Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products

JES-00608; No of Pages 13 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 6 ) XX X–XXX

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Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products Jing Li, Yu Bo, Shaodong Xie⁎ College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing, China

AR TIC LE I N FO

ABS TR ACT

Article history:

With the objective of reducing the large uncertainties in the estimations of emissions from

Received 17 June 2015

crop residue open burning, an improved method for establishing emission inventories of crop

Revised 12 August 2015

residue open burning at a high spatial resolution of 0.25° × 0.25° and a temporal resolution of

Accepted 19 August 2015

1 month was established based on the moderate resolution imaging spectroradiometer

Available online xxxx

(MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to

Keywords:

improve the accuracy of related activity data. Locally observed emission factors were used to

Crop residue open burning

calculate the primary pollutant emissions. MODIS satellite data were modified by combining

Air quality

them with county-level agricultural statistical data, which reduced the influence of missing

Emission inventory

fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4,

Moderate resolution imaging

nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5),

spectroradiometer (MODIS)

organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ±169% for NH3. © 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Introduction Since the pioneering study by Crutzen et al., (1979), biomass burning has been considered an important source of atmospheric trace species and primary fine particles that has a significant impact on regional air quality and global climate change (Crutzen and Andreae, 1990; Mauzerall et al., 1998; Gustafsson et al., 2009; Akagi et al., 2012; Permadi and Oanh, 2013). Moreover, some of the emissions with hazardous pollutants have adverse impacts on human health (Johnson et al., 2005). Biomass burning may be divided into several major categories: temperate forest, boreal forest, savanna

and grassland, peat, deforestation (tropical), degradation, crop residues, and residential wood combustion (van der Werf et al., 2010). In China and other agriculture-based-economy countries, crop residue open burning accounts for a major fraction of total biomass burning (Streets et al., 2003b). Crop residue open burning is a human-initiated activity used to prepare a field for the next crop, remove residues, control weeds and release nutrients for the next crop cycle (Gadde et al., 2009). It began to receive government and scientific attention within China as early as the 1990s. Crop residue open burning is an important contributor to global biomass burning (van der Werf et al., 2010) and poses a serious

⁎ Corresponding author. E-mail: [email protected] (Shaodong Xie).

http://dx.doi.org/10.1016/j.jes.2015.08.024 1001-0742/© 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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threat to human health and air quality (Marlier et al., 2013). Streets et al., (2003b) estimated that of 730 Tg of biomass burned Asia in 2000, 250 Tg was from the open burning of crop residues; China and India accounted for 110 and 84 Tg, respectively of the residue burning. It is essential to establish accurate estimates of residue open burning emissions to evaluate their roles in local air quality and climate change. Emission inventories could provide information on emission sources and their characteristics, facilitate air quality simulation and forecasting, and guide policy-making for pollution control (Streets et al., 2003a; Zhang et al., 2008). Methods for establishing an emission inventory of residue open burning are based on either a bottom-up approach or top-down approach. The bottom-up approach refers to the use of the Seiler and Crutzen (1980) style equation to estimate emissions by multiplying the total mass of crop residues burned by the corresponding emission factors (EFs). The top-down approach often refers to the use of satellite or airborne observations, usually in conjunction with atmospheric modeling, to estimate emissions (Ichoku and Ellison, 2014). The top-down approach provides a reliable method for quantifying biomass burning consistently over large areas, such as forest burning and savanna/grassland burning (Korontzi et al., 2006). However the method has many limitations, when used to quantify small fires (Roy et al., 2008). In China, the average farming area of a farming household is only two acres (0.001334 km2) (National Bureau of Statistics of China, 2013). Moreover, beginning in 1997, the Chinese government has enacted a series of regulations and laws to prohibit field burning (Yan et al., 2006); thus, it occurs on a small scale in dispersed locations, making it difficult to assess well via satellite (Streets et al., 2003b). Considering the limitations of the top-down approach to quantify the small fires in residue burning, we chose the bottom-up approach to build the emission inventory. Crop residue open burning emissions have been estimated using the bottom-up method (Streets et al., 2003b; Yan et al., 2006; Tian et al., 2011; Zhang et al., 2008; Huang et al., 2012; Jain et al., 2014). However, the parameters used to calculate emission inventories were imprecise, which can result in errors in simulations and mislead policy-making. The reliabilities of emission factors (EFs), grain-to-straw ratios, dry matter content, proportion of crop residue burned in the field, and burning efficiency are the major challenges in producing an accurate emission inventory based on the bottom-up method. Many early studies used EFs determined by foreign studies or applied the same factor to the burning of different crops. The EFs of crop residue burning are related to residue types, conditions, or burning practices (Zhang et al., 2008). Therefore, crop-specific EFs measured in local experiments can better represent emissions from the burning of crop residues. Several studies have determined local EFs for particulates and trace gases from residue open burning in China, but the results have not been widely incorporated into emissions inventories (Zhu, 2004; Tian et al., 2011; Li et al., 2007a; Zhang et al., 2013). The proportion of crop residue burned in fields is the most important activity information influencing the estimation of emissions from the open burning of agricultural residue, but previous studies assumed this by using statistical reports or values based on past experience (Streets et al., 2003b; Yan et al., 2006; Wang and

Zhang, 2008). It is reported that the use of combine harvesters is the main reason for crop residue burning (Badarinath et al., 2009; Erenstein, 2011; Mishra and Shibata, 2012), and the use of a mechanical harvest ratio to estimate the proportion of crop residues burned in fields may be more reliable. The grain-to-straw ratios used in previous studies were derived from data provided by the China Association of Rural Energy Industry or the United Nations Food and Agriculture Organization, and were based on the results of studies conducted from 1980 to 1984. These outdated data are not relevant to the current situation in China. For example, a recent study indicated that the residue ratio of corn is 1.2, but most previous studies have used 2, which would overestimate the production of corn straw (Wang et al., 2012). This recent published data has never been used in emission inventories. In published studies, the spatial and temporal resolution of such burning was either too low or was presented in grids using satellite data. While active fire detection may be more sensitive than the observation of burned areas for sub-pixel size fires, many small fires are missed by polar orbiting satellites. To determine an accurate spatial distribution for the emission inventory of residue open burning, new approaches are required. In this study, an improved method for establishing a reliable emission inventory of residue open burning was developed. Agriculture mechanization ratios and recently published provincial grain-to-straw ratios were used to improve the accuracy of calculations of the amounts of residue burning. CO2, CO, CH4, nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5), organic carbon (OC), and black carbon (BC) emissions from residue open burning in 2012 were estimated using crop-specific local EFs. Emissions were distributed temporally and spatially by the moderate resolution imaging spectroradiometer (MODIS) Thermal Anomalies/Fire product, and were modified by combining the data with county-level agricultural statistical data.

