spring and summer (autumn and winter), but the reverse was true for O3 concentrations.

Fig. 3.

Monthly variations of NOx, SO2, CO, and O3 at the Xianghe site in 2012.

Fig. 3.

Monthly variations of NOx, SO2, CO, and O3 at the Xianghe site in 2012.

VOC concentrations of SOA gaseous precursors.

A total of 104 samples were collected at Xianghe during 2012. The samples were collected at 1400 local time every Thursday and stored in preevacuated 1-L specially designed stainless flasks (Fig. 4). Analyses of the samples were performed within 2 weeks using the preconcentrator equipment (Entench 7100) and GC–MS instrument (Finnigan Trace GC/Trace DSQ). As many as 78 VOCs (C4–C10) were measured in the samples, except for five samples from 12 September to 10 October in which 56 VOCs were measured, including alkanes, alkenes, aromatic hydrocarbons, and halocarbons. The average concentration of total VOCs at Xinghe was 28.2 ppbv, with the highest concentration being 79.4 ppbv in April and the lowest being 5.8 ppbv in August. Aromatic hydrocarbons were the highest abundant, accounting for 37.1%, followed by halocarbons (30.2%), alkanes (26.8%), and alkenes (10.5%). The mean concentration of NMHCs in the Beijing urban area have obvious seasonal variation, with the highest concentrations generally occurring in March and November and a decreasing trend found from spring to summer.

Fig. 4.

Monthly variations of VOCs at the Xianghe site in 2012.

Fig. 4.

Monthly variations of VOCs at the Xianghe site in 2012.

The nine-size distribution of aerosol mass concentrations.

Size-resolved aerosol mass concentrations at Xianghe are shown in Fig. 5. It can be seen that the average annual mass concentration of TSP was 196.5 µg m–3, ranging from 68.1 to 404.5 µg m–3. The concentrations of TSP were higher in winter and spring than in summer and autumn. As shown in Table 3, the mean mass concentration of TSP in winter, spring, summer, and autumn, from high to low, was 230.4, 220.9, 175.0, and 159.1 µg m–3, respectively. PM2.1 and PM9.0 shared the same seasonal trends as TSP, and the average mass concentration of PM2.1 was 97.6 µg m–3, ranging from 24.9 to 301.3 µg m–3. Meanwhile, the average mass concentration of PM9.0 was 169.7 µg m–3, ranging from 56.2 to 389.2 µg m–3. Figure 6 shows the size-resolved aerosol mass concentrations across the four seasons. The size distributions of atmospheric particulate matter in the different seasons were all found to have a bimodal logarithm normal structure; the fine mode showed the maxima peak at radius 0.43–0.65 µm, and the coarse mode showed the maxima peak at radius 4.7–5.8 µm. The maxima peak of the coarse mode in spring was higher than in other seasons mainly because of dust particles with relatively larger sizes. The maxima peak of the fine mode was significantly higher than that of the coarse mode in winter, mainly because of coal combustion and stable weather, which is not conducive for the diffusion of pollutants.

Fig. 5.

Size-resolved aerosol mass concentrations at the Xianghe site in 2012.

Fig. 5.

Size-resolved aerosol mass concentrations at the Xianghe site in 2012.

Table 3.

Seasonal variation of aerosol mass concentration (µg m–3) at the Xianghe site.

Seasonal variation of aerosol mass concentration (µg m–3) at the Xianghe site.
Seasonal variation of aerosol mass concentration (µg m–3) at the Xianghe site.
Fig. 6.

Size-resolved aerosol mass concentration across the four seasons.

Fig. 6.

Size-resolved aerosol mass concentration across the four seasons.

The nine-size concentration distribution of inorganic salts in particles.

