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Emission inventory research of typical agricultural machinery in Beijing, China
Atmospheric Environment 216 (2019) 116903
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Emission inventory research of typical agricultural machinery in Beijing, China Xiuning Hou, Jinling Tian, Changbo Song, Jie Wang, Jiyun Zhao, Xuemin Zhang
T
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College of Engineering, China Agricultural University, Beijing, 100083, China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: PEMS Agricultural machinery Emission factor Emission inventory
Non-road mobile machinery, especially agricultural machinery, has increasingly become an important source of air pollution in China. Hence, it is important to study these emissions. In this study, Beijing is used as an example, and the emission characteristics of 22 types of agricultural machinery (tractors, combine harvesters, and micro-tillers) in idling mode, moving mode, and working mode were tested using a portable emission measurement system (PEMS). The results indicate that the net power-based emission factor in working mode was larger than that in the idling and moving modes. Based on information from actual emission factors, ownership and activity levels, a typical agricultural machinery emission inventory for Beijing from 2006 to 2016 is established using the complex method from the Technical Guidelines for Compiling Non-Road Mobile Source Emission Inventory. In addition, the spatial distribution of the emission inventory is analyzed using Geographic Information System (GIS). Typical agricultural machinery in Beijing emitted carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), and the total, and these decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively, from 2006 to 2016, which proved the validity of emission control technologies and relevant regulations in China. In addition, the emission intensities of the different agricultural machines and different regions differed significantly. In 2016, the overall contribution of tractor emissions accounted for more than 80%. In the Yanqing District, Shunyi District, and Miyun District, the total emissions reached nearly 50%. The Chaoyang District was less than 1%. Therefore, in the future, government should pay more attention to high emission intensity agricultural machinery and its regional impacts.
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Corresponding author. E-mail address: [email protected] (X. Zhang).
https://doi.org/10.1016/j.atmosenv.2019.116903 Received 4 March 2019; Received in revised form 30 July 2019; Accepted 17 August 2019 Available online 19 August 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
Atmospheric Environment 216 (2019) 116903
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1. Introduction
example for other provinces and municipalities. In this study, eleven tractors, three combine harvesters, and eight micro-tillers are used as the research objects. Their emission characteristics under different working modes are measured using PEMS. Then the emission factors based on net power are calculated and compared. Due to this investigation, the three-level ownership and activity level of agricultural machinery in Beijing are obtained. Finally, according to the complex methods outlined in the Guidelines, an emission inventory of typical agricultural machinery in Beijing from 2006 to 2016 is obtained. The emission inventory shows the differences in emission intensities during different years, using different agricultural machinery, and in different regions of Beijing. This emission inventory of typical agricultural machinery in Beijing is of great significance for monitoring the atmospheric environment and controlling air pollution in Beijing.
According to the China Vehicle Environmental Management Annual Report (2017), non-road mobile sources emitted 704,000 tons of hydrocarbons (HC), 5,639,000 tons of nitrogen oxides (NOx), and 472,000 tons of particulate matter (PM) in 2015. Among these figures, agricultural machinery was responsible for emitting 352,000 tons of HC, 2,105,000 tons of NOX, and 210,000 tons of PM, accounting for 50.0%, 37.3%, and 44.4% of total emissions, respectively. Therefore, agricultural machinery is an important source of pollution from non-road mobile machinery. Hence, more in-depth research is needed regarding the emissions from these vehicles. The portable emission measurement system (PEMS) is a device that is directly placed in a driving machine to collect the pollutant emission