1. Methodology Residues of crops including rice, wheat, corn, legumes, tubers, cotton, peanut, and rapeseed are widely burned in fields in China (Cao et al., 2008). In this study, burning emissions of such residues (throughout this study, we are referring to those burned in fields) were initially estimated at the provincial level by multiplying the mass of each type of crop residues burned and the corresponding EF, as shown in Eq. (1): Em;i ¼

X

j

E Fi; j  Mm; j

ð1Þ

where, Em,i (Gg) is the amount of pollutant i emitted annually from residue open burning in province m, EFi,j (g/kg) is the emission factor of crop type j for pollutant i, and Mm,j (Tg) is the mass of crop residues of crop j burned in the field in province m.

1.1. Provincial mass of residue open burning The provincial mass of crop residues burned of each crop type (M) was calculated on the basis of crop production using Eq. (2): Mm; j ¼ Q m; j  σ  δ j

ð2Þ

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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where, Q (Tg) is the crop residue burned in the field, σ is the proportion of dry matter in the crop residue, and δ is the burning efficiency. Previous studies suggested that the dry matter fractions of crop residues varied from 80%–90% based on field measurements (Zhang et al., 2013; Jain et al., 2014). As farmers usually burn crop residues before they are fully dried, we used 80% dry matter (σ) to calculate the crop residue yields. Burning efficiencies (δ) specific to various crops were compiled from Turn et al., (1997) and de Zárate et al., (2005), with values obtained for rice (0.89), wheat (0.86), corn (0.92), beans (0.68), tubers (0.8), cotton (0.8), peanuts (0.8), and rapeseed (0.82). Q could not be obtained directly from statistical data, and had to be estimated from crop residue production using Eq. (3): Q m; j ¼ Pm; j  φm

which refers to recent data shown in Table 1 (Wang et al., 2012). The proportion of crop residues burned in the field (φ) was an important factor to be determined. Field investigations have confirmed that mechanical harvesting is the main reason for residue burning in China. To further study the influence of mechanical harvesting on residue burning, we took the following steps. First, a field survey carried out in a typical agricultural region determined that the height of the stubble left behind in fields after the use of a combine harvester was about 30 cm, which inevitably increases the difficulty of sowing, making it necessary to burn the residue (Wang and Zhang, 2009). Second, fragments of machineharvested crops are scattered throughout the field, making recovery difficult and reducing the potential for reuse as an industrial raw material. For these reasons, together with the high cost of straw recycling, mechanical harvesting has become the main driver of residue burning (Wang, 2012). Field surveys have shown that when a crop is harvested by a combine harvester, the proportion of residue burned is much greater than that when crops are harvested manually (Yang et al., 2008). It can be concluded that mechanized harvesting has encouraged residue open burning. Based on the statement above, Q was calculated twice: once for crops harvested manually, and again for those

ð3Þ

where, P (Tg) is the crop residue production and φ is the proportion of crop residue burned in the field. Crop residue production was calculated using Eq. (4): Pm; j ¼ Cm; j  Rm; j

ð4Þ

where, C (Tg) is the crop production (National Bureau of Statistics of China, 2013) and R is the grain-to-straw ratio,

Table 1 – Provincial level grain-to-straw ratio for different crops. Province

Rice

Wheat

Corn

Beans

Tubers

Cotton

Peanut

Rapeseed

Average Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shaanxi Gansu Qinghai Ningxia Xinjiang

1.04 1.1 1.33 0.95 1 0.83 1.03 1.03 0.92 1.28 1.24 1.07 1.09 1.14 1.03 1.29 0.97 0.96 0.98 1.07 1.1 1.2 0.91 0.9 1.14 1.14 1.07 0.94 0.84 – 0.99 0.74

1.28 1.29 1.16 1.22 1.25 1.39 1.22 1.25 1.05 1.09 1.41 1.2 1.12 1.34 1.36 1.39 1.29 1.39 1.38 1.27 1.22 – 1.08 1.12 1.29 1.2 1.22 1.27 1.26 1.31 1.08 1.36

1.07 1.02 0.99 1.05 1.16 1.3 1.03 1.09 1.16 0.93 1 0.96 1 0.93 0.95 0.96 1.07 0.98 0.96 0.93 0.94 0.94 0.96 0.98 0.94 0.93 0.95 1.1 1.11 1.1 1.21 1.15

1.35 1.36 1.36 1.36 1.36 1.36 1.29 1.5 1.13 1.52 1.52 1.52 1.52 1.52 1.52 1.36 1.36 1.52 1.52 1.52 1.52 1.52 1.52 1.52 1.52 1.52 1.36 1.36 1.36 1.36 1.36 1.36

0.53 0.42 0.42 0.42 0.42 0.62 0.6 0.6 0.6 0.53 0.53 0.53 0.53 0.58 0.52 0.42 0.42 0.52 0.52 0.58 0.58 0.58 0.49 0.49 0.49 0.49 0.75 0.62 0.62 0.75 0.62 0.62

2.87 2.62 2.62 2.62 2.62 2.62 2.62 2.62 2.62 3.35 3.35 3.35 3.35 3.35 3.35 2.64 2.41 3.35 3.35 3.35 3.35 3.35 3.35 3.35 3.35 3.35 2.62 2.62 2.62 2.62 2.62 2.85

0.99 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 1.26 1.26 1.26 1.26 1.08 1.26 0.89 0.86 1.26 1.26 1.26 1.26 1.26 1.26 1.26 1.26 1.26 0.86 0.86 0.86 0.86 0.86 0.86

2.9 2.57 2.57 2.57 2.57 2.57 2.57 2.57 2.57 2.98 2.98 2.98 2.98 2.98 2.98 2.57 2.57 2.98 2.98 2.98 2.98 2.98 2.98 2.98 2.98 2.98 2.57 2.57 2.57 2.57 2.57 2.57

–: not detectable.

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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harvested using a combine harvester. Thus, Eq. (3) can be transformed into Eq. (5):   Q m; j ¼ A j  αm; j  ð1−βm Þ  Pm; j þ Bm  1−αm; j  Pm; j

ð5Þ

where, αm,j is the proportion of the crop harvested mechanically for crop type j in province m, 1–αm,j is the proportion of the crop harvested manually, and βm is the proportion of straw returned to the field by machines in province m, A and B are the proportions of the crop burned in the field for crops harvested by combine harvester and by hand, respectively (the rest of the variables have already been defined). P was calculated from Eq. (4), and αm,j and βm were taken from the China Agricultural Machinery Industry Yearbook (Table 2). In recent years, with the strict prohibition of such burning, mechanization techniques that place straw back into farm land have become popular. We used the coefficient (1–βm) to subtract this effect. The coefficient A was determined from field investigations. About 82% of mechanically harvested wheat was burned, whereas the proportion of other straw crops was about 37% (Yang et al., 2008). The value of B was more complicated. The proportion of a manually harvested crop that is burned can be influenced by topography, the economy, population density, climate, and other factors, and the results of previous investigations in a specific province may not be appropriate for other provinces.