Figure 7 shows the seasonal variations of SO42–, NO3, and NH4+ at Xianghe. A total of 21 series of 189 samples were collected in 2012. The most abundant ions in Xianghe were SO42–, NO3, and NH4+, and the annual concentrations of these three ions were 12.4, 6.9, and 9.2 µg m–3, respectively. The mass of SO42– was higher than NO3, indicating that the stationary source was still predominant in Xianghe, and NH4+ pollution cannot be ignored as its concentration was higher than NO3. The seasonal variations of SO42–, NO3, and NH4+ are presented in Fig. 7. The concentration of SO42– was much higher in summer and winter because of a higher sulfur oxidation ratio in summer and the wide use of coal burning and stagnant weather conditions during winter. Dependent on lower temperatures, the concentration of NO3 was much higher in autumn and winter, while NH4+ was much higher in spring and winter. These three ions mainly gathered in fine particles, especially in winter (86.6%, 70.4%, and 86.6% of SO42–, NO3, and NH4+, respectively, was distributed in particles less than 2.1 µm in size annually).

Fig. 7.

Seasonal variations of SO42–, NO3, and NH4+ at the Xianghe site in 2012.

Fig. 7.

Seasonal variations of SO42–, NO3, and NH4+ at the Xianghe site in 2012.

The nine-size concentration distribution of EC and OC.

The measured abundances of OC and EC are summarized in Fig. 8. OC and EC of PM2.1 and PM9.0 in spring and autumn were lower than those in summer and winter. In summer, OC of PM2.1 and PM9.0 was 28.0 and 51.1 µg m–3, while for EC the values was 1.7 and 2.6 µg m–3, respectively. In winter, OC values of PM2.1 and PM9.0 were 41.6 and 53.0 µg m–3, while for EC the values were 5.5 and 6.5 µg m–3, respectively. OC and EC were enriched in PM2.1. In winter, approximately 78% of OC and 85% of EC were concentrated in PM2.1, and in summer the ratios were 55% and 66%, respectively. This indicates that carbonaceous aerosols were more enriched in fine particles in winter than in summer.

Fig. 8.

EC and OC abundances in different particle size fractions at the Xianghe site in 2012.

Fig. 8.

EC and OC abundances in different particle size fractions at the Xianghe site in 2012.

The species of OC.

Figure 9 shows that the mean total 17-alkane concentration of PM9.0 was 610 ng m–3, ranging from 147 to 1226 ng m–3, and the average alkane concentration of PM9.0 in spring, summer, autumn, and winter was 629, 396, 566, and 850 ng m–3, respectively. Moreover, the average concentrations of n-alkanes in the three different sizes (PM1.1, PM1.1–3.3, and PM3.3–9.0) were 311, 142 and 157 ng m–3, respectively. The highest concentration was observed in winter, which was due to the increased use of fossil fuels in winter and meteorological conditions causing the poor dispersion of pollutants in the air. It can be seen that the mean total of the concentration of 21 fatty acids of PM9.0 was 2124 ng m–3, ranging from 297 to 3182 ng m–3, and the average mass concentration of PM9.0 in spring, summer, autumn, and winter was 2461, 1271, 2087, and 2245 ng m–3, respectively. Moreover, the average mass concentrations of fatty acids in the three different sizes (PM1.1, PM1.1–3.3, and PM3.3–9.0) were 749, 631, and 636 ng m–3, respectively. The average concentration of levoglucosan, a tracer of biomass burning (Simoneit et al. 1991; Urban et al. 2012), was higher in autumn and winter, indicating that the contribution of biomass burning to aerosol levels over Xianghe should not be neglected. Furthermore, levoglucosan was detected in all three fractions of the size-segregated aerosol samples, with values from 35% to 67% of the total concentration contained within particles smaller than 1.1 µm (Fig. 9). These results are similar to those found for aerosols in other studies (Schkolnik et al. 2005; Decesari et al. 2006; Li et al. 2013).

Fig. 9.

The seasonal variation and size distributions of n-alkane, fatty acids and levoglucosan at the Xianghe site in 2012.

Fig. 9.

The seasonal variation and size distributions of n-alkane, fatty acids and levoglucosan at the Xianghe site in 2012.

Mass closure of aerosols.

