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Aerosol and Air Quality Research, 16: 3290–3301, 2016 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2016.07.0324 Emission Characteristics and Control Prospects of Primary PM2.5 from Fossil Fuel Power Plants in China Yan Wang1,2, Ke Cheng2*, He-Zhong Tian3**, Peng Yi4, Zhi-Gang Xue4 1 Laboratory of Environmental Pollutants and Health Effects Assessment, School of Public Health, Xinxiang Medical University, Xinxiang 453003, China 2 Henan Key Laboratory for Environmental Pollution Control, Key Laboratory for Yellow River and Huai River Water Environment and Pollution Control, Ministry of Education, School of Environment, Henan Normal University, Xinxiang 453007, China 3 State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China 4 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China ABSTRACT

In this study, a unit-based approach was used to establish an integrated emission inventory of primary PM2.5 from fossil fuel (coal, oil, and natural gas) power plants in China. The inventory was of high spatial and temporal resolution, and composed of detailed chemical speciation. In 2014, the total emissions were estimated to be approximately 669.53 kt. The emissions of primary PM2.5 from coal-fired power plants (CFPs) were 668.56 kt, making CFPs the largest contributor. The emissions of primary PM2.5 from oil-fired power plants (OFPs) and natural gas-fired power plants (GFPs) were approximately 17.41 t and 945.60 t, respectively. Spatial distribution features demonstrated that the emissions in the eastern and central provinces of China were much higher than those in the west, except for provinces involved in the “west-to-east power transmission” project. For CFPs, crustal elements and water-soluble inorganic ions were the primary species of PM2.5. By contrast, for OFPs and GFPs, carbonaceous components were the predominant species of PM2.5. Moreover, this study conducted a scenario analysis of changes in PM2.5 emissions resulting from technical advancement, penetration, and substitution of CFPs by GFPs for the target years.

Keywords: PM2.5 emission; Unit-based approach; Chemical composition; Scenario projection. INTRODUCTION

Particulate matter with aerodynamic diameters less than or equal to 2.5 mm (PM2.5) is a serious concern, because it can lead to significant deterioration of visibility and adverse health risks (Meng et al., 2013; Yang et al., 2013; Pui et al., 2014; Kioumourtzoglou et al., 2015). The regional combined pollution characterized by PM2.5 in Asia is of increasing concern, particularly in China (Hu et al., 2015). High PM2.5 concentrations are associated with severe haze events, which have occurred in most regions in Central and * Corresponding author.

Tel.: +86-373-3325971; Fax: +86-373-3325971 E-mail address: [email protected]

** Corresponding author. Tel.: +86-10-5880-0176; Fax: +86-10-5880-0176 E-mail address: [email protected]

Eastern China since the beginning of 2013. The Chinese government is under considerable pressure to control haze pollution, and PM2.5 concentrations have been added to the latest version of air quality standards (GB3095-2012), which came into effect nationwide in January 2016.

In 2015, among the 161 cities conducting PM2.5 monitoring, up to 83.8% reported an annual PM2.5 concentration that exceeded Level II of the new standards (MEP, 2015). Frequent haze in China can be mainly attributed to the heavy reliance on coal to fuel power generation and industrial processes (Cao et al., 2011; Hu and Jiang, 2013; Wang and Zheng, 2013). Among all sectors, the power sector has always been ranked as the largest coal consumer and accounts for more than half of the total coal consumption (NBSC, 2015). Recently, air quality legislation, including the establishment of rigorous guidelines and legally binding limit values, has been increasingly implemented to reduce emissions from thermal power plants.

To meet the increasingly strict emission regulations, most existing power plants have undergone continuous technical

Wang et al., Aerosol and Air Quality Research, 16: 3290–3301, 2016 3291

renovation and coal quality improvement. Nearly 94.0% of pulverized coal power units have installed electrostatic precipitators (ESPs), and the remaining 6.0% have installed fabric filters (FFs) (Zhang et al., 2015). All units equipped with fluidized-bed furnaces have installed ESPs. Currently, flue gas desulfurization (FGD) technology for reducing SO2 emissions has been adopted nationwide, and more than 99.0% of units have adopted FGD technology (MEP, 2015). Although ESPs and FGD devices can effectively remove large particles, their removal efficiency for PM10 and PM2.5 is relatively low. Therefore, 50.0%–80.0% of the particles in stack aerosols are in the PM2.5 range (Nielsen et al., 2002; Wang et al., 2011). Moreover, selective catalytic reduction (SCR) technology has become increasingly widely used to reduce NOx emissions in China, and approximately 92.0% of coal-fired power units adopted SCR technology by the end of 2015 (MEP, 2015). Although SCR is an effective strategy for controlling NOx emissions and thus secondary PM2.5 (nitrate aerosol) in the atmosphere, the enhanced formation of both SO4

2– and NH4+ particles resulting from

SCR installation, on such a massive scale in China, is expected to significantly increase primary PM2.5 emissions from coal-fired power plants (CFPs) (Li et al., 2015).

