An Improved Approach to Estimate Methane Emissions from Coal

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An Improved Approach to Estimate Methane Emissions from Coal Mining in China Tao Zhu, Wenjing Bian, Shuqing Zhang, Pingkuan Di, and Baisheng Nie Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01857 • Publication Date (Web): 28 Sep 2017 Downloaded from http://pubs.acs.org on October 3, 2017

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Environmental Science & Technology

An Improved Approach to Estimate Methane Emissions from Coal Mining in China Tao Zhu1,2, *, Wenjing Bian1, Shuqing Zhang1, Pingkuan Di1,*, Baisheng Nie3

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ABSTRACT

School of Chemical & Environmental Engineering, China University of Mining & Technology (Beijing), No.11, Xueyuan Road, Haidian District, Beijing 100083, China. 2 State Key Laboratory of Coal Resources and Safe Mining (CUMTB), Beijing 100083, China. 3 College of Resources & Safety Engineering, China University of Mining & Technology (Beijing), No.11, Xueyuan Road, Haidian District, Beijing 100083, China. * Authors to whom correspondence should be addressed.

China, the largest coal producer in the world, is responsible for over 50% of the total global methane (CH4) emissions from coal mining. However, the current emission inventory of CH4 from coal mining has large uncertainties because of the lack of localized emission factors (EFs). In this study, province-level CH4 EFs from coal mining in China were developed based on the data analysis of coal TOC Art production and corresponding discharged CH4 emissions from 787 coal mines distributed in 25 provinces with different geological and operation conditions. Results show that the spatial distribution of CH4 EFs is highly variable with values as high as 36 m3/t and as low as 0.74 m3/t. Based on newly developed CH4 EFs and activity data, an inventory of the province-level CH4 emissions was built for 2005-2010. Results reveal that the total CH4 emissions in China increased from 11.5 Tg in 2005 to 16.0 Tg in 2010. By constructing a grey forecasting model for CH4 EFs and a regression model for activity, the province-level CH4 emissions from coal mining in China are forecasted for the years of 2011 to 2020. The estimates are compared with other published inventories. Our results have a reasonable agreement with USEPA’s inventory and are lower by a factor of 1-2 than those estimated using the IPCC default EFs. This study could help guide CH4 mitigation policies and practices in China.

1. INTRODUCTION Methane (CH4) is the second most important anthropogenic greenhouse gas and has a 28-fold greater global warming potential than CO2 over one hundred years.1 Atmospheric CH4 concentration has increased from 700 ppb during pre-industrial times2 to 1803 ppb in 2011.3 1 ACS Paragon Plus Environment


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Globally the total CH4 emissions have doubled in the past three decades, and most of this increase took place in the 2000s. China has contributed between 14% and 22% of the global anthropogenic CH4 emissions in the 2000s.4 This fast increase of the total CH4 emissions after 2000 is mainly driven by CH4 emissions from coal mining.5 With rapid growth of the Chinese economy, coal production has nearly tripled in the past three decades, causing an increase in CH4 emissions. Global CH4 emissions from coal mining were estimated to be about 584 MtCO2e in 2010, accounting for approximately 8% of the worldwide anthropogenic CH4 emissions.6 These emissions are projected to rise by 15% over the next 10 years.6 China’s estimated CH4 emissions from coal mining in the same year were about 295 MtCO2e,7 which accounted for about 50% of the global estimate in 2010.8 Coal is a primary energy source in China, which accounted for 67.5% of China's total primary energy consumption in 2013.9 Due to the fact that 90% of China's fossil energy reserves are coal reserves, it is likely that coal will remain a major energy source in China for a long-time period in the future. Thus, there is a need to estimate CH4 emissions from coal mining for the current and future years. There have been some studies on estimating CH4 emissions from coal mining in China.5,10-16 Unfortunately, there appears to be discrepancies among the CH4 emission inventories developed by the investigators who may have used different estimation methods, EFs, activity data, and investigated areas. The Emission Database for Global Atmospheric Center Version 4.2 (EDGARv4.2)11 seemed to have overestimated the EFs by a factor of 2 for the coal mining in China due to the fact that the EDGARv4.2 inventory compilation used the European averaged EFs for CH4 from coal mine production in substitution of missing data, which seems to be too high for China.17 According to the default EFs in the Intergovernmental Panel on Climate Change (IPCC)12, we estimated the average annual CH4 emission from coal mining in China to be about 36 Tg (range of 20-50) in 2010. A recent global CH4 budget study by a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project17 showed that China has an average annual CH4 emission of 21.5 Tg (range of 13-25) from coal mining, assuming that China’s contribution to the global coal mining CH4 budget was 50%. Based on the four regional EFs of CH4 in China from coal mining, Peng et al.5 estimated an annual CH4 emission of 17.7 Tg (range of 16.7-20.3) for 2010. The U.S. Environmental Protection Agency (EPA)10 reported that China emitted 14.0 Tg CH4 from coal mining in 2010. It is apparent that the estimates vary considerably among different studies. The quantity of CH4 emitted from coal mining is a function of many factors: geology, in-situ CH4 content, coal rank, mining depth, mining methods, etc. Among these, coal rank and mining depth are the major factors. Coal rank is a measure of the carbon content of the coal, with higher coal ranks corresponding to higher carbon content and generally higher CH4 content. Pressure increases with depth and as a result, deeper mining generates more CH4 emissions than shallow mining of the same coal rank.18 The Revised 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC)12 provide two general approaches to estimate CH4 emissions from coal mining. The first approach, outlined in the Tier 1 and Tier 2 methodology is based on coal production and EF. Coal production activity data can be provided by enterprises or statistical divisions. The EF used is the global average range of EFs for Tier 1 and country- or basin-

