Introduction
Open biomass burning (OBB), which includes forest, shrubland, grassland and
crop residue fire burning (van der Werf et al., 2010; Qiu et al., 2016), is
one of the most important sources for gaseous and particulate matter (PM)
especially for fine particulate particles (PM2.5) and associated
carbonaceous aerosols (elemental carbon, EC; organic carbon, OC) (Zha,
2013; Yan et al., 2014; Zong et al., 2016; Zhou et al., 2017). Previous
studies have shown that OBB contributed to approximately 40 % of the
annual average submicron EC emission and 65 % of primary OC emission
globally (Bond et al., 2013), and contributed more than 45 % of PM2.5
concentration on days of heavy air pollution (Deng, 2011). The pollutants
with high emission amounts from OBB posed significant impacts on regional
and global climate change, air quality and human health (Seiler and Crutzen,
1980; Crutzen and Andreae, 1990; Andreae and Merlet, 2001; Bond et al.,
2004; Akagi et al., 2011; H. Zhang et al., 2016).
From the 1970s (Crutzen et al., 1979), emission estimation of
biomass burning has been a research hot topic from global (Seiler and
Crutzen, 1980; Levine et al., 1995; Liousse et al., 1995; Bond et al., 2004;
Randerson et al., 2012; Kaiser et al., 2012) to regional scale (Yevich and
Logan, 2003; Liousse et al., 2010; Li et al., 2017).
China is suffering from severe air pollution with 100 million tons of
biomass open burned each year (Zhang et al., 2015). The quantitative
estimation of pollutant emissions for all of China (Streets et al., 2003;
Tian et al., 2002; Cao et al., 2005; Zhou et al., 2017) or a certain region
(Liu et al., 2015; Zhou et al., 2015; Jin et al., 2017a) is also a vital
practice, which is the base for assessing the impact of OBB on regional air
quality deterioration. Central and eastern China (CEC), including
central China (Hunan, Henan and Hubei) and eastern China – part of the
North China Plain (Shandong), the Yangtze River Delta (YRD, including
Zhejiang, Jiangsu, Anhui and Shanghai) and part of the Pan-Pearl River Delta
(Fujian and Jiangxi) (Fig. 1) – is an area with plenty of vegetation
coverage (as listed in Fig. S1 of the Supplement). Yin et al. (2017)
have indicated that the crop residue fire burning during summer harvest time can
lead to the increase of PM2.5 concentration in China's middle east
region. As one of the most heavily polluted regions in China (Chang et al.,
2009; Fu et al., 2013), many large cities are included in this region, such
as Nanjing, Wuhan, Shanghai and Hangzhou. Former studies have highlighted
the role of OBB on worsening air quality regionally or in megacities,
especially for crop residue burning during harvest periods (Yamaji et al., 2010;
Zhu et al., 2010; Huang et al., 2012b; Su et al., 2012;
Cheng et al., 2014; Zhou et al., 2016; Zhang et al., 2017).
Locations of central and eastern China and the key megacities.
![]()
Previous studies mainly focused on crop residue burning emissions with
relatively low spatial and temporal resolution (Yamaji et al., 2010; Huang
et al., 2012b), which may limit its adoption in air quality modeling to give
an accurate result. An accurate estimation of monthly emissions from OBB
with a long timescale and high spatial resolution is still limited. It
should be noted that OBB activities showed spatial–temporal variation
properties and have changed greatly during the last two decades in China,
especially for forest land fire burning (Huang et al., 2011) and crop residue
burning, considering the implementation of related policies (Tables S1 and
S2 in the Supplement). As a big agricultural country, the Chinese government has placed
a high priority on environmental pollution prevention caused by OBB. From
1965 to 2015, 51 management documents for crop straw have been formulated
and 34 documents were intensively issued after 2008 (Chen et al., 2016). Up
to now, few studies have accurately estimated the biomass burning emissions
in a long time period (Fu et al., 2013; Cheng et al., 2014).
The role of the pollution prevention policies on the spatial–temporal
variation of pollutants emitted needs to be better clarified.
In addition, most previous studies have adopted the top-down method (Seiler
and Crutzen, 1980) to estimate OBB emissions by national or
provincial statistical data, and then the total emission amounts of
pollutants were re-allocated in grids by population, land cover area or
even equal sharing, which is one of the key reasons for the high
uncertainties of OBB emission inventories (Streets et al., 2003; Klimont and
Streets, 2007; Gadde et al., 2009; He et al., 2011; Zhou et al., 2015, 2017). Quantitative estimation of biomass burning was highly
improved by the satellite observations of fire burned area or active burning
fires (Freitas et al., 2005; Wooster et al., 2005; Roy et al., 2008; Giglio
et al., 2008; Reid et al., 2009; Sofiev et al., 2009;
Liousse et al., 2010; Huang et al., 2012a; Li et al.,
2016). The improvement of spatial–temporal distribution evolution was
achieved by active fire products (e.g., the AVHRR fire count product, Setzer
and Pereira, 1991; Moderate Resolution Imaging Spectroradiometer (MODIS) active fire satellite products, Cooke et al.,
1996; and VIRS fire count product, Ito and Akimoto, 2007). The burned area
detection was improved by burned area products (e.g., GBA2000 product, Ito
and Penner, 2004; Korontzi, 2005; MODIS burned area dataset, Ito and Akimoto,
2007; and Global Fire Emissions Database (GFED), Randerson et al., 2012).
However, satellite observation also exhibited weakness in estimating fire
burning emissions (Duncan et al., 2003; He et al., 2015). One is the burned
area product, which provides fire burned areas of the whole month. It is
limited by the lower pixel resolutions. The sizes of many small burn scars
are below the detection limit of these products (Eva and Lambin, 1998; Laris,
2005; McCarty et al., 2009; Roy and Boschetti, 2009). Therefore, the
contribution of small fires to fire burned area and the corresponding fire
burning emissions are still poorly understood (Randerson et al., 2012). The
other is the active fire product, which can provide information on small
fire locations, occurrence time and small fire burned area (Prins and
Menzel, 1992; Giglio et al., 2006; Chuvieco et al., 2008; Roberts et al.,
2009; Aragao and Shimabukuro, 2010; Bowman et al., 2011; Lin et al., 2012;
Arino et al., 2012). The uncertainty of fire detection is mainly due to the
limitation of satellite overpass periods. To reduce the uncertainty of
emission estimation by satellite products, the combination of two satellite
datasets has proven to be an effective practice recently (Qiu et al.,
2016).
Forest, shrubland and grassland biomass fuel loading (kt km-2)
in each province.
Province
Forest
Forest
Shrublandc
Grasslandd
(2003–2008)a
(2009–2015)b
Shandong
4.26
2.95
6.94
0.78
Henan
5.66
4.16
6.94
0.77
Anhui
6.32
3.61
12.2
0.77
Jiangsu
4.7
2.64
6.86
0.72
Hubei
5.34
3.28
7.87
0.88
Hunan
4.79
2.52
17.4
0.8
Jiangxi
4.75
3.08
18.5
0.76
Fujian
6.29
5.91
18.9
0.85
Zhejiang
3.51
3.11
18.4
0.86
Shanghai
6.09
2.99
6.86
0.93
References: a Fang et al. (1996). b This study. c Pu et al. (2004).
d Hu et al. (2006).
The lack of local biomass data (biomass loading data and vegetation
speciation data) and local emission factors could introduce uncertainty in
emission estimates. Currently, local biomass loading data need to be updated
and accurately measured. Local high-spatial-resolution vegetation speciation
data have been rarely adopted in OBB estimations. Meanwhile, a lot of
research on OBB has used the same emission factors for pollutants
emitted from OBB without considering the various biomass species and
combustion conditions (Andela et al., 2013; Giglio et al., 2013). All these
should be considered and improved in the establishment of an OBB emission
inventory.
