Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014

JES-01371; No of Pages 16 J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 7 ) XX X–XXX

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Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014 Quanfeng Jin1,2,3 , Xiangqing Ma1,2 , Guangyu Wang4 , Xiajie Yang1,2 , Futao Guo1,2,⁎ 1. 2. 3. 4.

College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China Co-innovation Center for Soil and Water Conservation in the Red Soil Region of the Cross-straits, Fuzhou 350002, China Li Shui Vocational Technical College, Lishui 323000, China Asia-Pacific Forest Research Centre, Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

AR TIC LE I N FO

ABS TR ACT

Article history:

Based on satellite image data and China’s Statistical Yearbooks (2000 to 2014), we estimated

Received 22 June 2017

the total mass of crop residue burned, and the proportion of residue burned in the field vs.

Revised 27 October 2017

indoors as domestic fuel. The total emissions of various pollutants from the burning of crop

Accepted 21 November 2017

residue were estimated for 2000-2014 using the emission factor method. The results

Available online xxxx

indicate that the total amount of crop residue and average burned mass were 8690.9 Tg and 4914.6 Tg, respectively. The total amount of emitted pollutants including CO2, CO, NOx,

Keywords:

VOCs, PM2.5, OC (organic carbon), EC (element carbon) and TC (total carbon) were 4212.4–

Agricultural pollutants

8440.9 Tg, 192.8–579.4 Tg, 4.8–19.4 Tg, 18.6–61.3 Tg, 18.8–49.7 Tg, 6.7–31.3 Tg, 2.3–4.7 Tg, and

Crop residue

8.5–34.1 Tg, respectively. The emissions of pollutants released from crop residue burning

Straw burning

were found to be spatially variable, with the burning of crop residue mainly occurring in

Air pollution

Northeast, North and South China. In addition, pollutant emissions per unit area (10 km ×

Temporal and spatial variations

10 km) were mostly concentrated in the central and eastern regions of China. Emissions of CO2, NOx, VOCs, OC and TC were mainly from rice straw burning, while burning of corn and wheat residues contributed most to emissions of CO, PM2.5 and EC. The increased ratio of PM2.5 emissions from crop residue burning to the total emitted from industry during the study period is attributed to the implementation of strict emissions management policies in Chinese industry. This study also provides baseline data for assessment of the regional atmospheric environment. © 2017 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Introduction Biomass burning is an important source of particulate matter and gaseous pollution, thereby playing a significant role in the global atmosphere, ecosystems and climate change (Miura and Kanno, 1997; Keshtkar and Ashbaugh, 2007). Previous studies have reported that in 1979, 1990 and 2001, the annual biomass combustion worldwide was 6800 Tg, 8680 Tg and 8600 Tg, respectively (Andreae and Merlet, 2001), of which

crop residue burning accounted for about 8% (Werf et al., 2010). Crop residue burning emits large quantities of gaseous pollutants, such as SO2, NOx, NH3, CO and VOCs (volatile organic compounds), significantly affecting regional air quality. It is unanimously agreed that high concentrations of CO, NOX and VOCS promote the formation of photochemical smog and haze, seriously affecting regional air quality and human health (Andreae and Merlet, 2001; Reinhardt and Ottmar, 2004; Kulle, 2008). The emission and deposition of large amounts of

⁎ Corresponding author. E-mail: [email protected] (Futao Guo).

https://doi.org/10.1016/j.jes.2017.11.024 1001-0742/© 2017 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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NOx also promote the formation of acid rain and negatively affect soil pH and other physical and chemical soil properties (Singh and Agrawal, 2008). Additionally, EC (element carbon) has a strong influence on the absorption and scattering of sunlight, and is believed to be one of the main causes of global warming and reduced atmospheric visibility (Cao et al., 2005; Stone et al., 2010). China is a large agricultural country with the largest mass of crop residue in the world, accounting for 17.29% of the total global mass (Bi et al., 2010) and 63.4 % of the total mass in Asia (Yevich and Logan, 2003). Residues of wheat, corn, rice, and beans are the major crop residues in China, which are generated after harvesting and before the next planting (Gupta et al., 2004). The total mass of crop residue in mainland China was estimated to be 60.6 Tg, 61.0 Tg and 62.3 Tg in 2000, 2001 and 2002, respectively (Guo et al., 2004). The utilization rate of crop residue is low, with large quantities either discarded or burned in the field (Bhatia et al., 2013). A previous study showed that the emissions of PM2.5, BC, OC, SO2, NOx, NH3, CO and VOCs resulting from the burning of crop residue in mainland China in 2007 were 13.2 Tg, 1.4 Tg, 3.0 Tg, 31.6 Tg, 23.2 Tg, 16.0 Tg, 164.9 Tg and 35.5 Tg, respectively (Cao et al., 2011). To date, studies have focused on the concentrations and total gaseous emissions from crop residue burning in a given year (Cao et al., 2008a, 2008b; Zhang et al., 2016), but dynamic analyses over a longer continuous time period are lacking. Previous studies have also tended to use a single combustion emission factor instead of specific emissions factors for different crops (Yan et al., 2006; Zhang et al., 2008), or have ignored the difference between indoor and outdoor combustion emission factors (Sahai et al., 2007), which is likely to increase estimation errors. Based on data from China's Statistical Yearbooks and previous research, this study uses more specific emissions factors to analyze the spatial and temporal trends of pollutant emissions from crop residue burning in different regions in mainland China from 2000-2014. The main objectives of this research are to: (1) estimate the type of crops burned and their total mass from 2000 to 2014; (2) estimate the total emissions of CO2, CO, NOx, VOCs, PM2.5, OC and EC from crop residue burning; (3) analyze the spatial and temporal changes in pollutants in different regions; and (4) provide a scientific basis for relevant model-based research and atmospheric pollution control policies.

1. Materials and methods This study is based on data from 31 regions of mainland China, including provinces, autonomous regions and municipalities. Crop residues are currently used in three main ways in mainland China: natural decomposition, burning in the field, and burning as domestic fuel.

1.1. Study area The mainland of China (Fig. 1) is located between 6°06′−53°30′ N and 73°20′−135°30′ E. China has a large population of ~1.36 billion (2014). China's crop area is about 1.5 × 109 ha, accounting for 15.63% of its total land area and about 7% of the world's arable land. Rice, corn and wheat are the dominant food crops in China,

and beans, rapeseed and peanuts are the major oil crops (https:// data.worldbank.org/indicator/AG.LND.A). From 2003 to 2010 China experienced 273,418 crop fires, with an average of 34,177 crop fires per year (Huang et al., 2012).

