Zhiyong Li 1,2, Yutong Wang1, Yao Hu1, Lan Chen1,2, Hongtao Zhu1,2

Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China


Received: July 26, 2019
Revised: August 8, 2019
Accepted: August 26, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2019.07.0363  


Cite this article:

Li, Z., Wang, Y., Hu, Y., Chen, L. and Zhu, H. (2019). Emissions of NOx, PM, SO2, and VOCs from Coal-fired Boilers Related to Coal Washing, Iron-steel Production, and Lime and Gypsum Making in Shanxi, China. Aerosol Air Qual. Res. 19: 2056-2069. https://doi.org/10.4209/aaqr.2019.07.0363


Highlights

  • Studied EFs for coal washing, iron-steel making, and lime-gypsum production.
  • EFs were expressed by threeways.
  • Weak correlations of EFs vs. REs, EFs vs. coal components were found.
  • VOCs of coal washing dominated among 3 industries.
  • EFs of SO2, NOx, and VOCs increased with coal washing enterprise scales.
 

ABSTRACT


The accurate pollutant inventories are important for the development of pollution control policies, which further rely on detailed emission factors (EFs) to some extent. However, detailed air pollutant EFs for coal-fired boilers (CFBs) associated with coal washing (CW), iron-steel production (IS), and lime and gypsum manufacturing (LG) are lacking in China at present. CFBs of 91 enterprises involving CW, IS, and LG were investigated to obtain their pollutant EFs associated with coal consumption (EFI, kg t–1), outputs (EFII, kg MY–1), and product yields (EFIII, kg t–1) through field investigation and sampling. The weak correlation between EFs of 4 air pollutants vs. corresponding removal efficiencies (REs), and EFs vs. coal compositions among three industries implied the impact of actual combustion conditions and operating status of removal facilities (RFs). EFs of VOCs from small-scale CW enterprises (SSEs) were much higher than those of large- and medium-scale enterprises (LSEs and MSEs) owning to the incomplete combustion of coal. Also the SO2 and NOx EFs of CW increased with decreasing enterprise scale, while the maximum PM occurred at MSEs. The mean EFI values of LG for the 4 air pollutants was PM > NOx > VOCs > SO2, differed from PM > SO2 > NOx for the IS, VOCs > PM > NOx > SO2 for the CW LSEs and MSEs, and VOCs > NOx > PM > SO2 for the CW SSEs, which suggested the influence of combined factors including coal composition, production processes, combustion conditions, and pollutant removal technologies and removal efficiencies. EFI values for the 8 IS factories followed the order PM > SO2 > NOx, while they were PM > NOx > SO2 for EFII values due to their output fluctuation. For the EFII and EFIII values of SO2, NOx, and PM, LG dominated within the 3 industries, while the corresponding maximum VOCs occurred at the CW industry.


Keywords: Emission factor; Coal washing; Lime-gypsum making; SO2; NOx; VOCs.


INTRODUCTION


With rapid economic development and urbanization, air pollution has become an increasingly serious issue in China. The severe regional air pollution is mostly characterized by high concentration of fine particulate matter happened frequently in recent China (Hu et al., 2017). Serious haze episodes have occurred in recent Southeast Asia, degrading the air quality in this region including China, Malaysia, and Thailand (Sharma and Balasubramanian, 2018). Particulate matter (PM) had been always a topic of general interest in air pollution issue in China, which has received more and more concerns from Chinese people and government (Lang et al., 2017; Li et al., 2017, 2018). In present China, PM,SO2, NOx, and VOCs originated from different industries are widely recognized as primary air pollutants, which can damage the environment, climate, and human health if they enter into the respiratory and vascular systems of the human body (Yan et al., 2017; Hu et al., 2018; Liu et al., 2018; Zhao et al., 2018). Secondary inorganic aerosol (SIA, sum of sulfate, nitrate, and ammonium) is the main component of PM mainly formed through gaseous pollutant (SO2, NOx, and NH3) to particle conversion, and these gaseous pollutants are emitted from complicated anthropogenic sources including coal and biomass burning, cooking, and traffic-related and industrial emissions (Huang et al., 2014; Lee, 2015). Organic aerosol (OA) contains 100 chemical species and contributes high mass fraction of 20–90% to PM, which is mainly formed through photo-chemical reactions of organic pollutants (VOCs etc.) from anthropogenic combustion sources (Turpin et al., 2000; Carlton et al., 2009). VOCs in ambient air are receiving more and more concern ascribe to many of them have been identified to be human carcinogens, and precursors of both secondary organic aerosols and O3 (Zhang et al., 2015; Hu et al., 2018; Khan et al., 2018).