Previous studies have estimated China′s primary PM2.5 emissions from coal- or fuel oil-fired power plants within a national inventory (Zhang et al., 2007; Cao et al., 2011; Lei et al., 2011; Zhao et al., 2013a; Chen et al., 2014) or as a part of regional inventories (Huang et al., 2011; Zhao et al., 2012; Fu et al., 2013). Some of these studies have also reported emission trends (Lei et al., 2011; Zhao et al., 2013a). Using a bottom-up approach, these studies have calculated primary PM2.5 emissions as products of activity data and emission factors (EFs) and have investigated fuel, combustion type, emission controls, and their prevalence. According to the 2014 technical guidelines for establishing primary PM2.5 emission inventories, EFs of PM2.5 from the power sector should account for three aspects, fuel (coal, diesel, fuel oil, natural gas, etc.), combustion technology (pulverized furnace, grate furnace, circulating fluidized bed [CFB], etc.), and air pollution control devices (APCDs; ESPs, FFs, FGD, SCR, etc.). However, most studies have mainly focused on coal-fired units and have assigned coal consumption data on the basis of national statistical yearbooks because of the lack of detailed unit statistics. Furthermore, all available EFs, regardless of SCR installation, may also lead to an underestimation of primary PM2.5 emissions from CFPs (Li et al., 2015).

In addition, although the ambient concentrations have been analyzed extensively, relatively little attention has been paid to emission inventories of PM2.5 speciated with trace elements (Reff et al., 2009). Moreover, knowledge of PM2.5 speciation is crucial for studying source apportionment, haze formation, radiative forcing, and toxicity for human health (Tainio et al., 2010; Fu et al., 2013). In this study, a unit-based approach was used to establish an integrated emission inventory of primary PM2.5 from fossil fuel power plants with high spatial and temporal resolution. And detailed chemical speciation has been discussed.

METHODOLOGY AND DATA SOURCES Methodology

In this study, a unit-based approach was adopted to evaluate the atmospheric emissions of primary PM2.5 from fossil fuel power plants in China for the year of 2014. The emissions were calculated by combining the unit-based activity data with the optimal specific EFs of PM2.5. The unit-based emissions can be calculated as

, ,

c o g

i i j e j e k ki j e k

E E E E

CC CEF OC OEF GC GEF

(1) CCi = ICi × RTi × (UCc/0.7143) × 10–6 (2) OCj,e = ICj,e × RTj,e × UCo(e) × 10–6 (3) GCk = ICk × RTk × UCg × 10–3 (4)

CEFi = ACi × FAi(d) × MRi(d) × (1 – Pi(f)) (5) OEFj,e = PFo(e) × (1 – Pj(f)) (6) GEFk = PFg × (1 – Pk(f)) (7) where E is the total primary PM2.5 emissions from fossil fuel power plants utilizing different fuel types, in terms of t year–1; Ec, Eo, and Eg represent primary PM2.5 emissions from CFPs, oil-fired power plants (OFPs), and natural gas-fired power plants (GFPs), respectively, measured in t; CC is the annual coal consumption of CFPs, in terms of kt year–1; OC and GC are the annual consumption of OFPs and GFPs, in terms of kt year–1 and 106 m3 year–1, respectively; CEF

is the PM2.5 EFs of CFPs, in terms of kg t–1; OEF and GEF are the PM2.5 EFs of OFPs and GFPs, measured in kg t–1 and g m–3, respectively; IC represents the installed capacity of units, in terms of MW; RT is the annual running time (expressed in h); UCc is the standard coal consumption per unit of power generation, in terms of gce kW–1 h–1; 0.7143 is the constant conversion coefficient of raw coal to standard coal equivalent; UCo and UCg are the consumption of oil and natural gas per unit of power generation, in terms of g kW–1 h–1 and m3 kW–1 h–1, respectively; AC is the ash content of coal as consumed (expressed as %); FA is the percentage of ash converted into fly ash (expressed as %); MR is the ratio of PM2.5 in fly ash; PFo and PFg are the PM2.5 production coefficients of OFPs and GFPs, in terms of kg t–1 and g m–3, respectively; P represents the removal efficiency of APCDs; i, j, and k represent the units of CFPs, OFPs, and GFPs, respectively; d represents the type of CPF boiler; e represents the type of fuel consumed by OFPs, including diesel and fuel oil; and f represents the type of APCDs.