1.2. Data sources The data used for crop residue burning is from the MODIS wildland fire data product, which is a reliable source for monitoring vegetation fires (Amraoui et al., 2015). Even when affected by natural factors, the success rate of MODIS in fire monitoring is nearly 90%. By removing the interference of noise, flares and cloud, the success rate in monitoring forest fire in different areas and seasons is as high as 100% (Zhou and Wang, 2006). This study uses MOD14A1 (daily scale at 1 km resolution) to extract the geographical coordinates and the ignition time of fire spots in mainland China from 2000 to 2014. Modis14A1 has been used in previous studies to estimate the emissions from agricultural fires in China (Liu et al., 2015, 2016). Other studies have pointed out that some agricultural fires may not be detected by MODIS sensor, but in this study, the MODIS fire product is mainly used to show the temporal and spatial distribution of agricultural fires in different regions of China; in this context, the fires "undetected" by MODIS sensors can be considered a systematic error that will not significantly impact our findings. In addition, the emissions of pollutants from crop burning are calculated based on statistical data and literature review using emissions factors and grain-to-straw ratios. The function of the MODIS fire product is to calculate the spatial weight for each region of China and to allocate the estimated emission of pollutants to each region based on the weight (for more details please see Section 0). The MODIS fire data were then overlaid on a vegetation type map (also of 1 km resolution) to identify crop fires. The vegetation type map was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). Crop yield data was obtained from China’s Statistical Yearbooks (2001-2015) and used to calculate the mass of crop residue with the residue-to-product ratios (RPR) obtained from previous studies (Liu and Shen, 2007; Xie et al., 2011a, 2011b) (Table 1). For the combustion efficiency of different crop residues burned in the field, this study refers to the previous study by Huang et al. (2012) (Table 1), and for the combustion efficiency of different crop residues burned as domestic fuel, an average of 92.5% was adopted based on Zhang et al. (2008). Due to limited information on the ratio of burned crops in the field to the total amount of crop residue in China, we combined results of previous studies conducted for a specific year including 2000, 2004, 2006 and 2010 (Cao et al., 2008a, 2008b; Tian et al., 2011; Zhang et al., 2008; Peng et al., 2016) to estimate the ratio for each region from 2000 to 2014. The average annual change ratio of burned crop residue in the field was then calculated based on the value of four specific years and the ratio for 2011–2014 fixed at the 2010 value (see ratio values in Appendix 1). In addition, the ratio of crop residue burned indoors as domestic fuel to the total amount of crop residue burned each year were mainly obtained from the China Energy Statistical Yearbook (2001-2009); however, due to a lack of statistical information, the ratio between 2009-2014 was fixed at the 2008 value (see ratio values in Appendix 2).

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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Fig. 1 – Overview of the study area, including the study regions and cropland locations. The data is provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn).

For the combustion efficiency of different crop residues burned in the field, we used the average combustion efficiency (80%) derived from previous studies (Zárate et al., 2000; Wang et al., 2012). For the combustion efficiency of different crop residues burned as domestic fuel, an average of 92.5% was adopted based on Zhang et al. (2008).

Given a set of N items to be clustered, the basic process of hierarchical clustering is: (1) assign each item to its own cluster; (2) find the closest (most similar) pair of clusters and merge them into a single cluster so that there is one less cluster; (3) calculate distances (similarities) between the new cluster and each of the old clusters; (4) repeat steps (2) and (3) until all items are clustered into a single cluster of size N.

1.3. Clustering of different regions by crop residue mass 1.4. Mass of crop residues burned in the field and as domestic fuel The spatial and temporal dynamics of the gases emitted from crop residue burning were identified using hierarchical cluster analysis (SPSS 19.0 software) based on the mean mass of crop residues in the 31 regions during the study period. Since the area of municipalities including Beijing, Tianjin, Hainan, Chongqing and Shanghai are very small, they were grouped in with their corresponding provinces; therefore, the cluster analysis contains 26 provinces.

Table 1 – Residue to product ratio combustion efficiency for different crops. Crop type

Rice Wheat Corn Rape Soybean

N (residue-to-product ratio, RPR) Range

Mean

0.85–1.33 1.00–1.38 0.89–1.38 2.13–3.35 1.08–2.85

1.18 1.19 1.14 2.74 1.97

and

outdoor

Combustion efficiency (%) in the field

The mass of crop residues burned in the field and as domestic fuel were calculated using Eq. (1) (Lu et al., 2011): M ¼ ∑m ðPm  Nn  B  ηÞ

where m is the crop type; P is the mass (t) of crop m; N is the residue-to-production ratio (RPR) of crop m; B is the proportion of crop residue burned as domestic fuel or burned in the field; and η is the combustion efficiency of crop residues burned as domestic fuel or burned in the field.

1.5. Emissions of pollutants from crop residue burning Total emissions of pollutants in each region were calculated using Eq. (2) (Lu et al., 2011): Ei: j ¼ 10−3 

89 86 92 82 68

ð1Þ

X

M j  EFi: j

ð2Þ

where Ei.j is the emissions (t) of pollutant i; j is the mass (t) of burned crop residues j; and EFi.j is the emissions factor (g/kg) of pollutant i from burned crop residues j.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

Rice Wheat Corn Soybean Rape Rice Wheat Corn Soybean Rape

Residue type

1105–1446 1311–1558 1262–1548 1446–1548 1446–1548 1024–1585 1203–1585 1203–1585 1203–1585 1203–1585

Range

CO2

1287 1452 1408 1490 1490 1324 1384 1360 1363 1363

Mean 27.7–91.1 47.9–141.2 53.0–114.7 47.0–81.0 47.0–81.0 68.0–102.0 60.0–107.9 53.0–107.9 76.1–108.0 76.1–133.5

Range

CO

PM2.5

VOCs

OC

56.1 77.5 75.6 68.3 68.3 89.5 92.2 90.7 93.0 108.0

1.4–3.8 1.2–3.3 1.3–4.3 1.1–3.3 1.5–3.3 1.5–3.4 1.5–3.3 1.5–4.3 1.5–3.1 1.5–3.1

2.4 2.1 2.8 2.0 2.4 2.5 2.4 2.4 2.2 2.1

4.2–12.9 6.4–11.4 5.3–11.7 3.7–6.6 6.4–6.6 6.2–9.1 6.3–6.6 3.9–6.6 6.3–7.7 3.3–12.8

8.74 8.2 6.6 5.6 6.6 8.0 6.5 5.6 6.0 7.2

6.1–13.3 6.0–7.5 6.0–10.0 6.0–7.0 6.0–7.0 5.1–20.2 5.1–6.2 4.5–6.2 5.1–6.2 5.1–6.2

8.8 6.8 8.3 6.5 6.5 9.8 5.6 5.3 5.6 5.6

2.0–8.9 2.7–5.1 2.2–3.9 2.1–3.7 2.2–3.7 1.3–8.8 2.3–3.3 2.3–3.3 1.2–3.3 1.2–3.3

5.1 3.7 3.0 3.1 3.1 3.7 2.8 2.8 2.2 2.3

Mean Range Mean Range Mean Range Mean Range Mean

NOx

I: Li et al., 2007; Li et al., 2009; Cao et al., 2008a, 2008b; Zhang et al., 2008; Wang et al., 2012; Zhang et al., 2013; Jing et al., 2014 II: Li et al., 2007; Cao et al., 2008a, 2008b; Zhang et al., 2008; Li et al., 2009; Wang et al., 2012; Jing et al., 2014 III: Li et al., 2007; Cao et al., 2008a, 2008b; Zhang et al., 2008; Zhang et al., 2013 IV: Tian et al., 2011; Zhang et al., 2013; Tang et al., 2014;V: Tian et al., 2011; Tang et al., 2014 VI: Li et al., 2007; Cao et al., 2008a, 2008b; Wei et al., 2012; Zhang et al., 2013; Tang et al., 2014 VII: Li et al., 2007; Cao et al., 2008a, 2008b; Wei et al., 2012 VIII: Li et al., 2007; Cao et al., 2008a, 2008b; Wei et al., 2012; Zhang et al., 2013 IX: Li et al., 2007; Cao et al., 2008a, 2008b; Wei et al., 2012; Zhang et al., 2013; Tang et al., 2014; X: Qin and Xie, 2011; Zhang et al., 2013; Tang et al., 2014. VOCs: volatile organic compounds.

Burned as domestic fuel

Burned in the field

Burning type

Table 2 – Range and mean emissions factors (g/kg) of different crops from various studies.

0.17–0.49 0.36–1.30 0.35–1.30 0.36–1.30 0.46–1.30 0.10–0.91 0.66–0.75 0.66–0.75 0.67–0.77 0.66–1.09

Range

EC

0.42 0.62 0.72 0.75 0.77 0.57 0.71 0.70 0.87 0.80

2.5–9.1 3.2–5.5 3.0–5.0 2.5–4.0 2.6–5.0 1.4–9.1 3.1–4.0 3.0–4.0 1.9–4.0 1.9–4.0

5.5 4.3 3.8 3.8 3.8 4.3 3.5 3.6 3.1 3.1

Mean Range Mean

TC

I II III IV V VI VII VIII IX X

Reference

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Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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1.6. Determination of emissions factors

for the relevant region to calculate the emissions of each pollutant for each grid square.