In this study, the multiple satellite data (MCD14 ML and MCD64Al), local
high spatial resolution of vegetation speciation data, updated local biomass
loading data, local emission factors and survey results were used to
estimate multi-year OBB emissions from 2003 to 2015 in CEC. High
spatial–temporal resolution of emission allocation was achieved. The
possible driving factors like local habits, social customs, rural
population, economic level, agricultural production, energy and pollution
control policies which may impact the spatial distribution and temporal
variation of OBB emissions were explored. They have been overlooked in
previous studies (Song et al., 2009; Chen et al., 2013; Shi et al., 2015a).
The results here will provide scientific evidence for policy making on
controlling OBB emission and modeling its regional impact on air quality,
climate and human health. The methods are also helpful for other regions for
OBB emission estimation.
Methods
Estimation of burned areas
OBB emissions in CEC were initially estimated based on the local biomass
data (biomass loading data and vegetation speciation data), satellite burned
area data (Fig. S2) and emission factors. The fire burning emission amount was
calculated by the following equation (Wiedinmyer et al., 2011; Shi et al.,
2015b):
Ei,x,t=∑j=1nBAx,t×CEx×BLx×EFi,j,
where j stands the different aggregated vegetation types; i stands for
different pollutant species; Ei,x,t is the emission amount of
pollutant i in location x and month t; BAx,t is the total burned area
(km2) of aggregated vegetation class in location x and month t;
CEx is defined as the combustion efficiency in location x; BLx is
the biomass fuel loading (kg) in location x; EFi,j is the emission
factor of pollutant specie i for vegetation type j.
MODIS burned area product (MCD64Al: http://modis-fire.umd.edu, last access: 7 August 2018) and MODIS
active fire product (MCD14 ML:
https://earthdata.nasa.gov/faq#ed-firms-faq, last access: 7 August 2018) were combined to obtain
accurate open biomass burned area data. MCD64Al had a 500 m spatial
resolution and monthly temporal resolution, which could accurately detect
the burned area at 500 m pixels. A much lower pixel resolution burning was
difficult to detect by this satellite. Therefore, we used MODIS active fire
product MCD14 ML as a supplemental tool to obtain the small fire burned
area. The active fire detection method based on thermal anomalies could
detect fires as low as 1/20 of a pixel. We resampled the two fire products'
data into a 1 km × 1 km grid. The total burned area in each grid cell
was estimated by the following equation (Randerson et al., 2012):
BAtotal(i,t,j)=BAMCD64Al(i,t,j)+BAsf(i,t,j),
where BAtotal(i,t,j) is the total fire burned area in grid cell i,
month t and aggregated vegetation class j; BAMCD64Al(i,t,j) is the
MCD64Al burned area in grid cell i, month t and aggregated vegetation class
j; BAsf(i,t,j) is the small fire burned area in grid cell i, month
t
and aggregated vegetation class j.
BAMCD64Al(i,t,j) was directly detected from the MCD64Al product. MCD14 ML
active fire points in each grid included two parts: active fire points with
or near MCD64A1 burned area (FCin) and active fires outside the MCD64Al
burned area (FCout). BAsf(i,t,j) was the burned area of FCout. The BAsf(i,t,j) was used as a supplement.
Because the active fire product existed as the fire points and could not directly obtain
the burned area data, the burned area of the small fire was estimated based on
the following method (Randerson et al., 2012):
BAsf(i,t,j)=FCout(i,t,j)×α(r,s,j)×γ(r,s,j),
where BAsf(i,t,j) is the small fire burned area of Fout in grid
cell i, month t and aggregated vegetation class j; FCout(i,t,j) is the
total number of MCD14 ML active fires outside of the burned area in grid
cell i, month t and aggregated vegetation class j; α is the ratio of
BAMCD64A1 to Fin and α is equal to the value of
the surrounding grid cell if BAMCD64A1 is equal to 0; γ is an
additional unit less scalar which indicates the difference between Fin
and Fout and γ is assumed equal to 1 in this research;
r denotes the burning region; s indicates the burning period.
Biomass fuel loading
For forest land, most previous studies used the forest biomass loading data
from Fang et al. (1996). The forest biomass loading data in recent years need
to be updated. In this study, the forest loading data between 2003 and 2008
were collected from Fang et al. (1996). From 2008 to 2015, the forest loading
data were calculated based on the eighth Chinese National Forest Resource
Inventory (Xu, 2014). The forest biomass density data (Table 1) were
estimated by the following equation:
Bi,r=Ti,r/Ai,r,
where i stands for different forest species (broadleaf forest, coniferous
forest and mixed forest); r indicates each province; Bi,r is the biomass
density of forest specie i in province r; Ti,r indicates the total biomass
of forest specie i in province r; Ai,r denotes the total area of forest
specie i in province r.
The total biomass of different forest species was calculated based on the
forest stock volume method as follows (Fang et al., 1996):
Ti,r=∑j=1nEj,r=∑j=1n(aVj,r+b),
where j stands
for different tree types of forest specie i; Ej,r indicates
the biomass of different tree type j in province r; Vj,r indicates the
forest stock volume of different tree type j in province r; a and b are set
as correlation coefficients.
The correlation coefficients “a” and “b” for different tree types were
derived from previous studies (Fang et al., 1996; Tian et al., 2011; Lu et
al., 2012; Li et al., 2014; Wang and Deng, 2014; Wen et al., 2014) (Table 2).
Aj,r and Vj,r were collected from the eighth Chinese National
Forest Continuous Inventory. As shown in Table 1, the forest biomass density
in recent years has changed a lot, which highlighted the importance of the
update for improving the emission estimation.
Parameters of the linear regression model for biomass and stock
volume of dominant tree species.
Tree species
a
b
Tree species
a
b
Larix
0.967a
5.7598a
Cinnamomum camphora
1.0357a
8.0591a
Pinus koraiensis
0.5185a
18.22a
Phoebe
1.0357a
8.0591a
Pinus sylvestris var. mongolica
1.11a
Elm
0.7564f
8.3013f
Pinus densiflora
1.0945b
2.004b
Robinia
0.7564a
8.3103a
Pinus thunbergii Parl
0.5168b
33.237b
Schima superba
0.76e
8.31e
Chinese pine
0.7554a
5.0928a
Sweetgum
0.76e
8.31e
Pinus armandii
0.5856a
18.7435a
Other hard broadleaf
0.7564b
8.3103b
Pinus massoniana
0.52a
Tilia
0.7975b
0.4204b
Pinus yunnanensis
0.52a
Sassafras
1.0357a
8.0591a
Pinus kesiya var. langbiamensis
0.510b
1.045b
Populus
0.4754a
30.603a
Pinus densata
0.5168b
33.237b
Salix
0.4754c
30.6034c
Foreign pine
0.5168
33.2378
Paulownia
0.8956d
0.0048d
Pinus elliottii
0.51e
1.05e
Eucalyptus
0.7893a
6.9306a
Pinus taeda
0.5168f
33.2378f
Rich acacia
0.4754a
30.60a
Mount Huangshan pine
0.5168f
33.2378f
Casuarina equisetifolia
0.7441b
3.2377b
Joe pine
0.5168f
33.237f
Melia azedarach
0.4754b
30.603b
Other pine
0.5168a
33.2378a
Other soft broadleaf
0.4754b
30.603b
Cunninghamia lanceolata
0.399a
22.54a
Coniferous mixed
0.5168f
33.2378f
Cryptomeria fortunei
0.4158a
41.3318a
Broadleaf mixed
0.8392b
9.4157b
Metasequoia
0.4158a
41.3318a
Coniferous and broadleaf mixed
0.7143b
16.9154b
Taxodium ascendens
0.399a
22.541a
Betula
0.9644a
0.8485a
Abies
0.4642a
47.499
White birch
0.9644a
0.8485a
Picea
0.4642a
47.499a
Betula costata
0.9644a
0.8485a
Tsuga
0.4158a
41.3318a
Water, beard and yellow
0.7975b
0.4202b
Keteleeria
0.4158
41.3318
Manchurian ash
0.798c
0.42c
Cupressus
0.6129a
26.1451a
Juglans mandshurica
0.798c
0.42c
Yew
0.4642b
47.499b
Amur cork tree
0.798c
0.42c
Other fir
0.399a
22.541a
Quercus
1.3288a
-3.8999a
References: a Fang et al. (1996). b Wen et
al. (2014). c Lu et al. (2012). d Tian et al. (2014). e Wang and Deng (2014). f Li et al. (2014).
