Estimation of Methane Emissions from Municipal Solid Waste Landfills in China Based on Point Emission Sources

Abstract

The methane (CH₄ ) emissions from municipal solid waste (MSW) landfills in China in 2007 were estimated based on database of the three-dimensional emission factors matrix and point sources, by an IPCC recommended FOD (firstorder decay) model. The location, capacity and age of landfills constitute the three dimensions of the emission factors matrix, which were obtained by laboratory analysis and in situ investigation. Key parameters such as waste composition, degradable organic carbon ratio, CH₄ correction factor, oxidation factor and recovery rate, were carefully analyzed in terms of these three dimensions. The point sources database consists of 2,107 MSW landfills in cities and towns of China in 2007. The results show that the CH₄ emissions from MSW landfills were 1.186 Mt in 2007. Compared with the CH₄ emissions of 2.20 Mt in 2005, the significant discrepancy mainly comes from statistical data of landfills, e.g., number of landfills and amount of waste disposed in landfills. CH₄ emissions were lower than 700 t for most of the landfills, whereas there were 279 landfills with emissions larger than 1,000 t, and only 10 landfills with emissions larger than 10,000 t. Jiangsu province ranks the largest emitter with 98,700 t while Tibet is the smallest emitter with 2,100 t. In general, the emissions from eastern provinces, such as Jiangsu, Guangdong and Zhejiang, were larger than those from western provinces, such as Ningxia, Tibet and Qinghai.

Keywords

municipal solid waste landfill ; CH4 emissions ; point emission sources

1. Introduction

Methane (CH₄ ) is the second largest driver of climate change behind carbon dioxide and one of the six greenhouse gases (GHGs) listed in the Kyoto Protocol, with global warming potential of 25 over 100 years (IPCC, 2007a ). CH₄ is also a short-lived climate pollutant with an average life-time around 12 years in the atmosphere. According to the IPCC Fourth Assessment Report (IPCC, 2007b ), the total CH₄ emissions and those from waste management accounted for 14.3% and 2.8% respectively, of the global GHG emissions in 2004. The CH₄ emissions from waste management shared 4% of the global total GHG emissions in 2010 (UNEP, 2012 ), with about half both from municipal solid waste (MSW) landfill and waste water treatment (JRC and PBL, 2012 ). The CH₄ emissions from MSW landfill rose fast from 16.50 Mt in 1970 to 29.50 Mt in 2008, with the total growth of 78.79% (JRC and PBL, 2012 ). About 73% of safely disposed MSW in China was landfilled in 2012 (NBSC, 2013 ). Landfill is the dominant treatment of MSW disposal at present and will continue to be the dominant and most economically viable MSW management in the mid-term future of China.

With the development of economy, advance of urbanization and improvement of people’s living standards, both the waste generation and landfill are substantially increasing. Comprehensive and accurate estimation of the CH₄ emissions from landfills is increasingly important in waste recycling and CH₄ emission reduction in China. The regional specific emission factors and detailed inventory for CH₄ emissions are essential to regional GHG inventories and climate change programs at provincial level.

Waste decomposition does not begin immediately after the disposal but typically with a time delay. Therefore, the CH₄ emissions by the waste decomposition will last a period of time (roughly 50 years) after the MSW landfilled (IPCC, 2006 ). The annual emissions vary significantly due to variation of landfill capacity, management level and operation time. Research has identified that the regional specific and finer classified emission factors and landfill specific activity data are essential to the CH₄ emission estimation due to the notable impacts of capacity and management level on the emissions (Börjesson et al., 2009 , Ishigaki et al., 2008 , Kumar et al., 2004  and Wangyao et al., 2010 ). However, studies on landfill CH₄ emissions in China were mostly based on nationwide emission factors and national or regional waste disposal data (Jiang et al., 2004 , Xu, 1997 , Gao et al., 2006 , Gao et al., 2007 , Qu and Yang, 2011 , Luo et al., 2009  and Gong et al., 2007 ). The uncertainty of estimation of CH₄ emissions from landfills in China based on the general emission factors and regional activity data is relatively high due to the special emission process of landfills.