rate. This device is widely used to study the regular pattern of emission changes, and it can obtain a sensitive response, is anti-vibrational and stable, and can achieve real-time data collection (Xu et al., 2006). Currently, PEMS is widely used to test motor vehicles emissions in China, and PEMS is also being used to test agricultural machinery. Some scholars have studied the emission characteristics of tractors and combine harvesters under actual operating modes using PEMS (SEMTECH-DS and MAHA-MPM4), which provides a basis and reference for the research of agricultural machinery emissions (Fu et al., 2013; Ge et al., 2013). An emission inventory can show the temporal and spatial distributions and changes in pollutants in a certain area over a period of time. Currently, the global level of non-road machinery activities and emission data are difficult to obtain, and the development of emission models and inventories is still being undertaken. Only the NONROAD model, developed by the U.S. Environmental Protection Agency (EPA), has been widely used (Lin, 2009). For a long time, the NONROAD model has been used for the research and compilation of non-road emission inventories in China. On December 31, 2014, the Ministry of Ecology and Environment of the People's Republic of China issued the Technical Guidelines for the Compilation of Emission Inventories of Nonroad Mobile Pollution Sources (Trial Implementation), hereinafter referred to as the Guidelines. Since then, a method for compiling air pollutant emission inventories of non-road mobile sources has been formally proposed, and standardization for the compilation of the emission inventories from non-road mobile sources has also been realized. Based on the current situation of non-road mobile sources in China, these Guidelines provide basic emission factors and establish calculation methods for major pollutant emissions from vehicles including construction machinery, agricultural machinery, small general machinery, diesel generating units, ships, railway diesel locomotives, and civil aviation aircraft. According to the accuracy of the calculation results, the inventory can be divided into complex methods based on net power or time, general methods based on mileage, or simple methods based on fuel consumption. Currently, some experts and scholars have established pollutant emission inventories in different regions, such as Beijing, Nanchang, Tianjin, Chengdu, Nanjing, the Yangtze River Delta, and the Pearl River Delta (Lu et al., 2017; Ji, 2015; Zhang et al., 2010, 2017; Xu., 2016; Xie and Yang, 2017; Xie and Zheng, 2016). Most of them have adopted the least accurate method in the Guidelines, estimations based on fuel consumption. They have also drawn primarily on the non-road mobile machinery emission factors provided by the United States given in the NONROAD model, or they have used the recommended values in the Guidelines. This has resulted in greater uncertainty in emission inventories. National Aeronautics and Space Administration(NASA)released a global air quality map in September 2010, showing that China has one of the highest concentration of fine particulate matter in the world (Zhang et al., 2016). In recent years, fog and haze have frequently occurred in Beijing, which has become one of the most serious areas for fog and haze pollution in China. Therefore, Beijing needs to formulate stricter local emission standards than the national standards and set an
2. Experimental method 2.1. Portable emission measurement system The PEMS is composed of the Testo 350 flue gas analyzer, the Sailsors TF100 thermal mass flowmeter, and the Pegasor PPS-M fast particle sensor. Among these, the Testo 350 flue gas analyzer is used to measure the volume concentration of CO, NOx, and HC. The TF100 thermal mass flowmeter is used to measure the mass flow of exhaust gas. It is suitable for high temperature, high humidity, and dirty gas emissions. It is stable and reliable and has been widely used in vehicle exhaust gas testing. The Pegasor PPS-M type fast particle sensor is used for instantaneous and long-term monitoring of ultrafine particles. It has high resolution and sensitivity and can measure instantaneous mass concentration and quantity concentration of particles. 