For grassland and shrubland, local biomass density data were collected (Pu
et al., 2004; Hu et al., 2006) in Table 1. To determine the accurate provincial
amounts of crop residue burning, we gathered the production of different
species of crops from the China Statistical Yearbook (NBSC, 2004–2016).
Detailed data of crop residue to production ratio (dry matter) were
collected from local statistical data (Table 3), and the updated data for
crop straw burned ratio were derived from survey results (Table 4). Using
the updated biomass data, the accuracy of the estimation of OBB emission is
expected to be improved.
Detailed crop residue to production ratio data for each province.
Province
Rice
Corn
Wheat
Cotton
Rapeseed
Soy bean
Sugar cane
Peanut
Potato
Sesame
Sugar beet
Tobacco
Anhui
1.09a
1a
1.12a
3.35a
2.98a
1.52a
0.34a
1.26a
0.53a
2.01a
0.37a
0.71a
Fujian
0.85b
1.04c
1.17c
2.91d
2.87d
1.5d
0.43d
1.08m
0.57d
2.01d
0.43d
0.56d
Henan
1c
0.96c
1.08h
2.41i
2.87d
1.5d
0.34d
0.89d
0.57d
1.78d
0.43d
0.49d
Hubei
1.17e
1.04c
1.17c
4.09j
3.17k
1.5d
0.43d
1.14d
0.57d
2.01d
0.43d
0.71d
Hunan
0.94f
1.11g
1.17c
2.91d
3l
1.5d
0.43d
1.38n
0.57d
2.23d
0.43d
0.85d
Jiangsu
1.04a
1a
1.41c
2.61i
2.98a
1.52a
0.34a
1.26a
0.53a
2.01a
0.37a
0.71a
Jiangxi
1c
1.04c
1.17c
2.91d
2.87d
1.5d
0.43d
1.14d
0.57d
2.01d
0.43d
0.71d
Shandong
1c
0.96c
1.33c
2.91d
2.87d
1.5d
0.43d
0.85d
0.57d
2.01d
0.43d
0.71d
Shanghai
1.28a
0.93a
1.09a
3.35a
2.98a
1.52a
0.34a
1.26a
0.53a
2.01a
0.37a
0.71a
Zhejiang
1.07a
0.96a
1.2a
3.35a
2.98a
1.52a
0.34a
1.26a
0.53a
2.01a
0.37a
0.71a
References: a Zhu et al. (2017). b Chen et
al. (2008). c Xie et al. (2011a). d Xie et al. (2011b). e Zeng et al. (2007).
f Ao et al. (2007). g Lei et al. (2009). h Zhao and
Chen (2008). i Xue et al. (2006). j Yu et al. (2009). k Zou et al. (2008).
l Liu et al. (2010). m Tang et al. (2009). n Li et al. (2008).
Detailed crop straw burned ratio data for each province.
Region
Crop straw
burning percentage
Anhui
0.10a
Fujian
0.188b
Henan
0.208c
Hubei
0.207c
Hunan
0.278c
Jiangsu
0.10a
Jiangxi
0.18c
Shandong
0.178c
Shanghai
0.148d
Zhejiang
0.319c
References: a Tian et al. (2011.
b Huang (2014). c Peng et
al. (2016). d Zhou et al. (2017).
Combustion efficiency
In previous studies (Wang and Zhang, 2008; Tian et al., 2011), the combustion
efficiency (CE) of OBB was mainly set as a constant, which may bias the
emission estimation. To improve the accuracy, for cropland, the CE was set
as 0.68 for soy bean and 0.93 for other types (Koopmans and Koppejan, 1997;
Wang and Zhang, 2008; Zhang et al., 2011). For forest, shrubland and
grassland, the CE of fires at each grid cell was assumed as a function of
forest cover of the corresponding grid cell (Ito and Penner, 2004; Wiedinmyer et al.,
2006). If areas had tree coverage exceeding 60 %, the CE for woody and
herbaceous cover was set as 0.3 and 0.9, respectively; the CE was set as 0
and 0.98 for woody and herbaceous cover with tree coverage less than 40 %;
for 40–60 % tree cover of fires, the CE was defined as 0.3 for woody
fuels,
and the calculation of herbaceous areas was referred to the following
equation:
CEs=e-0.13×TB,
where TB stands for the percent tree cover for fires in each grid cell.
It should be noted that though we improved the selection of CE values for
different biomass burning types by reviewing literature, the CE value
should not be a constant during burning and the pollution emissions were not
uniform in different burning phase, such as smoldering (Kondo et al., 2011)
and flaming burning (Burling et al., 2010). Emission inventories in this
research and currently published papers (Wang and Zhang, 2008; Zhang et al.,
2011; Lu et al., 2011) were estimated for a long time period or a whole year
with the timescale as months instead of hours. Therefore, the CE values used
here reflected the average biomass burning condition. In the future, for
research on developing an emission inventories with hourly or daily resolution,
corresponding high-time-resolution activity data and emission factors for
different burning stages should be considered.
Emission factors
Emission factors (EFs) of different OBB were summarized in Table 5. EFs for
cropland burning were mainly collected from previous research carried out
in CEC (Tang et al., 2014). As there was a lack of EF research on some crop species
conducted in CEC and forest, grassland and shrubland conducted in China, EFs
were collected from previous research (Cao et al., 2008; Wang and Zhang,
2008; Akagi et al., 2011; He et al., 2015). In addition, some emission
factors measured by our research group in CEC were included in this study.
The emission factors of open biomass burning emissions for various
pollutants (g kg-1 dry matter).
Vegetation
OC
EC
CO
CH4
NOx
NMVOCs
SO2
NH3
CO2
PM2.5
Corn
1.457j
0.14a
70.2a
4.4b
3.36a
10c
0.45c
0.68g
1261f
5c
Rice
1.96a
0.52c
52.32c
3.9b
1.42d
6.05f
0.147a
0.53g
791f
3.03d
Wheat
2.7b
0.49a
61.90c
3.4b
1.19d
7.5c
0.147c
0.37b
1557f
7.6a
Cotton
3.06c
0.57f
70.29c
4.4b
2.98c
10c
0.23c
0.68b
1445h
11.7c
Rapeseed
1.08d
0.23d
34.3d
3.9b
1.12d
8.64c
0.25c
0.53g
1445h
5.76c
Soy bean
1.05d
0.13d
32.3d
3.9b
1.08d
8.64c
0.25c
0.53g
1445h
3.32d
Sugar cane
2.03c
0.41c
40.08f
3.9b
2.03c
11.02f
0.25c
0.53g
1445h
4.12f
Peanut
2.03c
0.41c
55.13c
3.9b
2.11c
8.64c
0.25c
0.53g
1445h
5.76c
Potato
2.03c
0.41c
55.13c
3.9b
2.11c
8.64c
0.25c
0.53g
1445h
5.76c
Tobacco
2.03c
0.41c
55.13c
3.9b
2.11c
8.64c
0.25c
0.53g
1445h
5.76c
Sesame
2.03c
0.41c
55.13c
3.9 b
2.11c
8.64c
0.25c
0.53g
1445h
5.76c
Sugar beet
2.03c
0.41c
55.13c
3.9b
2.11c
8.64c
0.25c
0.53g
1445h
5.76c
Coniferous forest
7.8e
0.2e
118e
6e
2.4e
28e
1i
3.5e
1514e
9.7e
Broadleaf forest
9.2e
0.6e
102e
5e
1.3e
11e
1e
1.5e
1630e
13e
Mixed forest
9.2e
0.6e
102e
5e
1.3e
14e
1i
1.5e
1630e
9.7e
Grassland
2.6e
0.4e
59e
1.5e
2.8e
9.3e
0.5e
0.5e
1692e
5.4e
Shrubland
6.6e
0.5e
68e
2.6e
3.9e
4.8e
0.7e
1.2e
1716e
9.3e
References: a Cao et al. (2008). b Li et al. (2007). c He et al. (2015).
d Tang et al. (2014). e Akagi et al. (2011).
f Zhang et al. (2008). g EPD (2014). h Wang et
al. (2008). i Andreae and Rosenfeld (2008). j This study.