Using specific emission factors (EFs) for different types of crops is important for accurately estimating the total emissions of pollutants. This study uses the EFs of crop residue combustion from various studies (Table 2), prioritizing EFs reported for mainland China since emissions factors are affected by crop type and regional environment. Both range and mean of EFs from previous studies were used; the range of EFs was used to estimate the emission of pollutants, and the mean value of EFs was used to analyze the dynamic change of pollutants.

1.7. Analysis of the temporal dynamics of smoke emissions The Mann–Kendall trend test was applied to determine if there were any significant temporal trends in total emissions of pollutants in each region during the study period. This non-parametric statistical test has been widely used in the analysis of many hydrologic and climatic time series because: (1) no assumptions are made about the distribution of the data and (2) it is not influenced by outliers (Mann, 2004; Shadmani et al., 2012; Blain, 2014). The significance level used was p < 0.05.

1.8. Mapping of the spatial distribution of pollutants from crop biomass burning in the field Satellite remote sensing data provides spatial information with high resolution and accurate positioning (Huang et al., 2012). The spatial distribution of crop fire spots was mapped based on the MODIS forest fire product, and a vegetation distribution map and spatial distribution of emitted pollutants were identified accordingly. The kernel density estimation approach was applied to map crop fire density. This approach was originally introduced in wildfire occurrence mapping by Koutsias et al. (2002) and some researchers have applied the kernel density approach to analyze the spatial patterns of forest fires (De La Riva et al., 2004; Koutsias et al., 2014). The kernel density estimation in this study was applied under the ArcGIS 10.2 environment. The specific methodology for mapping the spatial distribution of pollutants at a 10 km × 10 km grid scale is as follows: 1) Total number of crop fires was calculated in each region (province or municipality); 2) ArcGIS 10.2 was used to create a 10 km × 10 km grid of the entire research area. The number of fire spots in each grid square was then extracted and the spatial weight of each grid square was calculated according to Eq. (3): Ei: j ¼

FCi  Ei:k FCk

5

ð3Þ

where Ei.j is the emissions of pollutant “j” in grid square “i”, FCi is the number of crop fire spots in grid square i, FCk is the total number of crop fire spots in region k (province or municipality) within which grid square i occurs, and Ei.k is total emission of pollutant “j” in region k; 3) The weight of each grid was combined with the total amount of pollutants from crop residue burning in the field

2. Results and discussion 2.1. Mass of crop residues The 26 provinces in mainland China were grouped into five clusters according to mass of crop residues during the study period (Fig. 2). Fig. 3 shows significant differences in the mass of crop residues and the proportion of each crop type in different regions. In cluster I, rice residues account for the largest proportion, the mass of wheat, corn and rapeseed residues are relatively small, and bean residue is the smallest. In cluster II, wheat and corn residues account for about 90% of the total crop residue mass. In cluster III, nearly 85% of crop residues are rice residue. The total mass of crop residues in cluster IV are mainly produced by corn and wheat, with both accounting for about 30% of the total residue. Corn, followed by rice, are the major crops in cluster V, accounting for about 45% and 30% of the total mass, respectively. According to Fig. 3, rice residues dominate in southern China, while wheat and corn are more important in northern China. Soybean and rapeseed residues account for only a small percent of total crop residues in all clusters.

2.2. Total quantity of crop residues burned Based on China's Statistical Yearbooks, estimates were made for the total amount of residue from in-field and domestic fuel burning of major crops from 2000–2014. According to Table 3, rice straw is the dominant contributor to crop residue burning in cluster I for both field and domestic fuel. Wheat and corn straw are the two largest crop types for residue burning in cluster II, and corn straw accounts for the greatest percentage of crop residue burning in cluster V. Clusters III and IV have few burned crop residues compared to other clusters. Estimates show that the average annual mass of crop residue burned as domestic fuel from 2000 to 2014 was 212.6 Tg, accounting for 40.5% of total crop residue. This result is smaller than that found in previous studies, which reported 47% of crop residues burned as domestic fuel in 2000 (Yan et al., 2006; Zhang et al., 2008). The most likely explanation for this result is increasing industrialization in China since 2000, which has resulted in decreasing use of crop residues as domestic fuel in rural areas and small towns, and an increase in the utilization of electricity instead (Guo et al., 2004; Sahai et al., 2007). Yan et al. (2006) and Zhang et al. (2008) estimated that an average of 20.04% and 23.58% of crop residues were burned in the field in 2000 and 2004, respectively, which are similar to the results of this study (22.04%) (Table 3). Fig. 4 shows the annual trends in crop residue burned in the field and as domestic fuel from 2000 to 2014. Overall, the amount of crop residue burned in the field increased over this time period, with key differences between regions. The amount burned in the field increased in clusters I, IV and V, remained unchanged in cluster II, and decreased in cluster III.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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Fig. 2 – Hierarchical cluster analysis of regions in mainland China by the total mass of crop residues from 2000 to 2014.

From 2000 to 2014, the amount of crop residue burned indoors as domestic fuel remained unchanged overall, but again the temporal trend differs between clusters. Similar to residue burned in the field, the amount burned as domestic fuel remained unchanged in cluster II and increased in cluster

V over the study period. However, the dynamic change of annual mass of residue burned as domestic fuel can be clearly divided into two periods, 2000–2006 and 2007–2014. During 2000–2006, except for cluster III, all clusters saw increases in the amount of residue burned as domestic fuel (same trend as

Fig. 3 – Mass of crop residues by crop type (in 10 Tg) in mainland China from 2000–2014 between the five region clusters (I, II, III, IV, V, as defined in Fig. 2). Note the different y-axis scales for different clusters. Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

26.83 18.63 33.43 15.89 16.64 21.94 45.67 36.51 29.13 35.91 39.07 40.57 77.5 5.6 5.5 9.3 1.1 98.9 139.2 10.0 4.8 29.5 3.4 186.8 60.2–94.7 4.3–6.8 4.2–6.7 7.2–11.3 0.8–1.3 76.9–120.9 108.2–170.2 7.7–12.2 3.7–5.8 22.9–36.0 2.6–4.1 145.2–228.4 36.8 10.8 5.5 12.7 41 106.9 71 20.7 5.0 27.3 97.2 221.2

residue burned in the field). However, during the period 2007–2014, clusters I, III, IV and China as a whole remained unchanged, although there is a significant increasing trend in residues burned in the field, indicating that the demand for crop residue as domestic fuel in most regions of China has reached saturation. Previous studies (Zhang et al., 2008; Liu et al., 2011) have shown that the total amount of crop residues burned indoors and in the field are affected by the regional economic situation, household income, and crop area of the household, as well as the production of coal and natural gas. The proportion of crop residue burned in the field is positively correlated with the income level of farmers, the consumption of other energy sources, and the crop area of households, and negatively correlated with the economic situation and rural population density. In addition, there is a significant negative correlation between crop residue burned indoors and that burned in the field. Cluster III represents a more developed region in China, with a high degree of industrialization and energy supplied mainly from coal, electricity and natural gas, which results in a low percentage of crop residues burned as domestic fuel (29.14%). In contrast, cluster I represents a less

453.1–708.9 13.9–21.7 55.4–86.7 24.5–38.3 56.9–89.1 603.9–945.0 718.9–1124.9 25.6–40.1 48.4–75.7 50.8–79.5 128.8–201.5 972.5–1521.7