2.2. Agricultural equipment and test site selection In the selection process of the testing machinery, three key factors were considered: (1) The ownership and use of agricultural machinery were considered synthetically. (2) The tested agricultural machinery should cover typical power ranges. (3) Stage II equipment with the largest number was chosen. In this study, twenty-two agricultural machines were tested, including eleven tractors, three combine harvesters, and eight micro-tillers. The engine power range of the tractor was 40.4–75 kW, the engine power of the combine was 86 kW, and the engine power range of the micro-tiller was 3.7–4.1 kW. According to the divisions of power ranges for agricultural machinery engines in the Guidelines, these engine powers fell into the following standardized power ranges: 37–75, 75–135, and 0–37 kW. Testing machines were leased from the Beijing Pinggu Agricultural Machinery Industry Association, which includes both foreign brands (John Deere) and domestic brands (YTO, LOVOL, YUEDA, and DUOLI). The experiment lasted from April to May, 2018. The test site was leased farmland in the Pinggu District, Beijing. The experimental process did not affect the agricultural activities of nearby farmland. The diesel oil used in the tests came from a gas station in the Pinggu District, and the sulfur content was less than 50 ppm. Table 1 presents the specifications of the tested agricultural machinery. 2.3. Test operating modes Previous studies have shown that the emission rates of pollutants from agricultural machinery vary greatly under different operating conditions (Fu et al., 2013; Ge et al., 2013). Therefore, in this study, the idling, moving, and working modes were selected to test. The operating modes are described in Table 2. The activity type distributions come from statistics obtained from the actual work of the agricultural machines in the field, an investigation of senior agricultural machinery operators, and references from relevant literature (Fu et al., 2013). 2
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Table 1 Specifications of the tested agricultural machinery. ID
Equipment type
Manufacturer
Mode year
Power (kW)
Emission standard
1 2 3 4 5 6 7 8 9 10 11 12
Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Combine harvester Combine harvester Combine harvester Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller
YTO JOHN DEERE JOHN DEERE JOHN DEERE JOHN DEERE LOVOL YUEDA YTO JOHN DEERE JOHN DEERE JOHN DEERE JOHN DEERE
2013 2012 2015 2011 2013 2015 2012 2012 2016 2014 2012 2013
40.4 55.1 55.1 55.1 55.1 55.1 59 62.5 75 75 75 86
Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage
JOHN DEERE
2011
86
Stage II
JOHN DEERE
2011
86
Stage II
DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI
2013 2014 2014 2013 2012 2014 2016 2014
3.7 3.7 4.1 4.1 4.1 4.1 4.1 4.1
Stage Stage Stage Stage Stage Stage Stage Stage
13 14 15 16 17 18 19 20 21 22
II II II II II II II II II II II II
Fig. 1. The general view of the PEMS installation.
quantity concentration of PM. Because the measurement time of each instrument was different, in order to synchronize time, it was necessary to correct the test data before calculating the instantaneous emission rate. 2.5.2. Calculation of the emission factor For gaseous pollutants, since the measured tail gas concentration is the dry basis concentration, and the tail gas in the actual discharge that contains water, it was necessary to convert the concentration under a dry base to that under a wet base. Equation (1)−(3) show the calculation process:
II II II II II II II II
Ci, w = Ci, d × K,
(1)
where i is the type of pollutant; Ci, w is the wet base concentration of i; Ci, d is the dry base concentration of i; and K is the conversion coefficient of the dry and wet bases.
2.4. Test procedure
K= Typically, the entire test procedure took 4–5 h for each device and generally included the following four steps: (1) PEMS Installation. The PEMS installation followed two principles: a. The instrument must be firmly and reliably installed to ensure continuous data recording during the testing process; b. The installation of the instrument must not affect the normal work of the testing machinery. Considering the complex structure of agricultural machinery and the small cab space, the test system was fixed on the top of the test machine. Alternately, the test system was connected with the machine to be tested and placed in a vehicle to collect the data during the agricultural operation. The installation process took approximately 40–60 min. Fig. 1 shows the general view of the PEMS installation. (2) PEMS warm up. The Testo 350 and PPS-M needed to warm up for 30 min each. (3) Emission tests were performed for at least 20 min per mode. (4) The PEMS was removed.
1 , 1 + 0.5 × (CCO, d + CCO2, d ) × y − CH2, d
(2)
where y is the atomic ratio of hydrogen to carbon in fuel. For diesel, it is usually 1.85.