Spatial and temporal allocation
In order to estimate high spatial resolution of OBB emission in CEC, a
high-resolution vegetation map (1:1 000 000) (Fig. S1) together with the burned
area of every open biomass species was used. All the data were relocated
into a 1 km × 1 km grid to identify and estimate spatial variations
of OBB emission. The monthly distributions of OBB emissions were estimated
based on the monthly burned area of different vegetation cover types.
The emission in tth grid was calculated by the following equation:
Et,j=BAt,j/BAi,j×Ei,j,
where j means different biomass species; i denotes different provinces;
Et,j is the emission of different biomass specie j in tth grid;
BAt,j is the burned area in tth grid cell; BAi,j is the
total burn area of different vegetation types in province i; Ei,j is
the total emission amounts from OBB in province i.
Other factors influencing OBB emission
Several detailed statistical data in the NBSC were collected, such as rural
population, per capita net income of rural residents, agricultural output
and forestry output in each province and each year. They may impact OBB
emissions. Correlation analyses between OBB emissions and these
influencing factors were conducted. Rural population data in 2003, 2004 and
2010 were lacking as the detailed data were not reported in NBSC.
Uncertainty analysis
The Monte Carlo method together with the Oracle Crystal Ball software was
used to evaluate the estimation uncertainty quantitatively for all the
pollutants. Pollutant emissions were estimated from 20 000 Monte Carlo
simulations with a 95 % coincidence interval.
Results and discussion
Accumulated pollutant emissions from OBB in CEC
Cumulative emissions of major pollutants from open biomass burning
in central and eastern China during 2003–2015 (Gg).
Province
OC
EC
CH4
NOx
NMVOCs
SO2
NH3
CO
CO2
PM2.5
Shandong
783.9
48.56
669.4
479.3
1505
54.55
95.56
10 880
226 705
1007
Henan
1068
63.19
738.3
512.1
1629
54.23
101.3
11 869
260 239
1155
Anhui
238.2
20.24
197.7
115
410
12.94
29.75
2939
63 623
283.1
Jiangsu
201.6
19.88
178
98.48
341
9.29
23.89
2543
53 106
228.5
Hubei
234.2
33.92
337.7
173.1
660.7
19.86
48.5
4555
97 788
415.8
Hunan
202
40.34
376.8
179.1
738.4
24.33
64.3
5239
96 338
418.8
Jiangxi
132.8
27.88
236.1
109
447.6
14.2
40.55
3305
57 692
252.3
Fujian
97.15
15.15
148.1
71.14
347.4
12.81
34.45
2285
40 095
190.2
Zhejiang
91.41
16.22
147.9
70.53
290.9
9.62
25.83
2055
39 142
167.8
Shanghai
14.34
2.09
17.14
8.56
29.89
0.76
2.29
233.8
4392
17.88
Total
3064
287.5
3047
1816
6399
212.6
466.5
45 904
939 120
4136
Table 6 presented the cumulative OBB emission amounts during 2003–2015, and
multi-year emissions of different provinces were detailedly listed in Table S3.
By the end of 2015, the cumulative emissions of OC, EC, CH4,
NOx, NMVOCs, SO2, NH3, CO, CO2 and PM2.5 were
3.64×103, 2.87×102, 3.05×103,
1.82×103, 6.4×103, 2.12×102,
4.67×102, 4.59×104, 9.39×105 and
4.13×103 Gg, respectively. For better revealing the
spatial–temporal variation of OBB emissions, the PM2.5 variation was
detailedly discussed as an example. From 2013 to 2015, the highest emission
amounts of PM2.5 were found in Henan and Shandong, accounting for
27.93 % and 24.35 % of the total emission amounts, respectively. The
lowest emission appeared in Zhejiang and Shanghai, which only contributed
4.05 % and 0.43 %. For other provinces, Hunan, Hubei, Fujian, Anhui,
Jiangxi and Jiangsu accounted from 5.52 % to 10.13 % of the total
emissions.
The contributions of different biomass burning types for various pollutants
were shown in Fig. 2a. Cropland burning contributed the most emission for
all the pollutants, by 84 %–96 %. The forest fire exhibited higher
emission of NH3, SO2, NMVOCs and PM2.5, accounting for
12 %, 11 %, 7 % and 5 % of the corresponding total emission,
respectively. As shown in Fig. 2b, for cropland, wheat, corn and rice
straw burning were the top three emission source types for all the
pollutants. Corn straw burning contributed the most to SO2 (48 %),
NOx (37 %), NMVOCs (33 %), CO (32 %) and CO2
(28 %) emissions. Highest contributions of EC (45 %), OC (33 %)
and CH4 (32 %) from rice straw burning were found, while wheat
straw burning contributed the most (31 %) to PM2.5 emission.
The mean contributions of different types of biomass to biomass
burning pollutant emission (a) and the mean contributions of different types
of crops to cropland accumulative pollutant emission (b) from 2003 to 2015.
![]()
The averaged contributions of different biomass burning types to
PM2.5 emission in each province.
![]()
In Fig. 3, except for Fujian, cropland burning emission was the largest
contributor to PM2.5 emission, with the contributions ranging from
75.25 % (Jiangxi) to almost 100 % (Shanghai). The higher rural
agglomeration, abundant crop production and more crop residue burning
activities in these provinces can explain the higher contributions. Shanghai
is one of the most developed cities in China. The highest contribution of
cropland burning is not related to its high levels of agricultural
activities but is due to the lack of emissions from other open biomass
burning sources. Highest contribution from the forest fire burning and
shrubland fire burning were found in Fujian (45.29 %) and in Jiangxi
(23.95 %), respectively. For forest fire burning, the southern provinces
(Fujian, Zhejiang, Jiangxi, Hunan, Hubei and Anhui) exhibited higher values,
varying from 3.66 % (Hubei) to 38.3 % (Fujian), and for shrubland fire
burning, the contributions varied from 1.5 % (Hubei) to 7.23 %
(Zhejiang). The relatively high emission contributions of forest and shrubland
fire burning in the southern provinces can be explained by the large forest
and shrubland coverage, frequent human forestry activities, low
precipitation and dry weather in spring and winter (Cao et al., 2015), which
may easily lead to forest and shrubland fires. However, for the northern
provinces (Shandong, Henan and Jiangsu), the contributions ranged around
0.76 %–1.97 %, which can be neglected. PM2.5 emission from
grassland in CEC was negligible with the following provinces holding higher
contributions: Jiangxi (0.8 %), Hunan (0.25 %), Fujian (0.11 %) and
Anhui (0.1 %).
From Fig. 4, emissions from wheat and corn straw burning mainly
concentrated in Shandong and Henan (totally accounting for 82 % and 78 %
of the total emissions, respectively), and the rice straw burning exhibited
higher concentrations in the Hunan, Jiangxi and Hubei provinces, by 25 %,
18 % and 16 %, respectively. The total contributions of rapeseed,
cotton, potato and peanut straw burning to the PM2.5 emission were
relatively small, accounting for 21 %–24 % of the total emissions. Most
emissions from cotton, peanut and potato straw burning were located in Shandong
(totally accounting for 35 %, 35 % and 20 %) and Henan (totally
accounting for 19 %, 40 % and 15 %). Hubei (32 %) and Hunan (31 %)
were the major provinces for rapeseed straw burning emissions. In addition,
emissions from soy bean, sugar cane, tobacco, sesame and sugar beet straw
burning were negligible, and never exceeded 1 % of total crop residue
burning emission in this study.
The averaged contributions of various crop straw burning to
cropland PM2.5 emission in different provinces.
![]()
Temporal variation and spatial distribution for OBB emissions in
CEC
Yearly variation
Multi-year emissions of OBB from 2003 to 2015 in CEC were shown in Fig. 5.
