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Spatial and temporal distribution of open bio-mass burning in China from 2013 to 2017
Atmospheric Environment 210 (2019) 156–165
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Spatial and temporal distribution of open bio-mass burning in China from 2013 to 2017
T
Huabing Kea, Sunling Gonga,∗, Jianjun Hea, Chunhong Zhoua, Lei Zhanga,b, Yike Zhoua a b
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China Climate and Weather Disasters Collaborative Innovation Center, Nanjing University of Information Science & Technology, Nanjing, 210044, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Open bio-mass burning Spatial-temporal variations MODIS Vegetation types El Niño
Open bio-mass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing the trace gases and particulate matters into the atmosphere. A better understanding of spatial-temporal variations of open bio-mass burning in China is required to assess its impacts on the air quality and especially on the heavy haze pollution. The MODIS fire spots data and the calculated burned areas were used in this research, which shows the varying number of fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. Meanwhile, the fire spots were found mainly concentrated in three key periods (March–April, June and October–November) and two zones (Zone 1 and Zone 2) with inter-annual variations of burned areas. In addition, the contribution of major vegetation types burning was studied, the cropland occupied the largest proportion of burned area of more than 70% in any period time, followed by forest. Finally, from the perspective of climate and human activities, the causes of inter-annual variations were discussed. By comparing the average temperature and precipitation in the two zones from 2013 to 2017, it was found that the burned forest area is positively correlated with the average temperature of the zones and negatively correlated with the average precipitation. Meanwhile, the relationship between the El Niño events and the bio-mass burning was discussed.
1. Introduction Open bio-mass burning, from wildfires and agricultural field burning, makes up an essential part of atmospheric trace gases and particulate matter emitted to the atmosphere (Dennis et al., 2002; Jenkins et al., 1992; Zhang et al., 2008), including CO, CO2, CH4, BC, OC, NOx, NH3, SO2, NMOCs and PM. The emissions of PM can degrade visibility (McMeeking et al., 2006) and cause health problems (Pope and Dockery, 2012). The emitted gases, such as NMOCs and NOx, can react downwind of bio-mass burning location and lead to the formation of ozone (Pfister et al., 2008). A large amount of CO2 released into the atmosphere from burning may have non-negligible impacts on the carbon cycle (IPCC, 2007; Wiedinmyer and Neff, 2007). Meanwhile, bio-mass burning can not only lead to periods of degraded air quality (Field et al., 2016), but also influence the weather and climate through changes primarily in radiation and cloud formation. China has the world's largest crop production with an estimated area of 1.4 × 106 km2 (Frolking et al., 1999; Xiao et al., 2003). In addition, the total amount of crop residue in China occupies the front rank
in the world, accounting for about 18% of global production (Bi et al., 2010). With no suitable methods for treating crop residue, a large amount of crop residue is burned in the cropland in few days after harvest in order not to delay planting in the next season, which is a major contribution to the formation of heavy pollution events (Deng et al., 2011; Zhang et al., 2016; Chen et al., 2017b; Mehmood et al., 2017). During the 46-year period from 1965 to 2010, there were 1614 forest fires in the Great Xing'an Mountains of north-eastern China, with an average annual rate of 35.09. The total forest over-fired forest area reached 3523011.86 hm2, and the annual average over-fired forest area was 7.66 × 104 hm2 (Hu et al., 2012). In the northeastern part of Inner Mongolia Autonomous Region, there are an average of more than 400 grassland fires per year, with a total burned area of about 8000 km2 (Li et al., 2017). It is precisely because there was a large amount of open bio-mass burning of different vegetation types in China, and considering its impact on the atmospheric environment and climate change, it is necessary to conduct a detailed study on the spatial and temporal distribution of bio-mass burning in China. Many studies have been devoted to the spatial and temporal
∗ Corresponding author. Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, ZhongGuanCun South Ave. 46, HaiDian District, Beijing, 100081, China. E-mail address: [email protected] (S. Gong).
https://doi.org/10.1016/j.atmosenv.2019.04.039 Received 18 February 2019; Received in revised form 16 April 2019; Accepted 17 April 2019 Available online 22 April 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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Tianjin, Hebei Province, Henan Province, Shandong Province, Anhui Province, Jiangsu Province, Shanxi Province, Hubei Province, as well as parts of Zhejiang Province and Inner Mongolia Autonomous Region), which together can explain 60%–70% of bio-mass burning in China (Yi et al., 2017). The study area includes most of China, except for the western and southern provinces, with the spatial resolution of 18 km. In addition, in order to study the impact of climate factors on the interannual variation of bio-mass burning, 13 major urban meteorological observation sites within Zone 1 and Zone 2 were selected (50953 Harbin Station, 54161 Changchun Station, 53698 Shijiazhuang Station, 54823 Jinan Station, 58238 Nanjing Station, 57083 Zhengzhou Station, 58321 Hefei Station, 54342 Shenyang Station, 54511 Beijing Station, 54517 Tianjin Station, 53772 Taiyuan Station, 57494 Wuhan Station, 58457 Hangzhou Station), as shown in Fig. 1(b).
distribution of bio-mass burning, using different remote sensing data and processing methods. An average burned area of approximately 3.2 × 106 ha occurred in China per year, which was mainly concentrated in spring and autumn from 2001 to 2012 (Yi et al., 2017). Researches on crop residue burning in different regions from 2003 to 2017 showed an overall upward trend with a slight fluctuation, by employing the MODIS products MOD14A1/MYD14A1 (Zhuang et al., 2018). The North China is dominated by agricultural fires, which mainly occurred in the two harvest periods of June in summer and October in autumn (Huang et al., 2012). By using MODIS active fire and burned area data, many forest fires were found in Northeast China, which were mainly concentrated in the relatively dry spring and autumn, with large inter-annual variations, implying high probability of accidental causes (Chen et al., 2017a). However, few studies have mentioned the spatial and temporal distribution of bio-mass burning with different vegetation types and the proportion of corresponding contributions. At the same time, the reasons for their inter-annual variations are also worth exploring. Therefore, in this study, by using MODIS fire spots data and calculated burned areas, firstly, the spatial and temporal distribution of open biomass burning in the study area was obtained. Secondly, the burned area of different vegetation types and the corresponding proportion was analyzed from 2013 to 2017. Finally, from the perspective of climate and human activities, the causes of inter-annual changes were explored. Meanwhile, the relationship between the El Niño events and the biomass burning was discussed.
2.2. The dataset of land use In this study, the International Geosphere-Biosphere Programme (IGBP)-Modified MODIS Land Use was used, identifying 20 land cover classes (Table 1), provided by the Boston University and modified by the NCEP (Friedl et al., 2002). According to previous research results (Friedl et al., 2010; Chen et al., 2017a), around 75% of China's land is covered with various vegetation types (mainly forest, savanna, grassland and cropland). At the same time, the proportion of grid points for each land use in the study area was counted (Fig. 2). The major vegetation types were forests (1, 2, 3, 4, 5), savannas (6, 7, 8, 9), grasslands (10) and croplands (12). Shrublands and savannas were classified to the same vegetation type based on their similar structure. Non-vegetated land cover classes, including wetlands (11), urban and built-up (13), cropland/natural vegetation mosaic (14), snow and ice (15), barren or sparsely vegetated (16), water (17) and tundra (18, 19, 20) were all excluded from the analysis in this study (Chen et al., 2017a).
2. Materials and methods 2.1. Study area Since the start of the state “Air Pollution Prevention and Control Action Plan” in 2013, air quality issues have received extensive attention from society and the state. North China, East China and Southwest China are the regions with the most serious air pollution (Ma et al., 2016; Guo et al., 2017; Wang et al., 2017; Zhang et al., 2018). Bio-mass burning, one of the main sources of emissions, is severe in the Northeast, Central China, and East China, so this study will focus on these regions (Fig. 1(a)) and defines them by two zones: Zone 1 (Liaoning Province, Jilin Province, Heilongjiang Province), and Zone 2 (Beijing,
2.3. MODIS active fire The Near real-time MODIS(C6) active fire data set was used in this study, which were obtained directly from Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/active_ fire/). The data set provides fire detection with a nominal horizontal resolution of about 1 km2 and a temporal resolution of four times per
Fig. 1. (a) The study area of two zones in China. The serial number 1–20 in the color bar corresponds to different land use, as shown in Table 1. (b) Major urban meteorological stations. 157
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Fig. 2. Proportion of different land use in the study area.
day, including the latitude and longitude of the fire detection, the acquisition time (in UTC), the bright temperature, the fire radiation power, and the estimation of confidence. Despite some uncertainties in the fire detections of this data set, they are widely used in many fields (Wiedinmyer et al., 2011). All fire detections from 2013 to 2017 were used in this study, with confidence less than 30% excluded. For a more reliable analysis result, the low-confidence burning spots were discarded and nominal-confidence and high-confidence burning spots were counted.