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specific EF for Tier 2. The second approach, the Tier 3 method, uses mine-specific measurement data to develop national estimates for underground mines, which is usually impracticable. Global inventories generally rely on country-level EFs and socio-economic statistics, which hardly fully reflect the local to regional mining conditions and economic growth. This is especially the case in China where mining conditions and economic activities vary largely between provinces. To reduce uncertainties on estimates of CH4 emissions from coal mining in China with an aim to guide the Central Government to allocate momentary resources to mitigate CH4, it is therefore of particular importance to develop province-level CH4 EFs and emission inventories using more detailed information on activity and EF. In fact, this is an integrated method that generally follows IPCC Tier 2 and Tier 3 methods. Very recently, Maasakkers, et al.19 have used the method to develop a gridded inventory of U.S. anthropogenic CH4 emissions. In this study, detailed information on coal production and corresponding discharged CH4 emissions was collected from 787 coal mines distributed in 25 provinces with different geological and operation conditions for 2009. On the basis of the analysis of the collected data and using regression techniques, the CH4 EFs for three types of coal mines in China were developed: low CH4-content mines, high CH4-content mines, and outburst CH4-content mines. The province-level CH4 emissions were inventoried for 2005-2010 using the official coal production data and the newly developed CH4 EFs. The estimated CH4 emission inventories were compared with other published inventories. Finally, the province-level CH4 emissions for 2011-2020 were forecasted based on the grey forecasting model and regression techniques. 2. ESTIMATION OF PROVINCE-LEVEL CH4 EFs In this study, the coal mines in China are classified into three types based on the CH4 EFs: low CH4-content mines with an EF range of 0 – 10 m3 CH4 emitted per ton of coal mined (m3/t), high CH4-content mines with an EF range of 10.1 - 30 m3/t, and outburst CH4-content mines with an EF > 30 m3/t at the normal production conditions. These classifications are based on the guideline of the China Coal Mining Safety Rule.20,21 Based on the data collected from 787 underground coal mines distributed in 25 provinces and two enterprises in China, a mathematical relationship between cumulative coal production and CH4 EFs was established, and CH4 EFs for the three coal mine types were developed. 2.1 Data Collection. There have been little public sources and/or published literatures on minespecific CH4 emissions in China. The data on annual coal production, discharged CH4 emissions and others for 2009 were collected from the State Administration of Work Safety (SAWS) for 787 underground coal mines located in 25 provinces (municipalities and autonomous regions) and two enterprises (A and B) with various geological and operation conditions (unpublished data). The collected data were subject to the strict and standardized procedures established by SAWS for data quality assurance/quality control. Details on how the data were collected, compiled, verified, and analyzed are presented in SI S1.1-S1.2. Note that there are 23 provinces, four municipalities, five autonomous regions, and two Special Administrative Regions in China. For the sake of brevity, the municipalities and autonomous regions in China are also referred to 3 ACS Paragon Plus Environment


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as provinces in this study. The enterprises A and B are two the Central Government owned coal mine companies with their business mainly in Shanxi, Shaanxi and Inter-Mongolia with lower CH4 contents. These provinces and enterprises are believed to be representative of the vast majority of provinces with coal resources in China. After sorting these mines by CH4 EF, it was found that most of the coal mines in China are of low CH4 contents. Provincial distribution of the selected 787 coal mines and their distribution by CH4 EFs are presented in SI Figures 1 and 2, respectively. The low CH4-content mines, high CH4-content mines, and outburst CH4-content mines accounted for 66.4%, 22.6% and 11.0% of the total mines, respectively. The distributions of coal production by three mine types for each province are presented in SI Table S1. It should be noted that the collected dataset wasn’t the actual total coal production for 2009 in China. To check whether the collected dataset was a good representation of the actual total coal production, the relative fractions of coal production for each mine type over the total (909 million tons) in the dataset were compared to those reported by the China Yearly Energy Statistical Book22 (with a total of 2,975 million tons in 2009). It was found that the relative contributions of each coal mine type’s coal production in the collected dataset and in the Yearly Book were similar (see SI Table S2). 2.2 EFs Development. On the basis of the analysis of the collected coal production versus calculated CH4 EFs from the dataset (787 mines) developed above, the curve of cumulative coal production versus CH4 EFs was found to be of exponential or polynomial characteristics. Initially nine nth-order polynomial functions (1th-order to 9th-order) and three exponential functions (ExpGrow1, ExpGrow2 and ExpGro3 in MATLAB) were used to fit the curve of cumulative coal production vs. CH4 EFs. Finally, the exponential model (ExpGrow2 function) was selected to describe the relationship between cumulative coal production and CH4 EFs because of its better goodness-of-fit statistics.