The multi-year variation of OBB emissions for various pollutants was similar
(Fig. 6).
The increase of crop residue burning dominated the significant growth of
OBB emission. Pollutants emitted from OBB all increased obviously from 2003
to 2008. Then, with the adoption of strict control policies (Table S1 in
Supplement), the growth of crop residue burning emission gradually slowed
down. The forest, shrubland and grassland fire burning were related to
weather conditions and human activities. Their emissions were difficult to
predict and control, and random yearly variation existed. Therefore, we
discussed the multi-year variation during 2003–2015 instead of the overall
trend for the whole period (Fig. S3). Taking PM2.5 as example, emissions
exhibited a clearly increasing trend from 2003 (256 Gg) to 2008 (353 Gg) and
then decreased in the following 2 years to 322 Gg. After 2010, there
existed higher (2011, 2013 and 2015) and lower values (2010, 2012 and 2014)
alternately. The values in 2011, 2013 and 2015 all did not exceed the peak
values in 2008.
Emissions from forest, shrubland and grassland fire burning have an obvious
trend of declining from 2003 to 2006 and rising from 2006 to 2008. Peak
emissions for PM2.5 from forest, shrubland and grassland fire burning
were found in 2008, as 49, 8.9 and 0.7 Gg, respectively. In 2008,
intensive policies for utilization of straw energy (Table S1) and
strengthening the forestry fire prevention (Table S2) were published, which
effectively limited the emissions from forest and shrubland fire burning as
shown in Fig. 6a. Obvious decreasing was found from 2008 to 2010, down to 19, 4.8 and 0.24 Gg, respectively. Then, they exhibited interannual
oscillation from 2010 to 2015, with higher emission in 2011, 2013 and 2015
and lower emission in 2012 and 2014 (Jin et al., 2017a). The multi-year
tendency for forest, shrubland and grassland fire burning was mainly
affected by the variations in climate, management measures and other human
forcing. It can also be concluded that the yearly fluctuation of pollutants from
OBB was mainly impacted by the emissions of forest, shrubland and grassland
fire burning, but not the crop residue burning.
The emission of PM2.5 from crop residue burning exhibited quite
different yearly variation trend with other three types of biomass burning,
which gradually increased from 2003 (228 Gg) to 2015 (323 Gg), by 29 %.
The increase of crop residue production can primarily explain the increasing
of pollutant emission. Meanwhile, as shown in Fig. S6 and Table S1, the
controlling of pollutants from crop residue burning in China started from
1965. In 2000, the law for prevention of air pollution was published. Then,
in 2003, the regulations on straw banning and comprehensive utilization were
released. In Fig. 6, we found that the emission of PM2.5 from crop
residue burning significantly increased from 2003 (228 Gg) to 2008 (294 Gg),
due to the increase of crop production and deficiency of strict control
policies in this period (Table S1). Although emissions from forest,
shrubland and grassland fire burning fluctuated markedly during this period,
the obvious increase of crop residue burning dominated the total growth of
OBB emission from 2003 to 2008 as their higher emission amounts. From 2008
to 2015, strict policies were developed to improve the straw energy
utilization and reduce the air pollution raised by its burning. However, it
has to be noted that the policies may not be well implemented, with the annual
averaged increasing amounts of 7.3 Gg for PM2.5. From Fig. 6b, the
large contributions to PM2.5 (22 %–28 % and 29 %–33 %) and
increasing trends for corn straw burning and wheat straw burning could be
found, which should be further focused. The contribution of rice straw
burning has slightly decreased in the research period, by about 19 %. Other
types of biomass totally accounted for averaged 25 % of PM2.5
emission and all exhibited a slightly increasing trend from 2003 to 2015, by
about 21 %–29 %.
Yearly emissions of open biomass burning from 2003 to 2015.
![]()
The multi-year PM2.5 emissions of different opening biomass
burning sources (a) and various crop types (b) from 2003 to 2015.
![]()
The multi-year PM2.5 emissions of the four types of biomass
burning in different provinces from 2003 to 2015.
![]()
Figure 7 showed that the crop residue burning emissions in Henan, Shandong,
Anhui, Jiangsu, Hubei, Hunan and Jiangxi exhibited obvious increasing
trends, which suggested the importance of crop residue burning control in
these provinces. For Fujian and Zhejiang, no obvious increase for cropland
burning emission was found, implying that the emission has been well
controlled in these years. It should be noted that in Fujian and Zhejiang,
the main crop is rice, while in other provinces, the main crops are corn and
wheat, especially in the northern provinces. To conclude, pollutants emitted
from crop straw burning (wheat, corn and rice) are now still the key sources
for air pollution, in view of its increasing emission trend. The randomness
of burning activities and corresponding widespread and scattered
distribution make it difficult to control them. The wheat and corn emissions
in the northern provinces and rice burning emissions in the southern provinces
should be controlled specially in the future.
The monthly PM2.5 emissions of different open biomass
burning from 2003 to 2015 for all of central and eastern China (a) and
each province (b).
![]()
In Fig. 8, the PM2.5 emission from crop residue burning exhibited
higher amounts for the Henan and Shandong provinces in 2015 (100 and 82 Gg,
respectively), which are 200 %–1200 % of those for other provinces.
As the main source regions for air pollution of the Yangtze River Delta (YRD)
and Beijing–Tianjin–Hebei (BTH) region (Fu et al., 2013; Zhou et al., 2015),
the enforced and effective control of crop residue burning in the two
provinces during summer and autumn harvest periods is important for improving
the air quality of these regions.
Monthly distribution
The monthly PM2.5 emission variation of different OBB in CEC was shown
in Fig. 8a. PM2.5 emission demonstrated higher amounts in May and June (90.4–179.3 Gg), followed by December to March of next year (32.2–127.3 Gg)
and September–October (8.2–89.2 Gg), and was lowest during July–August
(14.3–65.9 Gg). As the emission amount of cropland fire burning was 1
or 2 orders of magnitude higher than other three types of biomass burning, the
monthly variation of total PM2.5 emission was dominantly controlled by
the crop residue fire burning (L. Zhang et al., 2016). The periods with highest
PM2.5 emissions were the summer and autumn harvest times, when the
burning activities were more frequent. The peak of open biomass fire burning
occurred in May and June, totally accounted for 42 % of the total
PM2.5 emissions in 2003–2015, which is caused by the harvest and open
residue burning of winter wheat, especially in Henan, Shandong, Jiangsu and
Anhui (Fig. 8b). Large amounts of wheat straw were burned after the
harvest to increase the soil fertility and prepare for following corn
cultivation (Levine et al., 1995). The small peak of open biomass
burning emission in September to October (totally accounted for
13.82 % of the total PM2.5 emissions in 2003–2015) can be attributed
to the burning of corn straw after corn harvest. Though the open biomass
burning was strictly forbidden in recent years, scattered burning activities
still existed in these regions. As shown in Fig. S4, the PM2.5 emissions in CEC and major agricultural provinces during harvest time have
shown a rapid decline in recent years, in accordance with the change
tendency of burned area due to increased government management. Considering
the yearly increasing fact of crop straw burning, it is worth noting
that fire burning during the harvest season as a way of circumventing
governmental polices needs to be well regulated. From December to February,
the crop residue burning emissions decreased to the lowest level in the
entire year (18.9 % of the total PM2.5 emissions in 2003–2015). However,
the emissions of PM2.5 from forest, shrubland and grassland burning
achieved peak values from December to March, being 67 % of those in
2003–2015.
The monthly PM2.5 emissions from open biomass burning in each
province.