581 17.8 71.1 31.4 73 774.5 921.9 32.9 62.1 65.2 165.2 1247.1

129.7–179.0 191.7–264.6 2.7–3.8 45.6–62.9 6.1–8.4 375.9–518.8 254.7–351.5 371.6–512.8 2.2–2.9 106.6–147.2 16.4–22.6 751.5–1037.1

154.4 228.2 3.3 54.3 7.3 447.4 303.1 442.2 2.6 126.9 19.5 894.3

41.8–64.8 70.9–109.9 1.0–1.6 40.6–62.9 79.2–122.7 233.5–362.1 82.6–128.2 145.4–225.5 0.9–1.4 87.4–135.4 185.2–287.1 501.5–777.6

53.3 90.4 1.3 51.8 100.9 297.8 105.4 185.5 1.2 111.4 236.2 639.6

20.2–53.4 5.9–15.6 3.0–8.0 6.9–18.4 22.5–59.5 58.7–155.1 39.0–103.0 11.3–30.0 2.7–7.2 15.0–39.6 53.4–140.9 121.5–320.8

7

Total

Total Burned as domestic fuel (Tg)

I II III IV V 1349.1–2102.0 I II III IV V 2492.4–3885.7 Burned in the field (Tg)

Mean Range Mean Range Mean Range Mean Range Mean Range

Rapeseed straw Soybean straw Corn straw Wheat straw Rice straw

Agricultural crop residue combustion Area Type

Table 3 – Range and mean of the total mass of crop residue (Tg) burned in field and as domestic fuel from 2000-2014 in mainland China in each cluster.

Combustion ratio (%)

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Fig. 4 – Annual mass of major crop residue burned in the field and as domestic fuel in the five cluster areas (defined in Fig. 4) in China from 2000–2014.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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developed area of China, with high proportions of rural residents and low degrees of industrialization, which results in the burning of crop residue as a main source of domestic energy. Although Guangzhou, a developed province in China, is included in cluster I, it may not have significant influence on the combustion ratio of domestic fuel due to the small area of its agriculture region.

2.3. Temporal and spatial distribution of crop fire spots There were 387,683 crop fires in mainland China from 2000-2014, with an annual average of 25,846 fires. The kernel density estimation method was applied to map crop fire density (Fig. 5) and found crop fires to be mainly concentrated in the eastern and central parts of mainland China, with the highest percent occurrence in Anhui, Jiangsu, Heilongjiang, Henan, Shandong, Zhejiang and Hebei provinces, accounting for 12.02%, 9.98%, 8.86%, 8.83%, 8.47%, 5.44% and 4.85% of total fires, respectively. These regions are the major agricultural areas in China, where the crop rotation period is short, as farmers prefer to burn crop residues in the field in order to not delay crop sowing and to maximize economic benefits. Overall, the annual number of crop fires in China increased from 2000 to 2014, but the trend differs between clusters (Fig. 6). The annual number of fires increased from 2000 to 2014 in clusters I, II, IV and V, although there were decreases in clusters II and IV in some years, partly linked to air pollution controls enacted during the 2008 Beijing Olympic Games. In cluster III, the annual number of crop fires increased from 2000 to 2008 but then remained constant and decreased slightly to 2014. Fig. 7 shows that the temporal and spatial distribution of crop fires in China varies within the year. Crop fires are most widespread across mainland China in March, April, May and

Fig. 6 – Annual number of crop fires in China and the five cluster areas (defined in Fig. 2) from 2000 to 2014.

October. In January and February, crop fires occur predominantly in southern China, in provinces such as Yunnan, Guangdong, Guangxi, Fujian and Jiangxi, accounting for 60% of the crop fires at this time of year. Most crop fires occurring in March and April are in eastern and southern China. From May to August, crop fires are mostly concentrated in central and eastern China. From September to November, crop fires occur mostly in northeastern, central, eastern and southeastern China, while 56% of December crop fires occur in southeastern China. The timing of crop fires is mainly controlled by the harvest and due to climatic conditions, triple-cropping within one year is the dominant cropping pattern in southern China, double-cropping is the main cropping pattern in central

Fig. 5 – The density of crop fires in China from 2000 to 2014. Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 7 ) XX X–XXX

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Fig. 7 – Monthly variation in crop fires in China from 2000 to 2014. Dark red points represent fire ignition.

China, and in northern China mono-cropping in one year or triple-cropping over two years occurs most often. Consequently, fires occur in southern China more frequently than in other parts of the country. These results are consistent with those reported by Zhang et al. (2016) and Huang et al. (2012).

2.4. Emissions of pollutants from crop residue burning The total emissions of pollutants from crop residue burning in the field from 2000-2014 are mainly concentrated in northeast, eastern and southern China (Fig. 8). Eastern China is the major grain producing area of China, where corn and wheat are sown

alternately in a year in Shandong, Henan, Anhui and Jiangsu provinces. Because the time from harvest to re-sowing is short in these provinces, to save time and the cost of clearing up crop residues, farmers prefer to burn the crop residues in the field, resulting in the highest emissions of pollutants. High emissions of pollutants from crop residue burning in the field also occur in some areas of northeastern China where rice, corn and beans are the major food crops. Crop residues are burned in the field directly after crop harvesting in October or during the following spring, and the remaining crop residues are burned indoors as domestic fuel. In southern China, wheat, rapeseed and rice are the major crops and the main cropping pattern is

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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Fig. 8 – Spatial distribution of the total amount of different pollutants from crop residue burning in the field from 2000 to 2014.

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Table 4 – Total emissions (Tg) of different pollutants from both field and domestic crop burning in the five clusters (defined in Fig. 2) in mainland China from 2000–2014. Emission (Tg) CO2 CO NOx VOCs PM2.5 OC EC TC Total

Cluster I

Cluster II

Cluster III

Range

Mean

Range

Mean

Range

2071.8–4174.4 95.3–280.6 2.4–9.4 9.3–36.2 9.3–26.3 3.1–18.5 1.2–2.2 3.7–19.6 2196.4–4567.5

3123.1 187.9 5.9 22.8 17.8 10.8 1.7 11.7 3381.9

948.3–1767.3 42.7–128.0 1.0–4.0 4.1–8.3 4.3–9.2 1.8–4.4 0.4–1.0 2.3–5.1 1004.9–1928.2

1357.8 85.4 2.5 6.2 13.5 3.1 0.7 3.7 1466.6

131.4–272.6 5.5–17.6 0.2–0.6 0.6–2.7 0.6–1.9 0.2–1.4 0.1–0.1 0.2–1.5 138.7–298.5

Cluster IV

Mean 202.0 11.6 0.4 1.7 1.3 0.8 0.1 0.9 218.6

Range 452.8–899.1 21.0–63.3 0.5–2.1 1.9–5.1 1.9–4.9 0.8–2.6 0.2–0.5 0.9–2.9 480.2–980.6

Cluster V

Mean 675.9 42.2 1.3 3.5 3.4 1.7 0.4 1.9 730.4

Total

Range

Mean

Range

Mean

608.1–1327.4 28.3–89.1 0.6–3.2 2.6–8.9 2.5–7.3 1.0–4.4 0.3–0.7 1.2–4.9 644.6–1445.9

967.8 58.7 1.9 5.8 4.9 2.7 0.5 3.1 1045.3

4212.4–8440.9 192.8–579.4 4.8–19.4 18.6–61.3 18.8–49.7 6.7–31.3 2.3–4.7 8.5–34.1 4464.9–9220.7

6326.7 386.1 12.1 39.9 34.3 19.0 3.5 21.3 6842.8

VOCs: volatile organic compounds.