In this paper, the region specific emission factors and detailed information on each landfill in China were used to calculate the landfill CH₄ emissions in 2007. The emissions at provincial and national levels were aggregated from individual landfills. This bottom-up method reduces the uncertainties associated with emission factors and activity data, and therefore significantly improves the estimation quality.

2. Methodology

The FOD (first-order decay) model recommended by IPCC guidelines (IPCC, 2006 ), is applied in calculation in this paper. It is currently the mainstream approach and utilized by EPA (2013) for landfill CH₄ emissions. The advantage of this model is that it incorporates time parameters to reflect the decay process of carbon in waste. The FOD model involves relatively more parameters than other methods, e.g., mass balance method. The FOD model can be presented as:

$E_{{\mbox{C}}{\mbox{H}}_{4}}=M\cdot \sum _{i=1}^{4}C\cdot f_{i}\cdot D_{i}\cdot D_{F}\left(e^{-\left(T-1\right)\cdot k_{i}}-\right.$$\left.e^{-T\cdot k_{i}}\right)\cdot F\cdot 16/12\cdot \left(1-\right.$$\left.R\right)\times \left(1-O\right){\mbox{,}}$

( 1)

where E_CH4 is the CH₄ emitted in inventory year T ; M is the mass of MSW landfilled at time 0, when the reaction starts; C is the correction factor; f_i is the fraction of waste type i (kitchen waste, paper, textile, and wood); D_i is the fraction of degradable organic carbon in waste type i ; D_F is the fraction of degradable organic carbon which decomposes; k_i is reaction constant; F is the fraction of CH₄ , by volume, in generated landfill gas; R is the CH₄ recovery rate; O is the oxidation factor.

Because the waste composition in China is different from that in developed countries, the IPCC default emission factors for FOD, most of which are derived from studies of developed countries, are not appropriate in our estimation. We developed a China landfill emission factors matrix by field investigation and laboratory analysis. The activity data, i.e., mass of MSW in each landfill, time of landfill operation are all from landfill-level survey.

The estimation does not include Taiwan, Hong Kong and Macao. The scope of survey covers all the landfills including sanitary landfill and simple landfill (open dump).

3. Emission factors

An emission factors matrix is built up based on field investigation, laboratory analysis and literature review. The key parameters in this study are the composition of waste type, fraction of degradable organic carbon, correction factor, CH₄ recovery rate and oxidation factor. Other parameters, e.g., fraction of CH₄ in generated landfill gas, are relatively stable. Therefore the IPCC default values are adopted.

China is divided into 7 regions according to regional climate, economic development and the people’s habits and customs, which could significantly affect composition of MSW. Compositions of MSW in 55 cities in the 7 regions were investigated to determine the average value for each region (Table 1 ). According to discussion and interview with experts and landfill managers, the management level (directly affecting the correction factor, recovery rate and oxidation factor) of landfill in China could be largely determined by its capacity. The larger the landfill capacity is, the more regular and stricter management it exhibits. Thus, three scales of capacity are identified, namely I (> 5 million m³ ), II (2–5 million m³ ) and III (< 2 million m³ ) for parameters determination.

Table 1. Waste composition in 7 regions of China

Region	Province	Number of cities investigated	Degradable contents (%)
			Kitchen waste	Paper	Textile	Wood
Northwest	Xinjiang, Gansu, Qinghai, Ningxia, Shaanxi	7	39.26	5.00	2.38	3.92
North China	Shanxi, Inner Mongolia, Hebei, Beijing, Tianjin	7	53.25	7.67	3.26	3.21
Northeast	Heilongjiang, Jilin, Liaoning	9	59.90	7.30	1.54	2.30
Central China	Henan, Hubei, Hunan	6	35.57	4.58	1.07	1.73
East China	Shandong, Jiangsu, Anhui, Shanghai, Zhejiang, Fujian, Jiangxi	16	54.07	7.27	4.45	1.55
Southwest	Chongqing, Sichuan, Guizhou, Yunnan, Tibet	6	45.54	7.14	3.14	3.64
South China	Guangdong, Guangxi, Hainan	4	44.05	5.80	2.00	3.17

Note: The time span covers 2000–2010 for these 55 cities

3.1. MSW components

The MSW with degradable contents could be classified into 4 types according to the IPCC guidelines and characteristics of MSW in cities of China. The diaper as an individual type listed in IPCC is merged into textile type due to China statistical situation. The waste composition in seven regions is shown in Table 1 . The waste composition data are compiled from field investigation and literature reviews of major landfills in 55 cities.