= + ∙

+ ∙

(1)

where: y is the CH4 EF (m3/t), x is the cumulative coal production (104 t), yo, xo, a1, a2, t1, and t2 are constants. Note that in order to smooth the curve of cumulative coal production vs. CH4 EFs, the cumulative coal production quantities were sorted by an interval of unit CH4 EF (i.e., 0-1, 1– 2, 2–3…., so on). Integrating equation (1) gives the CH4 emissions for coal mine type i (i =1,2,3) given that the low and upper integration limits (cumulative coal production) are α and β, respectively.

| = y dx = + ∙ ∙

! !" #$

+ % ∙ % ∙

! !" #&

'

(2)

Given the cumulative coal production for coal mine type i, the CH4 emission for coal mine type i was calculated. Then CH4 EFi for coal mine type i was calculated as follows:


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() =

*+ ,*+ + ,+

（i=1,2,3）

（3）

Figure 1 presents the relationship between the cumulative coal production and CH4 EFs for the fitted (from eq.1) and the collected datasets, respectively. Based on the analysis of the collected dataset and operation of equations (2) and (3), the CH4 EFs for the three mine types (low CH4content mines, high CH4-content mines, and outburst CH4-content mines) were determined to be: ( = 0.74 12 /t，(% = 11.43 12 ，(2 = 40.95 12 /, respectively. The corresponding CH4 emissions were also calculated as shown in Table 1. The overall EF was determined to be 7.60 m3/t, which is quite different from the default range of 10-25 m³ /t for underground coal mining used in IPCC.12 2.3 Province-Level CH4 EFs. The primary goal of this study was to develop province-level CH4 EFs from coal mining in China. To do so, province-level annual coal production for the three types of mines were collected from the China Energy Statistical Yearbook,22 the China Environment Statistical Yearbook,23 and the China Statistical Yearbook24 for 2005-2010. The percentages of each mine type’s coal production over the total for each province and each enterprise were calculated. For a given year, the CH4 EFs for the provinces and enterprises were calculated by a matrix operation. Let A be a matrix of [27x3] representing the coal production fractions of the three mine types in 25 provinces and 2 enterprises, and B be a matrix of [3x1] representing the EFs of the three mine types determined above. Coal production weighted CH4 EFs for these provinces and enterprises were calculated by the matrix product AxB. Figure 2a presents the spatial distribution of the province-level CH4 EFs in China for 2009. It is obvious that the CH4 EFs vary significantly from province to province. There were 9 provinces with EFs greater than 10 m3/t in 2009, including Chongqing, Anhui, Guizhou, Jiangxi, Heilongjiang, Hunan, Sichuan, Liaoning, and Hubei. Chongqing had the largest EF (36.71 m3/t), followed by Anhui with an EF of 22.9 m3/t. These regional EFs correlate well with the properties of the regional mines. For example, the southwest of China has higher EFs than other regions because the coal mines in that region have deeper depth and higher coalbed CH4 content, especially in Chongqing and Guizhou. The lowest EFs were found in Beijing, Fujian and Qinghai, which are 0.739 m3/t. There are not high and outburst CH4-content mines in these areas. It should be noted that six provinces-Zhejiang, Guangdong, Hainan, Shanghai, Tianjin and Xizang, were not included in this study because they either had negligible coal production or had no coal mines reported. The province-level CH4 EFs developed in this study for the period of 2005-2010 are presented in SI Table S4. It is noted that the developed CH4 EFs for 2005-2010 varied temporally and spatially. The results in SI Table S4 reveal that 1) Beijing and other 5 provinces showed a constant CH4 EF trend probably due to their limited coal reserves and production; 2) Shanxi and other 8 provinces exhibited a slight increasing trend, while Heilongjiang had the greatest increase rate (about 40%) probably due to their deeper mining depth; and 3) the remaining 10 provinces indicated a downward trend with different slopes, especially Shaanxi had the greatest decreasing of about 50% probably due to their new mines operation and/or some deeper mines’ closures. 3. ESTIMATION OF PROVINCE-LEVEL CH4 EMISSIONS: 2005-2010



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The second goal of this study was to use the newly developed EFs coupled with activity data (coal production) to develop an improved province-level CH4 emission inventory for China’s coal mining industry for 2005-2010. The developed emission inventory was compared with other published inventories. 3.1 Methodology for Estimating CH4 Emissions. Total CH4 emissions into the atmosphere should include those discharged from underground mining, post-mining and surface mining, and exclude the utilized portion of the collection quantity. That is, annual CH4 emissions emitted into the atmosphere for each province were calculated by summing the CH4 emissions from the underground coal mining (Eum), post-mining (Epm), and surface mining (Esm) and then subtracting the utilized CH4 emissions (Eut) as follows: ( = (9: + (;: + ( E@A = ∑2DE EFD ∗ PD =

∑J+K$ H*+ ∗I+ ∑J+K$ I+

(4) ∗ L>

(5)