3. Results 3.1. Temporal and spatial distribution of open bio-mass burning Fig. 4 shows the changes of the total number of MODIS fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. In addition to the exceptionally high number of fire spots in January 2014, the fires were mainly concentrated in March–April and October–November, followed by a slightly higher number of fire spots in June. Therefore, the following section will estimate the burned area for these periods based on the two zones, obtain the inter-annual variation, and analyze the climate and human factors that leads to this change. Fig. 5 shows the spatial distribution of burned area in March–April, June, and October–November from 2013 to 2017. Obviously, Zone 1 and Zone 2 covered almost the most severe area of China's bio-mass burning, except for Southwest China, which was not discussed in this paper. The detailed burned areas for Zone 1 and Zone 2 are summarized in Table 2. In Zone 1, bio-mass burning occurred mainly in the two periods of March–April and October–November. It was mainly concentrated in the Songnen Plain and Sanjiang Plain in Heilongjiang, the western part of Jilin Province and the northern part of Liaoning Province. The burned
2.4. Method of calculating the burned area For each detected fire spot, the burned area will initially be assigned to 1 km2. However, if the vegetation type at the fire spot is grassland or savanna, the burned area will be allocated 0.75 km2 (Wiedinmyer et al., 2006; Al-Saadi et al., 2008). In addition, the percent of bare cover in MODIS VCF product was then used to adjust the burned area. The VCF product identified the percent of three vegetation types (Fig. 3) of tree, non-tree vegetation and bare cover at each active fire location (Hansen et al., 2003, 2005). For example, a forest fire is designated as a burned area of 1 km2 and the VCF data set allocates 30% bare cover for the same location, the burned area will ultimately be assigned 0.7 km2.
Fig. 3. (a) Percent of tree cover from VCF; (b) percent of non-tree vegetation cover from VCF. 158
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Fig. 4. (a) Annual variation of MODIS fire spots; (b) Monthly variation of MODIS fire spots from 2013 to 2017.
fires in this area occurred mainly in June, with crops reaching an average of 95%. The volatility is basically in line with the timing of agriculture. Winter wheat (planted in mid-October, harvested at the end of May) and summer maize (planted in mid-June, harvested at the end of September) are the two most important crops in the zone (MOA, 2000–2001). Among them, the peak of the fires in June was resulted from the harvest of winter wheat. To increase the soil fertility for next cultivation, plenty of wheat residue will be burned after harvest. The bio-mass burning in October–November may be attributed to the maturity of the corn and subsequent burning of corn stalks, but it was not as concentrated as in June, and lower than the June in the total area of combustion. The burned area was lowest in March–April, and the crops accounted for the least. Meanwhile, the spatial distribution was the most discrete, possibly from a small amount of last year's crop residue removal. In Zone 1, the burned area and proportion of crops in March–April increased year by year, from 2028.1 km2, 78.1% in 2013 to 16051.4 km2 and 92.8% in 2017. The burned area of the forest was basically stable at 600–700 km2, but the proportion has been decreasing gradually because of the increase in total combustion areas. In June, the phenomenon of bio-mass burning was relatively rare, the burned rate of crops was basically maintained at about 80%, and the burned area of forests accounted for about 15%. The burned area and proportion of crops in October–November have the same annual change, which was the highest in 2014, and then decreased to the lowest in 2016, and increased again in 2017. There was no obvious change in the burned area of forests with the lower values in 2014 and 2016, however, the proportion of forests showed a trend of increasing year after year, and gradually approached the proportion of 12.6% in 2013. Grasslands and savannas occupied a relatively small proportion of combustion throughout the zone and were not detailed here. In Zone 2, the burned area of crops in March–April showed a general trend of decreasing, and eventually remained stable, but the proportion increased year by year, from 72.6% in 2013 to 82.3% in 2017. The burned area of forests is decreasing compared to 2013 and the proportion fluctuated between 5.7% and 16.7%. In June, the bio-mass burning almost came from crops, with an average ratio of over 95%. The burned area has been decreasing year by year but rebounded in 2017. In October–November, the burned area of crops has the same annual change as in June, but the proportion is decreasing after 2015, from 93.0% in 2015 to 76% in 2017. At the same time, the proportion of forest burned area has increased with year, although the increase in its burned area is not significant. The entire Zone 2, savannas still accounted for a very small proportion, while the proportion of grasslands in the March–April and October–November average can reach 7.6% and 7.8%, mainly distributed in the Inner Mongolia region.
area in June accounted for a very small proportion, and the inter-annual variation was relatively small, being basically stable at around 250 km2. The burned area of Zone 1 has grown rapidly year by year in March–April, and the value in 2017 even reached 6.7 times that of 2013. However, the burned area of Zone 1 in October–November overall showed a downward trend. The year with the largest burned areas was 2014, followed by 2015, 2017 and 2013, with the smallest area of burning in 2016, and the maximum fire area was four times as large as the minimum one. An interesting phenomenon was found in Zone 1, the burned area in March–April from 2013 to 2015 has been significantly less than that in October–November, but after 2015, the situation has reversed and the burned area in March–April began to exceed that in October–November, and the gap was obvious. In Zone 2, the burned area in the three periods of March–April, June, and October–November was not much different. Generally, the burned area in June was relatively larger and more concentrated, mainly distributed in the northern part of Jiangsu Province and Anhui Province, eastern part of Henan Province and southern part of Shandong Province. The burned area of Zone 2 has been decreasing year by year in March–April, and the fire area of Zone 2 in 2017 has decreased by 44% compared with that in 2013. The burned area of Zone 2 in June and October–November was rapidly decreasing until the rebound in 2017. The area reached the minimum in 2016, which was 70% and 80% lower than that in 2013. 3.2. Contributions of different vegetation types burning In addition to the analysis of the inter-annual variation of bio-mass burning at specific periods and in specific regions, this paper also counted the four major vegetation types of fires and calculates the corresponding contribution ratios. As shown in Fig. 6, for Zone 1 and Zone 2, the vegetation type of croplands occupied the largest proportion of burned area of more than 70% in any period time, since two zones have a substantial amount of crop residue, resulting from relatively flat land and large agricultural activities (Mehmood et al., 2017). In Zone 1, the three provinces of Heilongjiang, Jilin and Liaoning in Northeast China, around 44 Tg of corn is produced per year. In this region, the sowing time for spring maize and japonica rice is late April, and beans are sown in mid-May. Early October is regarded as the harvest season for almost all crops (MOA, 2000–2001). A large number of fires in October arise from the remnant crop residue after harvest, and the large burned areas in April could be explained as the result of local clearing of farmland to prepare for next planting. Due to cold weather conditions, most of the cropland is vacant in winter except for some planting of oilseed rape, with much crop residue after harvest in autumn retained to spring. In Zone 2, eastern Henan, southern Shandong, northern Anhui, and northern Jiangsu are the areas with the fires most concentrated. The 159
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Fig. 5. Temporal and spatial distribution of burned area.