3.2. Ratio of degradable organic carbon

The ratios of degradable organic carbon in dry wastes (Table 2 ) are obtained by laboratory analysis conducted by the School of Environment, Tsinghua University. Fifteen samples for each waste type largely from Beijing landfills were analyzed by external heating method of potassium dichromate volumetric method. The ratios of degradable organic carbon in wet wastes are calculated based on the water content of different waste types. The resultant ratios can represent the national level values due to the slight differences of water content in MSW among different regions.

Table 2. The wet based ratio of degradable organic carbon in different components of MSW

Source	Kitchen waste	Paper	Textile	Wood
Results in this paper	0.11	0.24	0.27	0.33
IPCC default value	0.15	0.40	0.24	0.43

By comparison, the ratios in kitchen waste, paper and wood in this study are significantly lower than the IPCC default values due to higher water content in MSW of China. The higher ratio in textile in this study is largely attributed to the higher content of cotton materials in clothes in China.

3.3. CH₄ correction factor

CH₄ correction factor reflects the effect of landfill management on the CH₄ emissions. IPCC provided default values for 5 types of landfill management (shallow, semi-aerobic, unmanaged-deep, managedanaerobic, and uncategorized). We derived the correction factors for three different capacities by statistical analysis on the 79 landfills samples. We investigated the management level (conforming to the IPCC category) of the 79 landfills samples, assigned the IPCC value to each sample according to its management and calculated the weighted average correction factor for the 3 landfill capacities (Table 3 ).

Table 3. Key parameters in the FOD model

Type of landfill	CH4 oxidation factor			CH4 correction factor	CH4 recovery rate (%)
	Northwest	North China, Northeast, Central China, East China	Southwest, South China
I	0.00	0.00	0.00	1.00	40
II	0.08	0.10	0.15	0.92	24
III	0.15	0.20	0.30	0.61	5

3.4. CH₄ oxidation factor

The waste composition, landfill coverage materials, coverage thickness, humidity and temperature, etc. can affect the CH₄ oxidation factor. According to 79 landfills surveys, large landfills usually implement geomembrane covering to avoid odorous gases and improve the collection rate of landfill gas. Therefore the oxidation factors for large capacity landfills are relatively lower than those of medium and small capacity landfills (Table 3 ), which use the soil coverage, and thus favors CH₄ oxidation. The humid and rainy climate in southern China can also improve the CH₄ oxidation, whereas the dry weather and low annual temperature in Northeast China reduces the CH₄ oxidation.

3.5. CH₄ recovery rate

The CH₄ recovery can be of benefit to the landfill and climate change by CH₄ combustion for power generation and heat supply. The high share of kitchen waste in MSW of China leads to a high value of reaction constant or short degradable period. The results in the low recovery rate of landfill CH₄ because part of degradable organic carbon has been degraded before being landfilled. CH₄ recovery rates were determined by the field investigation as shown in Table 3 . Some landfills collect the CH₄ and directly vent the CH₄ after collection. This collection management has no effect on the recovery rate.

3.6. Other parameters

The reaction constants k_i for different waste types are IPCC default values for boreal and temperate (wet) climate zone (0.18, 0.06, 0.06 and 0.03 for kitchen waste, paper, textile and wood, respectively). The value of the fraction (F) of CH₄ in generated landfill gas is 0.5, which is relatively stable and range 0.4–0.6 in our investigation and consistent with the IPCC default value. The fraction of degradable organic carbon decomposed D_F is 0.5, same as the IPCC default value. According to the calculation, 97% of the degradable organic carbon will be decomposed 40 years later after the waste is landfilled. Therefore, the maximal inventory year of T is set to 40.