Coal burning is an important source of aerosol and gaseous pollutants, and impetus for haze formation (Li et al., 2017; Liu et al., 2017; Li et al., 2018; Hu et al., 2019). PM originates from coal combustion contain organic compounds, black carbon (BC), inorganic ions (SO42, NO3, Cl, and NH4+ etc.), and trace metals, which can lead to visibility deterioration, damage ecosystems and human health, and affect climate change substantially, which has attracted widespread attention (Bruns et al., 2015; Hu et al., 2017; Jayarathne et al., 2018; Masekameni et al., 2018). During coal burning process, sulfur contained in coal enters into atmosphere in the forms of SO2, SO3, and SO42 etc., the main forms lie in combustion residues are FeS2, CaSO4, FeSO4, and ZnS etc. (Hussain and Luo, 2019). The tremendous emissions of SO2 from coal burning can promote the sulfate aerosols and acid rain, which can result in the destruction of infrastructure and plant growth, and the formation of PM and haze (Saidur et al., 2011). In addition, gaseous emissions from combustion of coal also include CO2, CO, NOx, CH4, VOCs, and inorganic acid (Stockwell et al., 2015; Goetz et al., 2018).

Shanxi and Inner Mongolia are two predominant coal districts in China, whose coal production accounts for 49.2% and 51% of China’s total output in 2016 and 2017, respectively. The coal goaf area of Shanxi is as high as 20,000 km2, accounting for one seventh of its total area of 15 km2 (Niu et al., 2006). A large number of coal related enterprises including coal mining and washing, iron-steel production, and lime and gypsum making cluster in Shanxi Province of China (Li et al., 2018). Coal washing (CW) processes including heavy medium separation, jigging processes, and flotation separation and so on for the reduction of noncombustible minerals, acid precursors of sulfur-bearing minerals, and hazardous trace elements to produce high energy content fuels (Wen, 2000). CW can eliminate coal containing 50–80% ash, and 30–40% of total sulfur, and further achieve substantial cuts of air pollutants emitted from coal burning. Washing of 1 × 108 t coal can reduce the emission of 6–7 × 105 t SO2, and 1.61 × 107 t of coal gangue. In addition to the reduction of air pollutant emissions, improvement of coal utilization efficiency and conservation of energy were also obtained. Although the coal washing capacity of China is top ranking, the washing coal accounted only 22% of the total coal consumptions (Tang et al., 2005; Gu et al., 2012). China is the largest country in iron steel producing and consuming. Crude steel production in China was 717 million metric tons in 2012, which accounted for 46% of the world’s production (WSA, 2013). The emission amounts of PM and SO2 from iron-steel enterprises contributed 26.2% and 13.3% to the correspondingly Chinese industrial PM and SO2 emissions (Yan et al., 2015). Gypsum used in building industry was obtained through calcining of dihydrate natural gypsum or chemical gypsum at a certain temperature and subsequently dehydrated and decomposed to hemihydrates gypsum. More than 90% of lime and gypsum in China were linked with construction industry, and the industry associated with cement-, lime-, and gypsum-production contributes 0.73% to Chinese GDP in 2007.

Huge quantities of coal burned in coal fired boilers were needed to provide heating and vapor for the production processes in CW, IFP, and LG industries, and large amounts of SO2, NOx, PM, and VOCs are released. To our knowledge, few studies were conducted focus on emissions of coal fired boilers applied in these 3 types of industries.

Improved emission inventories (EIs) combined with detailed source information, in terms of emission factors and rate, and spatial distribution of sources, are imperative for better understanding of the sources and the formation mechanism of serious air pollution, and subsequently effective pollution control policies development (Zhou et al., 2017; Horák et al., 2018; Yue et al., 2018; Zheng et al., 2019). However, gigantic uncertainty existed in EIs establishment using poorly constrained global or regional models, so the detailed EFs specific to industry size or different industries were urgently need to obtain the accurate EIs (Li et al., 2018; Horák et al., 2018; Hu et al., 2019).

In this study, a total of 91 enterprises related to coal washing, iron-steel production, and lime and gypsum making were investigated. Field sampling and subsequent laboratory measured to obtain the information about their annual outputs, product yields, pollutant removal efficiencies, coal compositions, and pollutant concentrations in flue gas. All of these works aimed to achieve the following goals: 1) acquisition of localized EFs associated with SO2, PM, NOx, and VOCs for these enterprises; 2) obtainment of three expressions of EFs associated with coal consumption, output, and product yield to improve their practicability.


MATERIALS AND METHODS



Field Investigation of the Related Companies

In this study, 91 companies in Shanxi Province involving 79 coal washing enterprises, 8 iron-steel factories, and 4 lime and gypsum production plants were on-site investigated for their production and coal related information in 2017. SO2, PM, and NOx were real-time monitored using a flue gas analyzer, while VOCs in flue gas were sampled and subsequently analyzed by a GC-MS system.

Tables 13 listed the statistics including product types and yields, output, and fuel coal compositions in boilers for 79 CW enterprises involving 19 large-scale enterprises (LSEs), 37 medium-scale enterprises (MSEs), and 23 small-scale enterprises (SSEs). The corresponding statistic values for 8 iron-steel (IS) factories, and 4 lime and gypsum (LG) making plants were also provided in Tables 4 and 5, respectively. It should be pointed that the scale of enterprise was designated based on the initially designed product yield and output, not that the actual yield under the actually annual running time.