To examine the mass closure of aerosols, the chemical components were divided into seven categories as follows: mineral matter (MM), construction dust (CD), heavy metals (HM), organic matter, elemental carbon, sea salt (SS), and secondary inorganic aerosol (SIA), as shown in Table 4. Table 5 gives the contributions of the different components at the site. OM and SIA dominated the particulate mass of different sizes and shared a far greater contribution to PM2.1 (47.3% and 23.5%) than to PM9.0 (43.7% and 16.3%) and TSP (41.2% and 15.1%). High contributions of MM were also observed in PM9.0 and TSP, accounting for 22.6% and 26.4% of total PM9.0 and TSP mass, respectively. Overall, contributions of OM, SIA, and EC were much higher in fine particles; however, MM and CD were much higher in coarse particles. HM and SS all had low contributions to the particulate mass, of less than 2.1%. A lower contribution of SIA was found in spring, while a higher contribution was found in summer. CD and MM shared a far greater contribution in spring and autumn than in summer and winter. Meanwhile, the contribution of EC was much higher in winter than the other seasons, and higher contributions of OM were observed in spring and summer compared to autumn and winter.

Table 4.

Chemical components used in the mass closure study.

Chemical components used in the mass closure study.
Chemical components used in the mass closure study.
Table 5.

Contribution (%) of various chemical components to PM2.1, PM9.0, and TSP (mass closure).

Contribution (%) of various chemical components to PM2.1, PM9.0, and TSP (mass closure).
Contribution (%) of various chemical components to PM2.1, PM9.0, and TSP (mass closure).

Aerosol optical properties.

Figure 10 presents the aerosol optical properties and aerosol radiative forcing at the site. The annual means of the AOD, α, SSA, ARF, SRF, and TOA atmospheric radiative forcing were 0.46 ± 0.44, 1.07 ± 0.28, 0.84 ± 0.13, 43 ± 19, –50 ± 29, and –7 ± 26 W m–2, respectively. With the rapid urbanization and industrialization that has taken place in the Beijing–Tianjin–Hebei region, large amounts of industrial aerosols and dust were emitted in 2012, which led to serious particulate matter pollution and declined atmospheric visibility in the region. Because of the northward transport of dust and local dust in spring, the seasonal mean AOD was high at 0.50, with a relatively small Angström exponent of 0.92. In summer, good levels of vegetation and rainfall effectively reduced the surface dust emissions and cleared the hydrophilic aerosols from the atmosphere, meaning the seasonal mean of AOD reached a minimum value of 0.30. At the same time, the dominant mode of aerosol changed little, which was affected by surrounding crop/straw-burning emissions. The dominant mode of aerosol reached a minimum in autumn and winter, with the seasonal mean of the Angström exponent ranging from 1.03 to 1.13 and with a high AOD of 0.41. This implies that the biomass and fossil fuel burning produced heavy loadings of fine-mode aerosols during autumn and winter in the region (Xin et al. 2011). The high aerosol concentration also led to strong ARF and SRF. Apart from in summer, there was negative forcing on the atmosphere–surface system (TOA) in other seasons. The aerosols were cooling the regional atmosphere–surface system throughout year, which could have offset the greenhouse effect or confuse the regional climate change (Li et al. 2010). However, the ARF was powerfully warming the atmosphere and cooling the surface, which could have increased the atmospheric stability and suppressed pollutant diffusion. There was positive feedback between aerosol radiative forcing and air pollution, which would have resulted in a vicious circle of serious atmospheric pollution in the region.

Fig. 10.

The aerosol optical properties and aerosol radiative forcing at the Xianghe site in 2012.

Fig. 10.

The aerosol optical properties and aerosol radiative forcing at the Xianghe site in 2012.

The examples of the distribution of sulfate ion, OC, and EC over China.