3.7. CH₄ emission factors

Based on the aforementioned key parameters and Eq. 1 , the resultant landfill CH₄ emission factors were calculated as shown in Table 4 . On average, the emission factors were larger in capacity II than those in

Table 4. Landfill CH₄ emission factors (unit: kg CH₄ t^–1 )

Years of being landfilled	Landfill capacity I								Landfill capacity II								Landfill capacity III
	North west	North China	North east	Central China	East China	South west	South China	Mean	North west	North China	North east	Central China	East China	South west	South China	Mean	North west	North China	North east	Central China	East China	South west	South China	Mean
1	1.71	2. 31	2.49	1.48	2. 35	2.02	1.96	2.05	1. 84	2.42	2. 61	1.56	2.47	2.00	1.94	2. 12	1.44	1. 82	1.97	1. 17	1.85	1.40	1.36	1.57
2	1.46	1.97	2. 11	1.26	2.00	1.73	1.68	1.75	1.57	2.07	2.22	1.32	2.10	1.71	1.67	1.81	1.23	1.55	1.66	0.99	1.58	1.20	1.17	1.34
3	1.26	1.69	1.80	1.07	1.71	1.48	1.45	1.49	1.35	1.77	1.89	1.13	1.79	1.47	1.43	1.55	1.05	1.33	1.42	0.84	1.35	1.02	1.00	1.14
4	1.08	1.45	1.53	0.92	1.46	1.28	1.25	1.28	1.16	1.52	1.61	0.96	1.53	1.26	1.24	1.32	0.90	1.14	1.20	0.72	1.15	0.88	0.87	0.98
5	0.93	1.24	1.31	0.78	1.25	1.10	1.08	1.10	1.00	1.30	1.37	0.82	1.31	1.09	1.07	1.14	0.78	0.98	1.03	0.62	0.99	0.76	0.75	0.84
6	0.80	1.07	1.12	0.67	1.07	0.95	0.94	0.95	0.86	1.13	1.17	0.71	1.13	0.94	0.93	0.98	0.67	0.84	0.88	0.53	0.84	0.66	0.66	0.73
7	0.70	0.93	0.96	0.58	0.93	0.83	0.82	0.82	0.75	0.97	1.01	0.61	0.97	0.82	0.81	0.85	0.59	0.73	0.75	0.46	0.73	0.57	0.56	0.62
8	0.61	0.81	0.82	0.50	0.80	0.72	0.72	0.71	0.65	0.85	0.87	0.52	0.84	0.71	0.71	0.74	0.51	0.63	0.65	0.39	0.63	0.50	0.50	0.54
9	0.53	0.70	0.71	0.43	0.69	0.63	0.63	0.62	0.57	0.74	0.75	0.45	0.73	0.63	0.62	0.64	0.44	0.56	0.56	0.34	0.54	0.44	0.44	0.47
10	0.46	0.61	0.62	0.37	0.60	0.55	0.56	0.54	0.50	0.64	0.65	0.39	0.63	0.55	0.55	0.56	0.39	0.48	0.48	0.29	0.47	0.38	0.38	0.41
11	0.41	0.54	0.53	0.33	0.53	0.49	0.49	0.47	0.44	0.57	0.56	0.34	0.55	0.48	0.49	0.49	0.35	0.42	0.42	0.26	0.41	0.34	0.34	0.36
12	0.36	0.48	0.47	0.29	0.46	0.43	0.44	0.42	0.39	0.50	0.49	0.30	0.48	0.43	0.43	0.43	0.30	0.38	0.37	0.23	0.36	0.30	0.30	0.32
13	0.32	0.42	0.41	0.25	0.40	0.39	0.39	0.37	0.35	0.44	0.43	0.26	0.42	0.38	0.39	0.38	0.27	0.33	0.32	0.20	0.32	0.26	0.27	0.28
14	0.29	0.37	0.36	0.22	0.36	0.34	0.35	0.33	0.31	0.39	0.38	0.23	0.37	0.34	0.35	0.34	0.24	0.29	0.28	0.17	0.28	0.24	0.24	0.25
15	0.26	0.33	0.32	0.20	0.32	0.31	0.32	0.29	0.28	0.35	0.33	0.21	0.33	0.31	0.31	0.30	0.22	0.26	0.25	0.15	0.25	0.21	0.22	0.23
16	0.23	0.30	0.28	0.17	0.28	0.28	0.28	0.26	0.25	0.31	0.29	0.18	0.29	0.27	0.28	0.27	0.20	0.23	0.22	0.14	0.22	0.20	0.20	0.20
17	0.21	0.27	0.25	0.16	0.25	0.25	0.26	0.23	0.22	0.28	0.26	0.16	0.26	0.25	0.26	0.24	0.17	0.21	0.20	0.12	0.20	0.17	0.18	0.18