For example, because a combine harvester cannot be used in small fields or on sloping land, little mechanical harvesting occurs in mountainous and hilly areas, where manual harvesting is more common (Yi, 2013). Considering the limitations of human and material resources in these areas, we can speculate that the burning of residues following a manual harvest would be practiced more widely than in more populated regions (Wang et al., 2003). In non-agriculturally developed provinces, with a high proportion of mechanical harvesting and the comprehensive utilization of straw crops, the proportion of manual harvesting would be low. According to the proportion of mechanical harvesting in each province, the rural population, the gross domestic product (GDP), and the proportion of mountainous and hilly areas, China's provinces can be categorized into six groups. Each group has a burning proportion for manual harvesting (B), determined by field investigations in areas representative of each group, as shown in Table 3 (Wang and Zhang, 2008; Yang et al., 2008; Li, 2014). The provincial average of the proportion of crop residues burned in fields is shown in Table 4.

1.2. Emission factors Table 5 summarizes the EFs used in this study. EFs derived from local field measurements were selected preferentially

Table 2 – The proportion of the crop harvested mechanically (α), and the proportion of straw returned to the field by machines (β). α

Provinces

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Heilongjiang Henan Hubei Hunan Inner Mongolia Jiangsu Jiangxi Jilin Liaoning Ningxia Qinghai Shaanxi Shandong Shanghai Shanxi Sichuan Tianjin Tibet Xinjiang Yunnan Zhejiang

β

Rice

Wheat

Corn

Soybeans

Tubers

Cotton

Peanut

Rapeseed

0.98 0.55 0.36 0.36 1.00 0.70 0.58 0.25 0.72 0.67 1.00 0.78 0.95 0.71 0.83 0.96 0.71 0.80 0.69 0.96 0.00 0.53 0.65 0.99 0.56 0.46 1.00 0.00 0.63 0.17 0.90

0.97 1.00 0.04 0.02 0.50 0.00 0.43 0.01 0.00 1.00 1.00 0.97 0.89 0.27 1.00 1.00 1.00 0.00 1.00 0.79 0.72 0.82 0.98 1.00 0.88 0.28 0.98 1.00 0.86 0.16 1.00

0.44 0.75 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.44 0.70 0.58 0.10 0.01 0.38 0.47 0.00 0.40 0.27 0.44 0.10 0.38 0.81 0.00 0.39 0.00 0.77 0.18 0.43 0.00 0.00

0.56 0.08 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.08 0.02 0.43 0.12 0.00 0.50 0.08 0.00 0.24 0.16 0.01 0.00 0.02 0.14 0.00 0.07 0.00 0.48 0.00 0.45 0.00 0.00

0.00 0.00 0.00 0.00 0.10 0.01 0.00 0.00 0.00 0.27 0.46 0.06 0.02 0.00 0.54 0.00 0.00 0.07 0.39 0.52 0.10 0.15 0.26 0.00 0.58 0.00 0.00 1.00 0.49 0.00 0.00

0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00

0.08 0.02 0.00 0.00 5.60 0.00 0.00 0.00 0.00 0.19 0.08 0.29 0.00 0.00 0.00 0.02 0.04 0.25 0.84 0.00 0.00 0.01 0.59 0.00 0.08 0.00 0.26 0.00 0.41 0.00 0.00

0.17 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.38 0.00 0.16 0.33 0.12 0.82 0.15 0.05 0.00 0.00 0.00 0.33 0.01 0.30 0.02 0.00 0.01 0.00 0.42 0.50 0.01 0.10

0.27 0.34 0.23 0.13 0.36 0.10 0.19 0.13 0.32 0.49 0.23 0.62 0.44 0.19 0.54 0.62 0.34 0.16 0.27 0.03 0.10 0.00 0.41 0.35 0.57 0.23 0.62 0.20 0.16 0.27 0.35

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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Table 3 – Proportion of crop residues burned in different regions. A Type of region 1. 2. 3. 4. 5. 6.

Non-agriculturally developed Northern plains Northeast China Southern plains Hilly and mountains Remote

Province

Wheat

Other crops

B

Beijing, Guangdong, Hainan, Shanghai, Tianjin Hebei, Henan, Shandong, Shanxi, Shaanxi Heilongjiang, Jilin, Liaoning, Inner Mongolia, Anhui, Hubei, Hunan, Jiangsu, Zhejiang Chongqing, Fujian, Guangxi, Guizhou, Jiangxi, Sichuan, Yunnan Gansu, Ningxia, Qinghai, Tibet, Xinjiang

82% 82% 82% 82% 82% 82%

37% 37% 37% 37% 37% 37%

8% 6% 3% 10% 20% 8.5%

A and B stands for burning proportions for harvest by combine harvester and by hand, respectively.

(Li et al., 2007b; Cao et al., 2008; Kudo et al., 2014). Those EFs for pollutants lacking field measurements were taken from local laboratory measurements (Cao et al., 2008; Zhang et al., 2013). Wheat, corn and rice are the three major agricultural crops in China, and we found very few corresponding studies of other crop types. For some crops that lacked local measurements, the EFs were derived from two publications that are used widely by foreign crop residue emission inventories (Andreae and Merlet, 2001; Akagi et al., 2011). Due to the lack of data, it is difficult to quantify a wide range of emitted species from crop residue open burning exactly. It is important to note that the EFs presented in this study may not totally represent the reality, especially for the straws of beans, tubers, peanuts, and rapeseed. Residues of wheat, rice, and corn straw were the most commonly burned types of biomass, accounting for 90% of the total residue burned. Therefore we believe that the error caused by the inaccurate EF values for the other crops would be small.

1.3. Allocation to county level After gaseous and particulate emissions from residue fires were calculated using Eq. (1), provincial emissions were further allocated to the county level. We selected MODIS Thermal Anomalies/Fire gridded level-3 product (MOD/ MYD14A1) to determine the fire counts, which provided data from both the Terra and Aqua satellites. Each satellite made two observations per day, with the Terra overpass at 10:30 local time (LT) and 22:30 LT and the Aqua overpass at 1:30 LT and 13:30 LT (Giglio, 2010). MOD/MYD14A1 is produced at 1-km resolution per day. Collection 5 of the MODIS fire detection data was used in this analysis, and the MODIS data were obtained through the NASA Land Process Distributed Active Archive Center (LPDAAC), USA (https://lpdaac. usgs.gov/products/modis_products_table).