Figure 11 shows the annual average concentrations and size distributions of the sulfate ion (SO42–) in 2012 over China. Green and red pillars on the map represent the concentration of the sulfate ion in the PM9.0 and PM2.1, and the line graphs around the map represent the particle size of the sulfate ion in different sites. There were the high concentration of SO42– in north China and the central and eastern region: SO42– in PM2.1 was from 30 to 70 µg m–3 and SO42– in PM9.0 was from 50 to 100 µg m–3. The concentration of SO42– was the least in the west, especially in the Qinghai–Tibet Plateau area: SO42– in PM2.1 was from 0.5 to 6.0 µg m–3 and SO42– in PM9.0 was from 1.0 to 10 µg m–3. The concentrations of SO42– were dramatically higher in the cities than in the background sites, which inferred the fossil fuel burning was emitting a lot of SO2 and SO42– in the urban regions. The particle size distribution of SO42– shows a bimodal size distribution with the fine mode at 0.43–1.1 µm and the coarse mode at 4.7–5.8 µm. The mass size distributions of SO42– are mostly bimodal at the city sites, with a dominant peak in the fine mode, while at the background or the rural sites with a dominant peak in the coarse mode.

Fig. 11.

The annual average concentrations and size distributions of the sulfate ion in 2012 over China.

Fig. 11.

The annual average concentrations and size distributions of the sulfate ion in 2012 over China.

Figures 12 and 13 show the annual average concentrations and size distributions of OC and EC over China in 2012. The high concentrations of OC and EC can be found in northern China, Sichuan basin, and the eastern coastal areas. In the regions, OC in PM2.1 was about 20–30 µg m–3, SO42– in PM9.0 was about 40–55 µg m–3, EC in PM2.1 was about 1.5–4.5 µg m–3, and EC in PM9.0 was about 2.5–7.5 µg m–3. The low OC and EC concentrations mainly appeared in the northwest and the Tibetan Plateau. The lowest concentration of OC was 6 µg m–3 in PM2.1 and 11 µg m–3 in PM9.0. EC in PM2.1 was about 0.2–1.0 µg m–3 and EC in PM9.0 was about 0.3–1.5 µg m–3. The high concentrations of OC and EC can be found in most of the city sites, such as Beijing, Chengdu, Hefei, Xi‘an, and Taiyuan. In the contrast, the low OC and EC concentrations mainly appeared in the regional background sites and Tibetan Plateau. The average size distributions of OC and EC were all considered to be bimodal mode in different sites. Fine modes commonly showed the maxima peak at 0.43–0.65 and 0.65–1.1 µm in summer, and the coarse modes showed the maxima peak at 4.7–5.8 µm in all the sites. The amplitude of the fine mode was found to be larger than that of the coarse mode in most of the polluted sites, such as Guangzhou, Shanghai, Chengdu, Changsha, and Xi‘an. However, the amplitude of coarse mode was found to be larger in most of the sites which are affected heavily by dust (Dunhuang, Fukang, and Shapotou).

Fig. 12.

The annual average concentrations and size distributions of OC in 2012 over China.

Fig. 12.

The annual average concentrations and size distributions of OC in 2012 over China.

Fig. 13.

The annual average concentrations and size distributions of EC in 2012 over China.

Fig. 13.

The annual average concentrations and size distributions of EC in 2012 over China.

Sulfate ion, OC, and EC were the main components of aerosol. There are large differences in the spatial and temporal distribution over China. The concentrations of SO42–, OC, and EC were dramatically higher in northern China, the central and eastern regions, and the cities than in the western region and the background sites, especially in the Qinghai–Tibet Plateau. SO42–, OC, and EC of aerosol have two similar peaks in the fine mode and coarse mode at almost all of sites. The phenomenon is largely different from the usual cognition that SO42–, OC, and EC are mainly accumulated in the fine mode of aerosol. So many aerosol models and climate models must be calibrated or adjusted by the real aerosol parameters in China, which is the only way to decline their errors and uncertainties in evaluating aerosol effects on the regional and global climate change.

CONCLUSIONS.