18	0.19	0.24	0.22	0.14	0.22	0.23	0.24	0.21	0.20	0.25	0.23	0.15	0.24	0.23	0.23	0.22	0.16	0.19	0.17	0.11	0.17	0.16	0.17	0.16
19	0.17	0.22	0.20	0.13	0.20	0.21	0.21	0.19	0.19	0.23	0.21	0.13	0.21	0.21	0.21	0.20	0.14	0.17	0.16	0.10	0.16	0.14	0.15	0.14
20	0.16	0.20	0.18	0.11	0.18	0.19	0.20	0.17	0.17	0.21	0.19	0.12	0.19	0.19	0.19	0.18	0.14	0.16	0.14	0.09	0.14	0.13	0.14	0.14
21	0.15	0.18	0.16	0.10	0.16	0.17	0.18	0.16	0.16	0.19	0.17	0.11	0.17	0.17	0.18	0.16	0.12	0.14	0.13	0.08	0.13	0.12	0.13	0.12
22	0.13	0.17	0.15	0.09	0.15	0.16	0.17	0.15	0.14	0.18	0.15	0.10	0.16	0.16	0.17	0.15	0.11	0.14	0.11	0.08	0.12	0.11	0.11	0.11
23	0.12	0.15	0.13	0.09	0.14	0.15	0.15	0.13	0.13	0.16	0.14	0.09	0.14	0.15	0.15	0.14	0.11	0.12	0.11	0.07	0.11	0.11	0.11	0.11
24	0.11	0.14	0.12	0.08	0.12	0.14	0.14	0.12	0.12	0.15	0.13	0.08	0.13	0.14	0.14	0.13	0.10	0.11	0.10	0.06	0.10	0.10	0.10	0.09
25	0.11	0.13	0.11	0.07	0.11	0.13	0.13	0.11	0.11	0.14	0.12	0.08	0.12	0.13	0.13	0.12	0.09	0.11	0.09	0.06	0.09	0.09	0.09	0.09
26	0.10	0.12	0.10	0.07	0.11	0.12	0.12	0.11	0.11	0.13	0.11	0.07	0.11	0.12	0.12	0.11	0.08	0.10	0.08	0.05	0.08	0.08	0.08	0.08
27	0.09	0.11	0.09	0.06	0.10	0.11	0.12	0.10	0.10	0.12	0.10	0.06	0.10	0.11	0.11	0.10	0.08	0.09	0.08	0.05	0.08	0.08	0.08	0.08
28	0.09	0.11	0.09	0.06	0.09	0.10	0.11	0.09	0.09	0.11	0.09	0.06	0.09	0.10	0.11	0.09	0.08	0.08	0.07	0.05	0.07	0.08	0.08	0.07
29	0.08	0.10	0.08	0.05	0.08	0.10	0.10	0.09	0.09	0.10	0.08	0.06	0.09	0.10	0.10	0.09	0.07	0.08	0.06	0.05	0.07	0.07	0.07	0.07
30	0.08	0.09	0.08	0.05	0.08	0.09	0.10	0.08	0.08	0.10	0.08	0.05	0.08	0.09	0.09	0.08	0.07	0.08	0.06	0.04	0.06	0.06	0.07	0.06
31	0.07	0.09	0.07	0.05	0.07	0.09	0.09	0.07	0.08	0.09	0.07	0.05	0.08	0.08	0.09	0.08	0.06	0.07	0.05	0.04	0.06	0.06	0.06	0.06
32	0.07	0.08	0.07	0.04	0.07	0.08	0.08	0.07	0.07	0.09	0.07	0.05	0.07	0.08	0.08	0.07	0.06	0.07	0.05	0.04	0.05	0.05	0.06	0.05
33	0.07	0.08	0.06	0.04	0.06	0.08	0.08	0.07	0.07	0.08	0.06	0.04	0.07	0.08	0.08	0.07	0.05	0.06	0.05	0.03	0.05	0.05	0.05	0.05
34	0.06	0.07	0.06	0.04	0.06	0.07	0.08	0.06	0.07	0.08	0.06	0.04	0.06	0.07	0.07	0.06	0.Œ	0.06	0.05	0.03	0.05	0.05	0.05	0.05
35	0.06	0.07	0.05	0.04	0.06	0.07	0.07	0.06	0.06	0.07	0.06	0.04	0.06	0.07	0.07	0.06	0.05	0.05	0.05	0.03	0.05	0.05	0.05	0.05
36	0.06	0.06	0.05	0.03	0.05	0.06	0.07	0.06	0.06	0.07	0.05	0.04	0.05	0.06	0.07	0.06	0.05	0.05	0.04	0.03	0.04	0.05	0.05	0.05
37	0.05	0.06	0.05	0.03	0.05	0.06	0.06	0.05	0.06	0.06	0.05	0.03	0.05	0.06	0.06	0.05	0.05	0.05	0.04	0.02	0.04	0.05	0.05	0.04
38	0.05	0.06	0.05	0.03	0.05	0.06	0.06	0.05	0.05	0.06	0.05	0.03	0.05	0.06	0.06	0.05	0.05	0.05	0.04	0.02	0.04	0.04	0.05	0.04
39	0.05	0.05	0.04	0.03	0.04	0.06	0.06	0.05	0.05	0.06	0.04	0.03	0.05	0.05	0.06	0.05	0.04	0.05	0.03	0.02	0.04	0.04	0.04	0.04
40	0.05	0.05	0.04	0.03	0.04	0.05	0.05	0.04	0.05	0.05	0.04	0.03	0.04	0.05	0.05	0.05	0.04	0.04	0.03	0.02	0.03	0.04	0.04	0.04