where EFi and Pi are the CH4 EF and coal production for coal mine type i, respectively. Pt is the total coal production. The province-level CH4 EFs for 2005-2010 derived in section 2.3 were used to calculate CH4 emissions from underground mining. CH4 emissions from post-mining include emissions during subsequent handling, processing and transportation of coal. There have not been any measurements available in China for CH4 emissions from coal post-mining. According to Zheng et al.’s estimates14, the post-mining CH4 emissions accounted for about 11% and 12% of the total CH4 emission from coal mining in China in 1994 and 2000, respectively. The value of 12% was used in this study. CH4 emissions from surface coal mining are generally much smaller than those from underground mining12 up to a factor of 10.17 Only 5% coal was mined from surface mines on average at the country scale.14 Since there have been very few measurements of CH4 emissions from surface mining, it was assumed that the CH4 EFs from surface mining were the same as those of low CH4-content mines, which were derived in this study. Not all CH4 emissions from underground coal mines are released into the atmosphere. A fraction of CH4 from coal mining are usually collected and utilized. The utilized fraction of CH4 from coal mining increases with economic growth and enhancement of coal safety.25 There had been few public sources and/or literatures on the utilization of CH4 in China when we conducted this study. We collected and compiled the utilized CH4 quantities for 2005 to 2010 from the National Work Safety Yearly Reports (unpublished data, SAWS) and analyzed for the yearly fractions of utilization over the total CH4 emissions. It was found that the ratio increased from 4.6% in 2005 to 9.4% in 2010, with an average annual rate of 7.3% during the time period of 2005-2010. (The data are provided in SI Table S3.) These utilization fractions are consistent with those reported by three very recent literatures.5,16,26 By linear regression of the six years’ data, a good linear relationship with a R2 (coefficient of determination) value of 0.81 was established as follows: U = 0.0092 ∗ t + 0.0414 6 ACS Paragon Plus Environment

(6)

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where U is the utilization fraction, and t is the number of years from 2005 (t = 1 for 2005). 3.2 Province-Level CH4 Emissions. Figures 2b and 2c show the geographical representations of the coal production and estimated CH4 emissions from coal mining in China for 2009, respectively. It is clear from Figure 2c that a large proportion of the CH4 emissions occurred within five administrative areas: the southwest region (Guizhou, Chongqing, Sichuan), the north region (Shanxi, Inner-Mongolia, Hebei, Beijing), the central region (Hubei, Hunan, and Henan), the east region (Anhui, Jiangxi, Jiangsu, Fujian, and Shandong), and northeast (Heilongjiang, Jilin, and Liaoning). The five areas accounted for about 31.3%, 20.6%, 16.5%, 16.1% and 10.5% of the total CH4 emissions, respectively. By individual provinces, Shanxi, Guizhou, Anhui and Henan were ranked as the top four, which accounted for about 17.0%, 16.5%, 13.3% and 12.6% of the total CH4 emissions, respectively. As expected, the provinces with high CH4 emissions in Figure 2c generally correspond to those provinces with high CH4 EFs (Figure 2a) and/or high coal production (Figure 2b). The total annual emissions of CH4 from coal mining in China were estimated by aggregating the emissions in each province for the period of 2005-2010 and are presented in Figure 3 (for the purpose of comparison, the unit has been converted into Tg/yr). The results show that within the period of 2005−2010, the CH4 emissions increased continuously from 11.5 Tg in 2005 to 16.0 Tg in 2010, an increase of about 40%. On the average, the growth rate was about 0.9 Tg per year, or an annual growth rate of 6.8%. The CH4 EFs and coal production for each province and enterprise for the years of 2006 to 2010 are presented in SI Tables S4-S5, respectively. The CH4 emissions of the individual provinces and enterprises for the period 2005-2010 are presented in SI Figure S3. The results presented in SI Figure S3 show that six provinces - Hebei, Liaoning, Jiangsu, Jiangxi, Beijing, and Guangxi showed a downward trend with different slopes; while all other provinces showed a growth trend. These trends of CH4 emissions from coal mining were mainly attributed to changes in coal production and/or EFs resulting from different mining conditions in the individual provinces. 3.3 Comparison with Other Inventories. Figure 4 shows the comparisons of the CH4 emissions inferred in this study with EPA10 and others’ inventories. Our estimates of the total CH4 emissions were very close to EPA10 estimates with the differences of -6% and 12% for 2005 and 2010, respectively. Our estimates for 2007 and 2008 were consistent with those reported by Cheng et al.15 with a difference of about 1-2% and with those estimated by Peng et al.5 for 2010 with a difference of about -12%. Our estimate for 2010 was lower than Peng et al.’s estimate, probably due to the fact that Peng et al. used the default EFs of IPCC12 for post-mining activity and surface mining, and a slightly lower utilization fraction of CH4 in 2010. For comparison purposes, we calculated the total CH4 emissions using the default 25 m3/t for high- and 10 m3/t for low- CH4-content coal mines with an average EF of 18 m3/t for underground coal mining in IPCC12 for 2005-2010. The results show that the CH4 emissions estimated using the EFs in IPCC12 were about 2-3 times of this study. The CH4 emission inventories from Saunois et al.17 seemed to be too high for China based on EDGARv4.2.11 It should be noted that EDGARv4.2 adopted IPCC12’s default EFs. The above comparisons highlight that significant discrepancies on emission estimates may result from inappropriate use of emission factors and that applying “Tier 1” approach for estimating CH4 emissions from coal mining in China may cause large 7 ACS Paragon Plus Environment