Table 1. Statistical values for large scale coal washing enterprises.


Table 2. Statistical values for medium scale coal washing enterprises.


Table 3. Statistical values for small scale coal washing enterprises.


Table 4. Statistical values for 8 iron-steel production enterprises.


Table 5. Statistical values for 4 lime and gypsum production enterprises.

It is worth noting that all the 91 enterprises were not equipped with VOC removal facilities (RFs). NOx RFs were installed in a small number of factories, while PM RFs were installed in all the 91 factories.


Sampling and Measurement of SO2, NOx, and PM

The same method for sampling and measurement of SO2, NOx (sum of NO and NO2), and PM (particles with all size in flue gas) detailed described in Li et al. (2018) and Hu et al. (2019) was adopted in this study.

The gaseous pollutants SO2, NOx and CO originated from the coal fired boilers applied in all the enterprises were monitored online by a flue gas analyzer (Laoying-3012H, Qingdao LaoYing Environmental Science and Technology, Co., Ltd.) placed at outlet channel of flue gas after RFs to get the information related to pollutants actually released into atmosphere. The additional parameters such as temperature and flow velocity (m s−1) of flue gas were also measured and provided by this gas analyzer. PM was also sampled by this analyzer and the collected mass divided by the corresponding sampling volume of flue gas was used to represent PM concentration. The analyzer was calibrated with zero gas and standard gases (NOx, SO2 and O2) before measurement for the elimination of possible interferences. The mass emissions of SO2, NOx, and VOCs were calculated as their concentrations multiplied by sampling volumes of flue gas.

Two pathways including request from enterprises and field sampling and subsequent laboratory measurement were used to obtain the information about coal components for all the enterprises. The proximate- and ultimate-analysis of fuel coal burned in coal-fired boilers referred to the Chinese standards of GB/T-212-2008 and GB/T 476-2001, which aimed to the data acquisition associated with contents of ash, sulfur, carbon, hydrogen, nitrogen, oxygen, and water.


VOCs Sampling and Analysis

VOCs sampling and analysis were conducted based on the standard method designated by Chinese Ministry of Environmental Protection (HJ 734-2014) for smoke containing VOCs derived from stationary sources. The sampling and analysis procedures of VOCs were divided to 3 steps: adsorption using a stainless pipe used in TD-100 system (Markes International, UK), VOCs thermal desorption by TD-100, and detection by a HP6890 GC/5973i MS system. The adsorption tubes installed at the outlet of the sampling gun (lasted 5 mins at a flow rate of 40 mL min–1) were applied to the VOCs sampling. The interference from water was eliminated through condensation using an ice bath impact bottle before adsorption pipe. VOCs sample pretreatment and analysis methods were all subordinated to Hu et al. (2019). The calibration standards were prepared by diluting 100 ppbv of PAMS (Photochemical Assessment Monitoring Station, USA) (Yan et al., 2016; Widiana et al., 2017). The sum of 57 VOC species was used to represent total VOCs in this study.

The quality assurance and quality control measures were detailed described in Hu et al. (2019). Detection within 24 h, penetration experiment and activation of the adsorption tube, field blank and instrumental calibration before test were all included in this study. The MDLs were same to the reported values of Hu et al. (2019) and listed in Table S1.


Calculation of Emission Factors in Three Expressions

The same calculation methods of EFs described by Li et al. (2018) and Hu et al. (2019) were adopted in this study. In order to enhance EFs practicability in EIs establishment, coal consumption (EFI, kg t–1), output (EFII reported in kg MY–1), and product yield (EFIII, kg t–1) associated EFs were provided and calculated using the Eqs. (1)–(3).

 

where C is mass concentrations of gaseous pollutants, VFA is the actual flue gas volume derives from 1 kg coal burning, which can be induced by VAT.

  

VAT (m3 kg–1) is the theoretical air volume needed by burning of 1 kg of coal and obtained using Eq. (4). 

Car, Har, Oar, Nar, and Sar are corresponding element contents contained in fuel coal on the received basis.

VFT is theoretically generated flue gas volume from 1 kg coal combustion and calculated by Eq. (5).

  

where VCO2, VSO2, and VNO2 refer to volumes of CO2, SO2, and NO2 derived from burning of C, H, and N in coal, VN2 is the N2 volume in VAT and equal to 0.79VAT, and VH2O is the water vapor volume sum of coal containing H burning (0.112ω(Har)), vaporization of coal containing water (0.00124ω(Mar)), and vapor in air (0.0161VAT).

Finally, VAT derived from 1 kg coal combustion is calculated by Eq. (7).

 

where α is the excess air coefficient, which is provided by corresponding enterprise. 