The general methodology for calculating PM2.5 speciation is to multiply the total emissions of PM2.5 from each power plant by the averaged chemical speciation profiles. The


equation is given by Etr = Ec × PRc(tr) + Eo × PRo(tr) + Eg × PRg(tr) (8) where Etr is the emission of a specific trace element of PM2.5 from CFPs, OFPs, and GFPs, with the unit of t year–1; PRc, PRo, and PRg are the speciation profiles for CFPs, OFPs, and GFPs, respectively (expressed as %); and tr represents a specific trace element of PM2.5. Activity Data Collection and Analysis

The unit-based statistics of CFPs were compiled from the List of Key Emission Reduction Projects of Air Pollutants issued by the Ministry of Environmental Protection of the People′s Republic of China (MEP) in 2014; statistics were taken from a total of 4467 units operated by 2098 CFP enterprises (MEP, 2014a). The database of OFPs was established using the environmental statistics of MEP, containing 26 units of 9 OFP enterprises. The unit-based information for GFPs was obtained from China Natural Gas Map published by Allied Resources Allocator (ARA) International Limited Research and Publication Division, consisting of 164 units of 68 GFPs enterprises (ARA, 2015). According to the data released by the China Electricity Council (CEC), in 2014, the total installed capacities of CFPs and GFPs were 832.33 GW and 56.97 GW, respectively (CEC, 2015). In this study, the capacity of all units was summed up to 778.76 GW for CFPs and 51.80 GW for GFPs, accounting for 93.6% and 90.9% of the released total, respectively. For OFPs, units with capacities less than 6 MW were not included in the official statistics; thus, because of the lack of other detailed information, only 9 OFP enterprises with a total capacity of 1.77 GW were considered in this study, representing 65.6% of the total (ECCEPY, 2014).

The provincial power structure of fossil fuel power plants in China in 2014 is shown in Supporting Information (SI) Fig. S1. For CFPs, units with capacities greater than or equal to 600 MW accounted for more than 45.3% of the total, and the proportion of units with capacities greater than or equal to 1000 MW was 8.2%. For OFPs, units with capacities less than 200 MW accounted for the largest proportion at 81.3%. For GFPs, units with capacities greater than or equal to 300 MW but less than 600 MW accounted for 47.4%; GFP units with capacities greater than or equal to 1000 MW accounted for more than 20.0%. Thus, it can be concluded that the power structure is dominated by high-capacity CFPs and GFPs. Thus, emissions are more likely to be reduced by improving the efficiency of power generation. However, the existing OFPs in China are mainly small-scale and self-supplying plants of oil-processing enterprises.

Using the aforementioned statistical data and the compilation of other useful information, a comprehensive database of power units in China was established in this study, which contained essential information on the enterprise name, geographical position, number of units, unit capacity, RT, boiler types, fuel types (coal, diesel, fuel oil, and natural gas), fuel consumption per unit of power generation (UC), fuel properties (ash content of coal), and APCDs such as dust collectors and FGD devices. The unit-based fuel consumption

was then calculated from this database by Eqs. (2)–(4). In general, the unit-based approach has higher demands

of data availability. However, in the actual calculation, it is difficult to obtain all necessary parameters for each power unit. Hence, in this study, key indexes, such as RT, UC, and the ash content of coal, were assigned the averaged provincial values corresponding to the location of the enterprise. The averaged values of RT and UC of CFPs in each province were obtained from the CEC, as shown in SI Fig. S2 (ECCEPY, 2014; CEC, 2015). Recently, the unit capacity of CFPs has shown a steady annual growth rate of 6.8% since 2010. However, the rate at which power consumption increases has begun to decrease slowly because of China′s declining economic growth. Moreover, all these factors have led to the overcapacity of power industry and the resulting remarkable decline in RT of CFPs, from 5240 h in 2010 to 4489 h in 2014. In China, electric power generated by OFPs and GFPs is believed to supplement that generated by CFPs. Thus, the RT values of OFPs and GFPs were lower than those of CFPs, with values of only 883 h and 2721 h, respectively (ECCEPY, 2014). With the upgrading of power generation devices, in 2014, the averaged value of UC sharply decreased from 347.00 gce kW–1 h–1 in 2010 to 302.03 gce kW–1 h–1 (CEC, 2015). Moreover, the UC of OFPs and GFPs in 2014 was derived from the averaged data of power generation and fuel consumption of previous years.

Emission Factors of PM2.5

In this study, the EFs of primary PM2.5 from fossil fuel power plants were determined according to factors including fuel types, fuel properties, boiler types, removal efficiency, and penetration of APCDs. The EFs of PM2.5 were calculated using Eqs. (5)–(7), and the variables were compiled from the available references and official published reports (Yi et al., 2006; Zhao et al., 2008; Wang et al., 2010a; MEP, 2014b).