Croplands were identified using the European Space Agency Climate Change Initiative Land Cover Maps (EAS CCI-LC Maps) (http://maps.elie.ucl.ac.be/CCI/viewer/index. php). The CCI-LC Maps are 3-epoch series of global land cover maps at 300 m spatial resolution, where each epoch covers a 5-year period (2008–2012, 2003–2007, 1998–2002). In this study we choose the most recent map (2008–2012) to identify the croplands. The CCI-LC Maps characterize the land cover with 24 different classes using UN-LCCS (land cover classification system) classifiers. Only MODIS active fire detections in the land cover classes defined as “Cropland” or “Mosaic cropland (> 50%)” in the CCI-LC Maps were identified as crops burning in fields. In this study, if a fire event detected by a MODIS sensor was located in one 300 m crop land pixel in CCI-LC Maps, it could be recognized as an agricultural fire (Liu et al., 2015). To reduce the effect of satellites missing fires due to cloud cover and the limited duration of agricultural fires, we used county-level agricultural statistical parameters to modify the burning area of crop residues. Based on the pixels of fire counts and the seeded area of each county, emissions from the residue open burning were allocated to the county-level using Eq. (6): En;i ¼ σ  FCn =FCm  Em;i þ ð1−σÞ  CAn =CAm  Em;i

ð6Þ

where Em,i is the emissions from residue open burning for species i in province m, estimated by Eq. (1), En,i (Gg) is the emissions from residue open burning for species i in county n, FCn is the fire count in county n, FCm is the total fire count in province m, CAn (hectare) is the crop-seeded area of county n, CAm (hectare) is the total crop-seeded area of province m, and σ is a distribution coefficient. The limited duration of agricultural fires combined with the low temporal resolution of MODIS (four observations per day of about 5 min each) means many agricultural fires

Table 4 – Proportion of crop residues burned in agricultural fields (φ) in Chinese provinces. Provinces

φ

Provinces

φ

Provinces

φ

Provinces

φ

Tibet Shaanxi Anhui Shandong Ningxia Beijing Xinjiang Shanghai

44.00% 35.07% 34.11% 33.13% 30.22% 29.61% 27.66% 27.52%

Qinghai Hebei Guangxi Fujian Sichuan Jiangxi Heilongjiang Guangdong

24.57% 24.46% 24.42% 23.55% 23.04% 22.69% 22.22% 22.05%

Henan Chongqing Guizhou Yunnan Zhejiang Hunan Hubei Jiangsu

21.58% 21.33% 21.14% 20.82% 20.70% 20.65% 20.20% 18.87%

Tianjin Hainan Gansu Jilin Shanxi Liaoning Inner Mongolia Average

18.72% 17.99% 16.12% 15.64% 14.71% 12.66% 11.41% 23.35%

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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Table 5 – Emission factors for pollutants emitted from crop residue burning (unit: g/kg dry fuel). Pollutants CO2 CO CH4 NMVOCs N2O NH3 SO2 NOx PM2.5 OC BC

Wheat straw a

1470 60 a 3.36 a 7.48 a 0.07 a 0.37 a 0.85 a 3.3 a 7.58 a 2.69, 5.02 b, 2.63 c 0.491, 0.4 b, 0.64 c

Corn straw a

1350 53 a 4.41 a 10.40 a 0.14 a 0.68 a 0.44 a 4.3 a 11.7 a 3.94, 2.43 i, 2.55 j, 1.48 k, 2.37 l 0.35, 0.28 i, 2.32 j, 0.31 k, 0.22 l

Rice straw m

1105 53.2 m 5.82 6.05 m 0.07 n 2.2 0.4 n 3.83 m 12.1 m 3.3,3.04 d, 1.97 e 1.61 f, 1.38 g, 1.81 h 0.49 d, 0.45 e, 0.28 f, 0.66 g, 0.74 h

Cotton straw o

1345 106 o 5.82 9.42 0.07 n 2.2 0.4 n 2.49 o 6.3 1.83 o 0.82 o

Others 1584 102 5.82 9.42 p 0.07 n 2.2 0.4 n 5.3 6.3 2.3 0.8

Other values are from Akagi et al. (2011). NMVOCs: nonmethane volatile organic compounds; OC: organic carbon; BC: black carbon. a Li et al. (2007b). b Cao et al., (2008) for Shanghai province. c Cao et al., (2008) for Hebei province. d Cao et al., (2008) for Changzhou city in Jiangsu province. e Cao et al., (2008) for Huai'an city in Jiangsu province. f Cao et al., (2008) for Hubei province. g Cao et al., (2008) for Sichuan province. h Cao et al., (2008) for Guangxi province. i Cao et al., (2008) for Shandong province. j Cao et al., (2008) for Jilin province. k Cao et al., (2008) for Henan province. l Cao et al., (2008) for Inner Mongolia province. m Zhang et al., (2013). n Andreae and Merlet (2001). o Cao et al., (2008). p Kudo et al., (2014).

cannot be detected simply because they are not burning at the time of a MODIS overpass. The poor temporal coverage of satellite products and the short duration of agricultural fires, as well as cloud cover, may contribute to underestimating fire counts. Schroeder et al., (2008) found high count omission in the MOD14 data associated with crop-dominant areas on Brazilian Amazonia, and the omission errors can be as much as 75%. Therefore, we introduced the distribution coefficient σ to reduce the error in spatial distribution. The pollutant emissions of each province were divided into two parts, according to σ. One part (σ) was allocated to the county level by satellite fire counts and the other part (1–σ) was allocated using statistical information. First, China's provinces were classified according to cloud cover data and land statistics. Cloud cover data were taken from Wang and Wang (2009), and land statistics were obtained from the China county statistical year book. A province was defined as cloudy when the total annual mean cloudiness was more than 65%, and was defined as mountainous if more than 70% of its land was hilly and mountainous. Then the provinces were categorized into three types: cloudy-mountainous, low cloudiness-flat, and other (Table 6). Cloud cover and the small areas being burned were

the two major reasons for satellites underestimating fire counts, and mountainous regions usually led to a small area of farming and small fire size. In this study, we assumed that the values of σ for cloudy-mountainous, low cloudiness-flat, and other are 0, 1, and 0.5, respectively.

2. Results and discussion 2.1. Mass of crop residue burned in 2012 The year of 2012 was selected to calculate the emission inventory of residue open burning. Based on Eq. (4), crop residue production in 2012 was 707 Tg for the whole country. We estimated that the total mass of crop residue burned in 2012 was 160 Tg (Eq. (5)), accounting for 23% of the total residue. The provincial production of crop residue and the mass of residue burned in the field in China is shown in Fig. 1. The production of residue in Jilin, Liaoning, and Gansu provinces was relatively high, but relatively little was burned. Conversely, Qinghai and Xinjiang provinces had low levels of

Table 6 – Distribution coefficient (σ) for different regions. Type of province

Province

Cloudy-mountainous Chongqing, Fujian, Guangxi, Guizhou, Hunan, Jiangxi Sichuan Anhui, Beijing, Hebei, Henan, Heilongjiang, Hubei, Jiangsu, Jilin, Liaoning, Ningxia, Shanxi, Shaanxi, Shandong, Low cloudiness-flat Shanghai, Tianjin, Xinjiang, Other Guangdong, Hainan, Inner Mongolia, Ningxia, Qinghai, Tibet, Yunnan, Zhejiang

σ 0 1 0.5

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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b

a

Tg/(year.province) Tg/(year.province)

Fig. 1 – Production of crop residue (a) and the mass of residue burned in the field (b) in China (GS(2015)2407). 1 Beijing; 2 Tianjin; 3 Hebei; 4 Shanxi; 5 Inner Mongolia; 6 Liaoning; 7 Jinlin; 8 Heilongjiang; 9 Shanghai; 10 Jiangsu; 11 Zhejiang; 12 Anhui; 13 Fujian; 14 Jiangxi; 15 Shandong; 16 Henan; 17 Hubei; 18 Hunan; 19 Guangdong; 20 Guangxi; 21Hainan; 22 Chongqing; 23 Sichuan; 24 Guizhou; 25 Yunnan; 26 Tibet; 27 Shannxi; 28 Gansu; 29 Qinghai; 30 Ningxia; 31 Xinjiang.