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burned forest area in 2014 was lower, but it corresponded to higher average temperatures and lower average precipitation. For an improved understanding of the quantitative relationship of annual variation among the average temperature, precipitation and the forest burned area, their cross-correlations were analyzed for two zones. Because there is some understanding of the relationship between the burned area and the meteorological elements, the one-tailed probability was used to evaluate the P value in this study. Acknowledging the possibility of falsely detecting relationships that might eventually prove unfounded and owing to the short time series (2013–2017), the P value threshold was chosen to increase to 0.1 to better understand potential relationships between climate and fires. Fig. 8 showed the correlation results between burned area, average precipitation and temperature in three periods from 2013 to 2017. The results revealed a positive correlation between the burned area and average temperature for Zone 1 (r = 0.81, P = 0.049) in March–April, whereas a negative correlation (r = −0.782, P = 0.059) could be found for Zone 2. There was a strong negative correlation between precipitation and burned area for Zone 1 (r = −0.71, P = 0.09) and Zone 2 (r = −0.81, P = 0.05) in March–April, and Zone 2 (r = −0.81, P = 0.049) in October–November. No significant correlations were detected for the other periods. Of course, it can be understood that the size of the burned area is not only affected by precipitation and temperature, but also influenced by wind and fuel conditions, or lightning strikes, which may be the reason why the law is sometimes not established. In addition, the relationship between the El Niño events and the biomass burning will be discussed below. A super El Niño event occurred over the equatorial center-eastern Pacific during 2014–2016, which peaked in November 2015 with its strength larger than two other super El Niño events (1982/1983 and 1997/1998 events), ranking as the strongest El Niño event since 1951 (Yuan et al., 2016). Due to the impacts of this strong event and the background of climate warming, the global surface temperature in China mainland reached a record high in 2015 (Zhai et al., 2016). The strong El Niño event brought severe weather and climate events to many regions in 2015, which in turn affected open bio-mass burning. In summer, the East Asian summer monsoon weakened, and the main monsoon rains were southward (Ren et al., 2012; Zhai et al., 2016), resulting in more precipitation in the southern region, especially in the Yangtze and Huaihe River basins (in Zone 2), while less rain and drought occurred in the northern regions (include Zone 1), which was in line with the statistical results in June of decreased precipitation and increased forest burned area for Zone 1, and increased precipitation and decreased forest burned area for Zone 2 (Figs. 7 and 8). In autumn and winter, the El Niño episode reached its peak strength, with a lower-troposphere anomalous anticyclonic circulation over the Philippines, bringing about abnormal southerly winds and significantly increased precipitation in south-eastern China (include Zone 2). At the same time, a negative phase of the Eurasia-Pacific teleconnection pattern dominated over the mid-high latitudes, which suppressed northerly winds in North China (include Zone 1), which was in line with the statistical results in October–November of increased temperature and increased forest burned area for Zone 1, and increased precipitation and decreased forest burned area for Zone 2 (Figs. 7 and 8). In conclusion, caused by the El Niño event, the increased temperature and decreased precipitation in Zone 1 and the increased precipitation in Zone 2, were in good agreement with the statistical results in Fig. 7, which may explain well the 33% increase of forest burned area for Zone 1 and 35% reduction for Zone 2 in 2015, compared to the fiveyear average. The above analysis reveals the impact of the El Niño event on climate change and bio-mass burning. The burning of crop residues was mainly controlled by human activities. After 2014, the burning of crop residues in Zone 1 and Zone 2 generally showed a downward trend. This may be due to the large-scale haze incidents in China, which led the government to pay more attention to the burning of crops and formulated a series of control policies. However, the burning of crop residue in March–April of Zone 1
Table 1 IGBP-modified MODIS 20-category land use categories. Index
Land Use Description
Index
Land Use Description
1
11
Permanent Wetlands
12 13
Croplands Urban and Built-Up
4
Evergreen Needleleaf Forest Evergreen Broadleaf Forest Deciduous Needleleaf Forest Deciduous Broadleaf Forest
14
5 6 7 8 9 10
Mixed Forests Closed Shrublands Open Shrublands Woody Savannas Savannas Grasslands
15 16 17 18 19 20
Cropland/Natural Vegetation Mosaic Snow and Ice Barren or Sparsely Vegetated Water Wooded Tundra Mixed Tundra Barren Tundra
2 3
Table 2 The burned areas at three specific periods in Zone 1 and Zone 2 from 2013 to 2017. unit: km2. Mar–Apr
2013 2014 2015 2016 2017
Jun
Oct–Nov
Zone 1
Zone 2
Zone 1
Zone 2
Zone 1
Zone 2
2595 8725 11827 12428 17303
3679 2853 2047 2157 2052
181 240 263 241 249
5043 4891 3419 1490 2097
6077 17967 14598 4454 8849
4244 3232 2534 854 1178
3.3. Causes of inter-annual variations The impact of climate change on fire regimes will vary with relative energy or water limitations of ecosystems (Littell et al., 2009). Temperature and precipitation are the two climate factors of greatest concern in any spatial or temporal analysis of fire patterns (Yi et al., 2017). Temperature variation can influence the evaporation of water in fuel and the surface temperature of fuels, and thus further affect the combustibility and burning point of fuels (Lu, 2011). Therefore, regional climatic characteristics were reflected by calculating the mean temperatures and precipitation levels for Zone 1 and Zone 2 at the three periods mentioned above. Since Zone 1 and Zone 2 are the main agricultural regions in China, and crop residue burning is a common practice as a cost-effective way to dispose of farm residues, therefore, the burning of crop residues is barely affected by regional climate, depending mainly on human activities (Cao et al., 2008; Zhang et al., 2016; Yang et al., 2008). So only the relationship between forest burning and climate elements (temperature, precipitation) will be discussed below. By comparing the average temperature and precipitation in the two zones from 2013 to 2017 (Fig. 7), it is found that the burned forest area showed a positive correlation with the average temperature and a negative correlation with the average precipitation. For example, in March–April, the burned forest area of Zone 1 in 2014, 2015 and 2017 was relatively high, with higher average temperature and lower average precipitation. At the same time, the burned forest area in 2013 was the smallest, corresponding to the lowest average temperature and the highest average precipitation. In Zone 2, there was a large burned area of forest in 2013, corresponding to a slightly lower average temperature and lower average precipitation. In June, the areas in Zone 1 and Zone 2 were small, as there was a large amount of precipitation during this period. In October–November, the above rule was still applicable in the Zone 2, such as 2013 and 2014, which have larger burned forest areas with higher temperatures and lower precipitation. However, the applicability of this law in Zone 1 was worse. For example, the burned forest area in 2013 was higher, but it corresponded to lower average temperature and higher average precipitation, the 161
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Fig. 6. The proportion of the four major vegetation types, where (a), (c), (e) correspond to Zone 1, (b), (d), (f) corresponds to Zone 2. Meanwhile (a), (b) represents March–April; (c), (d) represents June; (e), (f) represents October–November. (The columns correspond to the left vertical axis, indicating the burned area of four major vegetation types; and the broken lines correspond to the right vertical axis, indicating the percentage of burned area within four vegetation types).
fraction for Zone 1 in 2014 was relatively smaller than that in other years, and during October–November, when compared with other years, the average cloud fraction for Zone 2 in 2013 was relatively smaller. But overall, the inter-annual variation of average cloud fractions was relatively stable, which implied that the effects of cloud coverage on biomass burning in different years were relatively consistent, and the results in this study were relatively objective. In future work, the MODIS burned area product MCD64A1 will be used to improve the results from this study.
has increased year by year, which may be due to the anti-burning measures in the autumn, resulting in a large number of crop residue left in the spring. It is worth noting that the burning of crop residue in 2017 is beginning to show an increasing trend, so more effective treatment methods and stricter policies should be adopted. 4. Discussions 4.1. Influence of cloud coverage on open bio-mass burning
4.2. Comparison of the emissions from open bio-mass burning and anthropogenic sources
Considering the influence of cloud coverage on the detection of MODIS fire spots and the analysis of temporal and spatial distribution of open bio-mass burning, the MODIS cloud fraction data from Terra and Aqua was used in this study, with the spatial resolution of 1° × 1°. The detailed values of average cloud fraction for Zone 1 and Zone 2 at three periods from 2013 to 2017 were summarized in Table 3. It was obvious that the 5-year average cloud fractions for Zone 1 were slightly larger than those for Zone 2 in all three periods. However, there was no significant difference in the 5-year average cloud fractions between the two regions. This implied that the effect degree of cloud coverage on bio-mass burning in both two regions was relatively consistent. In addition, the inter-annual variation of average cloud fractions in the specific zone during three periods was also analyzed from Table 3. It was found that the inter-annual variation of average cloud fractions was not intense, and it was basically maintained within a relatively stable range based on the specific zone and period. Meanwhile, some special cases also existed, such as during March–April, the average cloud
To further illustrate the importance of open bio-mass burning, the comparison of the emissions from open bio-mass burning and anthropogenic sources was carried out. In this study, the bottom-up method was used to calculate the emission of open bio-mass burning. Satellite fire spot data, land cover products, vegetation cover products, bio-mass loadings and emission factors were used to provide an emission inventory of bio-mass burning with high spatial (1 km) and temporal (daily) resolution. The emissions are calculated by using the following equation (Wiedinmyer et al., 2011):
Ei = A (x , t ) × B(x) × FB × efi
(1)
Where the emissions (Ei ) are equal to the multiplication of burned area [A (x , t )], bio-mass loading [B(x)], the fraction of bio-mass burned (FB ) and the emission factors (efi ). Here i refers to the species emitted, and 162
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Fig. 7. Average temperature and precipitation over three periods in Zone 1 and Zone 2.