Fuel types and fuel properties were the main factors affecting PM2.5 emissions from power plants, particularly CFPs and OFPs. For CFPs, because of the quality diversity of coal produced and interprovincial coal flows, the ash content of coal consumed differs from one province to another. In this study, coal transfer matrixes were applied to determine the ash content of coal consumed in each province, which was consistent with the methodology in our previous studies (Tian et al., 2010, 2012; Cheng et al., 2015). The provincial ash content of coal produced was compiled from the averaged value of commercial coal from the primary mining area, as shown in SI Fig. S3.

The boiler type can affect the combustion efficiency of fuel, which can alter the emission characteristics of particles, particularly the ratio of fly ash to bottom ash and the percentage of PM2.5 among the total fly ash particles. Pulverized and CFB boilers are the predominant boiler types for CFPs. The proportion of units equipped with pulverized boilers has been reported to be as high as 88.0% of the total, and other units are equipped with CFB boilers (Zhang et al., 2015). FA values have been reported to be 75.0% for pulverized boilers and 56.0% for CFB boilers (MEP, 2014b). The aerodynamic diameters of particles are


also influenced by the boiler type. According to the available test data from different CFPs, the averaged ratio of PM2.5 in fly ash (MR) is 6.0% for pulverized boilers and 7.0% for CFB boilers (Zhao et al., 2008; MEP, 2014b). For OFPs, tube furnaces and waste heat boilers have been mostly adopted as combustion devices, whereas for GFPs, gas turbines are the predominant combustion devices (MEP, 2014b).

The removal efficiencies of APCDs were compiled from available literature, as shown in SI Table S1. The PM2.5 removal efficiencies of different technologies were averaged; the results were approximately 92.6% for ESPs, 97.5% for the combination of ESPs + Wet-FGD, and 99.0% for FFs (Yi et al., 2006; Zhao et al., 2008; Zhao et al., 2010; MEP, 2014a). It was estimated that SCR installation can increase primary PM2.5 emissions by at least 20.0% in comparison with the combination of ESPs + FGD (Li et al., 2015). In this study, all OFPs were assumed to be equipped with ESPs devices and to have the same application rate of Wet-FGD devices as CFPs did. The PM2.5 removal efficiencies of APCDs of OFPs were assumed to be consistent with those of CFPs. Moreover, it was assumed that GFPs are not equipped with APCDs because of the relatively low emissions of air pollutants.

Speciation Profiles of Primary PM2.5

In this study, for an improved understanding of the potential environmental effects of PM2.5, a comprehensive inventory of trace elements in primary PM2.5 from fossil fuel power plants was established using Eq. (8). It has been reported that APCDs can not only affect the mass concentration of particles but also alter the chemical speciation profiles of primary PM2.5 (Duan et al., 2015; Ma et al., 2015). The proportions of most components in PM2.5, particularly water-soluble inorganic ions and the trace element Cd, decrease by different degrees after ESPs and FFs installation. By contrast, the proportions of elemental carbon (EC), Se, Ni, Na, Fe, and Cu, which are mainly enriched in smaller-size particles, may increase after the installation of dust removal equipment (Meij, 1994; Wang et al., 2013). In the process of sulfur removal using limestone/lime plaster, some particles may be washed down, and small amounts of slurry may be entrained in the flue gas and may form particles after demisting and desiccation. Consequently, Wet-FGD has been found to have a significant impact on water-soluble inorganic ions and to increase the proportions of SO4

2–, NH4

+, and NO3– in PM2.5 by 566.7%, 400.0%, and 150.0%,

respectively (Ma et al., 2015). Detailed information is provided in SI Table S2.

Until now, few studies have focused on the chemical speciation profiles of PM2.5 from power plants. Several literature were found to summarize the average profiles of the PM2.5 components from power plants, which can be seen in SI Table S5 (US EPA, 2008; Reff et al., 2009; Chou et al., 2011; Liu et al., 2015). Large variation exists in the profiles of some components (e.g., SO4

2–, NH4+, and NO3

–, and Si for CFPs) among different researches. The probable reasons may attribute to the difference of burning conditions due to boiler types and fuel properties. Moreover, APCDs also have obvious impact on the speciation profiles of

PM2.5. By compiling available literature and considering the average speciation profiles of fossil fuel power plants, a full suite of speciation profiles of all components in PM2.5 for different fossil fuel power plants is proposed, which can be seen in SI Fig. S4. In this study, the inventory covered species including carbonaceous components (EC and OC), water-soluble inorganic ions (SO4

2–, NO3–, and NH4

+), hazardous trace elements (Hg, As, Pb, Cd, Cr, Sb, Ni, Mn, and Co), metal-bound oxygen (oxidation states of metals in PM2.5), noncarbon organic matter, other elements, excluding the aforementioned elements, and other unmeasured species.