capacity I and III, due to the relatively higher correction factors and lower recovery rates in capacity II than those in the other two capacity scales. The emission factors are always higher in Northeast China than in other regions in almost all three capacity scales. This reflects the comprehensive effects of key parameters on the emission process.

4. Activity data

All landfill information was from our survey data. There are 2,107 landfills in China, including 630 sanitary landfills and 1,477 simple landfills. The total waste disposed in landfills was 152 Mt. Comparison between national statistical data and the survey data is shown in Figure 1 . The number of landfills and amount of disposed waste in our data are about 5 and 2 times of the national statistical data, respectively. The main reason of this discrepancy is that the national statistical system mainly focused on large capacity landfills and excluded most of the simple landfills.

Figure 1. Comparison between statistical data and our survey data

The capacity III (small capacity) landfills dominated in quantity (Fig. 2 ), accounting for 83% of total in different regions of China landfills. The number of capacity III landfills in East China is the largest with 637, and that in Northeast China is the least with 105. Most of the large capacity (capacity I) landfills are also concentrated in East China with the total number of 28. There are only 8 capacity I landfills in Southwest China. The distribution mirrored the differences of population density and economic development among different regions. Because of the high population density, more MSW are generated in East China. In addition, the higher the economic development level is, the more regular and larger the capacity landfill are built.

Figure 2. Number of landfills and amount of disposed waste within the three capacity scales in different regions of China

The operation time (from the start of operation up to 2007) and volume of waste landfilled of each landfill were surveyed in the investigation. The histograms of these two data are shown in Figure 3 . The landfills with more than 30 years operation are very few. This means few landfills were built up in the 1970s in China. As shown in Figure 3 , the constructions of landfills began to increase in the mid-1980s, and developed rapidly after 2000. This implies that landfill CH₄ emissions will increase substantially in the next 10 years.

Figure 3. Histogram of the operating time (a) and annual disposed waste (b) in landfills

The annual waste landfilled in each landfill is assumed to be equal to the annual average level due to the fact that detailed historical data for each landfill are not available. The histogram of annual volume of disposed waste of all landfills is shown in Figure 3 b. Annual disposed volumes of most landfills are less than 200,000 m³ , with a few landfills exceeding 1,000,000 m³ . This also demonstrates that the small capacity landfills dominate the waste disposal by landfills in China.