RESULTS AND DISCUSSION



Emission Factors of NOx, VOCs, SO2, and PM for Boilers in Coal Washing

A total of 79 coal washing enterprises were investigated for their coal consumptions, fuel coal components, outputs, product yields, and large-, medium, and small-scale enterprises were all involved and the related information was listed in Tables 13. Obvious output fluctuation exhibited for 79 enterprises, which ranged from 0.21 to 4250 MY a–1 with the mean value as 463 ± 830 MY a–1. The large-, medium-, and small-coal washing enterprise possessed the mean output value as 1350 ± 1280, 249 ± 305, and 67.0 ± 77.1 MY a–1. The ash contents (air dried basis) of fuel coal were not differed from each other among three scale enterprises, which were in the range of 9.00–28.6% with the mean value as 16.4 ± 3.33%. The sulfur contents also showed no differences among three scale enterprises, they fluctuated from 0.27 to 2.80% with the mean value as 0.71 ± 0.63%. The products of these enterprises contained raw coal and clean coal with their yields ranged from 18.5 to 8070 and from 2.80 to 538 kt a–1, respectively. The annual outputs for all the 79 enterprises were correlated well with their product yields (R2 = 0.91, p < 0.05). Generally the coal consumptions were also correlated with the product yields (R2 = 0.59, p < 0.01) and output values (R2 = 0.66, p < 0.05).

Among 79 coal washing enterprises, only 8 ones were not installed with PM RFs, 18 ones were not equipped with SO2 RFs, while only 10 ones were equipped with NOx RFs. It should be noted that all the 79 enterprises were not equipped with VOCs RFs. The PM removal efficiencies (REs) ranged from 4.60% to 99.4% (80.9 ± 25.3%) and were not correlated with output values (R2 = 0.02), which implied the high PM RE technologies were not always adopted by high output enterprises. SO2 RE values of 60 enterprises with RFs fluctuated from 6.30% to 89.3%, not correlated with output values, were similar to PM RE values. NOx REs for 10 enterprises equipped with RFs ranged from 4.00% to 67.8%, and high RE values of 59.5% and 67.8% occurred at F35 and F78, which were designated as small-scale and medium-scale enterprises. VOCs RE values for all the 79 enterprises were zero due to the lack of RFs. Double alkali method and SNCR were main NOx removal technologies involved in these enterprises. The flue gas desulfurization methods contained double alkali, NH3·H2O absorption, and limestone gypsum absorption. Also NOx EFs were not correlated with NOx REs and N contents in coal, which suggested the formation of thermal-NOx and impact of the poor operation conditions of NOx RFs.

The RFs applied in coal washing industries should be further improved when all the present RFs were taken into account. Figs. 1 and 4 showed the EFs with 3 expressions for 19 coal washing enterprises belong to large-scale enterprises. In generally, EFI and EFII values showed the similar trends, VOCs possessed the highest EF value, followed by PM > NOx > SO2. For EFI values (in kg t–1), 0.06–180 (57.4 ± 75.5), 0.99–12.8 (5.17 ± 3.43), 0.44–15.8 (3.82 ± 3.56), and 1.06–4.18 (2.74 ± 0.721) were attributed to VOCs, PM, NOx, and SO2, respectively. Compared with other 3 air pollutants, SO2 EF values possessed smaller fluctuation, while Hu et al. (2019) reported that NOx has smaller fluctuation, which could be explained by the fluctuation of RFs. The mean value of SO2 EFI of F6, and F19 without SO2 RFs equipped was 2.80 kg t–1, which was higher than the mean value of 2.73 kg t–1 for the rest 17 enterprises. The highest SO2 EFI occurred at F11 (4.18 kg t–1), while the lowest value of 1.06 kg t–1 occurred at F5. EFI values of 19 enterprises were better correlated with SO2 REs (R = –0.50) than Sad values (R = –0.01), implied greater impact of REs than sulfur contents, which was similar to the reported results by Li et al. (2018) and Hu et al. (2019). PM EFI values were weak correlated with PM RFs and Aad values, suggested the impact of combustion conditions and actual operation status of RFs. For NOx EFI, 6 LSEs with RFs installed including F3, F10, F11, F12, F13, and F14 possessed the lower mean value of 3.52 kg t–1 than that (3.96 kg t–1) of the rest 13 enterprises, suggested the impact of NOx RFs. Only F6 was not equipped PM RFs, it had higher PM EFI value of 7.21 kg t–1 than the mean value of 5.06 kg t–1 for the rest 18 enterprises without PM RFs. VOCs possessed the highest EFI reflected the no VOCs RFs were installed in all the 19 LSEs.


Fig. 1. Emission factors of the coal-fired boilers used in large-scale coal washing enterprises.Fig. 1. Emission factors of the coal-fired boilers used in large-scale coal washing enterprises.