Scenario Projection

To explore the future emission trends of PM2.5 from fossil fuel power plants, the emissions for the period of 2015–2030 were projected according to various sets of assumptions regarding the future development of power demand and the application ratio of emission control technologies in China. The three scenarios designed were base level (BL), high emission level (HL), and low emission level (LL). For the BL scenario, the annual increasing rate of fossil fuel consumption and the application ratio of APCDs were based on those for the period of 2011–2014. The parameters of the HL scenario were based on high fuel consumption and lenient air pollutant control strategies. The parameters of the LL scenario were based on low fuel consumption and strict control measures. According to the plan of the energy industry and the annual increasing rate of power unit capacity during the period of the 12th five-year-plan (2011–2015), the unit capacity of different fossil fuel power plants was projected for three scenarios, as shown in SI Table S3 (CEC, 2011; ERI, 2011; SCC, 2014).

For OFPs and GFPs, the future projections focused mainly on the development of unit capacity, whereas for CFPs, both unit capacity and emission countermeasures were considered in the projections, covering the changes in the application ratio and the removal efficiency of APCDs. Detailed parameters are provided in SI Fig. S5. Uncertainty Analysis

To demonstrate the uncertainties in the estimation of PM2.5 emissions, Monte Carlo simulations were applied for a sensitivity analysis depending on activity data and EFs. The input parameters of activity data and EFs are determined based on the empirical judgment or to the related available published literature (Zhao et al., 2008; Tian et al., 2012; Liu et al., 2015), as listed in SI Table S4. RESULTS AND DISCUSSION Spatial Distribution Characteristics

In 2014, the total emissions of primary PM2.5 from fossil fuel power plants in China were estimated to be approximately 669.53 kt. The emissions of PM2.5 from CFPs were 668.56 kt, making CFPs the largest contributor. The emissions of PM2.5 from OFPs and GFPs were approximately 17.41 t and 945.60 t, respectively. As shown in Table 1, large amounts of PM2.5 emissions were contributed by Shandong, Jiangsu, Inner Mongolia, Guangdong, and Henan provinces,


Table 1. PM2.5 emissions from fossil fuel power plants by province in China, 2014.

Province CFPs OFPs GFPs Province CFPs OFPs GFPs (kt) (t) (t) (kt) (t) (t)

Beijing 1.63 / 235.53 Hubei 16.74 / / Tianjin 9.08 / 38.61 Hunan 14.19 / / Hebei 37.25 / 11.57 Guangdong 43.01 / 151.12 Shanxi 36.89 / / Guangxi 12.41 1.27 25.19 Inner Mongolia 53.92 / 5.48 Hainan 3.02 / 19.39 Liaoning 23.43 0.97 / Chongqing 6.88 / / Jilin 11.55 3.25 5.48 Sichuan 12.15 / 34.68 Heilongjiang 8.70 0.24 / Guizhou 18.69 / / Shanghai 9.42 8.51 123.41 Yunnan 8.39 / / Jiangsu 60.89 / 97.12 Xizang / / / Zhejiang 30.71 3.00 74.77 Shaanxi 24.90 / 2.19 Anhui 37.71 / / Gansu 6.61 / / Fujian 18.13 / 40.53 Qinghai 1.62 / / Jiangxi 15.18 / / Ningxia 20.77 / 16.28 Shandong 64.14 0.18 18.99 Xinjiang 16.69 / 18.26 Henan 43.85 / 27.02 National total 668.56 17.41 945.60

“/” means there were no CFPs, OFPs, or GFPs in this province.

which are mainly located in Eastern and Northern China. These five provinces altogether accounted for nearly 40.0% of the total primary PM2.5 emissions and approximately 38.4% of the national total from CFPs. Generally, the development of the electric power industry and the scaling-up of the power network are consistent with the development of the economy. When listed by gross domestic product, Guangdong, Jiangsu, Shandong, and Henan were the top five provinces among all provinces in China in 2014. Moreover, Inner Mongolia was ranked as the province with the highest emissions because of its abundant coal resources and high power output relative to other provinces.