Table 7 – Emissions from the field burning of crop residues in Chinese provinces in 2012 (unit: Gg). Provinces

CO2

CO

CH4

NMVOCs

N2O

NH3

SO2

NOx

PM2.5

OC

BC

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Heilongjiang Henan Hubei Hunan Jiangsu Jiangxi Jilin Liaoning Inner Mongolia Ningxia Qinghai Shaanxi Shandong Shanghai Shanxi Sichuan Tianjin Xinjiang Xizang Yunnan Zhejiang Total

13,553 367 2074 1398 1964 2709 3297 2413 335 9250 12,165 15,727 6294 6023 8120 4526 5174 2517 3447 1165 469 5243 18,995 355 2119 7817 349 235 7056 3679 1565 150,401

597 15 104 70 86 133 158 122 16 381 523 657 307 303 363 230 215 113 145 50 23 217 788 16 86 386 15 10 319 172 77 6698

43 1 9 7 6 14 16 10 2 24 48 40 24 30 27 23 19 10 11 4 1 14 48 1 6 30 1 1 19 15 7 509

73 2 13 8 12 15 19 15 2 53 82 86 35 34 44 25 37 17 23 7 3 30 107 2 14 46 2 1 41 23 9 879

1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 10

10 0 3 3 1 5 6 3 1 3 13 6 8 11 7 8 4 3 2 1 0 2 7 0 1 9 0 0 3 4 3 129

7 0 1 0 1 1 1 1 0 5 4 8 3 2 4 2 2 1 1 0 0 3 10 0 1 3 0 0 3 1 1 66

36 1 7 5 5 9 11 8 1 23 40 38 19 21 22 15 17 8 10 3 1 13 47 1 6 24 1 1 17 12 5 428

89 2 16 13 12 28 33 18 3 55 112 90 46 59 57 44 47 22 24 9 2 31 111 3 14 57 2 1 40 29 15 1087

29 1 5 4 4 8 9 6 1 19 35 28 14 17 18 12 11 7 6 3 1 11 35 1 7 18 1 0 14 9 4 336

5 0 1 1 1 1 2 1 0 4 4 5 2 3 3 2 7 1 1 0 0 2 6 0 1 4 0 0 3 1 1 61

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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residue, but a high burning rate. The five provinces with the highest burning levels were Shandong, Henan, Anhui, Heilongjiang, and Hebei, which together generated 72 Tg crop residue, accounting for 45% of the total. The five provinces with the lowest levels were Tibet, Tianjin, Beijing, Shanghai, and Hainan, which generated 28 Tg crop residue, accounting for 1% of the total. The most commonly burned biomasses were residues of wheat, rice, and corn straw (65, 48, and 33 Tg burned, respectively), accounting for 90% of the total residue burned. The proportion of each residue burned was 42% for wheat, 23% for rice, and 15% for corn. The burning of each crop type tended to be regional. The most corn, wheat, and rice were burned in northeast China, the North China Plain, and south China, respectively. This is consistent with the country's cereal crop distribution.

harvested in southern China in July; many kinds of crops are harvested in autumn; and cotton is harvested in November and December (Chinese Academy of Agricultural Sciences, CAAS, 1984). Because the Yellow-Huai River area is the major wheat-producing area and the Yangtse-Huai River area is the major early ripening rice-producing area in China, fire counts were more concentrated in summer, and especially concentrated in the area around Beijing, the capital of China (Fig. 4b). Fire counts in other seasons were relatively less regionally concentrated, and burning was uniformly scattered over most of the agricultural zone.

2.2.2. Spatial distribution of emissions To determine the spatial variation in emissions, the emissions from counties estimated by Eq. (6) were gridded to a resolution of 0.25° × 0.25° using geographic information system (GIS) and MapInfo software. Using CO as illustrative, the spatial variation of CO emissions is shown in Fig. 5. The regions with the highest emissions were the Yellow-Huai River and Yangtse-Huai River areas (Zone 1), which include the Henan, Jiangsu, Shandong, and Anhui provinces. The high CO emission grids also contained the Sichuan Basin and the south part of Shannxi (Zone 2), the northeast of China (Zone 3). The spatial distribution of county-level emissions indicates that, except for Xizang (Tibet), high emission counties were located in every province. The county with the highest emissions was Yongcheng in Henan province, with 104 Gg CO emissions. The second highest was Zaozhuang in Shangdong, with 84 Gg CO emissions.

2.2. Emissions from residue open burning The annual emissions of CO2, CO, CH4, NMVOCs, N2O, NOx, NH3, SO2, PM2.5, OC, and BC were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06 Tg, respectively, in 2012 (Eq. (1)). Emissions from each province are shown in Table 7. The crop-specific CO emissions in each province are shown in Fig. 2.

2.2.1. Spatiotemporal patterns of fire counts A total of 104,314 fires were recorded in China in 2012, of which 38,053 were recorded on farmland (36%). Monthly agricultural MODIS fire counts are shown in Fig. 3. The month when most crop fires occurred was June (37%), followed by April, March, and July. The spatial and seasonal distributions of fire counts are shown in Fig. 4. In China, the first harvest of triple cropping cultivation is from March to April; wheat is harvested in June; early-ripening rice is

2.3. Uncertainty analysis The overall uncertainty in our estimates was derived from the activity data and EFs. Monte Carlo simulation was performed

2000 1800

Rice

Wheat

Corn

Beans

Tubers

Cotton

Peanut

Rapeseed

1600

CO emission (Gg)

1400 1200 1000 800 600 400

Tibet

Hainan

Tianjin

Shanghai

Beijing

Fujian

Qinghai

Ningxia

Zhejiang

Chongqing

Guangdong

Gansu

Guizhou

Liaoning

Shanxi

Guangxi

Jiangxi

Yunnan

Inner Mongolia

Jilin

Hunan

Hubei

Shaanxi

Sichuan

Xinjiang

Hebei

Jiangsu

Anhui

Heilongjiang

Henan

0

Shandong

200

Province

Fig. 2 – Province-level crop-specific CO emissions in China in 2012. Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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18000 16000

MODIS fire counts

14000 12000 10000 8000 6000 4000 2000 0

1

2

3

4

5

6

7 8 Month

9 10

11 12

Fig. 3 – Monthly agricultural moderate resolution imaging spectroradiometer (MODIS) fire counts.