Due to the well correlation between output and coal consumption, EFII values (in kg MY–1) for these 79 enterprises exhibited the similar order as VOCs (407 ± 589) > PM (49.4 ± 53.4) > NOx (47.7 ± 109) > SO2 (26.1 ± 21.3), which was different with pharmaceuticals- and food-production industries reported by Li et al. (2019). Unlike the drastic fluctuation of prices of different medicines and food, the products and prices were consistent with each other for 79 coal washing factories, so EFI and EFII values showed the similar fluctuation. The EFII values (in kg MY–1) for PM, SO2, and NOx for the enterprises equipped with RFs had lower mean values as 39.3, 22.8, and 23.5 than the corresponding 231, 54.9, and 58.9 for the enterprises without RFs. SO2 EFII values showed a better correlation with SO2 REs (R = –0.45) than Sad values of fuel coals (R = –0.09), which was similar to EFI values.

Due to the differences of coal washing processes and product yields, EFIII values (in kg t–1) of 4 air pollutants for 19 LSEs differentiated from their EFI and EFII values, they complied with the order of PM (0.20 ± 0.77) > NOx (0.18 ± 0.70) > SO2 (0.14 ± 0.56) > VOCs (0.13 ± 0.16).

In this study, a total of 37 medium scale enterprises associated with coal washing were investigated and field measured. Considering the pollutant RFs, only 2 of 37 MSEs were equipped with NOx RFs with REs as 59.5% and 15.0%, 8 MSEs were not equipped with SO2 RFs, and all the 37 MSEs were not installed with VOCs RFs and were equipped with PM RFs. Figs. 2 and 4 listed the EFs and mean values of EFs for each MSE and 37 MSEs. The output values of 37 enterprises were well correlated with their product yields (R = 0.92, p < 0.01). EFI, EFII, and EFIII values of 4 air pollutants for 37 MSEs were consistent with EFI and EFII values of LSEs, which were subordinated to the order as VOCs (78.3 ± 59.1) > PM (5.78 ± 5.29) > NOx (5.37 ± 6.02) > SO2 (2.87 ± 1.34) for EFI (in kg t–1), VOCs (1740 ± 2560) > PM (149 ± 297) > NOx (113 ± 236) > SO2 (70.3 ± 106) for EFII (in kg MY–1), while they were VOCs (0.45 ± 0.67) > PM (0.04 ± 0.05) > NOx (0.03 ± 0.04) > SO2 (0.02 ± 0.02) for EFIII (in kg t–1), respectively (Fig. 4). VOCs possessed the overwhelming EFI, EFII, and EFIII values among 4 air pollutants attributed to the uninstalled VOCs RFs in all the 37 MSEs, which ranged from 0.21 kg t–1 of F43 to 252 kg t–1 of F39, 3.9 kg MY–1 of F56 to 14300 kg MY–1 of F51, and 1.00×10–3 kg t–1 of F56 to 1.56 kg t–1 of F46 (Fig. 2). The most value of VOCs EFI, EFII, and EFIII occurred at different factories implied the impact of production yield and product prices. PM REs varied from 9.40% to 99.4% for the 35 enterprises owning RFs, the mean value of PM EFI, EFII, and EFIII for these 35 factories were 5.23, 147, and 0.04, which were much lower than the corresponding 15.4, 187, and 0.06 for the 2 factories without RFs. PM EFs were not correlated with Aad values in fuel coal and PM REs, which suggested the greater impact of combustion conditions and degree of incomplete coal combustion resulted therefrom. Also the lower mean value of SO2 EFI of 29 factories owning SO2 RFs (REs from 10.0% to 89.3%) as 2.75 was obtained compared than 3.29 for the rest 8 ones without SO2 RFs installed. An opposite tendency occurred at SO2 EFII and EFIII values due to the fluctuations of product prices and yields among different enterprises, high values (73.9 kg MY–1 and 0.02 kg t–1) were attributed to enterprises owning RFs, while low values (57.6 kg MY–1 and 0.01 kg t–1) were obtained for the factories without RFs. In regard to NOx EFs, EFI and EFIII value for 2 factories owning NOx RFs (7.30 kg t–1 and 0.03 kg t–1) were higher than those (5.26 kg t1 and 0.03 kg t–1) under the influence of nitrogen contents in coal, combustion temperature, and NOx REs of RFs (Löffler et al., 2005; Li et al., 2018; Hu et al., 2019). Unlike SO2, NOx mainly originated from oxidation of air N2 at high temperature regardless of that small proportion of NOx formed by burning of fuel-nitrogen, so NOx emissions were more depended on the coal burning temperature and NOx RFs than SO2 owning to the different formation mechanisms between them (Löffler et al., 2005).


Fig. 2. Emission factors of the coal-fired boilers used in medium-scale coal washing enterprises.Fig. 2. Emission factors of the coal-fired boilers used in medium-scale coal washing enterprises.


Fig. 3. Emission factors of the coal-fired boilers used in small-scale coal washing enterprises.
Fig. 3. Emission factors of the coal-fired boilers used in small-scale coal washing enterprises.