Fig. 8. Correlations between burned area, annual average precipitation and temperature for each zone from 2013 to 2017.
t and x refer to the time and location of the fire. The anthropogenic emissions were derived from the Multi-resolution Emission Inventory for China version 1.2 (MEIC v1.2) with the resolution of 0.25° × 0.25°. The results of the comparison on main species (PM2.5, BC, OC, SO2, NOx, CO and NH3) between open bio-mass burning emissions and anthropogenic emissions are shown in Table 4. Obviously, it can be seen that the emissions of bio-mass burning are quite significant, especially in the Zone 1. The ratio of bio-mass burning emissions to anthropogenic emissions can reach about from few to 80%, depending on the species and time periods. Among all the species, the largest emissions were CO, PM2.5 and OC, and the highest ratios were OC, PM2.5 and CO during three periods. However, the ratios of bio-mass burning emissions to anthropogenic emissions are much smaller in Zone 2, due to the much larger anthropogenic emissions and smaller bio-mass burning emissions than Zone 1. Moreover, it is worth noting that in this method, satellite overpass
Table 3 The average cloud fractions at three periods in Zone 1 and Zone 2 from 2013 to 2017. Mar–Apr
2013 2014 2015 2016 2017 Ave
Jun
Oct–Nov
Zone 1
Zone 2
Zone 1
Zone 2
Zone 1
Zone 2
0.67 0.50 0.65 0.62 0.60 0.61
0.56 0.56 0.57 0.58 0.56 0.57
0.70 0.69 0.69 0.75 0.67 0.70
0.69 0.72 0.74 0.66 0.65 0.69
0.62 0.57 0.62 0.66 0.62 0.62
0.48 0.56 0.65 0.67 0.59 0.59
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Table 4 The comparison between open bio-mass burning emissions (BB) and anthropogenic emissions (MEIC). Mar–Apr
Zone 1
PM2.5 BC OC SO2 NOx CO NH3 PM2.5 BC OC SO2 NOx CO NH3
Zone 2
Jun
Oct–Nov
BB
MEIC
Ratio
BB
MEIC
Ratio
BB
MEIC
Ratio
88 7 56 6 37 1137 18 14 1 9 1 5 155 2
110 26 49 262 346 2510 180 540 113 157 1608 1660 11380 741
79.7 27.0 114.8 2.5 10.7 45.3 10.1 2.5 0.8 5.8 0.1 0.3 1.4 0.3
3 0 2 0 1 33 0 6 1 3 0 3 101 2
55 10 15 136 181 997 116 282 58 77 886 870 5694 500
6.3 1.8 15.8 0.2 0.5 3.3 0.2 2.0 1.1 4.4 0.0 0.4 1.8 0.4
88 5 59 7 26 888 9 10 1 6 1 3 101 1
129 36 81 319 387 3407 161 567 129 200 1738 1722 12572 634
67.8 14.7 73.3 2.1 6.8 26.1 5.5 1.7 0.5 3.2 0.0 0.2 0.8 0.2
a
BB: the average bio-mass burning emissions from 2013 to 2017; MEIC: anthropogenic emissions of 2012 (from MEIC inventory); Emissions unit: Gg; Ratio = BB/ MEIC*100, unit: %.
certain fire points will be missed, leading to some errors and uncertainties. Moreover, the relationship between fire detections and area burned is highly uncertain and requires more on-going research. Therefore, in future work, the MODIS burned area product MCD64A1 will be used to improve the results from this study. Finally, this study did not consider the impact of bio-mass burning on air quality. Further research, especially quantitative analysis, should be conducted to quantify the negative impacts of bio-mass burning on atmospheric environment field.
timing, cloud coverage and the omission of small fire spots may lead to an underestimation of the bio-mass burning emissions. Therefore, the real bio-mass burning emissions may be larger than the statistical results of this study, which further proves the importance of open biomass burning, and this is the significance of this study. 4.3. Comparison of the emissions from open bio-mass burning to FINN inventory In addition, a comparison with FINN fire inventory is added to ensure that the emission of bio-mass burning in this study was consistent with other sources. Here, October 2014 was selected as the study period due to its high bio-mass burning episodes (Fig. 4(b)). It can be found from Table 5 that the emission of bio-mass burning in this study was generally higher than that in FINN inventory, with the value of the BB/ FINN less than two. The differences in emission mainly arise from (1) different fuel loading datasets and (2) the removal of low-confidence fire spots. But overall, the estimates of open bio-mass burning emissions in this study could still be trusted.
5. Conclusions By using the MODIS fire spots and VCF products within Land Use dataset, this research attempted to explore the spatial and temporal distribution of open bio-mass burning in the study area from 2013 to 2017, the corresponding proportion of different vegetation types and the causes of inter-annual changes in China. The results revealed the annual and monthly changes of bio-mass burning from 2013 to 2017, meanwhile the spatial and temporal distribution of burned area at three specific periods in two zones (Zone 1 and Zone 2) was obtained. Specifically, the bio-mass burning in Zone 1 is dominant both in March–April and October–November. For Zone 2, the bio-mass burning is most concentrated in June. In Zone 1 and Zone 2, the vegetation type of cropland occupied the largest proportion of burned area of more than 70% in any period, followed by forest. It was found that the forest burned area showed a positive correlation with the average temperature of the zone and a negative correlation with the average precipitation. The super El Niño event (2014–2016) led to the increased temperature and decreased precipitation in Zone 1 and the increased precipitation in Zone 2, which may contribute the 33% increase of forest burned area for Zone 1 and 35% reduction for Zone 2 in 2015, compared to the five-year average. The burning of crop residues is mainly controlled by human activities. The methodology and results from this research provide a useful reference for policy-makers to better understand the characteristics and variations of bio-mass burning in China and lay the groundwork for simulating and predicting the impact of bio-mass burning on air quality in the next work. Accordingly, more effective measures to monitor and control bio-mass burning in these areas can be posed and carried out to enhance local air quality and protect human health.
4.4. Limitations and prospect Although this study has obtained some expected results, certain limitations remain. Firstly, the combined uses of several data sets such as MODIS fire spot product, VCF product and land use dataset will cause some deviations due to their different spatial resolutions. Higher resolution data products or more reasonable coupling methods will be considered in next work. Secondly, due to the limited temporal resolution of MODIS fire spot products and unfavorable observation conditions such as higher cloud coverage and severe haze pollution, Table 5 The comparison of the emissions between open bio-mass burning (BB) in this study and FINN fire inventory in October 2014. Zone 1
a
Zone 2
Species
BB
FINN
BB/FINN
BB
FINN
BB/FINN
PM2.5 BC OC SO2 NOx CO NH3
45163 4242 27562 3249 23654 694205 13568
30800 2349 16313 2293 14286 404964 7477
1.47 1.81 1.69 1.42 1.66 1.71 1.81
11176 921 7068 816 5047 152642 2888
7969 815 4459 562 4599 133381 2706
1.40 1.13 1.59 1.45 1.10 1.14 1.07
Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
Emissions unit: ton. 164
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influence the work reported in this paper.