The total unit capacity of CFPs for Shandong province was ranked second after Jiangsu, whereas Shandong province was the top contributor to PM2.5 emissions in China. By comparison, the proportion of units with a capacity of less than 300 MW was 37.5% in Shandong, which was significantly higher than that in Jiangsu province (15.5%). For small capacity units, the power generation indexes, particularly UC, were higher than those of large-capacity units; that is, much more coal was consumed and more air pollutants were emitted per unit of power generation. For OFPs, PM2.5 emissions were mainly contributed by Shanghai, Jilin, and Zhejiang, which are vital bases of oil-processing enterprises. For GFPs, Beijing, Guangdong, and Shanghai were the main contributors of PM2.5 emissions. This finding can be attributed to the increased substitution of CFPs by GFPs, because of the high political and economic status and stricter emission standards implemented in these districts.

To obtain more details of PM2.5 emissions from fossil fuel power plants, this study evaluated the spatial distribution of primary PM2.5 emissions according to the capacities of CFPs, OFPs, and GFPs in China, as shown in Fig. 1. The emissions of 4467 units of 2098 CFP enterprises, 26 units of 9 OFP enterprises, and 164 units of 69 GFP enterprises with different capacities were considered according to the latitude and longitude of the enterprises. Large quantities

of emissions from units with capacities greater than or equal to 600 MW were contributed by Jiangsu and Guangdong, whereas Henan was the primary source of emissions from units with capacities less than 200 MW. The emissions of primary PM2.5 from fossil fuel power plants in China showed remarkable geographical variation. The emissions in the eastern and central provinces of China were much higher than those in the west, except for provinces involved in the “west-to-east power transmission” project, such as Guizhou, Yunnan, and Shaanxi. The “west-to-east power transmission” project in China was proposed as early as 1986 and was eventually implemented in 1996. The main purpose of this project is to increase energy generation in Western China and completely utilize the coal resources in Central and Western China. This policy of energy source adjustment changed the spatial distribution characteristics of primary PM2.5 emissions from CFPs, showing a trend of “east-to-west.” Speciated Emissions of Primary PM2.5

The emissions of all PM2.5 species from fossil fuel power plants in China in 2014 are provided in Fig. 2, including carbonaceous components, water-soluble inorganic ions, hazardous trace elements, and crustal elements.

Carbonaceous aerosols are critical components of fine particles and mainly consist of OC and EC (Chou et al., 2011; Zhao et al., 2013). According to recent studies, carbonaceous components account for 17.0%–40.0% of the mass concentration of PM2.5 in aerosols (Wang et al., 2010b; Dong et al., 2013). OC and EC can affect the optical properties of the atmosphere through scattering and absorption and can significantly affect atmospheric visibility, radiation balance of the earth–atmosphere system, and climate change (Watson, 2002; Yang et al., 2010). Moreover, some substances containing OC have high physiological toxicity, and EC is enriched in semi-violate compounds and promotes chemical reaction processes. In this study, only the primary


Fig. 1. Spatial distribution of PM2.5 emissions by capacity in China, 2014.

emissions of carbonaceous components were considered. The emissions of carbonaceous components in PM2.5 from fossil fuel power plants were estimated to be 29.55 kt in 2014. For CFPs and OFPs, OC accounted for the largest proportions of 60.7% and 71.4%, respectively. By contrast, for GFPs, EC accounted for the largest proportion of 60.9%; thus, EC was the major contributor to all carbonaceous emissions from GFPs.

Water-soluble inorganic ions (SO42–, NO3

–, NH4+, etc.)

are mainly believed to be the products of secondary conversion of primary precursors (e.g. SO2, NOx, and NH3) in the atmosphere and are less likely to originate from the primary emissions of anthropogenic sources (Chou et al., 2011; Gao, 2012). The moisture absorption features of water-soluble inorganic ions can promote droplet formation under the conditions of saturated vapor pressure, which can affect atmospheric visibility and energy balance of the earth–

atmosphere system. (NH4)2SO4 is the greatest contributor to the extinction of visibility, followed by NH4NO3 (Yang et al., 2010). SO4

2– and NO3– are the primary acid substances

that directly affect human health and the ecosystem. In this study, the emissions of water-soluble inorganic ions in PM2.5 from fossil fuel power plants were estimated to be nearly 105.26 kt. For CFPs, SO4

2– accounted for the largest proportion of 80.8%. For GFPs, the proportions of SO4

2– and NO3

– in water-soluble inorganic ions were 80.4% and 19.6%, respectively. For OFPs, only SO4

2– was detected, with an emission of 3.31 t.

Ten hazardous trace heavy metals, which (in 1990) were listed as toxic air pollutants in the United States Clean Air Act, were considered in this study, including Hg, As, Se, Pb, Cd, Cr, Sb, Ni, Mn, and Co. Hazardous trace elements are usually ubiquitous in various raw materials, including fossil fuel (Lee et al., 2008; Cong et al., 2010; Tian et al.,


(a)

(b)

Fig. 2. Speciated emissions (a) and subclass of specific speciation (b) of primary PM2.5 from fossil fuel power plants in China, 2014.