![]()
Figure 9 clearly listed the monthly average emissions of PM2.5 from OBB
in different provinces. These provinces were classified based on the
correlation between their monthly emissions of 2003–2015. The Henan,
Shandong, Anhui and Jiangsu provinces (R2 higher than 0.92, p<0.01),
as one of the largest and contiguous wheat planting areas in China (Fang et
al., 2014), have two crop rotations. The highest monthly emissions were
observed for winter wheat harvesting (sown in October and harvested from May
to June) and corn harvesting (sown in middle June and harvested from
September to October). A large proportion of crop straw was always burnt
directly after the crop harvest (MEPC, 2015). For the Hubei province,
agricultural emissions fluctuated over the period from February to October
with several peaks because different crop species matured in succession. In
Jiangxi, Fujian and Hunan (R2 higher than 0.9, p<0.01), the largest
monthly emissions were observed with forest and shrubland fire burning during
the time between December and March, which is the dry season in these
provinces (Li et al., 2014, 2015), while in other months, the emissions were
limited. For Shanghai and Zhejiang (R2=0.7, p<0.01), lowest levels
of PM2.5 emission were found, with peak values occurring during the
summer and autumn harvest periods. An obvious two peaks were found for
April–May and July–August periods, which may reflect the rice harvesting at
these times. To sum up, the regional differences of monthly PM2.5
emissions from OBB were mainly caused by the different biomass burning types
and times, as well as corresponding environmental conditions.
Spatial distribution within 1 km <inline-formula><mml:math id="M650" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 1 km of PM<inline-formula><mml:math id="M651" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> emitted
from OBB in CEC
Annual spatial distribution (1 km × 1 km) of PM2.5
emissions from opening biomass burning in central and eastern China.
![]()
The spatial distribution of PM2.5 emitted from OBB within 1 km × 1 km
resolution was mapped based on the burned area and a high-resolution
vegetation map (1:1 000 000) in CEC. The multi-year averaged spatial
distributions of PM2.5 emission were shown in Fig. 10. It can be
found that OBB was widespread and scattered. The average emission
intensity of PM2.5 ranged from 0 to 15 tons per pixel in most
provinces. The variation range is mainly caused by the socioeconomic
development level, rural population and agricultural activities. The highest
values in the different provinces were all mainly raised by the cropland fire
burning due to the centralized burning in a relatively small area. Some
pixels with high emissions exceeding more than 100 tons each year were found
in Henan, Shandong and Hunan. It can be attributed to the large amounts of
crop straws in these provinces. The pixels of high emission intensity of more
than 70 tons from crop straw burning were also found in Hubei, Jiangsu and
Anhui. For forest and shrubland fire burning, the high emission points (more
than 30 tons per pixel) were found in Fujian and Jiangxi. Lower emission
intensities in Zhejiang (lower than 10 tons per pixel on average) and
Shanghai (lower than 7 tons on average) were mainly due to the highly
developed economy and limited agricultural activities (Su et al.,
2012). In addition, northern Anhui and eastern Jiangsu featured
high emissions of OBB with a relatively lower intensity (lower than 15 tons
per pixel on average), which may be due to the crop straw burning
in a large area in these regions.
Though the emission intensities varied in the past 10 years, the areas with
high emission amounts remain similar. They were mainly located in the main
agricultural areas in eastern Henan, southern Shandong, northern Anhui,
northern Jiangsu, eastern Hubei and northern Hunan. This result is in
accordance with previous studies (Huang et al., 2012b). The junction regions of the
four provinces (Henan, Shandong, Anhui and Jiangsu) should be paid more
attention, where the pollutant emissions from OBB joined together. This was
similar to recent research (Jin et al., 2017b). This region belongs to the
Huanghuai Plain, with a large area of cropland and low economic development
levels. The open burning activities and corresponding banning policies are
both abundant in village scale. The game of “cat and mouse” is frequently
played. More effective policies for guiding or helping farmers to utilize
straw energy rather than banning crop residue burning arbitrarily should be
considered sincerely. In Zhejiang and Shanghai, OBB emissions are sparsely
scattered, due to the relatively developed economic level, scarce biomass
sources and limited agricultural activities. The recycling of crop straw
faces many difficulties due in part to its high cost and the relative low
price of crop straw. Improving policies for effectively utilizing crop
residue straw is also an important challenge for the government.
Figure 11 highlights the spatial distribution of PM2.5 emitted from OBB
in different seasons of 2015. Emissions were more concentrated in summer,
followed by winter. In summer, the emission was concentrated in the
connection regions of Henan, Shandong, Anhui and Jiangsu, which is mainly
raised by the crop residue burning as discussed before. In winter, Jiangxi,
Hunan and Fujian showed the higher emission intensities from forest and
shrubland burning.
Seasonal emissions distribution (1 km × 1 km) of PM2.5
in 2015 from opening biomass burning in central and eastern China.
![]()
Correlation between PM2.5 emissions from crop residue burning
and agricultural output, rural population, per capita incomes of rural
residents (a) and correlation between PM2.5 emissions from forestry fire
burning and forestry output, rural population, per capita incomes of rural
residents (b) in different provinces from 2003 to 2015.
![]()
The impact of socioeconomic factors on OBB emission
Emissions from OBB were found to be in line with the local burning habit,
social customs, rural population, local economic level, agricultural level
and pollution controlling policies. Local burning habits have a great
influence on different types of OBB emissions. According to our survey, in
agricultural provinces, such as Henan, Shandong, Jiangsu and Anhui, people
always burn crop straws during sowing and harvest seasons. Despite the strict
implementation of crop residue burning management policies, the burning
habit is difficult to change in a short time. Less crop residue production
and crop burning activities are found in Jiangxi and Fujian, where people
are accustomed to using crop straw to feed draft animals and produce biogas
instead of open burning directly. Emission from crop residue burning is low.
However, due to the rich forest and shrubland resources, wood is served as
the staple household fuel, which mainly comes from felling trees or
collecting branches. These human activities can lead to an increase of
forest and shrubland fire burning, resulting in the elevated levels of
the corresponding emissions in these provinces.
Social customs also have an impact on OBB emissions. Biomass burning emissions
in April can be enhanced by human burning activities on the tomb-sweeping
day. The tomb-sweeping day (often on 4 or 5 April) is a time to
remember the dead. People sweep graves and burn sacrifices by ignited
straw, which can easily cause grass, shrub and forest fires (Qiu et al.,
2016). The fire points on the tomb-sweeping day in CEC were
22 %–38 % of the total fire points in April in some years (Fig. S5).
The Chinese government has also introduced policies to prevent forest,
shrubland and grassland fires on tomb-sweeping day (Table S2). The wildfires
caused by biomass burning from late January to early February are partially
related to the firework burning in the Chinese New Year (Zuo, 2004). The
firework burning activities for celebration and official sacrifices to
ancestors in the Chinese New Year easily lead to grass, shrub and forest
fires. All these activities can affect the emission levels and air quality
in a short timescale.
Correlation of the variation tendency between PM2.5 emission
from crop straw burning and rural population, agricultural output, per
capita incomes of rural residents in each province from 2003 to 2015.