As we know, air pollution and aerosol components are very serious and complex in China; they are aggravating the uncertainties of regional and global climate change. It is very necessary and significant to investigate the distribution of aerosol components over China. CARE-China is the first comprehensive attempt to systematically observe the physical, chemical, and optical properties of atmospheric aerosols across the country on an ongoing basis. Such networks are already well established in other corners of the international research community, and so the establishment of CARE-China fills the void of aerosol observations existing up until now in Asia. The network will provide significant amounts of data to help shed light on the spatial and temporal distributions of the concentrations of various aerosols and their precursors over China and to help investigate the quantitative relationship between aerosol concentrations, chemical components, and optical properties in typical areas; identify the sources of regional aerosols; and delineate the contribution from natural and anthropogenic origins. The project will provide physical and chemical parameters of aerosols and their precursors for both regional and global climate models and will provide improved means of verifying results from model simulations and satellite data. This will then help to reduce the uncertainties involved in the quantitative assessment of aerosol effects on regional climate and environmental changes in China. In short, the results emerging from this project will provide a significant basis for formulating and implementing a collaborative effort to produce reduction measures for aerosols and their precursors. The network data can be shared through the bilateral cooperation to maximize the research value.

ACKNOWLEDGMENTS

This study supported by the CAS Strategic Priority Research Program (XDB05020000 and XDA05100100) and NSFC (41222033, 41230642, 41375036, and 41321064). The project is a large-scale networking observation experiment, which is dependent on field stations and the analysis laboratories of the Chinese Ecosystem Research Network (CERN), Institute of Atmospheric Physics, Institute of Earth Environment, Institute of Tibetan Plateau Research, Guangzhou Institute of Geochemistry, and Capital Normal University. The authors duly acknowledge the tremendous efforts of all the scientists and technicians involved in the many aspects of the project.