triple-cropping within one year, with a short time between crop rotation. Therefore, most crop residues are burned in the field. The locations with high emissions of pollutants in southern China are mainly in Guangdong, Guangxi and Jiangxi. Table 4 shows that the total emissions of CO2, CO, NOx, VOCs, PM2.5, OC, EC and TC produced by crop residue burning are: 4212.4–8440.9 Tg, 192.8–579.4 Tg, 4.8–19.4 Tg, 18.6–61.3 Tg, 18.8– 49.7 Tg, 6.7–31.3 Tg, 2.3–4.7 Tg and 8.5–34.1 Tg, respectively, and in each cluster, CO2 is the dominant pollutant emitted, accounting for more than 90% of the total pollution, followed by CO, VOCs and PM2.5 (Table 4). The annual average emissions of each pollutant are: 421.8 Tg (CO2), 25.7 Tg (CO), 0.81 Tg (NOx), 2.7 Tg (VOCs), 2.3 Tg (PM2.5), 1.3 Tg (OC), 0.2 Tg (EC) and 1.4 Tg (TC). Wei et al. (2012) reported that the total emissions of CO2 and CO from crop residue burning in 2007 in mainland China were 721.7 Tg and 23.4 Tg. This value of CO2 is higher than our study (average 414.17 Tg), mainly because the average emission factor used in this study was derived from many other relevant studies and is lower than the one used by Wei et al. (2012). Yan et al. (2006) reported that the total annual emissions of CO2, CO, NOx, VOC, PM2.5, TC, OC and EC from crop residue burning in 2000 in mainland China were 371.58 Tg, 26.54 Tg, 0.61 Tg, 3.03 Tg, 1.58 Tg, 1.28 Tg and 0.30 Tg, respectively, which are similar to the results of this study.

2.5. Uncertainty in the estimation of pollutant emissions The uncertainty assessment method used in this study was provided by IPCC (1997). When variables in the estimation formula were combined by addition, the total uncertainty of the estimation was calculated by Eq. (4) and when variables Table 5 – Comparison of uncertainty of estimated emissions between this study and other similar studies. Estimated emissions Present study Lu et al. (2011) Streets et al. (2003) Zhang et al. (2000)

Uncertainty (%) CO2 CO NOx PM2.5 VOCs OC EC TC 61 68 — 33

61 77 185 70

62 84 37 31

VOCs: volatile organic compounds.

62 163 — 130

61 82 130 68

62 209 450 258

62 162 360 208

61 — — —

were combined by multiplication, the total uncertainty of the estimation was calculated by Eq. (5):

Utotal ¼

Utotal ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðU1  x1 Þ2 þ ðU2 þ x2 Þ2 þ ⋯ þ ðUn  xn Þ2 x1 þ x2 þ ⋯ þ xn qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U1 2 þ U2 2 þ ⋯ þ Un 2

ð4Þ

ð5Þ

where Utotal represents the overall uncertainty; xn and Un represent variables in the estimation formula and the corresponding percentage of uncertainty of each variable. The results show that combustion emissions from crop residues are affected by straw type, straw yield, grain ratio, combustion efficiency, and emissions factors. The data on straw type, yield and grain ratio are from China’s Statistical Yearbook. Cao et al. (2008a, 2008b, 2011) stated that government statistics are highly reliable and accurate, and the range of error is in the 5%-10% range, so the error of straw yield and grain ratio was set to 10% for this study. The combustion efficiency is a critical factor influencing pollutant emissions and is influenced by straw type, combustion mode, natural environment and other factors. However, very few studies have been conducted on the combustion efficiency of different crop residues. We refer to Cao et al. (2011) set the error to 200%. The emissions factor is another important factor for the estimation of particle emissions and in order to improve the accuracy of estimation, an average emissions factor from different crop residue burning studies was used with an error range of 10%-70%. The quantitative uncertainty in the emissions of each pollutant used in this study compared to other similar studies are shown in Table 5, which indicates that the estimation error of our study is less than Lu et al. (2011) and Streets et al. (2003), and close to Zhang et al. (2000).

2.6. Spatial and temporal trends in pollutants from crop residue burning The Mann–Kendall trend test was used to analyze the total emissions of pollutants in each cluster and was conducted based on annual mean emissions. The trends in emissions of different pollutants from crop residue burning from 2000 to 2014 are similar between the five clusters (Fig. 9). Overall, emissions of the

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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Fig. 9 – Change in total emissions of pollutants from crop residue burning from 2000 to 2014 in the five cluster areas (defined in Fig. 2) in mainland China. Note: ▲Increase; Significant increase; Significant decrease

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Table 6 – Total contribution rates (%) of crop residues to emissions of different pollutants from 2000-2014 in mainland China. Crop type CO2

CO

NOx VOCs PM2.5

OC

EC

TC

Wheat Corn Soybean Rapeseed Rice

27.7 19.5 6.3 6.3 40.2

25.3 19.9 5.5 5.0 44.4

22.6 14.6 4.8 4.6 53.4

28.2 24.0 8.0 7.5 32.2

23.5 16.9 4.8 4.5 50.3

27.2 19.1 6.2 5.7 41.8

20.9 15.7 4.7 4.3 54.3

24.9 15.4 4.9 5.3 49.5

VOCs: volatile organic compounds.

eight pollutants increased significantly in clusters I, II, IV, and V over the study period, but decreased significantly in cluster III. Some pollutants, such as CO2, EC and TC show a significant increasing trend, while other pollutants (NOx, PM2.5, OC and VOCs) increased non-significantly in cluster II.

2.7. Contribution of different crop residues to pollutant emissions The residues of different crops have different masses and various emissions factors and therefore make different contributions to the total emissions of different pollutants. The burning of rice residues contributed most to emissions of CO2, VOCs, OC and TC, while corn residues contributed most to CO, NOx, PM2.5 and EC emissions (Table 6). Our results are partly consistent with Sahai et al. (2011) who showed that the burning of corn residues contributes most to emissions of CO and NOx. The burning of residues from rapeseed and soybeans contributed comparatively little to total air pollutant emissions.

2.8. Comparison of PM2.5 emissions from crop residue burning and other sources over time In mainland China, especially in some agricultural provinces, emissions from industrial production and crop residue burning are considered to be two major sources of pollution. However, the composition of smoke, particularly chemical compounds in PM2.5, varies significantly between sources. Sulfur, hydrogen sulfide and heavy metal elements mainly exist in industrial dust (Carianos et al., 2000) and some nutrient elements for plants such as Na+, K+ and Ca2+ are emitted from crop residue burning (Cao et al., 2008a, 2008b),

which may affect human health and the atmosphere differently. This study therefore investigated the emission rate of PM2.5 from crop residue burning and industrial dust. Data for emissions of industrial dust from 2004 to 2010, available in China’s Statistical Yearbooks (Liu and Zhang, 2012), shows that the total emissions of industrial dust during this period are 49.3 Tg, and the average annual emissions are 7.0 Tg. Overall, emissions of industrial dust in mainland China decreased over time, with an average annual decrease of 0.6 Tg or 1.3% (Table 7). Emissions of industrial dust decreased in clusters I-V from 2004 to 2010, as also shown by Liu and Zhang (2012). In addition, Liu and Zhang (2012) reported that industrial dust emissions were significantly positively correlated with the development of mining, chemical and metal industries. Table 7 shows that during 2004-2010, the annual emission of PM2.5 from crop residue burning accounted for 24.4%, 23.9%, 25.7%, 31.1%, 33.0%, 43.7% and 52.5% of the annual emissions of industrial dust, showing a significant upward trend overall for mainland China. In cluster I, the ratio is gradually increasing from 2004 to 2010, while in clusters II and V , the rate shows a decreasing trend from 2007-2008, and in clusters III and IV the rate remains generally unchanged. Due to the government's promotion of the Green Olympics in 2008, some highly polluting chemical and metal industrial plants were closed and/or had pollution control measures implemented, and industrial dust emissions therefore significantly reduced in Beijing and adjacent areas in clusters I, II and V. However, the amount of crop residues burned indoors and in the field did not change significantly, resulting in an increase in the ratio of PM2.5 emissions from crop residue burning to industrial dust. Therefore, with increasingly strict emissions controls on industry, the importance of the role of crop residue burning as a PM2.5 contributor is expected to increase in mainland China, especially in the northeast where emissions of PM2.5 from crop residue burning now exceed emissions of industrial dust.