9

to quantify the uncertainty in our emission inventory using the following steps: First, the respective probability density function (PDF), of the activity data and EFs was determined. Then, the mean value and standard deviation for each PDF were determined. Next, each EF and each activity data value randomly selected was multiplied to obtain a sample of emissions. Finally, the process was repeated 100,000 times to produce the uncertainty data for this inventory at the 95% confidence interval. Four factors can lead to uncertainties in the activity data in this study: (1) crop production, (2) grain-to-straw ratio, (3) combustion efficiency, and (4) proportion of crop residues burned in fields. For crop production, we followed the assumption made in the Transport and Chemical Evolution over the Pacific (TRACE-P) experiment, and assumed a normal distribution with a coefficients of variation (CV) of ± 30% (Streets et al., 2003a). For other activity data, we generally followed the assumption by Zhao et al., (2011), i.e., uniform distributions are assumed for combustion efficiency, grainto-straw ratios, and the proportion of crop residues burned in agricultural fields. A Monte Carlo simulation was preformed repeatedly with new input values selected randomly from the respective probability distributions of the activity data for the four factors. This process was repeated 100,000 times. The crop residue production in 2012 in China ranged from 505 to

Fig. 4 – Spatiotemporal distribution of agricultural fires in China in 2012. (a) Spring (March–May); (b) summer (June–August); (c) autumn (September–November); (d) winter (December–February) (GS(2015)2407). Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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Zone 3

Zone 1 Zone 2

Based on the probability density distributions of the activity data and EFs (Table 8), and repeated Monte Carlo simulations (100,000 repeats), the ranges in uncertainty in the total emissions at 95% CI were as follows: CO2 (− 72%, 134%), CO (− 70%, 138%), CH4 (− 78%, 183%), NMVOCs (− 70%, 135%), N2O (− 74%, 126%), NH3 (− 82%, 169%), SO2 (− 71%, 143%), NOx (−72%, 157%), PM2.5 (−68%, 147%), OC (−68%, 151%), and BC (−69%, 166%). Fig. 6 illustrates the probability distribution of national CO emissions for China from residue open burning in 2012. Compared with the uncertainty of Streets et al., (2003b) (an average of within ±400%), the uncertainties in this study were substantially reduced.

2.4. Comparison against other emission inventories

kg/(year.grid) 0-1 1-50 100 500 >500 Fig. 5 – Spatial distribution (0.25° × 0.25°) of annual CO emissions from residue open burning in 2012 (GS(2015)2407).

907 Tg and the uncertainty at the 95% confidence interval (CI) ranged from − 28% to + 30%. The mass of crop residue burned in fields in China ranged from 55 to 259 Tg and the uncertainty at the 95% CI ranged from − 66% to + 62%. Regarding the EFs of gaseous pollutants, we followed the assumption by Streets et al., (2003a), and assumed that the underlying EF measurements were normally distributed. For EFs of particulate matter, we followed the assumption by (Bond et al., 2004), and assumed that EFs for BC, OC and PM2.5 followed a lognormal distribution. For EFs based on local observations, we assumed these had the least uncertainty, with a CV of ±50%, and EFs based on foreign studies had a CV of ± 150% (Qin and Xie, 2011).

Estimating emissions from the open burning of crop residues in China has been studied using different algorithms and data sources. Table 9 compares the results of previous bottom-up studies to those of our study. The cropland fire emissions of CO2 and CO calculated in our study were considerably higher than the emissions in Liu et al., (2015) and Huang et al., (2012), but roughly consistent with other results listed in Table 9. The emissions estimated in Liu et al., (2015) are based on the MODIS fire radiative power (FRP) data, and the emissions estimated in the other studies list in Table 9 are based on the bottom-up method. The limited duration of agricultural fires combined with the low temporal resolution of MODIS may lead to underestimation when estimating the crop residue open burning emissions using the FRP-based method. Compared with the results in this study and other bottom-up estimates listed in Table 9, the lower agricultural burning emissions in Huang et al., (2012) were caused mainly by the lower values used for the proportions of crop residues burned in fields. Huang et al., (2012) estimated crop burning in the field with an average ratio of 6.6%, which was based on a survey in 2000 by Gao et al., (2002). Yan et al., (2006) compared the results of Gao et al., (2002) with other estimates on the percentage of crop residues burned in the

Table 8 – Uncertainties of emission factors (EFs) and activity levels. Parameter EFs

Activity levels

CO2 CO CH4 NMVOCs N 2O NH3 SO2 NOx PM2.5 OC BC Agriculture production Combustion efficiency Proportion of crop residues burned in fields Grain-to-straw ratios

Distribution Normally Normally Normally Normally Normally Normally Normally Normally Lognormal Lognormal Lognormal Normally Normally Normally Uniform

Coefficients of variation Wheat

Corn

Rice

Others

±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±30% ±30% ±30% –

±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50% ±50%

±50% ±50% ±150% ±50% ±150% ±150% ±150% ±50% ±50% ±50% ±50%

±150% ±150% ±150% ±150% ±150% ±150% ±150% ±150% ±150% ±150% ±150%

Coefficients of variation are expressed as the standard deviation divided by the mean.

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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Forecast values

5200

0.05

4800 4400

0.04

4000 3200

0.03

2800 2400

0.02

Frequency

Probability

3600

2000 97.5% = 15683.30

1600

Mean = 7125.40 Median = 6549.28

0.01

1200

2.5% = 1924.52

800 400

0

-2000

0

2000

4000

6000

8000 10000 CO emissions (Gg)

12000

14000

0

16000

Fig. 6 – Probability distribution of China's national CO emissions from residue open burning in 2012, based on 100,000 Monte Carlo simulations.

fields and surmised that the survey data of Gao et al., (2002) understated these values. Our estimates were based on county surveys and mechanical harvesting data, which suggested an average value of 23.35% (ranging from 11% to 44%), similar to reported burning ratios of 10%–30% (Tian et al., 2011), 11%–33% (Wang and Zhang, 2008), 17% (Streets et al., 2003b) and 16.6%– 30% (Yan et al., 2006). Therefore, we estimated that 160 Tg of crop residues were burned in fields in 2012, which compares with the 43 Tg reported in Huang et al., (2012). The masses of crop residues burned in fields in Tian et al., (2011); Wang and Zhang (2008); Streets et al., (2003b) and Yan et al., (2006) were 98, 107, 110, and 122 Tg, respectively. The difference between the other pollutant emissions in our study and those reported in other studies is minor. Another important factor influencing the result was the EF value. Most of the reported Chinese biomass burning emission inventories were based on EFs obtained from studies in other countries and used a combined EF for different types of crop. Huang et al., (2012); Streets et al., (2003b) and Yan et al., (2006) derived emissions based on the EFs summarized by Akagi et al., (2011); Andreae and Merlet (2001) and Zhang et al., (2000), respectively; Wang and Zhang (2008) selected the EFs from local observations and foreign studies. Our study adopted crop-specific local EFs for calculations. The EFs of crop residue burning were related to the C and N contents of

each crop residue (Andreae and Merlet, 2001). Differences in the growing regions and crop type could be responsible for the different C and N contents in crop residues. Finally, the grain-to-straw ratios used in our study were updated. For example, the ratio for corn was 2 in our study, whereas a lower value of 1.2 was used in previous studies. The different parameters used in each study, including the proportions of crop residues burned in fields, EFs, and the grain-to-straw ratios, can explain the disparity in the emission results. This study applied detailed, reliable parameters, which potentially provide more realistic emission inventories for crop residue open burning in China.