REFERENCES

REFERENCES
Agarwal
,
S.
,
S. G.
Aggarwal
,
K.
Okuzawa
, and
K.
Kawamura
,
2010
:
Size distributions of dicarboxylic acids, ketoacids, α-dicarbonyls, sugars, WSOC, OC, EC and inorganic ions in atmospheric particles over northern Japan: Implication for long-range transport of Siberian biomass burning and East Asian polluted aerosols
.
Atmos. Chem. Phys.
,
10
,
5839
5858
, doi:.
Brooks
,
D. R.
, and
F. M.
Mims
III
,
2001
:
Development of an inexpensive handheld LED-based sun photometer for the GLOBE program
.
J. Geophys. Res.
,
106
,
4733
4740
, doi:.
Buseck
,
P.
,
2010
:
Nature and climate effects of individual tropospheric aerosol particles
.
Annu. Rev. Earth Planet. Sci.
,
37
,
17
43
, doi:.
Cavalli
,
F.
,
M.
Viana
,
K. E.
Yttri
,
J.
Genberg
, and
J.-P.
Putaud
,
2010
:
Toward a standardized thermal-optical protocol for measuring atmospheric organic and elemental carbon: The EUSAAR protocol
.
Atmos. Meas. Tech.
,
3
,
79
89
, doi:.
Charlson
,
R. J.
,
S. E.
Schwartz
,
J. M.
Hales
,
R. D.
Cess
,
J. A.
Coakley
,
J. E.
Hansen
, and
D. J.
Hofmann
,
1992
:
Climate forcing by anthropogenic aerosols
.
Science
,
255
,
423
430
, doi:.
Chow
,
J. C.
,
J. G.
Watson
,
L. C.
Pritehett
,
W. R.
Pierson
,
C. A.
Frazier
, and
R. G.
Purcell
,
1993
:
The DRI thermal-optical reflectance carbon analysis system: Description, evaluation and applications in U.S. air quality studies
.
Atmos. Environ.
,
27
,
1185
1201
, doi:.
Chow
,
J. C.
,
J. G.
Watson
,
L. W.
Chen
,
W. P.
Arnott
,
H.
Moosmuller
, and
K.
Fung
,
2004
:
Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols
.
Environ. Sci. Technol.
,
38
,
4414
4422
, doi:.
Chow
,
J. C.
,
J. G.
Watson
,
L. W.
Chen
,
M. C.
Chang
,
N. F.
Robinson
,
D.
Trimble
, and
S.
Kohl
,
2007
:
The IMPROVE_A temperature protocol for thermal/optical carbon analysis: Maintaining consistency with a long-term database
.
J. Air Waste Manage. Assoc.
,
57
,
1014
1023
, doi:.
Coakley
,
J.
,
2005
:
Reflections on aerosol cooling
.
Nature
,
438
,
1091
1092
, doi:.
Cwiertny
,
D. M.
,
M. A.
Young
, and
V. H.
Grassian
,
2008
:
Chemistry and photochemistry of mineral dust aerosol
.
Annu. Rev. Phys. Chem.
,
59
,
27
51
, doi:.
Decesari
,
S.
, and Coauthors
,
2006
:
Characterization of the organic composition of aerosols from Rondonia, Brazil, during the LBA-SMOCC 2002 experiment and its representation through model compounds
.
Atmos. Chem. Phys.
,
6
,
375
402
, doi:.
Dubovik
,
O.
,
B. N.
Holben
,
T. F.
Eck
,
A.
Smirnov
,
Y. J.
Kaufman
,
M. D.
King
,
D.
Tanre
, and
I.
Slutsker
,
2002: Variability of absorption and optical properties of key aerosol types observed in worldwide locations
.
J. Atmos. Sci.
,
59
,
590
608
, doi:.
Galloway
,
J. N.
,
F. J.
Dentener
,
E.
Marmer
,
Z.
Cai
,
Y. P.
Abrol
,
V. K.
Dadhwal
, and
A.
Vel Murugan
,
2008
:
The environmental reach of Asia
.
Annu. Rev. Environ. Resour.
,
33
,
461
481
, doi:.
Gantt
,
B.
, and Coauthors
,
2012
:
Global distribution and climate forcing of marine organic aerosol—Part 2: Effects on cloud properties and radiative forcing
.
Atmos. Chem. Phys.
,
12
,
6555
6563
, doi:.
GAW
,
2012
:
Recommendations for a composite surface-based aerosol network
. Global Atmosphere Watch Rep. 207, 68 pp.
Holben
,
B. N.
, and Coauthors
,
2001
:
An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET
.
J. Geophys. Res.
,
106
,
12 067
12 097
, doi:.
Huebert
,
B. J.
,
T.
Bates
,
P. B.
Russell
,
G.
Shi
,
Y. J.
Kim
,
K.
Kawamura
,
G.
Carmichael
, and
T.
Nakajima
,
2003
:
An overview of ACE-Asia: Strategies for quantifying the relationships between Asian aerosols and their climatic impacts