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uncertainties, as stated by the IPCC guidelines. The CH4 EFs of coal mining vary significantly by regions or provinces as shown in this study, because of different mining conditions (depth, CH4 concentration, coal seam permeability, etc.). These province-level CH4 EFs of coal mining ranged from ~36 m3/t in Chongqing to about 0.74 m3/t in Beijing and other two provinces (see Figure 2c or SI Table S4). 4. FORECASTING CH4 EMISSIONS: 2011-2020 The third goal of this study was to develop a methodology to forecast province-level CH4 emissions from coal mining in China for the future years, i.e. 2011-2020 in this study. Due to the fact that the official statistical data on coal production data after 2010 were not available when we conducted this study in 2011, the province-level CH4 EFs and activities (coal production) were forecasted, thus the corresponding CH4 emissions were also estimated. In the following subsections, the Grey forecasting model is briefly introduced and the forecasting model of CH4 emissions for the future years is developed. The forecasted results of coal production, CH4 EFs and the corresponding CH4 emissions for each province for 2011-2020 are presented, discussed and validated by comparing with some existing literatures. Lastly, possible uncertainties resulting from this study and potential implications of this study to China’s policy development are also discussed. 4.1 Grey Forecasting Model (GM(1,1)) for CH4 EFs. Grey system theory was initially developed by Deng27-28 to quantify uncertainty and information insufficiency. The grey forecasting model (GM) adopts the essential aspects of the grey system theory. The term GM(1,1) indicates one variable and first order differential. The grey model GM(1,1) is a time series forecasting model and uses the operations of accumulated generation to construct differential equations, thus it has the characteristics of requiring less data. (The mathematical description of the Grey forecasting model is described in SI S3.1.) The grey forecasting model is a local curve fitting extrapolation scheme. At least a four-data set is required by the predictor to obtain a reasonably accurate prediction28. Tan29 found that the grey model GM(1,1) can well fit equidistant and slow growth time sequences, but often performs very poor and makes delay errors for quick growth time sequences. On the basis of preliminary analysis of the CH4 EFs data developed in section 2.3 (see SI Table S4), it was found that there were some greater year-to-year variations for a few provinces during 2005-2010. For these provinces, the original time series of CH4 EF data with 6 entries (2005-2010) were first regressed for CH4 EF trends using the following exponential equation: ( = + ∑P)E O)

Q

! !" #+


(7)

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where EF(t) is the CH4 EF at year t, x is the year t, R indicates the increasing (+) or decreasing (-) trend, y0, x0, ti and Ai are constants. It is noted that the coal production weighted CH4 EFs for each province were used in this section for the forecasting model. The fitted time series of CH4 EFs by eq 7 for those provinces were used as the initial time series of CH4 EF data for the grey forecasting model. The grey modeling for 2011-2020 was implemented in MATLAB with some programming codes. An example of forecasting of the GM (1, 1) for CH4 EFs of Heilongjiang province is presented in SI S3.2. 4.2 Coal Production Forecasting Model. As stated in section 3.1, the coal production data for each province from 2004 and 2010 were collected from the China Energy Statistical Yearbook, the China Environment Statistical Yearbook, and the China Statistical Yearbook.22-24 Based on the preliminary analysis of the coal production from 2005-2010, it was found that there were some inter-annual variations in some provinces. To provide a reasonable coal production forecast for 2011 to 2020, the annual total coal production data for each province from 2005-2010 were pre-processed. The weighted moving average (WMA) method can help to smooth the coal production curve for better trend identification. Since the coal production in China has changed with time due to the rapid economic development and more requirements posed by resources, environments, greenhouse gas reduction, and work safety, the three-point WMA was used in this study to fit the coal production trend from 2006 to 2010 for each province, which places more weight on the coal production forecast that is closer to the year being forecasted. UW ST) = ∑XDE ∑VW V

(8）

V

where ST) is the weighted averaged coal production for year i, S) is the actual coal production for year i, WD is the weight of year i；n is taken as 3 in this study (3-point WMA), W 、W% 、W2 were assigned values of 0.1，0.2 and 0.7 for year i-2, year i-1 and year i, respectively. That is: [Z = 0.1 × ),% + 0.2 × ), + 0.7 × ) 2006 ≤ _ ≤ 2010 (9) S For example, the weighted moving average coal production for year 2010 is defined as: S% = 0.1 × %` + 0.2 × %a + 0.7 × %

(10)

Based on the processed data, the linear regression was made for coal production forecasting from 2011 to 2020 for each province. Considering that China’s economic growth will slow down,30 more policies and/or regulations on resources saving, air quality improvement, greenhouse gas reduction, and worker safety protection will be proposed and implemented, and energy utilization efficiency will improve as more renewable energy will be used during 2011 to 2020, as a result, coal production in China will be controlled. Thus, an adjusting factor of 0.66 was applied to the forecasted coal production between 2011 and 2020 for those provinces with a net growth in coal production after 2010. 9 ACS Paragon Plus Environment


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4.3 Model Validation. In this study, the mean absolute percentage error (MAPE) was used to validate the grey model GM(1,1) and the coal production forecasting model. In general, MAPE is a measure of prediction accuracy of a forecasting method in statistics, for example in trend estimation. It was implemented by comparing the actual value with the forecasted value to determine forecasting uncertainty.

d " >, de " >

bOL( = P ∑P>E c

d " >

c ∗ 100%

(11)