3. Conclusions This study combined crop fire data derived from satellite remote sensing data overlain with vegetation maps and emissions factors to determine the total residue mass of five major crops — corn, rice, wheat, rapeseed and beans — as well

Table 7 – Industrial dust emissions and ratio of the emissions of PM2.5 from crop residue burning to industrial dust for the five clusters (defined in Fig. 2) from 2004 to 2010 in mainland China. Year

2004 2005 2006 2007 2008 2009 2010

Cluster I

Cluster II

IE (Tg)

RE (%)

IE (Tg)

RE (%)

3.2 3.7 3.4 2.8 2.1 2.2 1.8

35.6 31.4 32.5 39.5 55.8 54.8 69.2

1.8 1.8 1.6 1.1 1.3 0.9 0.8

22.6 22.5 26.7 39.2 32.6 46.9 57.5

Cluster III IE (Tg) 0.3 0.4 0.4 0.4 0.3 0.3 0.3

Cluster IV

RE (%)

IE (Tg)

24.3 16.9 18.6 18.2 27.1 23.1 25.3

1.9 1.9 1.8 1.6 1.5 1.2 1.2

RE (%) 11.8 12.4 12.1 12.8 14.3 19.3 19.2

Cluster V

Mainland China

IE (Tg)

RE (%)

IE (Tg)

RE (%)

1.6 1.2 0.9 0.9 1.5 0.6 0.4

18.5 23.3 28.4 32.9 20.7 55.7 80.7

8.9 9.1 8.1 6.8 6.7 5.2 4.5

24.4 23.9 25.7 31.1 33.0 43.7 52.5

Note: IE represents industrial emissions; RE means ratio of the emission of PM2.5 from crop residue burning to industrial dust emissions.

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as the total emissions of CO2, CO, NOx, VOCs, PM2.5, OC, EC and TC from crop residue burning and their spatial and temporal distribution in mainland China from 2000 to 2014. We can conclude that: (1) From 2000 to 2014, the total emissions of CO2, CO, NOx, VOCs, PM2.5, OC EC and TC from crop residue burning both in the field and as domestic fuel were: 4212.4–8440.9 Tg, 192.8–579.4 Tg, 4.8–19.4 Tg, 18.6–61.3 Tg, 18.8–49.7 Tg, 6.7– 31.3 Tg, 2.3–4.7 Tg and 8.5–34.1 Tg, respectively. (2) There are significant spatial and temporal differences in the emissions of pollutants from crop residue burning in mainland China, with emissions predominantly occurring in the northeast, east, and south of China. (3) The burning of rice residues contributes most to emissions of CO2, VOCs, OC and TC, while burning of corn and wheat residues accounts for the majority of emissions of CO, NOx, PM2.5 and EC.

(4) From 2004 to 2010, the ratio of the emissions of PM2.5 from crop residue burning to the emissions from industrial dust increased, indicating that crop residue burning is becoming an increasingly important source of air pollution in China.

Acknowledgements This work was supported by the Fujian Agriculture and Forestry University Funds for Distinguished Young Scholar (No. xjq201613), the National Natural Science Foundation of China (No. 31400552), the International Science and Technology Cooperation Program of Fujian Agriculture and Forestry University (No. KXB16008A), and the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFnet/ 2010/FPF/001) Phase II.

Appendix 1. Annual outdoor combustion ratio of corn residue to total mass of crop residue (%) from 2000–2014

Regions (provinces)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Eilongjiang Shanghai Jiangshu Zhejiang Anhui Fujian Jiangxi Shandong Heinan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shanxi Gansu Qinghai Ningxia Xinjiang

20.28 20.43 24.20 20.97 21.28 21.07 20.98 22.42 48.75 23.94 42.64 18.67 32.57 21.69 26.54 19.69 23.37 22.29 36.44 16.74 30.26 19.38 19.92 14.97 11.47 8.33 14.81 16.63 9.93 20.25 19.14

19.95 20.13 23.56 20.62 20.49 20.30 20.22 21.54 47.34 26.31 41.74 19.77 32.51 20.78 25.70 19.67 22.32 23.18 36.15 18.08 30.47 18.66 19.15 14.62 11.40 9.03 14.96 16.63 10.51 19.90 18.92

19.62 19.83 22.92 20.26 19.70 19.53 19.45 20.65 45.94 28.68 40.83 20.87 32.46 19.87 24.86 19.65 21.27 24.06 35.86 19.43 30.68 17.93 18.38 14.28 11.34 9.74 15.10 16.63 11.08 19.55 18.70

19.28 19.52 22.28 19.91 18.91 18.75 18.69 19.77 44.53 31.05 39.93 21.97 32.40 18.95 24.02 19.63 20.21 24.95 35.56 20.77 30.88 17.21 17.61 13.93 11.27 10.44 15.25 16.62 11.66 19.19 18.47

18.95 19.22 21.64 19.55 18.12 17.98 17.92 18.88 43.12 33.42 39.02 23.07 32.34 18.04 23.18 19.61 19.16 25.83 35.27 22.11 31.09 16.48 16.84 13.58 11.20 11.14 15.39 16.62 12.23 18.84 18.25

17.73 17.86 19.07 18.03 14.96 14.89 14.86 15.34 37.51 32.66 35.46 27.49 32.12 14.37 19.84 18.06 14.93 29.37 34.09 27.51 32.00 13.59 13.77 12.14 10.95 13.77 15.95 16.51 14.32 17.62 17.33

16.50 16.50 16.50 16.50 11.80 11.80 11.80 11.80 31.90 31.90 31.90 31.90 31.90 10.70 16.50 16.50 10.70 32.90 32.90 32.90 32.90 10.70 10.70 10.70 10.70 16.40 16.50 16.40 16.40 16.40 16.40

14.43 14.43 14.85 18.45 10.50 12.18 12.53 14.50 31.43 31.63 31.90 33.78 30.18 13.83 16.83 17.58 14.20 35.45 34.23 32.60 33.10 11.85 13.23 9.20 14.98 14.80 15.98 14.60 13.43 17.15 16.13

12.35 12.35 13.20 20.40 9.20 12.55 13.25 17.20 30.95 31.35 31.90 35.65 28.45 16.95 17.15 18.65 17.70 38.00 35.55 32.30 33.30 13.00 15.75 7.70 19.25 13.20 15.45 12.80 10.45 17.90 15.85

10.28 10.28 11.55 22.35 7.90 12.93 13.98 19.90 30.48 31.08 31.90 37.53 26.73 20.08 17.48 19.73 21.20 40.55 36.88 32.00 33.50 14.15 18.28 6.20 23.53 11.60 14.93 11.00 7.48 18.65 15.58

8.20 8.20 9.90 24.30 6.60 13.30 14.70 22.60 30.00 30.80 31.90 39.40 25.00 23.20 17.80 20.80 24.70 43.10 38.20 31.70 33.70 15.30 20.80 4.70 27.80 10.00 14.40 9.20 4.50 19.40 15.30

8.20 8.20 9.90 24.30 6.60 13.30 14.70 22.60 30.00 30.80 31.90 39.40 25.00 23.20 17.80 20.80 24.70 43.10 38.20 31.70 33.70 15.30 20.80 4.70 27.80 10.00 14.40 9.20 4.50 19.40 15.30