Fig. 4. Mean values of EFs of coal fired boilers for large-scale, medium-scale, and small-scale coal washing factories.
Fig. 4. Mean values of EFs of coal fired boilers for large-scale, medium-scale, and small-scale coal washing factories.

A total of 23 small scale enterprises (SSEs) were involved in this study with their outputs ranged from 0.21 to 278 MY a–1 and product yields varied from 2.80 to 372 kt a–1. The weak correlation between output values and product yields (R = 0.68, p < 0.05) for SSEs compared with LSEs and MSEs were possibly attributed to the unstable sale prices resulted from the small enterprise size. Figs. 3 and 4 listed the EFs for each SSE and mean EF values for 23 SSEs. EF values defined as 3 expressions showed the same trends and followed the order as VOCs > NOx > PM > SO2, which were different from EFs of LSEs and MSEs. The increased PM EFs compared with MSEs and LSEs would be explained by back boilers applied in SSEs. The incomplete combustion of coal caused the enhanced carbon content (organic carbon and black carbon) in ash, further increased the mass of emitted PM. Meanwhile, the incomplete burning of coal also resulted in the increasing of VOCs emissions. The mean values of EFs for VOCs, PM, NOx, and SO2 from SSEs were EFI (110, 6.41, 5.31, and 2.97 kg t–1), EFII (2280, 867, 370, and 262 kg MY–1), and EFIII (0.50, 0.14, 0.07, and 0.04 kg t–1), respectively (Fig. 4). 8, 21, and 5 of 23 SSEs were not equipped with SO2, NOx, and PM RFs. The relatively low SO2 and NOx EFs occurred at SSEs with corresponding RFs equipped, while PM showed the reverse trend. The mean SO2 EFI, EFII, and EFIII values for 8 SSEs without RFs (3.45, 322, and 0.05) were much higher than the corresponding (2.71, 230, and 0.04) for 13 SSEs with RFs. The highest SO2 EFI value of 7.52 kg t–1 occurred at F59 without SO2 RFs, while the lowest value of 0.95 kg t–1 was possessed by F78 ascribe to its highest SO2 RE of 89.3% among 23 SSEs. F67 possessed the highest EFI and EFII values of the sum of SO2, PM, and NOx, due to its low output and no installation of any RFs (Fig. 3). The maximum of NOx EFI, EFII, and EFIII were all belong to F67, which could explained by its high nitrogen content, zero removal rate, and low output (Li et al., 2018; Hu et al., 2019). The mean NOx EFs for SSEs owning RFs were 1.03 for EFI, 52.4 for EFII, and 0.01 for EFIII, which were significantly lower than the corresponding 6.92, 945, and 0.15 for the rest SSEs without NOx RFs. PM EFI values of SSEs with PM RFs equipped possessed slightly higher value (5.33) than the corresponding 5.25 for the rest SSEs without RFs, which was possibly resulted from ash content fluctuations and actual RFs running status.

Fig. 4 listed the mean values of EFI, EFII, and EFIII of 4 air pollutants for 19 LSEs, 37 MSEs, and 23 SSEs. Except for PM EFs, EFs for the other 3 air pollutants increased with the decreasing of scale of enterprises, especially for VOCs. The incomplete coal combustion of backward boilers applied in SSEs would be the explanation of increasing of VOCs EFI values from 57.4 of LSEs to 110 of SSEs, EFII values from 407 of LSEs to 2280 of SSEs, and EFIII values from 0.13 of LSEs to 0.50 of SSEs.


Emission Factors of Coal Fired Boilers in Iron-steel Manufacturing

Due to the limited experimental condition, VOCs field sampling was not conducted for coal-fired boilers applied in 8 iron-steel (IS) enterprises. All the 8 IS factories were equipped with PM RFs and not equipped with NOx EFs, and SO2 RFs were partly installed in 5 of 8 factories (Table 4). Figs. 5 and 6 listed the EFs for each IS factory and the mean values of EFI, EFII, and EFIII for 8 IS enterprises. The lowest pollutant EFs occurred at I7, EFI values PM, NOx, and SO2 were 0.36, 0.07, and 0.74 kg t–1, and the corresponding values were 165, 33.2, and 338 kg MY–1 for EFII, and 1.03, 0.21, and 2.10 kg t–1 for EFIII, which might be explained by its lowest Sad content, pollutant RFs, and actual combustion condition and running status of RFs (Fig. 5). The highest EFI (73.0 kg t–1) of the sum of PM, NOx, and SO2 occurred at I5, while the corresponding values for EFII and EFIII were possessed by I4 (22900 kg MY–1) and I3 (11.8 kg t–1), respectively (Fig. 5).


Fig. 5. Emission factors of the coal fired boilers for each iron-steel production enterprise.Fig. 5. Emission factors of the coal fired boilers for each iron-steel production enterprise.


Fig. 6. Mean values of EFs for 3 air pollutants from coal fired boilers for 8 iron-steel production enterprises.
Fig. 6. Mean values of EFs for 3 air pollutants from coal fired boilers for 8 iron-steel production enterprises.