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Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 91744209, 91544232 and 41705080), the Science and Technology Development Fund of Chinese Academy of Meteorological Sciences (No. 2018KJ020), and CAMS Basis Research Project (No. 2017Y001). Appendix A. Supplementary data Supplementary data related to this article can be found at https:// doi.org/10.1016/j.atmosenv.2019.04.039. References Al-Saadi, J.A., Soja, A.J., Pierce, R.B., Szykman, J.J., Wiedinmyer, C., Emmons, L.K., Kondragunta, S., Zhang, X., Kittaka, C., Schaack, T., Bowman, K.W., 2008. Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data. J. Appl. Remote Sens. 2, 021504. https://doi.org/10.1117/1. 2948785. Bi, Y., Wang, Y., Gao, C., 2010. Straw resource quantity and its regional distribution in China. Agri. Mech. Res. 32, 1–7. https://doi.org/10.13427/j.cnki. Cao, G., Zhang, X., Wang, Y., Zheng, F., 2008. Estimation of emissions from field burning of crop straw in China. Chin. Sci. Bull. 53, 784–790. https://doi.org/10.1007/ s11434-008-0145-4. Chen, D., Pereira, J.M.C., Masiero, A., Pirotti, F., 2017a. Mapping fire regimes in China using MODIS active fire and burned area data. Appl. Geogr. 85, 14–26. https://doi. org/10.1016/j.apgeog.2017.05.013. Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M.S., Wang, S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L., Thai, P., Lam, Y.F., Pereira, G., Ding, A., Huang, X., Dumka, U.C., 2017b. A review of biomass burning: emissions and impacts on air quality, health and climate in China. Sci. Total Environ. 579, 1000–1034. https://doi.org/10.1016/j.scitotenv.2016.11.025. Deng, J., Wang, T., Jiang, Z., Xie, M., Zhang, R., Huang, X., Zhu, J., 2011. Characterization of visibility and its affecting factors over Nanjing, China. Atmos. Res. 101, 681–691. https://doi.org/10.1016/j.atmosres.2011.04.016. Dennis, A., Fraser, M., Anderson, S., Allen, D., 2002. Air pollutant emissions associated with forest, grassland, and agricultural burning in Texas. Atmos. Environ. 36, 3779–3792. https://doi.org/10.1016/s1352-2310(02)00219-4. Field, R.D., van der Werf, G.R., Fanin, T., Fetzer, E.J., Fuller, R., Jethva, H., Levy, R., Livesey, N.J., Luo, M., Torres, O., Worden, H.M., 2016. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl. Acad. Sci. U. S. A 113, 9204–9209. https://doi.org/10.1073/ pnas.1524888113. Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C., 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83, 287–302. https://doi.org/10.1016/S0034-4257(02)00078-0. Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., Huang, X., 2010. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182. https://doi.org/ 10.1016/j.rse.2009.08.016. Frolking, S., Xiao, X., Zhuang, Y., Salas, W., Li, C., 1999. Agricultural land-use in China: a comparison of area estimates from ground-based census and satellite-borne remote sensing. GCTE/LUCC RESEARCH LETTER, Glob. Ecol. Biogeogr. 8, 407–416. https:// doi.org/10.1046/j.1365-2699.1999.00157.x. Guo, H., Cheng, T., Gu, X., Wang, Y., Chen, H., Bao, F., Shi, S., Xu, B., Wang, W., Zuo, X., Zhang, X., Meng, C., 2017. Assessment of PM2.5 concentrations and exposure throughout China using ground observations. Sci. Total Environ. 601–602, 1024–1030. https://doi.org/10.1016/j.scitotenv.2017.05.263. Hansen, M.C., DeFries, R.S., Townshend, J.R.G., Carroll, M., Dimiceli, C., Sohlberg, R.A., 2003. Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7, 1–15. https:// doi.org/10.1175/1087-3562(2003)007<0001:gptcaa>2.0.co;2. Hansen, M.C., Townshend, J.R.G., DeFries, R.S., Carroll, M., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Remote Sens. 26, 4359–4380. https://doi.org/10.1080/01431160500113435. Hu, H., Wei, S., Sun, L., 2012. Estimation of carbon emissions due to forest fire in Daxing'an Mountains from 1965 to 2010. Chin. J. Plant. Ecolo. 36, 629–644. Huang, X., Li, M., Li, J., Song, Y., 2012. A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products. Atmos. Environ. 50, 9–15. https://doi.org/10.1016/j.atmosenv.2012.01.017.
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Spatial and temporal distribution of open bio-mass burning in China from 2013 to 2017
T
Huabing Kea, Sunling Gonga,∗, Jianjun Hea, Chunhong Zhoua, Lei Zhanga,b, Yike Zhoua a b
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China Climate and Weather Disasters Collaborative Innovation Center, Nanjing University of Information Science & Technology, Nanjing, 210044, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Open bio-mass burning Spatial-temporal variations MODIS Vegetation types El Niño
Open bio-mass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing the trace gases and particulate matters into the atmosphere. A better understanding of spatial-temporal variations of open bio-mass burning in China is required to assess its impacts on the air quality and especially on the heavy haze pollution. The MODIS fire spots data and the calculated burned areas were used in this research, which shows the varying number of fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. Meanwhile, the fire spots were found mainly concentrated in three key periods (March–April, June and October–November) and two zones (Zone 1 and Zone 2) with inter-annual variations of burned areas. In addition, the contribution of major vegetation types burning was studied, the cropland occupied the largest proportion of burned area of more than 70% in any period time, followed by forest. Finally, from the perspective of climate and human activities, the causes of inter-annual variations were discussed. By comparing the average temperature and precipitation in the two zones from 2013 to 2017, it was found that the burned forest area is positively correlated with the average temperature of the zones and negatively correlated with the average precipitation. Meanwhile, the relationship between the El Niño events and the bio-mass burning was discussed.
1. Introduction Open bio-mass burning, from wildfires and agricultural field burning, makes up an essential part of atmospheric trace gases and particulate matter emitted to the atmosphere (Dennis et al., 2002; Jenkins et al., 1992; Zhang et al., 2008), including CO, CO2, CH4, BC, OC, NOx, NH3, SO2, NMOCs and PM. The emissions of PM can degrade visibility (McMeeking et al., 2006) and cause health problems (Pope and Dockery, 2012). The emitted gases, such as NMOCs and NOx, can react downwind of bio-mass burning location and lead to the formation of ozone (Pfister et al., 2008). A large amount of CO2 released into the atmosphere from burning may have non-negligible impacts on the carbon cycle (IPCC, 2007; Wiedinmyer and Neff, 2007). Meanwhile, bio-mass burning can not only lead to periods of degraded air quality (Field et al., 2016), but also influence the weather and climate through changes primarily in radiation and cloud formation. China has the world's largest crop production with an estimated area of 1.4 × 106 km2 (Frolking et al., 1999; Xiao et al., 2003). In addition, the total amount of crop residue in China occupies the front rank
in the world, accounting for about 18% of global production (Bi et al., 2010). With no suitable methods for treating crop residue, a large amount of crop residue is burned in the cropland in few days after harvest in order not to delay planting in the next season, which is a major contribution to the formation of heavy pollution events (Deng et al., 2011; Zhang et al., 2016; Chen et al., 2017b; Mehmood et al., 2017). During the 46-year period from 1965 to 2010, there were 1614 forest fires in the Great Xing'an Mountains of north-eastern China, with an average annual rate of 35.09. The total forest over-fired forest area reached 3523011.86 hm2, and the annual average over-fired forest area was 7.66 × 104 hm2 (Hu et al., 2012). In the northeastern part of Inner Mongolia Autonomous Region, there are an average of more than 400 grassland fires per year, with a total burned area of about 8000 km2 (Li et al., 2017). It is precisely because there was a large amount of open bio-mass burning of different vegetation types in China, and considering its impact on the atmospheric environment and climate change, it is necessary to conduct a detailed study on the spatial and temporal distribution of bio-mass burning in China. Many studies have been devoted to the spatial and temporal
∗ Corresponding author. Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, ZhongGuanCun South Ave. 46, HaiDian District, Beijing, 100081, China. E-mail address: [email protected] (S. Gong).
https://doi.org/10.1016/j.atmosenv.2019.04.039 Received 18 February 2019; Received in revised form 16 April 2019; Accepted 17 April 2019 Available online 22 April 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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Tianjin, Hebei Province, Henan Province, Shandong Province, Anhui Province, Jiangsu Province, Shanxi Province, Hubei Province, as well as parts of Zhejiang Province and Inner Mongolia Autonomous Region), which together can explain 60%–70% of bio-mass burning in China (Yi et al., 2017). The study area includes most of China, except for the western and southern provinces, with the spatial resolution of 18 km. In addition, in order to study the impact of climate factors on the interannual variation of bio-mass burning, 13 major urban meteorological observation sites within Zone 1 and Zone 2 were selected (50953 Harbin Station, 54161 Changchun Station, 53698 Shijiazhuang Station, 54823 Jinan Station, 58238 Nanjing Station, 57083 Zhengzhou Station, 58321 Hefei Station, 54342 Shenyang Station, 54511 Beijing Station, 54517 Tianjin Station, 53772 Taiyuan Station, 57494 Wuhan Station, 58457 Hangzhou Station), as shown in Fig. 1(b).
distribution of bio-mass burning, using different remote sensing data and processing methods. An average burned area of approximately 3.2 × 106 ha occurred in China per year, which was mainly concentrated in spring and autumn from 2001 to 2012 (Yi et al., 2017). Researches on crop residue burning in different regions from 2003 to 2017 showed an overall upward trend with a slight fluctuation, by employing the MODIS products MOD14A1/MYD14A1 (Zhuang et al., 2018). The North China is dominated by agricultural fires, which mainly occurred in the two harvest periods of June in summer and October in autumn (Huang et al., 2012). By using MODIS active fire and burned area data, many forest fires were found in Northeast China, which were mainly concentrated in the relatively dry spring and autumn, with large inter-annual variations, implying high probability of accidental causes (Chen et al., 2017a). However, few studies have mentioned the spatial and temporal distribution of bio-mass burning with different vegetation types and the proportion of corresponding contributions. At the same time, the reasons for their inter-annual variations are also worth exploring. Therefore, in this study, by using MODIS fire spots data and calculated burned areas, firstly, the spatial and temporal distribution of open biomass burning in the study area was obtained. Secondly, the burned area of different vegetation types and the corresponding proportion was analyzed from 2013 to 2017. Finally, from the perspective of climate and human activities, the causes of inter-annual changes were explored. Meanwhile, the relationship between the El Niño events and the biomass burning was discussed.