2014; Cheng et al., 2015). They can evaporate entirely or partially during the high temperature of fuel combustion and can subsequently enter ambient air through exhaust gases generated after removal by various APCDs (Liu et al., 2002; Feng et al., 2003; Zhu et al., 2016). The combustion of fossil fuel, particularly coal, is believed to be the primary source of hazardous trace elements in the atmosphere and terrestrial and aquatic environments (Sakata et al., 2008;

Basile et al., 2009; Nguyen et al., 2010). The total emissions were estimated to be 2.43 kt, of which the most worrisome toxic elements, namely Hg, As, Pb, Cd, and Cr altogether account for 4.0% of the total emissions. The diversity of the profiles in PM2.5 is mainly attributed to the content in coals consumed and the volatility of different elements during combustion. Hg and Se are classified as volatile elements, whereas others are medium-volatile elements (Clarke and


Sloss, 1992). Moreover, the concentration of Se in coal is 10-fold higher than that of Hg (Tian et al., 2010). For all elements from OFPs and GFPs, only Cr was detected in the PM2.5 from OFPs, with a total emission of approximately 4.35 kg.

Ten crustal elements were considered in this study, namely Al, Ca, Fe, Mg, Ti, Ba, Sr, K, Na, and Si. Because of the ability to neutralize acidic species (such as sulfuric and nitric acids) in the atmosphere, crustal elements are crucial components of particles (Zhang et al., 2007; Lei et al., 2011). The emissions of crustal elements were estimated to be 127.35 kt in PM2.5 from fossil fuel power plants. For CFPs, Si, Al, and Ca were the primary components, with contributions of 41.9%, 21.8%, and 18.2%, respectively. Si and Fe were the major components of crustal elements in PM2.5 from OFPs, with proportions of 84.2% and 9.5%, respectively. Scenario Projection and Control Strategy Implications

The projections of primary PM2.5 emissions from fossil fuel power plants for the period of 2015–2030 in China are shown in Fig. 3. The total emissions will reach 1371.82 kt, 1471.37 kt, and 1077.24 kt by the end of 2030 under the BL, HL, and LL scenarios, respectively. The annual averaged increasing rates of PM2.5 emissions will be 4.6%, 5.0%, and 3.0% for the BL, HL, and LL scenarios during the period of 2014–2030, respectively. Moreover, the contribution of CFPs to total PM2.5 emissions will decline, whereas the contribution of GFPs will increase steadily. Energy structure adjustment is also required to meet the control target.

Currently, natural gas is considered the optimal clean energy alternative to coal and oil, and GFPs are the first choice for natural gas utilization worldwide. To promote the development of GFPs, a policy in which the electricity price for GFPs is increased by 0.35 yuan kW–1 h–1 compared with

that for CFPs was introduced by the National Development and Reform Commission (CGA, 2015; NDRC, 2015). According to the “Clean Air Action Plan of Beijing (2013–2017)” (implemented because of the tremendous pressure faced by China to reduce air pollution), all CFPs in Beijing are to be substituted by GFPs by the end of 2016. However, owing to the present limited supply of natural gas and the limits of associated pipelines, large-scale fuel substitution of coal by natural gas is a long-term process (CGA, 2015). Moreover, because of the restricted supply of fuel oil and diesel and the relatively high air pollutant emissions, the unit capacity of OFPs will be kept on an even keel, and PM2.5 emissions from OFPs will present a gradual downward trend.

In recent years, to meet the increasingly stringent emission standards and to obtain maximum reduction of PM2.5 emissions from CFPs, policies of “ultra-low emissions” or even “near-zero emissions” have been proposed. Consequently, the emission levels of primary pollutants from coal-fired units that adopted ultra-low emission control technologies are comparable with those from natural gas-fired plants. Technologies with high removal efficiencies for fine particles have been applied widely, including Electrostatic Fabric Filter, Dust Electric Coagulation, Movable Electrode technology, and Wet-ESP. However, CFPs contribute only 5.4% of the total primary PM2.5 emissions from all anthropogenic sources (Zhao et al., 2013b). Therefore, little can be achieved by reducing the fine particles in aerosols through the implementation of the “ultra-low” emission control policy for CFPs by making a huge financial investment. Moreover, the dust emissions of small-scale industrial boilers are nearly 40.0% of the total from all industrial sources, whereas the total amount of coal consumed is lower (only 20.0%) than that of CFPs (Mo et al., 2013; Zhu et al., 2014). Therefore, PM2.5 emission control strategies should focus on small-scale industrial coal-fired