399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433

where x(0)(t) and e are the actual and the grey forecasted values at time t, respectively, and n is the number of data entry or years in this study. The CH4 EF and coal production forecasting models were evaluated using the following MAPE criteria: 50% for poor forecasting.31 In addition, the coal production forecasting model was evaluated against the China National 12th 5-years Blue Plan30 and other published statistics. 4.4 Forecasted Results. The forecasted CH4 EFs and coal production for each province and enterprise for 2011 to 2020 are also presented in SI Tables S4-S5. The results in SI Table S4 reveal that 1) six provinces - Beijing, Fujian, Qinghai, Ningxia, Gansu and Xinjiang show a constant CH4 EF trend probably due to their very limited coal resources and low CH4 EFs; 2) nine provinces exhibit an increasing trend, and 3) the remaining twelve provinces indicate a downward trend with different slopes. For the forecasted coal production, twenty-one provinces are forecasted to have an increasing trend with significantly different slopes. The remaining six provinces show a dramatically decreasing trend (over 60%) probably due to the fact that coal resources in these provinces would approach their reserves. For those provinces with an upward coal production trend, it is found that the lower the CH4 EFs, the higher the growth rates. For example, Inner-Mongolia, Qinghai, Xinjiang, and Shaanxi provinces are forecasted to have an increase of over 60% in coal production from 2011 to 2020, while these provinces would have lower forecasted CH4 EFs of < 3.0 m3/t. The top three coal production provinces or enterprises – Inner-Mongolia, Shanxi, and Enterprises A are estimated to account for almost 50% of the national coal production during 2011-2020. Collectively, there will be an increase of about 37% from 2010 to 2020, or an annual growth rate of 3.2 % for coal production nationally. The forecasted national CH4 emissions for 2011 to 2020, coupled with those estimated in section 3.2 for 2005-2010 are presented in Figure 3. The CH4 emission trends for each province and enterprise are presented in SI Figure S3. It can be seen from Figure 3 that there would be an increase of about 6% from 2011 to 2020 (about 3% from 2011 to 2015 and about 2% from 2016 to 2020) nationally. For the individual provinces (see SI Figure S3), the nine provinces - Beijing, Hebei, Liaoning, Jiangsu, Fujian, Jiangxi, Hunan, Guangxi, and Shaanxi show a downward trend with the different slopes; while Anhui province indicates a trend of increasing first and then stabilizing; The rest of the provinces show a continued growth trend. These trends of CH4 10 ACS Paragon Plus Environment

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434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479


emissions from coal mining are mainly attributed to the changes of coal production and/or emission factors, and/or increased CH4 extraction and utilization in the individual provinces. 4.5 Validation of the Forecasted Results. For the forecasted CH4 EFs by the GM(1,1), the MAPEs for the individual provinces were calculated using the actual and forecasted values. The SI S3.2 presents an example of the detailed calculations. The last column in SI Table S4 represents the MAPE results of the CH4 EF GM(1,1) for 2005-2010, which reveal that the MAPEs of the GM(1,1) for 2005-2010 ranged from 0 to 14%. According to the criteria proposed by Lewis,31 the GM(1,1) developed in this study was adequate to forecast CH4 EFs in China. Similarly, the MAPEs were also calculated for the forecasted coal production for each province as showed in the last column of SI Table S5. The MAPEs of the coal production forecasting model for 2005-2010 were less than 10%, except for the three provinces - Xinjiang (11%), Qinghai (14%), and Ningxia (20%). In addition, the predicted national totals of coal production were compared with the China’s official statistics32. It was found that the relative residual errors were about 3.1%, 6.4%, and 0.2% for 2011, 2013 and 2014, respectively. According to the China National Development and Reform Commission’s 12th 5-years Blue Plan for Coal Industry,30 the coal production had to be controlled to about or below 3.9 billion tons by 2015. Our estimate for 2015 was 3.75 billion tons, which is very close to the Blue Plan. There have been very limited inventories developed for CH4 emissions from coal mining in China for after 2010. Our estimated CH4 emissions were close to EPA’s estimates10 with the differences of about 8% and 2% for 2015 and 2020, respectively. Saunois et al.’s estimate17 for 2012 was about two times that of this study. We also calculated the annual totals of CH4 emissions using IPCC’s default EFs for underground mining and the forecasted coal production in this study. The results showed a factor of 1-2 higher than our estimates for 2011-2020. All of the above comparisons are also presented in Figure 4. 4.6 Uncertainty. The CH4 EFs derived in this study are based on the database of 787 mines with a total production of 907 million tons in 2009, which represented about 31% of total actual coal production for the same year in China. The derived CH4 EFs for the three types of mines were used to develop the coal production weighted CH4 EFs for each province and each year of 20052020. In the other words, the EFs for the three types of mines derived in this study based on the 2009 data may not evolve with time. This may cause uncertainty for the time series of CH4 inventory. Besides the uncertainty on the EFs, the activity data and utilization fraction also have their own uncertainty. The coal production or activities for 2011-2020 were forecasted using the GM(1.1) Grey model based on the data of 2005-2010. The utilization fractions of CH4 for 20112020 were interpolated from the data of 2005-2010, which may have been underestimated because China has developed many programs to promote CH4 utilization in the recent years. In the past decade, China has been implementing a national policy of energy conservation and emission reduction as well as air quality improvement and has strengthened safety requirements for coal mining operation. As a result, some small coal mines have been closed. These closed small coal mines usually had very small coal production and very shallow mine shafts (because of low cost), thus the discharged CH4 into the atmosphere is relatively small. In this study, we were not able to obtain the information on how each coal mine does methane emission measurements and reports to SAWS. Thus, the uncertainties on the CH4 measurements from individual coal mines were not quantified and evaluated. There may be some variances in 11 ACS Paragon Plus Environment