8.20 8.20 9.90 24.30 6.60 13.30 14.70 22.60 30.00 30.80 31.90 39.40 25.00 23.20 17.80 20.80 24.70 43.10 38.20 31.70 33.70 15.30 20.80 4.70 27.80 10.00 14.40 9.20 4.50 19.40 15.30

8.20 8.20 9.90 24.30 6.60 13.30 14.70 22.60 30.00 30.80 31.90 39.40 25.00 23.20 17.80 20.80 24.70 43.10 38.20 31.70 33.70 15.30 20.80 4.70 27.80 10.00 14.40 9.20 4.50 19.40 15.30

8.20 8.20 9.90 24.30 6.60 13.30 14.70 22.60 30.00 30.80 31.90 39.40 25.00 23.20 17.80 20.80 24.70 43.10 38.20 31.70 33.70 15.30 20.80 4.70 27.80 10.00 14.40 9.20 4.50 19.40 15.30

Note: The outdoor combustion ratio of corn residues to total mass of crop residue for 2000, 2004, 2006 and 2010 were calculated based on the previous studies (Cao et al., 2008a, 2008b; Tian et al., 2011; Zhang et al., 2008; Peng et al., 2016). The average annual change ratio was calculated based on the two adjacent years of above four years, and the ratio from 2011-2014 was set up as a fixed value that are same as 2010.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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Appendix 2. Annual indoor combustion ratio of corn residue to total mass of crop residue (%) from 2000–2014

Regions (provinces)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Beijing Tianjn Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangshu Zhejiang Anhui Fujian Jiangxi Shandong Heinan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yuannan Xizang Shanxi Gansu Qinghai Ningxia Xinjiang

47.39 66.86 48.44 35.47 44.13 50.86 31.75 43.44 20.78 62.82 32.68 68.80 25.48 24.49 44.08 30.38 56.53 22.71 38.06 45.25 44.99 53.17 35.07 23.43 17.24 11.00 37.94 50.79 64.91 25.08 23.36

62.31 53.34 43.16 39.59 31.26 51.80 29.20 49.70 20.78 60.31 37.47 51.29 26.47 22.43 37.13 25.14 53.29 38.19 70.62 37.86 46.02 54.65 42.49 25.27 17.28 12.82 39.72 53.46 65.81 21.60 22.63

72.00 46.74 47.58 45.64 43.60 50.99 28.75 46.60 20.78 50.53 41.96 66.04 25.64 28.34 44.99 30.99 45.76 25.62 72.25 41.80 39.65 68.63 41.20 30.21 26.57 13.88 48.35 50.65 78.04 37.31 25.21

76.21 48.28 50.36 42.20 42.67 46.62 27.53 54.73 16.61 60.91 32.46 80.85 26.88 29.69 41.18 33.85 53.71 26.42 74.96 45.18 34.54 73.02 43.77 55.33 25.72 14.06 64.41 52.27 78.18 47.31 25.55

73.30 41.83 47.00 30.74 41.34 42.25 27.79 49.51 16.61 67.41 30.74 65.86 26.52 30.08 38.16 36.73 47.72 23.61 76.31 44.49 30.15 65.97 41.18 59.54 29.20 14.30 46.27 47.13 74.52 49.65 20.39

54.47 39.87 47.25 41.25 37.63 42.02 22.77 47.62 16.61 72.83 25.60 69.96 20.47 28.14 34.13 37.29 44.72 23.50 76.52 48.62 36.85 69.01 42.17 71.58 30.89 14.22 43.94 44.98 70.09 44.28 22.40

39.64 40.54 45.00 45.13 38.80 41.52 28.96 44.48 16.61 37.13 28.59 78.56 20.95 28.00 34.26 37.63 49.03 26.62 41.35 48.96 28.63 65.00 42.64 44.48 24.13 14.60 46.97 46.89 78.45 36.64 21.05

28.46 30.46 44.16 35.16 39.77 40.28 28.43 45.40 16.61 42.59 34.76 75.74 23.11 19.92 34.38 35.20 45.99 19.25 58.37 51.22 29.54 57.55 38.20 33.78 23.10 14.39 69.40 49.93 62.57 36.08 21.14

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

22.75 27.47 41.11 35.16 32.68 38.64 27.62 43.42 16.61 36.18 34.76 76.54 24.44 26.39 32.23 33.86 43.22 24.13 50.35 48.96 27.64 51.07 36.20 33.78 22.95 14.54 49.19 48.93 62.57 36.08 22.47

Note: The combustion ratio of corn residues to total mass of crop residue for each year are mainly from the China Energy Statistical Yearbook (2001-2009); however, due to a lack of statistical information, the ratio between 2009-2014 is fixed at the 2008 value.

REFERENCES Amraoui, M., Pereira, M.G., DaCamara, C.C., Calado, T.J., 2015. Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region. Sci. Total Environ. 524, 32–39. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15, 955–966. Bhatia, A., Jain, N., Pathak, H., 2013. Methane and nitrous oxide emissions from Indian rice paddies, agricultural soils and crop residue burning. Greenhouse Gases 3, 196–211. Bi, Y., Wang, Y., Gao, C., 2010. Straw resource quantity and its regional distribution in China. J. Agric. Mech. Res. 3, 1–7. Blain, G.C., 2014. Removing the influence of the serial correlation on the Mann–Kendall test. J. Chem. Phys. 29, 161–170. Cao, J.J., Wu, F., Chow, J.C., Lee, S.C., Li, Y., Chen, S.W., et al., 2005. Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi'an, China. Atmos. Chem. Phys. 5, 3127–3137. Cao, G.L., Zhang, X.Y., Gong, S.L., Zheng, F.C., 2008a. Investigation on emission factors of particulate matter and gaseous pollutants from crop residue burning. J. Environ. Sci. 20, 50–55.

Cao, G.L., Zhang, X.Y., Gong, S.L., An, X.Q., Wang, Y.Q., 2008b. Estimation of emissions from field burning of crop straw in China. Chin. Sci. Bull. 53, 784–790. Cao, G.L., Zhang, X.Y., Gong, S.L., An, X.Q., Wang, Y.Q., 2011. Emission inventories of primary particles and pollutant gases for China. Chin. Sci. Bull. 56, 781–788. Carianos, P., Galn, C., Alczar, P., Dominguez, E., 2000. Meteorological phenomena affecting the presence of solid particles suspended in the air during winter. Int. J. Biometeorol. 44, 6–10. De La Riva, J., Pérez-Cabello, F., Lana-Renault, N., Koutsias, N., 2004. Mapping forest fire occurrence at a regional scale. Remote Sens. Environ. 92, 363–369. Guo, H., Wang, T., Simpson, I.J., Blake, D.R., Yu, X.M., Kwok, Y.H., Li, Y.S., 2004. Source contributions to ambient VOCs and CO at a rural site in eastern China. Atmos. Environ. 38, 4551–4560. Gupta, P.K., Sahai, S.N., Singh, C.K., Dixit, D.P., Singh, C., Sharma, M.K., Tiwari, R.K., Garg, S.C., 2004. Residue burning in rice-wheat cropping system: causes and implications. Currentence 87, 1713–1717. Huang, X., Li, M., Li, J., Song, Y., 2012. A high-resolution emission inventory of crop burning in fields in China based on MODIS thermal anomalies/fire products. Atmos. Environ. 50, 9–15.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024

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J O U RN A L OF E N V I RO N ME N TA L S CIE N CE S X X (2 0 1 7 ) XXX –XXX