Fig. 3. Projections of primary PM2.5 emissions from fossil fuel power plants in China (the dotted lines represent the increasing trend of PM2.5 emissions).


boilers. Practical measures include replacing coal with electricity; that is, small-scale boilers should be shut down or should substitute natural gas for coal, and more coal should be diverted to large-scale CFPs for power generation. Comparison with other Inventories and Uncertainty Analysis

Until now, very few studies have focused on primary PM2.5 emissions from OFPs and GFPs. We compared our study findings with other published PM2.5 emission inventories of CFPs, as shown in Fig. 4 (Zhang et al., 2007; Zhao et al., 2008; Lei et al., 2011; Zhao et al., 2013a; Chen et al., 2014; He et al., 2015; Liu et al., 2015). It is hard to perform comparisons among different periods, because of the differences in fuel consumption and EFs of APCDs. However, the result of this study still keep pace of the declining trend during past decades as the result of the more strict control by the government. The difference in statistical methods and the limitation of official data sources can bring in bias of fuel consumption in different studies. The penetrations of APCDs in this study were compiled from available literatures, which are similar to other studies. However, the effect of SCR on EFs has not been considered in other studies, which can cause the difference of the total penetration of the combination of APCDs. Moreover, the upgrading of production technology and the widespread use of APCDs can also bring in the difference in EFs of different periods.

The uncertainties (expressed as 95% confidence interval [CI] around a central value) were estimated to range from approximately –31.2% to +30.5% for CPFs, from –43.3% to +45.2% for OFPs, and from –45.8% to +47.3% for GFPs. Relatively large uncertainties were observed in the emission estimations from OFPs and GFPs, which were mainly attributed to the lack of detailed information on production parameters of different boilers and emission control devices downstream, as well as localized EFs.

In addition, the difference in emissions estimated using

the same PM2.5 emissions but different speciation profiles based on various APCDs combinations are shown in SI Fig. S6, with water-soluble inorganic ions given as an example. In this study, the total emissions of SO4

2–, NO3–,

and NH4+ in primary PM2.5 from CFPs were remarkably

low under the combination of SCR + ESPs + FGD. When ESPs + FGD or only ESPs were adopted, the emissions of SO4

2– and NH4+ were overestimated, whereas the emission

of NO3– was underestimated. This finding might be attributed

to the different levels of the effects of various APCDs combinations on the proportions of components in PM2.5. Thus, large uncertainties were observed in the estimates of the chemical compositions of emissions owing to the use of only one set of speciation profile of primary PM2.5 for fossil fuel power plants, regardless of different APCDs combinations. However, comprehensive data sets of speciation profiles including all components in PM2.5 were still considerably limited. For a comprehensive understanding of the emission features of primary PM2.5 from fossil fuel power plants in China, more detailed investigation and field tests are required.

Despite the existing uncertainties in this study, the results are still meaningful for a comprehensive understanding of the total amount and the regional distribution of primary PM2.5 emissions from power plants, the contributions of units with different capacities, and the elemental compositions of particulates. These results provide valuable information for source apportionment of airborne particulate matter and regional air quality modeling. CONCLUSIONS

In the present study, the unit-based approach was used to establish a comprehensive emission inventory of primary PM2.5 from fossil fuel-burning electric power plants for the year of 2014. For an improved understanding of the potential environmental effects, the chemical composition of primary PM2.5 was demonstrated according to the available field test

Fig. 4. Comparison with published inventories of primary PM2.5 emissions from CFPs from fossil fuel power plants in China with 95% CI, 2014.


results. Moreover, the future emissions of primary PM2.5 until the end of 2030 were projected under different emission scenarios by considering the structure of the demand for electrical power and the application of emission control technologies. The present inventory has a high temporal and spatial resolution; it provides a scientific methodology for establishing an emission inventory and is also a favorable amendment and supplement to global primary PM2.5 emission inventories.

ACKNOWLEDGMENTS

This work is supported by the National Natural Science Foundation (NSFC) of China (21377012, 21177012, 40975061) of Beijing Normal University, National Natural Science Foundation (NSFC) of China (21407122) of Xinxiang Medical University, National Natural Science Foundation (NSFC) of China (21507024) and the Youth Science Foundation (2014QK23) of Henan Normal University, and the General Financial Grant from the China Postdoctoral Science Foundation (2015M580631). The authors wish to thank the editors and the anonymous reviewers for their valuable comments and suggestions on our paper. SUPPLEMENTARY MATERIAL

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Received for review, July 21, 2016 Revised, September 27, 2016

Accepted, September 30, 2016

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