PM2.5 emission (Gg)
Rural population (10 000)
Per capita income of rural residents (RMB)
Agricultural output (RMB 0.1 billion)
Shanghai
y=-0.001x+1.64 R2=0.17,P>0.05
y=-5×10-6x+1.4 R2=0.09,P>0.05
y=7×10-5x+1.36 R2=0.0005,P>0.05
Zhejiang
y=0.002x+6.19 R2=0.06,P>0.05
y=-6×10-5x+10.47 R2=0.19,P>0.05
y=-0.001x+10.72 R2=0.19,P>0.05
Fujian
y=-0.0002x+8.219 R2=0.01,P>0.05
y=-3×10-5x+8.1884 R2=0.06,P>0.05
y=-0.0002x+8.2144 R2=0.06,P>0.05
Jiangsu
y=-0.002x+23.41 R2=0.8,P<0.01
y=0.0002x+15.33 R2=0.66,P<0.01
y=0.001x+15.18 R2=0.69,P<0.01
Hubei
y=-0.008x+56.19 R2=0.94,P<0.01
y=0.0009x+25.39 R2=0.86,P<0.01
y=0.004x+24.31 R2=0.92,P<0.01
Anhui
y=-0.005x+37.11 R2=0.91,P<0.01
y=0.0007x+16.12 R2=0.79,P<0.01
y=0.004x+14.5 R2=0.85,P<0.01
Hunan
y=-0.01x+62.66 R2=0.78,P<0.01
y=0.0008x+20.66 R2=0.8,P<0.01
y=0.003x+20.1 R2=0.91,P<0.01
Jiangxi
y=-0.008x+33.73 R2=0.92,P<0.01
y=0.0006x+11.19 R2=0.82,P<0.01
y=0.006x+9.84 R2=0.87,P<0.01
Henan
y=-0.01x+150.14 R2=0.8,P<0.01
y=0.003x+70.41 R2=0.59,P<0.01
y=0.008x+62.79 R2=0.72,P<0.01
Shandong
y=-0.009x+122.46 R2=0.73,P<0.01
y=0.0014x+66.48 R2=0.66,P<0.01
y=0.004x+62.11 R2=0.77,P<0.01
In order to understand the impact of the rural population, local economic
level and agricultural level, correlation analyses between PM2.5
emissions from OBB and statistical data (rural population, per capita net
income of rural residents, agricultural output (crop straw burning) and
forestry output (forest, shrubland and grassland burning) in different
provinces were conducted. Significant positive correlations were found
between the rural population, agricultural output and the PM2.5
emissions from crop straw burning (R2 higher than 0.58, p<0.01) for the entire CEC (Fig. 12a). According to our survey, the high
rural population and agricultural output indicate that agricultural
activities are quite important in a certain region. With more crop residue
produced, it can easily cause high emissions from cropland fire burning. No
significant correlations were found for PM2.5 emission from crop straw
burning with the income of rural residents (Fig. 15), which indicates that
the rural economic level in different regions in CEC has no relationship
with the PM2.5 emission. Then, we calculated the correlations between
the change tendency of PM2.5 emission from crop fire burning and the
multi-year variation of other three socioeconomic factors as shown in Table 7
for different provinces. Significant positive correlations were found for
PM2.5 emission with per capita income of rural residents and
agricultural output (most R2 higher than 0.59, p<0.01), and
negative correlation were found for PM2.5 emission with rural
population (most R2 higher than 0.73, p<0.01) except for the
provinces of Shanghai, Zhejiang and Fujian, which are underdeveloped
agricultural provinces. From 2003 to 2015, with the increase of agricultural
outputs, more crop residue was produced. However, rapid economic development
and lower rural population in each province led to the popularization of commercial
energy and clean energy in rural areas. It decreased the demands in using
crop residue as fuel. As a consequence, more crop residues were directly
burned in the agricultural field. However, it was not suitable for Shanghai,
Zhejiang and Fujian (most R2 lower than 0.19, p>0.05),
which holds less crop residue production and high utilization efficiency of
crop straws.
Positive correlations were also found between forestry output and PM2.5
emission from forest land, shrubland and grassland fire burning (R2=0.14, p<0.01) in the entire CEC (Fig. 12b), which indicated
that human forestry activities played a positive role in open fire burning
(Yan et al., 2006). According to our survey, human forest activities such as
felling trees or picking up branches from trees can easily cause more forest
and shrubland burning. However, compared with the crop straw burning, no
correlation was found between PM2.5 emission and other statistical data
(the rural population and the per capita net income of rural residents)
(Fig. 13b and Table S4). It may indicate that the forestry fire burning
activities were not predominantly associated with the rural human living
activity. According to previous studies, forestry fire burning was affected
by environmental conditions and human activities with environmental factors
having a larger impact (Chen et al., 2013).
Comparison with others
Comparison of the emissions with previous studies in different
years (Gg).
Reference
Year
OC
EC
CH4
NOx
NMVOCs
SO2
NH3
CO
CO2
PM2.5
Wang et al. (2008)
2006
252
25.8
197
189
459
31.8
44.1
3841
81 225
1138
This study
215.3
21.13
220.7
131.9
451.1
14.33
31.46
3267
67 753
293.09
Huang et al. (2012a)
2006
54
17.4
136
123
1196
8.1
50.6
2379
36 886
146
This study
209.8
20.67
215.8
129.1
436.4
13.56
29.64
3172
66 088
283.3
Qiu et al. (2016)
2013
222
41.5
243
168
591
30.2
46.9
3273
78 633
475
This study
258.2
23.53
252.1
151.2
531.5
17.86
38.67
3817
78 050
343.44
Zhou et al. (2017)
2012
185
16.9
254
160
543
40.4
34.5
3330
92 797
484
This study
248.6
23.11
245.7
148.5
507.8
16.71
35.92
3688
75 785
329.46
Emission data from OBB in CEC during the past several years have been
compared with other studies for the similar year (Table 8). Compared with
the emissions derived from Wang et al. (2008) based on statistical data, the
differences of OC, EC, CH4, NOx, NMVOCs, NH3, CO2 and CO
emissions ranged from -41 % to 12 %. For SO2 (121 %) and
PM2.5 (288 %) emission, the differences were relatively high.
All these differences were mainly caused by the selection of EFs. The EFs
employed in Wang et al. (2008) were constant values for different biomass
species. In addition, the crop residue to production ratio data and the
burned ratio for various crop types were all specific to CEC in this study
based on literature and survey results, which increased the reliability of
these data. Similarly, Huang et al. (2012a) used the same EFS of
different crop straw burning for emission calculation. Compared with Wang
et al. (2008) and Huang et al. (2012a), the estimate in our study is believed
to be more accurate. An obvious underestimation of PM2.5 emission from
crop straw burning was found in Jin et al. (2017b), in which not all the
crop species were considered.
The estimation based on satellite observation was prevalent recently.
Compared to Zhou et al. (2017), who estimated the pollutant emission amounts
from the MODIS burned area product, the results in this study were much higher.
The reason may be that, when using a single satellite dataset, pollutant
emission can be underestimated because some actual fire activities could
not be detected (van der Werf et al., 2010). The lower emission of CO2,
NMVOCs, SO2 and NOx in our study is due to the adoption of more
accurate and suitable EF values as those in a previous study (Tang et al.,
2014). Our emission estimation of the pollutants is more similar to the
results of Qiu et al. (2016), who also used multiple satellite products
(MCD14 ML and MCD64Al) to estimate OBB emissions of China in 2013, with
the differences of the two studies ranging from -42 % to 22 %. For
CH4, NOx, NMVOCs, NH3 and CO2, the differences were less
than 10 %. The reason for the differences is due to the use of updated
local biomass data and EFs in this study. Therefore, the combination of
multiple satellite products with local EF data and updated local biomass
data (updated forest loading data, the crop residue to production ratio data
and the burned ratio for various crop types) is likely to have improved the
estimation of pollutant emission from OBB effectively.
Uncertainty analysis
Emission uncertainties in this study were associated with the satellite fire
products, biomass fuel loading data, combustion efficiency and emission
factors. It is difficult to assess the uncertainty of the satellite-derived
data for burned land area (Hoelzemann et al., 2004; Chang and Song, 2010). The
estimation of fire burned area was proven to be reliable by using the
burned area product MCD64Al (Giglio et al., 2013) and active fire product
MCD14 ML (Randerson et al., 2012). Although some active fires which burned
out at 10:30–13:30 LT each day could not be captured by MCD14 ML, the
burned area used in this study was more reliable due to the combination of
multiple satellite datasets (MCD64Al and MCD14 ML). The uncertainties in this
study were mainly caused by biomass loading data, combustion efficiency and
emission factors. These data were assumed to be normal distributions (Zhao
et al., 2011). The uncertainty of biomass loading data and combustion
efficiency was estimated to be approximately 50 % (Shi et al., 2015b) and
the uncertainty of EFs of each pollutant mainly ranged from 0.03 to 0.85
(Table S5). The reliability of emission factors played the most important role
in driving uncertainty. Considering all these parameters, 20 000 Monte Carlo
simulations were performed to evaluate the estimation uncertainty
quantitatively for pollutant emissions with a 95 % coincidence level. Table 9
showed the emission uncertainty for different pollutants from 2003 to 2015.