2.2. The dataset of land use In this study, the International Geosphere-Biosphere Programme (IGBP)-Modified MODIS Land Use was used, identifying 20 land cover classes (Table 1), provided by the Boston University and modified by the NCEP (Friedl et al., 2002). According to previous research results (Friedl et al., 2010; Chen et al., 2017a), around 75% of China's land is covered with various vegetation types (mainly forest, savanna, grassland and cropland). At the same time, the proportion of grid points for each land use in the study area was counted (Fig. 2). The major vegetation types were forests (1, 2, 3, 4, 5), savannas (6, 7, 8, 9), grasslands (10) and croplands (12). Shrublands and savannas were classified to the same vegetation type based on their similar structure. Non-vegetated land cover classes, including wetlands (11), urban and built-up (13), cropland/natural vegetation mosaic (14), snow and ice (15), barren or sparsely vegetated (16), water (17) and tundra (18, 19, 20) were all excluded from the analysis in this study (Chen et al., 2017a).
2. Materials and methods 2.1. Study area Since the start of the state “Air Pollution Prevention and Control Action Plan” in 2013, air quality issues have received extensive attention from society and the state. North China, East China and Southwest China are the regions with the most serious air pollution (Ma et al., 2016; Guo et al., 2017; Wang et al., 2017; Zhang et al., 2018). Bio-mass burning, one of the main sources of emissions, is severe in the Northeast, Central China, and East China, so this study will focus on these regions (Fig. 1(a)) and defines them by two zones: Zone 1 (Liaoning Province, Jilin Province, Heilongjiang Province), and Zone 2 (Beijing,
2.3. MODIS active fire The Near real-time MODIS(C6) active fire data set was used in this study, which were obtained directly from Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/active_ fire/). The data set provides fire detection with a nominal horizontal resolution of about 1 km2 and a temporal resolution of four times per
Fig. 1. (a) The study area of two zones in China. The serial number 1–20 in the color bar corresponds to different land use, as shown in Table 1. (b) Major urban meteorological stations. 157
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Fig. 2. Proportion of different land use in the study area.
day, including the latitude and longitude of the fire detection, the acquisition time (in UTC), the bright temperature, the fire radiation power, and the estimation of confidence. Despite some uncertainties in the fire detections of this data set, they are widely used in many fields (Wiedinmyer et al., 2011). All fire detections from 2013 to 2017 were used in this study, with confidence less than 30% excluded. For a more reliable analysis result, the low-confidence burning spots were discarded and nominal-confidence and high-confidence burning spots were counted.
3. Results 3.1. Temporal and spatial distribution of open bio-mass burning Fig. 4 shows the changes of the total number of MODIS fire spots in China from 2013 to 2017, with the highest in 2014 and the lowest in 2016. In addition to the exceptionally high number of fire spots in January 2014, the fires were mainly concentrated in March–April and October–November, followed by a slightly higher number of fire spots in June. Therefore, the following section will estimate the burned area for these periods based on the two zones, obtain the inter-annual variation, and analyze the climate and human factors that leads to this change. Fig. 5 shows the spatial distribution of burned area in March–April, June, and October–November from 2013 to 2017. Obviously, Zone 1 and Zone 2 covered almost the most severe area of China's bio-mass burning, except for Southwest China, which was not discussed in this paper. The detailed burned areas for Zone 1 and Zone 2 are summarized in Table 2. In Zone 1, bio-mass burning occurred mainly in the two periods of March–April and October–November. It was mainly concentrated in the Songnen Plain and Sanjiang Plain in Heilongjiang, the western part of Jilin Province and the northern part of Liaoning Province. The burned
2.4. Method of calculating the burned area For each detected fire spot, the burned area will initially be assigned to 1 km2. However, if the vegetation type at the fire spot is grassland or savanna, the burned area will be allocated 0.75 km2 (Wiedinmyer et al., 2006; Al-Saadi et al., 2008). In addition, the percent of bare cover in MODIS VCF product was then used to adjust the burned area. The VCF product identified the percent of three vegetation types (Fig. 3) of tree, non-tree vegetation and bare cover at each active fire location (Hansen et al., 2003, 2005). For example, a forest fire is designated as a burned area of 1 km2 and the VCF data set allocates 30% bare cover for the same location, the burned area will ultimately be assigned 0.7 km2.
Fig. 3. (a) Percent of tree cover from VCF; (b) percent of non-tree vegetation cover from VCF. 158
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Fig. 4. (a) Annual variation of MODIS fire spots; (b) Monthly variation of MODIS fire spots from 2013 to 2017.
fires in this area occurred mainly in June, with crops reaching an average of 95%. The volatility is basically in line with the timing of agriculture. Winter wheat (planted in mid-October, harvested at the end of May) and summer maize (planted in mid-June, harvested at the end of September) are the two most important crops in the zone (MOA, 2000–2001). Among them, the peak of the fires in June was resulted from the harvest of winter wheat. To increase the soil fertility for next cultivation, plenty of wheat residue will be burned after harvest. The bio-mass burning in October–November may be attributed to the maturity of the corn and subsequent burning of corn stalks, but it was not as concentrated as in June, and lower than the June in the total area of combustion. The burned area was lowest in March–April, and the crops accounted for the least. Meanwhile, the spatial distribution was the most discrete, possibly from a small amount of last year's crop residue removal. In Zone 1, the burned area and proportion of crops in March–April increased year by year, from 2028.1 km2, 78.1% in 2013 to 16051.4 km2 and 92.8% in 2017. The burned area of the forest was basically stable at 600–700 km2, but the proportion has been decreasing gradually because of the increase in total combustion areas. In June, the phenomenon of bio-mass burning was relatively rare, the burned rate of crops was basically maintained at about 80%, and the burned area of forests accounted for about 15%. The burned area and proportion of crops in October–November have the same annual change, which was the highest in 2014, and then decreased to the lowest in 2016, and increased again in 2017. There was no obvious change in the burned area of forests with the lower values in 2014 and 2016, however, the proportion of forests showed a trend of increasing year after year, and gradually approached the proportion of 12.6% in 2013. Grasslands and savannas occupied a relatively small proportion of combustion throughout the zone and were not detailed here. In Zone 2, the burned area of crops in March–April showed a general trend of decreasing, and eventually remained stable, but the proportion increased year by year, from 72.6% in 2013 to 82.3% in 2017. The burned area of forests is decreasing compared to 2013 and the proportion fluctuated between 5.7% and 16.7%. In June, the bio-mass burning almost came from crops, with an average ratio of over 95%. The burned area has been decreasing year by year but rebounded in 2017. In October–November, the burned area of crops has the same annual change as in June, but the proportion is decreasing after 2015, from 93.0% in 2015 to 76% in 2017. At the same time, the proportion of forest burned area has increased with year, although the increase in its burned area is not significant. The entire Zone 2, savannas still accounted for a very small proportion, while the proportion of grasslands in the March–April and October–November average can reach 7.6% and 7.8%, mainly distributed in the Inner Mongolia region.
area in June accounted for a very small proportion, and the inter-annual variation was relatively small, being basically stable at around 250 km2. The burned area of Zone 1 has grown rapidly year by year in March–April, and the value in 2017 even reached 6.7 times that of 2013. However, the burned area of Zone 1 in October–November overall showed a downward trend. The year with the largest burned areas was 2014, followed by 2015, 2017 and 2013, with the smallest area of burning in 2016, and the maximum fire area was four times as large as the minimum one. An interesting phenomenon was found in Zone 1, the burned area in March–April from 2013 to 2015 has been significantly less than that in October–November, but after 2015, the situation has reversed and the burned area in March–April began to exceed that in October–November, and the gap was obvious. In Zone 2, the burned area in the three periods of March–April, June, and October–November was not much different. Generally, the burned area in June was relatively larger and more concentrated, mainly distributed in the northern part of Jiangsu Province and Anhui Province, eastern part of Henan Province and southern part of Shandong Province. The burned area of Zone 2 has been decreasing year by year in March–April, and the fire area of Zone 2 in 2017 has decreased by 44% compared with that in 2013. The burned area of Zone 2 in June and October–November was rapidly decreasing until the rebound in 2017. The area reached the minimum in 2016, which was 70% and 80% lower than that in 2013. 3.2. Contributions of different vegetation types burning In addition to the analysis of the inter-annual variation of bio-mass burning at specific periods and in specific regions, this paper also counted the four major vegetation types of fires and calculates the corresponding contribution ratios. As shown in Fig. 6, for Zone 1 and Zone 2, the vegetation type of croplands occupied the largest proportion of burned area of more than 70% in any period time, since two zones have a substantial amount of crop residue, resulting from relatively flat land and large agricultural activities (Mehmood et al., 2017). In Zone 1, the three provinces of Heilongjiang, Jilin and Liaoning in Northeast China, around 44 Tg of corn is produced per year. In this region, the sowing time for spring maize and japonica rice is late April, and beans are sown in mid-May. Early October is regarded as the harvest season for almost all crops (MOA, 2000–2001). A large number of fires in October arise from the remnant crop residue after harvest, and the large burned areas in April could be explained as the result of local clearing of farmland to prepare for next planting. Due to cold weather conditions, most of the cropland is vacant in winter except for some planting of oilseed rape, with much crop residue after harvest in autumn retained to spring. In Zone 2, eastern Henan, southern Shandong, northern Anhui, and northern Jiangsu are the areas with the fires most concentrated. The 159
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Fig. 5. Temporal and spatial distribution of burned area.