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the energy per metric ton of coal (heat value) across the mines and a metric ton of coal may not be equivalent across different mines or provinces. It was expected to estimate CH4 EFs based on gram of methane per energy-equivalent unit. Unfortunately, we did not get the information on coal types (bituminous coal, lignite, anthracite, etc.) and their heat values from the individual mines. As a result, we were not able to convert the coal mass into heat value for individual mines in this study. According to the rule requirements of SAWS,20, 21 coal mines in China are required to regularly monitor each mine shaft’s CH4 emissions and report to SAWS for the purpose of work safety. We were not able to evaluate whether the methane emissions data from SAWS is comprehensive. In particular, there might be unreported/unmeasured methane emissions that are important from climate change perspective but unimportant for safety concerns. In addition, gridded CH4 emission inventories with high resolution were not able to be developed in this study due to the lack of individual mines’ geographical information. Such an inventory would be very useful in planning for criterial air pollutants and greenhouse gas emission reduction and air quality modeling. This effort will be pursued in the future time. It should be noted that this study was our first attempt to build a framework to estimate nationalwide or province-level or sub-regional CH4 EFs and emission inventories for China’s coal mining, based on the mine-specific information. Since there have been little public data sources and/or published literatures on mine-specific CH4 emissions in China, collecting CH4 emissions from the annual safety reports submitted by the individual coal mines to SAWS becomes a unique way to gather the data. The collected data were subject to the strict and standardized procedures established by SAWS for data quality assurance/quality control. As a consequence, the information on CH4 emissions for the selected mines used in this study is reasonably reliable, although the uncertainty cannot be quantitively estimated. The framework can be used in future updates when more information on CH4 emissions from more individual mines’ measurements becomes available. 4.7 Implications. In this study, the total annual CH4 emissions discharged into the atmosphere from coal mining in China were estimated to be 11.5 to 16.0 Tg/yr from 2005 to 2010. This vast amount of CH4 emission provides an opportunity for mitigation which has the co-benefits of reducing greenhouse gas emissions, improving air quality, and increasing clean energy supply. China has launched some national programs to mitigate CH4 emissions within the Global Methane Initiative (GMI) and the framework of the Clean Development Mechanism (CDM) on coal mine CH4.33All of these elements will contribute to reducing CH4 emissions from coal mining in China in the coming years to decades. As discussed above, there are significant differences in the CH4 EFs and coal production as well as CH4 emissions among different provinces due to their different mining conditions and reserves. The derived province-level CH4 EFs and the developed emission inventories in this study could help understand CH4 budgets at the provincial scale and guide CH4 mitigation policies and practices in China. For example, China may consider to shift coal mining operations from high CH4-content shafts to those with lower CH4-contents. For those provinces with high CH4 EFs such as Chongqing and Sichuan, local governments may consider switching energy use from coal to shale gas due to net carbon reduction benefits.26


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525 526 527 528 529 530 531 532 533

Author Information

534

Associated Content

535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568

Supporting Information Available Supporting information provided in this paper includes: Further details on data collection and processing, Supplemental results on provincial CH4 EFs, coal production and CH4 emission inventories from coal mining in China, and Grey forecasting model’s description and application example. This information is available free of charge via the Internet at http://pubs.acs.org.

Corresponding Authors Tao Zhu: Email: [email protected]. Phone: +86-1062339170. Pingkuan Di: Email: [email protected]. Phone: +1-530-220-3919.

Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work was supported by the Program for New Century Excellent Talents in China (No. NCET120967), the Beijing outstanding talent training project (No.2012ZG81), the Open Funds of State Key Laboratory of Coal Resources and Safe Mining (CUMTB) (No.SKLCRSM16KFA04), and the Fundamental Research Funds for the China Central Universities (No.2009QH03). We would like to thank the three anonymous reviewers for their valuable comments and suggestions to improve this paper.