.
J. Geophys. Res.
,
108
,
8633
, doi:.
Ichoku
,
C.
, and Coauthors
,
2002
:
Analysis of the performance characteristics of the five-channel Microtops II Sun photometer for measuring aerosol optical thickness and perceptible water vapor
.
J. Geophys. Res.
,
107
, doi:.
Jones
,
A.
,
D. L.
Roberts
, and
A.
Slingo
,
1994
:
A climate model study of indirect radiative forcing by anthropogenic sulphate aerosols
.
Nature
,
370
,
450
453
, doi:.
Kaufman
,
Y. J.
,
D.
Tanré
, and
O.
Boucher
,
2002
:
A satellite view of aerosols in the climate system
.
Nature
,
419
,
215
223
, doi:.
Kim
,
D. H.
,
B. J.
Sohn
,
T.
Nakajima
,
T.
Takamura
,
T.
Takemura
,
B. C.
Choi
, and
S. C.
Yoon
,
2004
:
Aerosol optical properties over east Asia determined from ground-based sky radiation measurements
.
J. Geophys. Res.
,
109
,
D02209
, doi:.
Knobelspiesse
,
K. D.
,
C.
Pietras
, and
G. S.
Fargion
,
2003: Sun-pointing-error correction for sea deployment of the MICROTOPS II handheld sun photometer
.
J. Atmos. Oceanic Technol.
,
20
,
767
771
, doi:.
Lee
,
K. H.
,
Z.
Li
,
M. S.
Wong
,
J.
Xin
,
Y.
Wang
,
W.-M.
Hao
, and
F.
Zhao
,
2007
:
Aerosol single scattering albedo estimated across China from a combination of ground and satellite measurements
.
J. Geophys. Res.
,
112
,
D22S15
, doi:.
Levy
,
R. C.
,
L. A.
Remer
,
S.
Matto
,
E. F.
Vermote
, and
Y. J.
Kaufman
,
2007
:
Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance
.
J. Geophys. Res.
,
112
,
D13211
, doi:.
Li
,
X.
,
L.
Wang
,
D.
Ji
,
T.
Wen
,
Y.
Pan
,
Y.
Sun
, and
Y.
Wang
,
2013
:
Characterization of the size-segregated water-soluble inorganic ions in the Jing-Jin-Ji urban agglomeration: Spatial/temporal variability, size distributions and sources
.
Atmos. Environ.
,
77
,
250
259
, doi:.
Li
,
Z.
,
2004
:
Aerosol and climate: A perspective from East Asia
. Observation, Theory, and Modeling of the Atmospheric Variability,
D.
Zhu
, Ed.,
World Scientific
,
501
525
.
Li
,
Z.
,
K.-H.
Lee
,
Y.
Wang
,
J.
Xin
, and
W.-M.
Hao
,
2010
:
First observation-based estimates of cloud-free aerosol radiative forcing across China
.
J. Geophys. Res.
,
115
,
D00K18
, doi:.
McMurry
,
P. H.
,
M. F.
Shepherd
, and
J. S.
Vickery
,
2004
: Particulate Matter Science for Policy Makers: A NARSTO Assessment.
Cambridge University Press
, 510 pp.
Menon
,
S.
,
2004
:
Current uncertainties in assessing aerosol effects on climate
.
Annu. Rev. Environ. Resour.
,
29
,
1
30
, doi:.
Morys
,
M.
,
F. M.
Mims
III
,
S.
Hagerup
,
S. E.
Anderson
,
A.
Baker
,
J.
Kia
, and
T.
Walkup
,
2001
:
Design, calibration, and performance of MICROTOPS II handheld ozone monitor and sun photometer
.
J. Geophys. Res.
,
106
,
14 573
14 582
, doi:.
Pan
,
Y. P.
,
Y. S.
Wang
,
Y. J.
Yang
,
D.
Wu
,
J. Y.
Xin
, and
W. Y.
Fan
,
2010
:
Determination of trace metals in atmospheric dry deposition with a heavy matrix of PUF by inductively coupled plasma mass spectroscopy after microwave digestion
.
Environ. Sci.
,
31
,
553
559
.
Pan
,
Y. P.
,
Y. S.
Wang
,
Y.
Sun
,
S. L.
Tian
, and
M. T.
Cheng
,
2013
:
Size-resolved aerosol trace elements at a rural mountainous site in northern China: Importance of regional transport
.
Sci. Total Environ.
,
461–462
,
761
771
, doi:.
Patashnick
,
H.
, and
E.
Rupprecht
,
1991
:
Continuous PM10 measurements using the tapered element oscillating microbalance
.
J. Air Waste Manage.
,
41
,
1079
1083
, doi:.
Penner
,
J. E.
,
R. E.
Dickinson
, and
C. A.
O'Neill
,
1992
:
Effects of aerosol from biomass burning on the global radiation budget
.
Science
,
256
,
1432
1434
, doi:.