On average, the uncertainties of the estimated OC, EC, CH4, NOx,
NMVOCs, CO, SO2, NH3, CO2 and PM2.5 were (-30 %,
30 %), (-48 %, 48 %), (-20 %, 20 %), (-20 %, 20 %), (-45 %,
45 %), (-18 %, 18 %), (-45 %, 45 %), (-35 %, 35 %), (-3 %,
3 %) and (-36 %, 36 %), respectively.
The uncertainty estimation of open biomass burning emissions for
various pollutants from 2003 to 2015.
Year
OC
EC
CO
CH4
NOx
NMVOC
SO2
NH3
CO2
PM2.5
2003
(-31 %, 31 %)
(-46 %, 46 %)
(-20 %, 20 %)
(-20 %, 20 %)
(-23 %, 23 %)
(-52 %, 53 %)
(-52 %, 51 %)
(-33 %, 33 %)
(-3 %, 3 %)
(-44 %, 44 %)
2004
(-29 %, 29 %)
(-47 %, 48 %)
(-21 %, 21 %)
(-22 %, 22 %)
(-24 %, 24 %)
(-45 %, 45 %)
(-56 %, 58 %)
(-34 %, 34 %)
(-3 %, 3 %)
(-47 %, 47 %)
2005
(-31 %, 31 %)
(-42 %, 44 %)
(-16 %, 16 %)
(-16 %, 17 %)
(-19 %, 19 %)
(-41 %, 40 %)
(-44 %, 44 %)
(-32 %, 33 %)
(2 %, 3 %)
(-35 %, 34 %)
2006
(-32 %, 33 %)
(-44 %, 44 %)
(-13 %, 13 %)
(-14 %, 14 %)
(-16 %, 17 %)
(-43 %, 43 %)
(-34 %, 35 %)
(-34 %,34 %)
(-3 %, 3 %)
(-25 %, 25 %)
2007
(-30 %, 30 %)
(-46 %, 46 %)
(-18 %, 19 %)
(-19 %, 19 %)
(-22 %, 22 %)
(-50 %, 51 %)
(-50 %, 50 %)
(-33 %, 34 %)
(-3 %, 3 %)
(-42 %, 42 %)
2008
(-26 %, 26 %)
(-52 %, 53 %)
(-25 %, 25 %)
(-28 %, 28 %)
(-29 %, 29 %)
(-69 %, 69 %)
(-62 %, 61 %)
(-38 %, 39 %)
(-3 %, 3 %)
(-55 %, 56 %)
2009
(-28 %, 28 %)
(-48 %, 48 %)
(-21 %, 21 %)
(-21 %, 22 %)
(-24 %, 24 %)
(-59 %, 59 %)
(-54 %, 54 %)
(-34 %, 35 %)
(-3 %, 3 %)
(-47 %, 47 %)
2010
(-31 %, 31 %)
(-44 %, 44 %)
(-16 %, 17 %)
(-17 %, 17 %)
(-19 %, 19 %)
(-45 %, 46 %)
(-42 %, 42 %)
(-33 %, 34 %)
(-3 %, 3 %)
(-34 %, 34 %)
2011
(-29 %, 29 %)
(-46 %, 46 %)
(-18 %, 18 %)
(-19 %, 19 %)
(-21 %, 21 %)
(-52 %, 53 %)
(-47 %, 47 %)
(-34 %, 35 %)
(-3 %, 3 %)
(-40 %, 40 %)
2012
(-32 %, 33 %)
(-44 %, 44 %)
(-14 %, 14 %)
(-14 %, 14 %)
(-17 %, 17 %)
(-35 %, 35 %)
(-35 %, 35 %)
(-34 %, 35 %)
(-3 %, 3 %)
(-27 %, 26 %)
2013
(-30 %, 30 %)
(-44 %, 44 %)
(-16 %, 16 %)
(-17 %, 17 %)
(-20 %, 20 %)
(-51 %, 51 %)
(-42 %, 43 %)
(-33 %, 34 %)
(-3 %, 3 %)
(-36 %, 36 %)
2014
(-32 %, 32 %)
(-45 %, 46 %)
(-15 %, 15 %)
(-16 %, 16 %)
(-19 %, 18 %)
(-43 %, 43 %)
(-42 %, 42 %)
(-35 %, 35 %)
(-3 %, 3 %)
(-33 %, 33 %)
2015
(-31 %, 31 %)
(-44 %, 44 %)
(-14 %, 146 %)
(-14 %, 13 %)
(-17 %, 17 %)
(-41 %, 41 %)
(-34 %, 34 %)
(-34 %, 35 %)
(-3 %, 3 %)
(-26 %, 26 %)
Compared with previous studies, the uncertainty was improved in our study
because the datasets used here were better and more suitable. The reliable
multiple satellites could better obtain burned area data. The local EF
data, updated forest loading data, the adoption of local crop residue to
production ratio data and the crop residue burned ratio data based on survey
results improved the emission estimation of forestry and cropland burning as
they could better reflect the actual situation in this region. Compared with
the constant combustion efficiency in previous research, the activity
combustion efficiency data could also reduce the uncertainty as they could
more accurately reflect the actual combustion conditions (Chen et al., 2013).
Therefore, due to the adoption of multiple satellite products, updated local
biomass data and local emission factors, the uncertainty ranges of different
pollutant emissions were narrowed and reliable in this study, which could
better reflect the real emissions.
Conclusions
In this study, a combination of the burned area product (MCD64Al) with the
active fire product (MCD14 ML), as well as local high-resolution vegetation
speciation data, updated local biomass data, local emission factors and
survey results were used to estimate the pollutant emissions from open
burning in CEC from 2003 to 2015. The emissions
from crop residue, forest, shrubland and grassland fire burning were
considered.
Crop residue burning was the major source type for pollutant emissions,
followed by forest and shrubland fire burning. The grassland fire burning
emissions were negligible in CEC. For cropland, the fire burning was mainly
concentrated in agricultural provinces, such as Henan and Shandong. For
forest and shrubland, the fire burning was mainly concentrated in the Fujian,
Jiangxi and Hunan provinces, with abundant forest resources. Wheat, corn and
rice straw were the major three types of crop straws for pollutant emission.
Wheat and corn straw burning dominated in Shandong and Henan, and the rice
straw burning dominated in the Hunan, Jiangxi and Hubei provinces. For various
pollutant emissions, corn straw burning was the largest contributor to
SO2, NOx, CO, NMVOCs, CO2 and NH3. OC, EC and
CH4 emissions were mainly produced by rice straw burning. Wheat straw
burning was the largest contributor to PM2.5. The spatial distribution
of open biomass residue burning in different years was similar. The high
emissions were mainly found in the major agricultural areas in eastern
Henan, southern Shandong, northern Anhui, northern Jiangsu, eastern Hubei
and northern Hunan, due to their abundant agricultural cultivated areas and
low straw utilization efficiency.
From 2003 to 2015, the multi-year tendency of opening biomass residue
burning emission for various pollutants was similar. Emissions from crop
straw burning continued to increase, due to the gradual increase of crop
residue production. While emissions from forest, shrubland and grassland
fire burning exhibited yearly fluctuations, which was mainly influenced by
the environmental conditions, management measures and other human driving
factors. Monthly distributions revealed that the pollutant emissions were at
the highest levels in May and June, with the lowest emissions in July and
August. The high emissions in May, June and October were mainly caused by
crop straw burning during sowing and harvest times. It is worth noting that the
fire burning activities during the harvest season need to be regulated continuously
by local governments, and emissions from forest and shrubland burning, which
accounted for the vast majority of total emissions in December to March,
should also be paid attention. The emissions of crop residue burning were
associated with the rural population, agricultural output and economic
levels, while the environmental conditions play an important role in the
emissions from forest land, shrubland and grassland fire burning.
The estimation of multi-year open biomass burning emissions by satellite
data in this study will provide objective and credible evidence for
assessing the role of pollution prevention policies on open burning
activities issued in the last decade. The high-spatial-resolution (1×1 km)
emission inventory in a monthly scale is also useful in modeling
regional air quality and human health risks in the future.