3. Conclusions An improved bottom-up emission inventory of crop residue burning in fields was established. Agriculture mechanization ratios were used to improve the accuracy of related activity data required for the compilation of the emission inventory. The most recent published regional-crop specific residue ratios were introduced in this study. Locally observed EFs were used to calculate the primary pollutant emissions from crop residue open burning. Emissions were distributed temporally and spatially with the MODIS Thermal Anomalies/Fire product, which was modified by

Table 9 – Comparison of the emissions calculated in our study with previous estimates. Region China (Tg/year)

Reference

This study (Tian et al., 2011) (Huang et al., 2012) (Wang and Zhang, 2008) (Qin and Xie, 2011) (Streets et al., 2003b) (Yan et al., 2006) This study The North China Plain (Gg/year) (Liu et al., 2015) This study The Pearl River Delta (Gg/year) (Zhang et al., 2013)

Year

CO2

CO

CH4 NMVOCs N2O NH3 SO2 NOx PM2.5 OC

EC

2012 2007 2006 2006 2005 2000 2000 2012 2012 2012 2008

140 142 68 155 – 160 184 68,675 26,000 697 –

11 9 4 7 – 10 11 5983 1700 46 48

0.33 0.43 0.25 0.37 – 0.28 0.32 158 94 1.2 –

0.06 0.06 0.03 0.05 0.07 0.07 0.08 23 13 0.4 0.3

0.87 0.76 2.2 0.87 – 1.70 1.92 377 835 5 8

0.01 – – – – – 0.01 4 – 0.1 –

0.08 – 0.09 0.08 – 0.13 0.16 27 36 1 –

0.07 0.04 0.02 0.06 – 0.04 0.05 36 7 0.5 –

0.17 0.36 0.23 0.36 – 0.40 0.47 64 50 2 2

1.22 0.54 0.27 2.17 – – 0.47 452 102 10 9

0.36 – 0.10 0.48 0.26 0.03 0.40 140 37 3 5

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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combining it with county-level agricultural statistical data. Based on this method, an improved emission inventory of residue open burning in China for 2012 was established. The annual emissions of CO2, CO, CH4, NMVOCs, N2O, NOx, NH3, SO2, PM2.5, OC, and BC from residue burning were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06 Tg, respectively, in 2012. The five provinces with the highest emissions, Shandong, Henan, Anhui, Heilongjiang, and Hebei, together accounted for 44.68% of the total crop residue burned in China. Most burning involved wheat, rice, and corn residues, which accounted for 90.6% of the total mass of residue burned. The temporal distribution showed that crop residue burning varied by season, being highest in June, followed by April. Emissions in summer were concentrated in the Yellow-Huai River and Yangtse-Huai River areas, whereas in other seasons the emission areas were dispersed more widely. Residue open burning is an important contributor to regional air pollution and global climate change. The control of such burning should focus on the wheat production areas of Henan, Shandong, Jiangsu, Anhui, and Hebei in June. The approach developed in this study can help reduce the large uncertainties in the estimations of emissions from residue open burning and guide policy-making for pollution control.

Acknowledgments This work was supported by the Environmental Protection Ministry of China for Research of Characteristics and Controlling Measures of VOCs Emissions from Typical Anthropogenic Sources (No. 2011467003) and the Natural Science Foundation key project (grant no. 91544106).

REFERENCES Akagi, S.K., Yokelson, R.J., Wiedinmyer, C., Alvarado, M.J., Reid, J.S., Karl, T., et al., 2011. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys. 11 (9), 4039–4072. Akagi, S.K., Craven, J.S., Taylor, J.W., McMeeking, G.R., Yokelson, R.J., Burling, I.R., et al., 2012. Evolution of trace gases and particles emitted by a chaparral fire in California. Atmos. Chem. Phys. 12 (3), 1397–1421. Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15 (4), 955–966. Badarinath, K.V.S., Sharma, A.R., Kharol, S.K., 2009. Impact of emissions from anthropogenic sources on satellite-derived reflectance. Adv. Space Res. 43 (10), 1545–1554. Bond, T.C., Streets, D.G., Yarber, K.F., Nelson, S.M., Woo, J.H., Klimont, Z., 2004. A technology-based global inventory of black and organic carbon emissions from combustion. J. Geophys. Res.-Atmos. 109 (D14), D14203. Cao, G.L., Zhang, X.Y., Gong, S.L., Zheng, F.C., 2008. Investigation on emission factors of particulate matter and gaseous pollutants from crop residue burning. J. Environ. Sci. 20 (1), 50–55. Chinese Academy of Agricultural Sciences (CAAS), 1984. Agricultural Regionalization of China. China Agriculture Press, Beijing, China. Crutzen, P.J., Andreae, M.O., 1990. Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250 (4988), 1669–1678.