A lot of heat provided by coal- and coal-gas fired boilers was demanded for the production processes of iron and steel (Yan, 2012). Compared with the Sad contents of fuel coal applied in CW LSEs (0.65%), MSEs (0.69%), and SSEs (0.80%), the lower mean value of 0.59% occurred at IS enterprises. Also lower mean value of Aad of fuel coal for 8 IS enterprises (15.4%) was found compared with corresponding value of CW LSEs (17.5%), MSEs (15.8%), and SSEs (16.4%). In regard to EFI values (reported in kg t–1) for 3 air pollutants, they followed the order as PM (28.3 ± 15.9) > SO2 (9.80 ± 6.12) > NOx (8.46 ± 7.10), which significantly differed from those of different scale coal washing enterprises, they were PM > NOx > SO2 for CW LSEs and MSEs, and NOx > PM > SO2 for CW SSEs. In regard EFI values (reported in kg t–1), PM possessed much higher value of 28.3 than 5.17, 5.78, and 5.31 for CW LSEs, MSEs, and SSEs regardless of low ash contents of coal in IS industries and similar PM REs between CW and IS industries, which possibly resulted from the differences of production processes between two industries. The mean NOx EFI (reported in kg t–1) for 8 IS enterprises (8.46) was also much higher than 3.82, 5.37, and 6.41 for CW LSEs, MSEs, and SSEs ascribe to no NOx RFs installation in all the IS enterprise and combustion conditions. The SO2 originated from IS enterprises also showed an enhanced average EFI compared with those of 3 different scale CW enterprises.

The EFII values of 3 air pollutants for 8 IS factories (reported in kg MY–1) were 3720, 951, and 883 for the PM, NOx, and SO2, respectively. The different sort order between EFI and EFII for 8 IS enterprises was mainly attributed to the fluctuation of output. The EFIII values (reported in kg t–1) for 8 IS factories followed the same order with EFI, they were PM (3.77 ± 1.75) > SO2 (1.49 ± 0.74) > NOx (1.22 ± 1.14). Due to the differences existed in product prices, product yields, and output values between CW and IS industries, EFII and EFIII values for IS were much higher than the corresponding values for CW.

The SO2 originated from 5 enterprises with SO2 RFs installed possessed lower mean EFI of 9.74 than 9.90 of the rest 3 ones without SO2 RFs, while higher EFII and EFIII values occurred at 5 factories owning RFs compared with those of the rest 3 factories without RFs resulted from the fluctuations of outputs and product yields.


Emission Factors of Coal-fired Boilers in Lime and Gypsum Making Factories

As shown in Table 5, all the 4 LG factories equipped with PM and SO2 RFs, and only 1 factory equipped with NOx RFs, while no factories were installed with VOCs RFs. The output values ranged from 0.50 to 44.8 MY a–1 with the mean value as 22.2 ± 22.9 MY a–1, which were much lower than those of CW and IS industries. The sulfur contents and ash contents for 4 LG factories were much lower than those of CW and LG enterprises. The highest SO2, VOCs, and NOx EFI values occurred at L3, they were 7.73, 11.5, and 10.2 kg t–1, while the corresponding lowest values were possessed by L2 (1.54 kg t–1), L4 (0.18 kg t–1), and L1 (1.40 kg t–1), respectively.

Fig. 7 list the EFI, EFII, and EFIII values of 4 air pollutants for coal fired boilers applied in 4 LG enterprises. The highest EFI values (reported in kg t–1) of SO2 (7.73), NOx (10.2), and VOCs (11.5) occurred at L3, while the correspondingly lowest values were attributed to L1 (1.37), L1 (1.40), and L4 (0.18), respectively. In regard to PM, EFI maximum was possessed by L1 (8.41), while the lowest one occurred at L4 (1.37) (Fig. 7). The weak correlation between EFs of air pollutants and corresponding RFs indicated the impact of actual running status of RFs and combustion of boilers.


Fig. 7. Emission factors of the coal fired boilers used in lime and gypsum making enterprises.Fig. 7. Emission factors of the coal fired boilers used in lime and gypsum making enterprises.

The mean values of EFI for 4 air pollutants followed the order as PM (5.10 ± 2.57) > NOx (3.93 ± 3.66) > VOCs (3.49 ± 4.66) > SO2 (3.21 ± 2.63), significantly differed from the orders PM > SO2 > NOx for IS industries, VOCs > PM > NOx > SO2 for CW LSEs, VOCs > PM > NOx > SO2 for CW MSEs, and VOCs > NOx > PM > SO2 for CW SSEs, which suggested the influence of comprehensive factors including coal compositions, production processes, combustion conditions, and pollutant removal technologies and removal efficiencies. VOCs EFI for LG enterprises were far less than those of CW factories, indicated the impact of combustion completeness and production procedures. PM EFI values of LG enterprises were compared to those of CW factories with different scale, but much lower than those of IS industry. Mean NOx EFI of LG plants was higher than that of CW LSEs, but much lower than that of CW MSEs, CW SSEs, and IS factories. In regard to SO2, they followed the order as IS > LG > CW.