Prather
,
K. A.
,
C. D.
Hatch
, and
V. H.
Grassian
,
2008
:
Analysis of atmospheric aerosols
.
Annu. Rev. Anal. Chem.
,
1
,
485
514
, doi:.
Ricchiazzi
,
P.
,
S.
Yang
, and
C.
Gautier
,
1998
:
SBDART: A research and teaching software tool for plane-parallel radiative transfer in the Earth’s atmosphere
.
Bull. Amer. Meteor. Soc.
,
79
,
2101
2114
, doi:.
Schkolnik
,
G.
,
A. H.
Falkovich
,
Y.
Rudich
,
W.
Maenhaut
, and
P.
Artaxo
,
2005
:
New analytical method for the determination of levoglucosan, polyhydroxy compounds, and 2-methylerythritol and its application to smoke and rainwater samples
.
Environ. Sci. Technol.
,
39
,
2744
2752
, doi:.
Seinfeld
,
J. H.
, and Coauthors
,
2004
:
ACE-ASIA: Regional climatic and atmospheric chemical effects of Asian dust and pollution
.
Bull. Amer. Meteor. Soc.
,
85
,
367
380
, doi:.
Shaw
,
G. E.
,
1976
:
Error analysis of multi-wavelength sun photometry
.
Pure Appl. Geophys.
,
114
,
1
14
, doi:.
Simoneit
,
B. R. T.
,
G.
Sheng
,
X.
Chen
,
J.
Fu
,
J.
Zhang
, and
Y.
Xu
,
1991
:
Molecular marker study of extractable organic matter in aerosols from urban areas of China
.
Atmos. Environ.
,
25
,
2111
2129
, doi:.
Smirnov
,
A.
, and Coauthors
,
2011
:
Maritime aerosol network as a component of AERONET—First results and comparison with global aerosol models and satellite retrievals
.
Atmos. Meas. Tech.
,
4
,
583
597
, doi:.
Solomon
,
S.
,
D.
Qin
,
M.
Manning
,
Z.
Chen
,
M.
Marquis
,
K. B.
Averyt
,
M.
Tignor
, and
H. L.
Miller
, Eds.,
2007
: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.
Streets
,
D. G.
, and
K.
Aunan
,
2005
:
The importance of China’s household sector for black carbon emissions
.
Geophys. Res. Lett.
,
32
,
L12708
, doi:.
Sun
,
Y.
,
Y. P.
Pan
,
X. R.
Li
,
R. H.
Zhu
, and
Y. S.
Wang
,
2011
:
Chemical composition and mass closure of particulate matter in mega cities, north China (in Chinese)
.
Environ. Sci.
,
32
,
2732
2740
.
Urban
,
R. C.
, and Coauthors
,
2012
:
Use of levoglucosan, potassium, and water-soluble organic carbon to characterize the origins of biomass-burning aerosols
.
Atmos. Environ.
,
61
,
562
569
, doi:.
Wang
,
G.
,
C.
Chen
,
J.
Li
,
B.
Zhou
,
M.
Xie
,
S.
Hu
,
K.
Kawamura
, and
Y.
Chen
,
2011
:
Molecular composition and size distribution of sugars, sugar-alcohols and carboxylic acids in airborne particles during a severe urban haze event caused by wheat straw burning
.
Atmos. Environ.
,
45
,
2473
2479
, doi:.
Wang
,
Y.
, and Coauthors
,
2011
:
Seasonal variations in aerosol optical properties over China
.
J. Geophys. Res.
,
116
,
D18209
, doi:.
Xia
,
X.
,
Z.
Li
,
P.
Wang
,
H.
Chen
, and
M.
Cribb
,
2007
,
Estimation of aerosol effects on surface irradiance based on measurements and radiative transfer model simulations in northern China
.
J. Geophys. Res.
,
112
,
D22S10
, doi:.
Xin
,
J.
, and Coauthors
,
2007
:
Aerosol optical depth (AOD) and Ångström exponent of aerosols observed by the Chinese Sun Hazemeter Network from August 2004 to September 2005
.
J. Geophys. Res.
,
112
,
D05203
, doi:.
Xin
,
J.
,
L.
Wang
,
Y.
Wang
,
Z.
Li
,
P.
Wang
,
2011
:
Trends in aerosol optical properties over the Bohai Rim in northeast China from 2004 to 2010
.
Atmos. Environ.
,
45
,
6317
6325
, doi:.
Xin
,
J.
,
Y.
Wang
,
L.
Wang
,
G.
Tang
,
Y.
Sun
,
Y.
Pan
, and
D.
Ji
,
2012
:
Reductions of PM2.5 in Beijing-Tianjin-Hebei urban agglomerations during the 2008 Olympic Games
.
Adv. Atmos. Sci.
,
29
,
1330
1342
, doi:.
Zhang
,
Q.
, and Coauthors
,
2007
:
Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes
.
J. Geophys. Res.
,
34
,
L13801
, doi:.