Crutzen, P.J., Heidt, L.E., Krasnec, J.P., Pollock, W.H., Seiler, W., 1979. Biomass burning as a source of atmospheric gases CO, H2O, N2O, NO, CH3Cl and COS. Nature 282 (5736), 253–256. de Zárate, I.O., Ezcurra, A., Lacaux, J.P., Van Dinh, P., de Argandoña, J.D., 2005. Pollution by cereal waste burning in Spain. Atmos. Res. 73 (1–2), 161–170. Erenstein, O., 2011. Cropping systems and crop residue management in the trans-Gangetic plains: issues and challenges for conservation agriculture from village surveys. Agric. Syst. 104 (1), 54–62. Gadde, B., Bonnet, S., Menke, C., Garivait, S., 2009. Air pollutant emissions from rice straw open field burning in India, Thailand and the Philippines. Environ. Pollut. 157 (5), 1554–1558. Gao, X., Ma, W., Ma, C., Zhang, F., Wang, Y., 2002. Analysis of the current status of utilization of crop straw in China. J. Huazhong Agric. Univ. 21, 242–247. Giglio, L., 2010. MODIS Collection 5 Active Fire Product User's Guide Version 2.4. Science Systems and Applications, Inc., Maryland, USA. Gustafsson, Ö., Krusa, M., Zencak, Z., Sheesley, R.J., Granat, L., Engström, E., et al., 2009. Brown clouds over South Asia: biomass or fossil fuel combustion. Science 323 (5913), 495–498. Huang, X., Li, M.M., Li, J.F., 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. Ichoku, C., Ellison, L., 2014. Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements. Atmos. Chem. Phys. 14 (13), 6643–6667. Jain, N., Bhatia, A., Pathak, H., 2014. Emission of air pollutants from crop residue burning in India. Aerosol Air Qual. Res. 14 (1), 422–430. Johnson, N.G., Bryden, M., Xiao, A., 2005. Risk analysis and safety evaluation of biomass cookstoves. Proceedings of IMECE 2005: Engineering/Technology Management. American Society of Mechanical Engineers, New York, USA. Korontzi, S., McCarty, J., Loboda, T., Kumar, S., Justice, C., 2006. Global distribution of agricultural fires in croplands from 3 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data. Glob. Biogeochem. Cycles 20 (2), GB2021. Kudo, S., Tanimoto, H., Inomata, S., Saito, S., Pan, X.L., Kanaya, Y., et al., 2014. Emissions of nonmethane volatile organic compounds from open crop residue burning in the Yangtze River Delta region, China. J. Geophys. Res. -Atmos. 119 (12), 7684–7698. Li, Y.F., 2014. Investigation of Agricultural Burning in the Field Master thesis Peking University, Beijing, China. Li, X.H., Duan, L., Wang, S.X., Duan, J.C., Guo, X.M., Yi, H.H., et al., 2007a. Emission characteristics of particulate matter from rural household biofuel combustion in China. Energy Fuel 21 (2), 845–851. Li, X.G., Wang, S.X., Duan, L., Hao, J.M., Li, C., Chen, Y.S., et al., 2007b. Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. Environ. Sci. Technol. 41 (17), 6052–6058. Liu, M.X., Song, Y., Yao, H., Kang, Y.N., Li, M.M., Huang, X., et al., 2015. Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power. Atmos. Environ. 112, 326–334. Marlier, M.E., DeFries, R.S., Voulgarakis, A., Kinney, P.L., Randerson, J.T., Shindell, D.T., et al., 2013. El Niño and health risks from landscape fire emissions in Southeast Asia. Nat. Clim. Chang. 3 (2), 131–136. Mauzerall, D.L., Logan, J.A., Jacob, D.J., Anderson, B.E., Blake, D.R., Bradshaw, J.D., et al., 1998. Photochemistry in biomass burning plumes and implications for tropospheric ozone over the tropical South Atlantic. J. Geophys. Res.-Atmos. 103 (D7), 8401–8423.

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024

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Mishra, A.K., Shibata, T., 2012. Synergistic analyses of optical and microphysical properties of agricultural crop residue burning aerosols over the Indo-Gangetic Basin (IGB). Atmos. Environ. 57, 205–218. National Bureau of Statistics of China, 2013. China Statistical Yearbook. China Statistics Press, Beijing, China. Permadi, D.A., Oanh, N.T.K., 2013. Assessment of biomass open burning emissions in Indonesia and potential climate forcing impact. Atmos. Environ. 78, 250–258. Qin, Y., Xie, S.D., 2011. Estimation of county-level black carbon emissions and its spatial distribution in China in 2000. Atmos. Environ. 45 (38), 6995–7004. Roy, D.P., Boschetti, L., Justice, C.O., Ju, J., 2008. The collection 5 MODIS burned area product-global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112 (9), 3690–3707. Schroeder, W., Prins, E., Giglio, L., Csiszar, I., Schmidt, C., Morisette, J., et al., 2008. Validation of GOES and MODIS active fire detection products using ASTER and ETM + data. Remote Sens. Environ. 112, 2711–2726. Seiler, W., Crutzen, P.J., 1980. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Chang. 2 (3), 207–247. Streets, D.G., Bond, T.C., Carmichael, G.R., Fernandes, S.D., Fu, Q., He, D., et al., 2003a. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 108 (D21), 8809. Streets, D.G., Yarber, K.F., Woo, J.H., Carmichael, G.R., 2003b. Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions. Glob. Biogeochem. Cycles 17 (4), 1099. Tian, H.Z., Zhao, D., Wang, Y., 2011. Emission inventories of atmospheric pollutants discharged from biomass burning in China. Acta Sci. Circumst. 31 (2), 349–357. Turn, S.Q., Jenkins, B.M., Chow, J.C., Pritchett, L.C., Campbell, D., Cahill, T., et al., 1997. Elemental characterization of particulate matter emitted from biomass burning: wind tunnel derived source profiles for herbaceous and wood fuels. J. Geophys. Res. 102 (D3), 3683–3699. van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Mu, M., Kasibhatla, P.S., et al., 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmos. Chem. Phys. 10 (23), 11707–11735. Wang, S.J., 2012. Research on rural householders' comprehensive utilization in Jiangsu province PhD thesis Nanjing Agricultural University, Nanjing, China.

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Wang, Y.Y., Wang, B.M., 2009. Total cloud amount difference between ISCCP product and ground observation over China. J. Appl. Meteor. Sci. 20 (4), 411–418. Wang, S.X., Zhang, C.Y., 2008. Spatial and temporal distribution of air pollutant emissions from open burning of crop residues in China Sciencepaper Online, 2008. 3(5) pp. 329–333. Wang, S.W., Zhang, S.H., 2009. Thinking of comprehensive utilization of crop straw. Anhui Agri. Sci. Bull. 15 (5), 88–90. Wang, A.L., Gao, W.S., Hong, C.M., 2003. Study on the ecological effect of crop residues burned or incorporated in field in North Central irrigated area of China. Chin. J. Eco-Agric. 11 (1), 142–144. Wang, X.Y., Xue, S., Xie, G.H., 2012. Value-taking for residue factor as a parameter to assess the field residue of field crops. J. China Agric. Univ. 17 (1), 1–8. Yan, X.Y., Ohara, T., Akimoto, H., 2006. Bottom-up estimate of biomass burning in mainland China. Atmos. Environ. 40 (27), 5262–5273. Yang, S.J., He, H.P., Lu, S.L., Chen, D., Zhu, J.X., 2008. Quantification of crop residue burning in the field and its influence on ambient air quality in Suqian. China Atmos. Environ. 42 (9), 1961–1969. Yi, Z.Y., 2013. Yearbook of Agricultural Mechanization in China. China Agricultural Science and Technology Press, Beijing, China. 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, 4537–4549. Zhang, H.F., Ye, X.N., Cheng, T.T., Chen, J.M., Yang, X., Wang, L., et al., 2008. A laboratory study of agricultural crop residue combustion in China: emission factors and emission inventory. Atmos. Environ. 42 (36), 8432–8441. Zhang, Y.S., Shao, M., Lin, Y., Luan, S.J., Mao, N., Chen, W.T., et al., 2013. Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region. China Atmos. Environ. 76, 189–199. Zhao, Y., Nielsen, C.P., Lei, Y., McElroy, M.B., Hao, J., 2011. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11 (5), 2295–2308. Zhu, B., 2004. Emission characteristic of PM2.5from crop straw burning Master thesis Peking University, Beijing, China.

Please cite this article as: Li, J., et al., Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.08.024