5. Results

Based on the FOD model coupled with detailed activity data and emission factors matrix, CH₄ emissions of each landfill in China are estimated and then aggregated to the regional and national levels. The emissions of landfills are clustered in eastern China due to a large number of landfills especially large capacity landfills (Fig. 4 ). The CH₄ emissions in 2007 of the majority of landfills are less than 700 t, emissions of 279 landfills are greater than 1,000 t, and emissions of only 10 landfills are greater than 10,000 t.

Figure 4. CH4 emissions of individual landfills in China in 2007 (unit: t)

Figure 5 shows the CH₄ emissions of landfills at the provincial level. Jiangsu province ranked the largest emitter with 98,727 t while Tibet ranked the lowest emitter with 2,086 t. The provinces in eastern China, such as Jiangsu, Guangdong, and Zhejiang, generally had larger emissions than the provinces in western China, such as Tibet, Ningxia, and Qinghai.

Figure 5. Landfill CH4 emissions at the provincial level in China

The total amount of CH₄ emissions from landfills in China was 1,186,094 t, which accounts for about 53.91% of the total CH₄ emissions from landfills of China in 2005, as stated in The People’s Republic of China Second National Communication on Climate Change (hereinafter referred to as Second National Communication) (NDRC, 2012 ).

6. Conclusions and policy implications

The CH₄ emissions from 2,107 landfills in China have been estimated individually and rolled up to obtain provincial and national level estimations. There are two merits in this study. The first is that the regional and capacity specific emission factors for landfills are used instead of the IPCC default values used in most of the other studies in China. The key parameters influencing the emission factors, i.e., waste composition, ratios of degradable organic carbon, CH₄ oxidation factor, and CH₄ recovery rate, are obtained by our laboratory analysis and field investigation. The second is the full-scope landfill data which greatly support the estimation of CH₄ emissions of landfill individually. These two merits can reduce the uncertainty of our estimations as much as possible.

There are significant discrepancies between this study and the estimation in the Second National Communication. There are three main reasons for the discrepancies. 1) Difference in estimation method. The estimation in the Second National Communication was based on regional or city level, whereas our estimation was based on individual landfill. 2) Difference in activity data. The activity data used in the Second National Communication were from the national statistical data, and our activity data were from the full scope survey. 3) Differences in emission factors and the base year. Emission factors of this study are much more detailed for landfills in different regions and capacities. The virtue of emission factors used in the Second National Communication was that it considered the waste composition change during the period of time, whereas the waste composition has been kept constant in this study. What’s more, the inventory year for the Second National Communication was 2005 and 2007 for this study. It is still difficult to quantitatively evaluate the source of differences and determine which value is closer to the real condition due to the lack of more detailed data. But this study shows that there are still gaps between statistical data and information requirements for the estimation of CH₄ emissions of landfills, and further work is still needed.

The composition of MSW in China is substantially different from that in developed countries. The MSW landfills in most European countries and the U.S. have relatively higher ratios of slowly degradable fractions. However, in China, the higher level of rapidly degradable fraction in MSW results in earlier landfill gases generation during landfilling. Part of the degradable organic carbon is decomposed during the waste collection and transportation. Therefore, a more detailed emission factors system and integrated controlling measures are necessary.

The potential increase of CH₄ emissions from landfill in China is very high. As more regulated sanitary landfills are being constructed and put into operation, with the growing operation time of current large capacity landfill, the amount of CH₄ generated annually will be large. Given the similar amount of waste disposed in landfills, emissions in China are still very low, compared with the U.S. landfill emission level. The CH₄ emission abatement in landfills should be a high priority for the decision makers. Co-management of pollution and CH₄ reduction is essential and feasible. The malodorous landfill gas has been one of the hot public issues in China. It has significant relationship with CH₄ emissions from the perspective of generation process. The degradable organic carbon is the key to the generation of pollutants and CH₄ . The technology and policy for co-management should be implemented according to the specific conditions.

Acknowledgements

This study was funded by the Project Study on Key Issues of China City Carbon Emission Inventory (No. 41101500) supported by National Natural Science Foundation of China.

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