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burned forest area in 2014 was lower, but it corresponded to higher average temperatures and lower average precipitation. For an improved understanding of the quantitative relationship of annual variation among the average temperature, precipitation and the forest burned area, their cross-correlations were analyzed for two zones. Because there is some understanding of the relationship between the burned area and the meteorological elements, the one-tailed probability was used to evaluate the P value in this study. Acknowledging the possibility of falsely detecting relationships that might eventually prove unfounded and owing to the short time series (2013–2017), the P value threshold was chosen to increase to 0.1 to better understand potential relationships between climate and fires. Fig. 8 showed the correlation results between burned area, average precipitation and temperature in three periods from 2013 to 2017. The results revealed a positive correlation between the burned area and average temperature for Zone 1 (r = 0.81, P = 0.049) in March–April, whereas a negative correlation (r = −0.782, P = 0.059) could be found for Zone 2. There was a strong negative correlation between precipitation and burned area for Zone 1 (r = −0.71, P = 0.09) and Zone 2 (r = −0.81, P = 0.05) in March–April, and Zone 2 (r = −0.81, P = 0.049) in October–November. No significant correlations were detected for the other periods. Of course, it can be understood that the size of the burned area is not only affected by precipitation and temperature, but also influenced by wind and fuel conditions, or lightning strikes, which may be the reason why the law is sometimes not established. In addition, the relationship between the El Niño events and the biomass burning will be discussed below. A super El Niño event occurred over the equatorial center-eastern Pacific during 2014–2016, which peaked in November 2015 with its strength larger than two other super El Niño events (1982/1983 and 1997/1998 events), ranking as the strongest El Niño event since 1951 (Yuan et al., 2016). Due to the impacts of this strong event and the background of climate warming, the global surface temperature in China mainland reached a record high in 2015 (Zhai et al., 2016). The strong El Niño event brought severe weather and climate events to many regions in 2015, which in turn affected open bio-mass burning. In summer, the East Asian summer monsoon weakened, and the main monsoon rains were southward (Ren et al., 2012; Zhai et al., 2016), resulting in more precipitation in the southern region, especially in the Yangtze and Huaihe River basins (in Zone 2), while less rain and drought occurred in the northern regions (include Zone 1), which was in line with the statistical results in June of decreased precipitation and increased forest burned area for Zone 1, and increased precipitation and decreased forest burned area for Zone 2 (Figs. 7 and 8). In autumn and winter, the El Niño episode reached its peak strength, with a lower-troposphere anomalous anticyclonic circulation over the Philippines, bringing about abnormal southerly winds and significantly increased precipitation in south-eastern China (include Zone 2). At the same time, a negative phase of the Eurasia-Pacific teleconnection pattern dominated over the mid-high latitudes, which suppressed northerly winds in North China (include Zone 1), which was in line with the statistical results in October–November of increased temperature and increased forest burned area for Zone 1, and increased precipitation and decreased forest burned area for Zone 2 (Figs. 7 and 8). In conclusion, caused by the El Niño event, the increased temperature and decreased precipitation in Zone 1 and the increased precipitation in Zone 2, were in good agreement with the statistical results in Fig. 7, which may explain well the 33% increase of forest burned area for Zone 1 and 35% reduction for Zone 2 in 2015, compared to the fiveyear average. The above analysis reveals the impact of the El Niño event on climate change and bio-mass burning. The burning of crop residues was mainly controlled by human activities. After 2014, the burning of crop residues in Zone 1 and Zone 2 generally showed a downward trend. This may be due to the large-scale haze incidents in China, which led the government to pay more attention to the burning of crops and formulated a series of control policies. However, the burning of crop residue in March–April of Zone 1
Table 1 IGBP-modified MODIS 20-category land use categories. Index
Land Use Description
Index
Land Use Description
1
11
Permanent Wetlands
12 13
Croplands Urban and Built-Up
4
Evergreen Needleleaf Forest Evergreen Broadleaf Forest Deciduous Needleleaf Forest Deciduous Broadleaf Forest
14
5 6 7 8 9 10
Mixed Forests Closed Shrublands Open Shrublands Woody Savannas Savannas Grasslands
15 16 17 18 19 20
Cropland/Natural Vegetation Mosaic Snow and Ice Barren or Sparsely Vegetated Water Wooded Tundra Mixed Tundra Barren Tundra
2 3
Table 2 The burned areas at three specific periods in Zone 1 and Zone 2 from 2013 to 2017. unit: km2. Mar–Apr
2013 2014 2015 2016 2017
Jun
Oct–Nov
Zone 1
Zone 2
Zone 1
Zone 2
Zone 1
Zone 2
2595 8725 11827 12428 17303
3679 2853 2047 2157 2052
181 240 263 241 249
5043 4891 3419 1490 2097
6077 17967 14598 4454 8849
4244 3232 2534 854 1178
3.3. Causes of inter-annual variations The impact of climate change on fire regimes will vary with relative energy or water limitations of ecosystems (Littell et al., 2009). Temperature and precipitation are the two climate factors of greatest concern in any spatial or temporal analysis of fire patterns (Yi et al., 2017). Temperature variation can influence the evaporation of water in fuel and the surface temperature of fuels, and thus further affect the combustibility and burning point of fuels (Lu, 2011). Therefore, regional climatic characteristics were reflected by calculating the mean temperatures and precipitation levels for Zone 1 and Zone 2 at the three periods mentioned above. Since Zone 1 and Zone 2 are the main agricultural regions in China, and crop residue burning is a common practice as a cost-effective way to dispose of farm residues, therefore, the burning of crop residues is barely affected by regional climate, depending mainly on human activities (Cao et al., 2008; Zhang et al., 2016; Yang et al., 2008). So only the relationship between forest burning and climate elements (temperature, precipitation) will be discussed below. By comparing the average temperature and precipitation in the two zones from 2013 to 2017 (Fig. 7), it is found that the burned forest area showed a positive correlation with the average temperature and a negative correlation with the average precipitation. For example, in March–April, the burned forest area of Zone 1 in 2014, 2015 and 2017 was relatively high, with higher average temperature and lower average precipitation. At the same time, the burned forest area in 2013 was the smallest, corresponding to the lowest average temperature and the highest average precipitation. In Zone 2, there was a large burned area of forest in 2013, corresponding to a slightly lower average temperature and lower average precipitation. In June, the areas in Zone 1 and Zone 2 were small, as there was a large amount of precipitation during this period. In October–November, the above rule was still applicable in the Zone 2, such as 2013 and 2014, which have larger burned forest areas with higher temperatures and lower precipitation. However, the applicability of this law in Zone 1 was worse. For example, the burned forest area in 2013 was higher, but it corresponded to lower average temperature and higher average precipitation, the 161
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Fig. 6. The proportion of the four major vegetation types, where (a), (c), (e) correspond to Zone 1, (b), (d), (f) corresponds to Zone 2. Meanwhile (a), (b) represents March–April; (c), (d) represents June; (e), (f) represents October–November. (The columns correspond to the left vertical axis, indicating the burned area of four major vegetation types; and the broken lines correspond to the right vertical axis, indicating the percentage of burned area within four vegetation types).
fraction for Zone 1 in 2014 was relatively smaller than that in other years, and during October–November, when compared with other years, the average cloud fraction for Zone 2 in 2013 was relatively smaller. But overall, the inter-annual variation of average cloud fractions was relatively stable, which implied that the effects of cloud coverage on biomass burning in different years were relatively consistent, and the results in this study were relatively objective. In future work, the MODIS burned area product MCD64A1 will be used to improve the results from this study.