Jing, C., Zhi, G.R., Chen, Y.J.M., Fan, Z.G., Xue, L.I., Yin, F., 2014. A preliminary study on brown carbon emissions from open agricultural biomass burning and residential coal combustion in China. Res. Environ. Sci. 27, 455–461. Keshtkar, H., Ashbaugh, L.L., 2007. Size distribution of polycyclic aromatic hydrocarbon particulate emission factors from agricultural burning. Atmos. Environ. 41, 2729–2739. Koutsias, N., Kalabokidis, K.D., Allgöwer, B., 2002. Fire occurrence patterns at landscape level: beyond positional accuracy of ignition points with kernel density estimation methods. 2002 World Conference on Natural Resources Modeling, “Modeling Natural and Biotic Resources in a Changing Planet”. Sigri, Lesbos, Greece. Koutsias, N., Balatsos, P., Kalabokidis, K., 2014. Fire occurrence zones: kernel density estimation of historical wildfire ignitions at the national level, Greece. J. Maps 10 (4), 630–639. Kulle, T.J., 2008. Acute odor and irritation response in healthy nonsmokers with formaldehyde exposure. Inhal. Toxicol. 5, 323–332. Li, X.H., Wang, D., Hao, J.M., Li, C., Chen, Y.S., Liu, Y., 2007. Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. Environ. Sci. Technol. 41, 6052–6058. Li, X.H., Wang, S.X., Lei, D., Hao, J.M., 2009. Characterization of non-methane hydrocarbons emitted from open burning of wheat straw and corn stover in China. Environ. Res. Lett. 4, 940–941. Liu, G., Shen, L., 2007. Quantitive appraisal of biomass energy and its geographical distribution in China. J. Nat. Resour. 22, 9–19. Liu, R.J., Zhang, Z.H., 2012. Decomposition of impact factors of industrial dust emission in China. Environ. Sci. Technol. 35, 244–248. Liu, L.H., Jiang, J.Y., Zong, L.G., 2011. A study for the emissions of greenhouse gases from crop residue burning: Jiangsu province as sample. Environ. Sci. 05, 1242–1248. Liu, M.X., Song, Y., Yao, H., Kang, Y.L., Li, M.M., Huang, X., 2015. Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power. Atmos. Environ. 112, 326–334. Liu, J., Bo, Y., Xie, S.D., 2016. Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products. J. Environ. Sci. 44, 158–170. Lu, Z., Zhang, Q., Streets, D.G., 2011. Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010. Atmos. Chem. Phys. 11, 9839–9864. Mann, M.E., 2004. On smoothing potentially non-stationary climate time series. Geophys. Res. Lett. 31, 1010–1029. Miura, Y., Kanno, T., 1997. Emissions of trace gases (CO2, CO, CH4, and N2O) resulting from rice straw burning. Soil Sci. Plant Nutr. 43, 849–854. National Bureau of Statistics of China, n.d.-a. China energy statistical yearbook (2001–2009) China Statistics Press. National Bureau of Statistics of China, n.d.-b. China statistical yearbook (2001–2015) China Statistics Press. Peng, L.Q., Zhang, Q., He, K.B., 2016. Emissions inventory of atmospheric pollutants from open burning of crop residues in China based on a national questionnaire. Res. Environ. Sci. 29 (8), 1109–1118. Qin, Y., Xie, S.D., 2011. Historical estimation of carbonaceous aerosol emissions from biomass open burning in China for the period 1990-2005. Environ. Pollut. 159, 3316–3323. Reinhardt, T.E., Ottmar, R.D., 2004. Baseline measurements of smoke exposure among wildland firefighters. J. Occup. Environ. Hyg. 1, 593–606. Sahai, S., Sharma, C., Singh, S.K., Dixit, N., Singh, P., Sharma, K., et al., 2007. A study for development of emission factors for trace gases and carbonaceous particulate species from in situ burning of wheat straw in agricultural fields in india. Atmos. Environ. 41, 9173–9186.

Sahai, S., Sharma, C., Singh, S.K., Gupta, P.K., 2011. Assessment of trace gases, carbon and nitrogen emissions from field burning of agricultural residues in India. Nutr. Cycl. Agroecosyst. 89, 143–157. Shadmani, M., Marofi, S., Roknian, M., 2012. Trend analysis in reference evapotranspiration using Mann–Kendall and Spearman's rho tests in arid regions of Iran. Water Resour. Manag. 26, 211–224. Singh, A., Agrawal, M., 2008. Acid rain and its ecological consequences. J. Environ. Biol. 29, 15–24. Stone, E.J., Schauer, T.A., Mahmood, A., 2010. Chemical characterization and source apportionment of fine and coarse particulate matter in Lahore, Pakistan. Atmos. Environ. 44, 1062–1070. Streets, D.G., Yarber, K.F., Woo, J.H., Carmichael, G.R, 2003. Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions. Glob. Biogeochem. Cycles 17 (4), 1759–1768. Tang, X.B., Huang, C., Lou, S.R., Qiao, L.P., Wang, H.L., Zhou, M., et al., 2014. A Study for the emission factors and particulate matters from crop residue burning in Yangtze River Delta. Environ. Sci. 05, 1623–1632. Tian, H.Z., Zhao, D., Wang, Y., 2011. The list of air pollutant emissions from biomass burning in China. J. Environ. Sci. 2, 349–357. Wang, Q., Geng, C., Sihua, L.U., Chen, W., Shao, M., 2012. Emission factors of gaseous carbonaceous species from residential combustion of coal and crop residue briquettes. Front. Environ. Sci. Eng. 7, 66–76. Wei, W., Zhang, W., Hu, D., Ou, L., Tong, Y., Shen, G., et al., 2012. Emissions of carbon monoxide and carbon dioxide from uncompressed and pelletized biomass fuel burning in typical household stoves in China. Atmos. Environ. 56, 136–142. Werf, G.R.V.D., Randerson, J.T.L., Giglio, G.J., Collatz, M., Mu, P.S., Kasibhatla, D.C., et al., 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735. Xie, H.H., Han, D.Q., Wang, X.Y., Lv, R.H., 2011a. Harvest index and residue factor of cereal crops in China. J. China Agric. Univers. 16, 1–8. Xie, H.H., Wang, X.Y., Han, D.Q., Xue, S., 2011b. Harvest index and residue factor of non-cereal crops in China. J. China Agric. Univers. 16, 9–17. Yan, X., Ohara, T., Akimoto, H., 2006. Bottom-up estimate of biomass burning in mainland China. Atmos. Environ. 40, 5262–5273. Yevich, R., Logan, J.A., 2003. An assessment of biofuel use and burning of agricultural waste in the developing world. Glob. Biogeochem. Cycles 17, 1084–1086. Zárate, I.O.de, Ezcurra, A., Lacaux, J.P, Dinh, P.V., 2000. Emission factor estimates of cereal waste burning in Spain. Atmos. Environ. 34 (19), 3183–3193. Zhang, J., Smith, K.R., Ma, Y., Ye, S., Jiang, F., Qi, W., et al., 2000. Greenhouse gases and other airborne pollutants from household stoves in China: a database for emission factors. Atmos. Environ. 34 (26), 4537–4549. Zhang, H., Ye, X., Cheng, T., Chen, J., Yang, X., Wang, L., Zhang, R., 2008. A laboratory study of agricultural crop residue combustion in China: emission factors and emission inventory. Atmos. Environ. 42, 8432–8441. Zhang, Y., Shao, M., Lin, Y., Luan, S., Mao, N., Chen, W., Wang, M., 2013. Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region, China. Atmos. Environ. 76, 189–199. Zhang, L., Liu, Y., Hao, L., 2016. Contributions of open crop straw burning emissions to PM2.5 concentrations in China. Environ. Res. Lett. 149, 629–635. Zhou, X.C., Wang, X.Q., 2006. Validate and improvement on arithmetic of identifying forest fire based on EOS-MODIS data. Remote Sens. Technol. Appl. 21, 206–211.

Please cite this article as: Jin, Q., et al., Dynamics of major air pollutants from crop residue burning in mainland China, 2000–2014, J. Environ. Sci. (2017), https://doi.org/10.1016/j.jes.2017.11.024