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Machida, T.; Maksyutov, S.; McDonald, K. C.; Marshall, J.; Melton, J. R.; Morino, I.; Naik, V.; O’Doherty, S.; Parmentier, J. W.; Patra, P. K.; Peng, C.; Peng, S.; Peters, G. P.; Pison, I.; Prigent, C.; Prinn, R.; Ramonet, M.; Riley, W. J.; Saito, M.; Santinil, M.; Schroeder, R.; Simpson, I. J.; Spahni, R.; Steele, P.; Takizawa, A.; Thornton, B. F.; Tian, H.; Tohjima, Y.; Viovy, N.; Voulgarakis, A.; van Weele, M.; van der Werf, G. R.; Weiss, R.; Wiedinmyer, C.; Wilton, D. J.; Wiltshire, A.; Worthy, D.; Wunch, D.; Xu, X.; Yoshida, Y.; Zhang, B.; Zhang, Z.; and Zhu, Q. The global methane budget 2000–2012. Earth Syst. Sci. Data, 2016, 8, 697–751, www.earthsyst-sci-data.net/8/697/2016/ doi:10.5194/essd-8-697-2016. (18) Kirchgessner, D. A.; Piccot, S.D.; Masemore, S. S. An improved inventory of methane emissions from coal mining in the United States. J Air Waste Management Assoc. 2000 Nov, 50(11),1904-1919. (19) Maasakkers, J. D.; Jacob, D. J.; Sulprizio, M. P.; Turner, A. J.; Weitz, M.; Wirth, T.; Hight, C.; DeFigueiredo, M.; Desai, M.; Schmeltz, R.; Hockstad, L.; Bloom, A. A.; Bowman, K. W.; Jeong, S.; and Fischer, M. L. Gridded National Inventory of U.S. Methane Emissions. Environ. Sci. Technol. 2016, 50, 12123-13133. (20) SAWS (The State Administration of Work Safety). Interim Specification for the Identification of Coal Mine Gas Grad;. 2011 (in Chinese). (21) SAWS (The State Administration of Work Safety). Specification for the Identification of Classification of Gaseous Mines; 2006 (in Chinese).. (22) CESY. China Energy Statistical Yearbook; China Statistical Press; Beijing, 1980-2010, (23) CEnSY. China Environment Statistical Yearbook; China Environment Yearbook Press, Beijing, 1980-2010. (24) CSY. China Statistical Yearbook; China Statistical Press; Beijing, 1980-2010. (25) The National Development and Reform Commission (NDRC). The People’s Republic of China national Greenhouse gas inventory;China Environmental Press; Beijing, 2014. (26) Qin, Y.; Edwards, R.; Tong, F.; and Mauzerall, D. L. Can switching from coal to shale gas bring net carbon reductions in China? Environ. Sci. Technol, 2017, 51, 2554-2562. (27) Deng, J. L. Control problems of Grey system. Syst Contr Lett 1982,1(5),288–294. (28) Deng, J. L. The Basis of Grey Theory; Press of Huazhong University of Science & Technology; Wuhan, China, 2002. (29) Tan, G. J. The structure method and application of background value in grey system GM(1,1) Model (I), J. Theor. Pract. Syst. Eng. 2000, 20 (4), 98–103. (30) China National Development and Reform Commission. 12th 5-Years Blue Plan for Coal Industry; March, 2012. (http://zfxxgk.nea.gov.cn/auto85/201203/W020120322368710161760.pdf, accessed May 12, 2017.) (31) Lewis CD. Industrial and business forecasting methods; Butterworth Scientific; London, 1982. (32) https://zh.wikipedia.org/wiki/中国煤炭工业 (accessed on May 12, 2017) (33) Higashi, N. Natural gas in China: Market evolution and strategy; International Energy Agency, 2009. (https://www.globalmethane.org/).



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Table 1. Calculation of CH4 emissions and EFs by eqs (2) and (3) for three types of mines in China (based on 787 mines’ statistics in 2009). Type of coal mines Total Low CH4content mines High CH4content mines

Upper and lower bounds of integral in eq 2 (million t)

Coal production （million t）

Calculated CH4 emissions by eq (2) (million m³）

Calculated CH4 EF by eq (3) (m3/t)

g = 0，h = 907.07

907.07

6,892.64

7.60

g = 0，h = 602.07

602.07

444.71

0.74

g = 602.07，h = 806.77

204.70

2,340.33

11.43

g = 806.77，h 100.30 4,107.60 40.95 = 907.07 Note: The values of α and β for each mine type are the lower and upper cumulative coal production quantities, respectively (for example, α=602.07 is the lower cumulative coal production quantity and β=806.77 is the upper cumulative coal production quantity for high CH4-content mines. Alternatively, the values of α and β can be read from the curve of cumulative coal production vs. CH4 EFs at EF =10.1 m3/t and EF=30.0 m3/t, respectively. Outburst CH4content mines


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Figure captions

90 80

CH4 EF (m3/t)

70 60 50

Fitted CH4 EF (m3/t)

40

Actual CH4 EF (m3/t)

30 20 10 0 0

10000 20000 30000 40000 50000 60000 70000 80000 90000 100000

Cumulative Coal Production (104 t)

Figure 1. CH4 EFs as a function of cumulative coal production for collected dataset (787 mines’ data in 2009) and fitted dataset from eq 1. Note that in order to smooth the curve of cumulative coal production vs. CH4 EFs, the cumulative coal production quantities were sorted by an interval of unit CH4 EF (i.e., 0-1, 1–2, 2–3…., so on). The middle points of the interval (e.g., 0.5 for the range of 0-1) were used as the reported EFs.



656 657 658 659 660 661 662 663 664 665 666 667 668 (2a). Distribution CH4 EFs


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(2b). Distribution of Coal Production

(2C). Distribution of CH4 Emissions

Figure 2. Geographical distributions of (a) CH4 EFs (m3/t), (b) coal production (million tons/yr) and (c) CH4 emissions (million m3/yr) in China in 2009.



20.0 18.0 CH4 Emissions (Tg/yr)

16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 -

Year 669 670 671 672

Figure 3. Trend in national totals of CH4 emissions (Tg/yr) from coal mining in China. The emissions for 2005-2010 are estimated based on newly developed CH4 EFs in this study and official coal production statistics. The CH4 emissions for 2011-2020 are forecasted by constructing a grey forecasting model for CH4 EFs and a regression model for activity. 20 ACS Paragon Plus Environment

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673

Figure 4. Comparison with other inventories of CH4 emissions from coal mining in China, including those estimated by EPA10 for 2005, 2010, 2015, and 2020; Cheng et al.15 for 2007-



2008; Peng et al.5 for 2010, Saunois et al.17 for the 2000-2009 average, the 2003-2012 average, and 2012; and those estimated using IPCC12 default CH4 EFs of coal mining for 2005-2020.

TOC Art


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An Improved Approach to Estimate Methane Emissions from Coal

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