has increased year by year, which may be due to the anti-burning measures in the autumn, resulting in a large number of crop residue left in the spring. It is worth noting that the burning of crop residue in 2017 is beginning to show an increasing trend, so more effective treatment methods and stricter policies should be adopted. 4. Discussions 4.1. Influence of cloud coverage on open bio-mass burning
4.2. Comparison of the emissions from open bio-mass burning and anthropogenic sources
Considering the influence of cloud coverage on the detection of MODIS fire spots and the analysis of temporal and spatial distribution of open bio-mass burning, the MODIS cloud fraction data from Terra and Aqua was used in this study, with the spatial resolution of 1° × 1°. The detailed values of average cloud fraction for Zone 1 and Zone 2 at three periods from 2013 to 2017 were summarized in Table 3. It was obvious that the 5-year average cloud fractions for Zone 1 were slightly larger than those for Zone 2 in all three periods. However, there was no significant difference in the 5-year average cloud fractions between the two regions. This implied that the effect degree of cloud coverage on bio-mass burning in both two regions was relatively consistent. In addition, the inter-annual variation of average cloud fractions in the specific zone during three periods was also analyzed from Table 3. It was found that the inter-annual variation of average cloud fractions was not intense, and it was basically maintained within a relatively stable range based on the specific zone and period. Meanwhile, some special cases also existed, such as during March–April, the average cloud
To further illustrate the importance of open bio-mass burning, the comparison of the emissions from open bio-mass burning and anthropogenic sources was carried out. In this study, the bottom-up method was used to calculate the emission of open bio-mass burning. Satellite fire spot data, land cover products, vegetation cover products, bio-mass loadings and emission factors were used to provide an emission inventory of bio-mass burning with high spatial (1 km) and temporal (daily) resolution. The emissions are calculated by using the following equation (Wiedinmyer et al., 2011):
Ei = A (x , t ) × B(x) × FB × efi
(1)
Where the emissions (Ei ) are equal to the multiplication of burned area [A (x , t )], bio-mass loading [B(x)], the fraction of bio-mass burned (FB ) and the emission factors (efi ). Here i refers to the species emitted, and 162
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Fig. 7. Average temperature and precipitation over three periods in Zone 1 and Zone 2.
Fig. 8. Correlations between burned area, annual average precipitation and temperature for each zone from 2013 to 2017.
t and x refer to the time and location of the fire. The anthropogenic emissions were derived from the Multi-resolution Emission Inventory for China version 1.2 (MEIC v1.2) with the resolution of 0.25° × 0.25°. The results of the comparison on main species (PM2.5, BC, OC, SO2, NOx, CO and NH3) between open bio-mass burning emissions and anthropogenic emissions are shown in Table 4. Obviously, it can be seen that the emissions of bio-mass burning are quite significant, especially in the Zone 1. The ratio of bio-mass burning emissions to anthropogenic emissions can reach about from few to 80%, depending on the species and time periods. Among all the species, the largest emissions were CO, PM2.5 and OC, and the highest ratios were OC, PM2.5 and CO during three periods. However, the ratios of bio-mass burning emissions to anthropogenic emissions are much smaller in Zone 2, due to the much larger anthropogenic emissions and smaller bio-mass burning emissions than Zone 1. Moreover, it is worth noting that in this method, satellite overpass
Table 3 The average cloud fractions at three periods in Zone 1 and Zone 2 from 2013 to 2017. Mar–Apr
2013 2014 2015 2016 2017 Ave
Jun
Oct–Nov
Zone 1
Zone 2
Zone 1
Zone 2
Zone 1
Zone 2
0.67 0.50 0.65 0.62 0.60 0.61
0.56 0.56 0.57 0.58 0.56 0.57
0.70 0.69 0.69 0.75 0.67 0.70
0.69 0.72 0.74 0.66 0.65 0.69
0.62 0.57 0.62 0.66 0.62 0.62
0.48 0.56 0.65 0.67 0.59 0.59
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Table 4 The comparison between open bio-mass burning emissions (BB) and anthropogenic emissions (MEIC). Mar–Apr
Zone 1
PM2.5 BC OC SO2 NOx CO NH3 PM2.5 BC OC SO2 NOx CO NH3
Zone 2
Jun
Oct–Nov
BB
MEIC
Ratio
BB
MEIC
Ratio
BB
MEIC
Ratio
88 7 56 6 37 1137 18 14 1 9 1 5 155 2
110 26 49 262 346 2510 180 540 113 157 1608 1660 11380 741
79.7 27.0 114.8 2.5 10.7 45.3 10.1 2.5 0.8 5.8 0.1 0.3 1.4 0.3
3 0 2 0 1 33 0 6 1 3 0 3 101 2
55 10 15 136 181 997 116 282 58 77 886 870 5694 500
6.3 1.8 15.8 0.2 0.5 3.3 0.2 2.0 1.1 4.4 0.0 0.4 1.8 0.4
88 5 59 7 26 888 9 10 1 6 1 3 101 1
129 36 81 319 387 3407 161 567 129 200 1738 1722 12572 634
67.8 14.7 73.3 2.1 6.8 26.1 5.5 1.7 0.5 3.2 0.0 0.2 0.8 0.2
a
BB: the average bio-mass burning emissions from 2013 to 2017; MEIC: anthropogenic emissions of 2012 (from MEIC inventory); Emissions unit: Gg; Ratio = BB/ MEIC*100, unit: %.
certain fire points will be missed, leading to some errors and uncertainties. Moreover, the relationship between fire detections and area burned is highly uncertain and requires more on-going research. Therefore, in future work, the MODIS burned area product MCD64A1 will be used to improve the results from this study. Finally, this study did not consider the impact of bio-mass burning on air quality. Further research, especially quantitative analysis, should be conducted to quantify the negative impacts of bio-mass burning on atmospheric environment field.
timing, cloud coverage and the omission of small fire spots may lead to an underestimation of the bio-mass burning emissions. Therefore, the real bio-mass burning emissions may be larger than the statistical results of this study, which further proves the importance of open biomass burning, and this is the significance of this study. 4.3. Comparison of the emissions from open bio-mass burning to FINN inventory In addition, a comparison with FINN fire inventory is added to ensure that the emission of bio-mass burning in this study was consistent with other sources. Here, October 2014 was selected as the study period due to its high bio-mass burning episodes (Fig. 4(b)). It can be found from Table 5 that the emission of bio-mass burning in this study was generally higher than that in FINN inventory, with the value of the BB/ FINN less than two. The differences in emission mainly arise from (1) different fuel loading datasets and (2) the removal of low-confidence fire spots. But overall, the estimates of open bio-mass burning emissions in this study could still be trusted.
5. Conclusions By using the MODIS fire spots and VCF products within Land Use dataset, this research attempted to explore the spatial and temporal distribution of open bio-mass burning in the study area from 2013 to 2017, the corresponding proportion of different vegetation types and the causes of inter-annual changes in China. The results revealed the annual and monthly changes of bio-mass burning from 2013 to 2017, meanwhile the spatial and temporal distribution of burned area at three specific periods in two zones (Zone 1 and Zone 2) was obtained. Specifically, the bio-mass burning in Zone 1 is dominant both in March–April and October–November. For Zone 2, the bio-mass burning is most concentrated in June. In Zone 1 and Zone 2, the vegetation type of cropland occupied the largest proportion of burned area of more than 70% in any period, followed by forest. It was found that the forest burned area showed a positive correlation with the average temperature of the zone and a negative correlation with the average precipitation. The super El Niño event (2014–2016) led to the increased temperature and decreased precipitation in Zone 1 and the increased precipitation in Zone 2, which may contribute the 33% increase of forest burned area for Zone 1 and 35% reduction for Zone 2 in 2015, compared to the five-year average. The burning of crop residues is mainly controlled by human activities. The methodology and results from this research provide a useful reference for policy-makers to better understand the characteristics and variations of bio-mass burning in China and lay the groundwork for simulating and predicting the impact of bio-mass burning on air quality in the next work. Accordingly, more effective measures to monitor and control bio-mass burning in these areas can be posed and carried out to enhance local air quality and protect human health.
4.4. Limitations and prospect Although this study has obtained some expected results, certain limitations remain. Firstly, the combined uses of several data sets such as MODIS fire spot product, VCF product and land use dataset will cause some deviations due to their different spatial resolutions. Higher resolution data products or more reasonable coupling methods will be considered in next work. Secondly, due to the limited temporal resolution of MODIS fire spot products and unfavorable observation conditions such as higher cloud coverage and severe haze pollution, Table 5 The comparison of the emissions between open bio-mass burning (BB) in this study and FINN fire inventory in October 2014. Zone 1
a
Zone 2
Species
BB
FINN
BB/FINN
BB
FINN
BB/FINN
PM2.5 BC OC SO2 NOx CO NH3
45163 4242 27562 3249 23654 694205 13568
30800 2349 16313 2293 14286 404964 7477
1.47 1.81 1.69 1.42 1.66 1.71 1.81
11176 921 7068 816 5047 152642 2888
7969 815 4459 562 4599 133381 2706
1.40 1.13 1.59 1.45 1.10 1.14 1.07
Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
Emissions unit: ton. 164
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influence the work reported in this paper.
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