Both EFII and EFIII values for LG enterprises were subordinated to the order PM > NOx > SO2 > VOCs, which was different from EFI due to the impact of output and product yield.


CONCLUSIONS


79 coal washing (CW) enterprises (19 large-, 37 medium-, and 23 small-scale factories), 8 iron-steel (IS) factories, and 4 lime and gypsum (LG) plants within Shanxi Province were investigated, sampled, and subsequently detected to obtain the coal consumption, output, and product yield associated emission factors for VOCs, NOx, SO2, and PM.

EFI values of SO2, NOx, and VOCs increased with decreasing of scale of CW enterprises, while medium scale coal washing enterprises possessed the highest PM EFI values. EFI of SO2 increased from 2.74 of LSEs to 2.97 kg t–1 of SSEs, and they were from 3.82 of LSEs to 6.41 kg t–1 of SSEs for NOx, and from 57.4 of LSEs to 110 kg t–1 of SSEs for VOCs, respectively. Considering PM EFI values (reported in kg t–1), the MSEs possessed higher value of 5.78 than 5.17 for LSEs and 5.31 for SSEs, which might be explained by the actual running status of RFs and ash content contained in fuel coal. EFII values (reported in kg MY–1) for all the 4 air pollutants also increased with the decreasing of enterprise scales, which increased from 49.4 of LSEs to 370 of SSEs for PM, from 26.1 of LSEs to 262 of SSEs for SO2, from 47.7 of LSEs to 867 of SSEs for NOx, and from 407 of LSEs to 2280 of SSEs for VOCs, respectively. However, EFIII values showed different trends compared with EFI and EFII values for 4 air pollutants except for VOCs.

The weak correlation of EFs air pollutants vs. designed REs of RFs, and EFs vs. coal components was found among three types of industries indicated the great impact of actual running status of RFs and burning condition. IS industry possessed much higher EFI values of PM, NOx, and SO2 than those of CW- and LG- industries. The mean values of EFI (reported in kg t–1) for 4 air pollutants for LG enterprises followed the order as PM (5.10 ± 2.57) > NOx (3.93 ± 3.66) > VOCs (3.49 ± 4.66) > SO2 (3.21 ± 2.63), significantly differed from the orders PM (28.3 ± 17.0) > SO2 (9.80 ± 6.54) > NOx (8.46 ± 7.60) for IS industries, VOCs (57.4 ± 75.5) > PM (5.17 ± 3.43) > NOx (3.82 ± 3.56) > SO2 (2.74 ± 0.72) for CW LSEs, VOCs (78.3 ± 75.5) > PM (5.78 ± 5.29) > NOx (5.37 ± 6.02) > SO2 (2.87 ± 1.34) for CW MSEs, and VOCs (110 ± 145) > NOx (6.41 ± 6.59) > PM (5.31 ± 1.74) > SO2 (2.97 ± 1.07) for CW SSEs, which suggested the influence of comprehensive factors including coal compositions, production processes, combustion conditions, and pollutant removal technologies and removal efficiencies. For EFII values of SO2, NOx, and PM, LG dominated in 3 industries, while the corresponding maximum of VOCs occurred at CW industry. Higher mean EFII value (reported in kg MY–1) for SO2 (1680), NOx (1870), and PM (3790) were owned by LG compared with 883, 951, and 3720 for IS, 26.1, 47.7, and 49.4 for CW LSEs, 70.3, 113, and 149 for CW MSEs, and 262, 867, and 370 for CW SSEs, respectively. In regard to SO2, NOx, and PM EFIII values (reported in kg t–1), higher values of 1.49, 1.22, and 3.77 occurred at IS, and the correspondingly lower values were 0.21, 0.23, and 0.58 for LG, 0.14, 0.18, and 0.20 for CW LSEs, 0.02, 0.03, and 0.04 for CW MSEs, and 0.04, 0.07, and 0.14 for CW SSEs, respectively.

In a word, EFs should be designated for the specific industry and the industry scale. The pollutant removal facilities must be improved, especially for VOCs removal equipments in CW industry. SO2 RFs have been installed for most of enterprises of three types of industries, and the SO2 RFs should be installed for the remaining small part of enterprises. The low cost NOx removal measures such as fuel and air classification combustion, injection of ammonia and urea into furnace, changing of burning temperature should be adopted as soon as possible for the most of enterprises within these 3 industries. The PM RFs were equipped for almost all the enterprises and the running status and time should be optimized to obtain the enhanced PM removal efficiencies. 


ACKNOWLEDGEMENTS


This study was supported by the Fundamental Research Funds for the Central Universities (2017MS142) and National Natural Science Foundation of China (21407048).


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