CH2, d =
0.5 × y × CCO, d × (CCO, d + CCO2, d ) , CCO, d + 3 × CCO2, d
(3)
The instantaneous mass emission rate (g/s) is calculated according to Equation (4):
ERi = Ci, w × Vstd × ρi, std ,
(4)
where ERi is the instantaneous mass emission rate of i; g/s, Vstd is the standard volume flowrate of the tail gas, m3/min; and ρi, std is the standard density of the pollutant i, g/m3. For the PM, the data measured using PPS-M was the mass concentration (mg/m3). The instantaneous mass emission rate (g/s) was calculated according to Equation (5):
2.5. Data processing
ERPM g s = CPM × Vstd,
2.5.1. Time synchronized treatment of pollutants In this study, the original data measured using PEMS were the volume concentration of gaseous pollutants (CO, HC, NOx), the instantaneous mass flow of the tail gas, the mass concentration and the
where ERPM is the instantaneous mass emission rate of PM, g/s; and Vstd is the standard volume flowrate of the tail gas, m3/min. The way to calculate Vstd refers to HJ857-2017 issued by Ministry of Environmental Protection (MEP) of China 2017. The Vstd is
( )
(5)
Table 2 Operating modes and the activity type distribution. Equipment type
Operating modes
Equipment activities
Distributions of activity types
Tractor
Idling
5%
Combine harvester
Moving Working Idling
The engine keeps running at a low speed (600–800 rpm), but the tested equipment is not moving or working The tractor is moving, the working mechanism is in a static state, and there is no power output. The tractor provides power for the operation mechanism (rotary tillage) while moving on the farmland. The engine keeps running at a low speed (600–800 rpm), but the tested equipment is not moving or working The combine harvester is moving, the working mechanism is in a static state, and there is no power output. The combine harvester does crop harvesting while moving in the field. Rotary tillage with micro-tiller while moving in the farmland
Moving
Micro-tiller
Working Working
3
15% 80% 5% 15% 80% 100%
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calculated by dividing the mass flow by the standard density, and the standard density of the tail gas is 1.293 kg/m3. The basic emission factor was calculated based on Equation (6):
Table 3 Activity level information of tractors in each power range in Beijing.
k
EFi =
∑n = j ERi, n × 3600 Pn
,
∑ EFi,m × FCm
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37 37 ≤ G < 75 ≤ G < G ≥ 135 G < 37 37 ≤ G < 75 ≤ G < G ≥ 135
400 400 400 400 600 600 600 600
22.07 50.7 89.74 152.42 22.07 50.7 89.74 152.42
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
Planting areas in northern Beijing such as Yanqing, Shunyi, Miyun, etc. Planting areas in southern Beijing such as Daxing, Fangshan, Tongzhou, etc.
(6)
where EF (g/kWh) is a net power-based emission factor; i is a certain pollutant; n (s) is the duration of a certain operating mode; J (s) and K (s) are the start and end times of the operating mode, respectively; ER (g/s) is the instantaneous emission rate of a pollutant at a certain operating mode; and P is the engine power at that operating mode (kWh). The total emission factors were calculated according to Equation (7):
TEFi =
Engine Power (kW)
(7)
75 135
75 135
Table 4 Activity level information of combine harvesters in each power range in Beijing.
where TEF is the total emission factor; m is the typical operating mode (such as idling, moving, working); and FC is the weight factors of the operating mode.
Engine Power (kW)
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37 37 ≤ G < 75 ≤ G < G ≥ 135 G < 37 37 ≤ G < 75 ≤ G < G ≥ 135
150 150 150 150 300 300 300 300
22.06 66 91.47 208.68 22.06 66 91.47 208.68
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
Planting areas in northern Beijing such as Yanqing, Shunyi, Miyun, etc. Planting areas in southern Beijing such as Daxing, Fangshan, Tongzhou, etc.
3. Method of establishing the emission inventory 3.1. Emission calculation method According to the method with the highest accuracy in the Guidelines, the pollutant discharge was calculated according to the Equation (8):
E=
∑ ∑ ∑ (Pj,k,n × Gj.k .n × LFj,k,n × hrj.k .n × EFj,k,n) × 10−6, j
k
n
(8)
where E (t) is the CO, HC, NOx, and PM emissions of a non-road mobile machine; j is the category of the non-road movable machine; k is the emission standard; n is the engine power range; P is the machine ownership; G is the average rated net power; LF is the load factor; hr (h) is the number of hours used in the year; and EF (g/kWh) is the pollutant emission factor. It is worthy to note that the emission factors were measured only for tractors, combine harvesters, and micro-tillers in stage II with power ranges of 37–75, 75–130, and less than 37 kW, respectively. Previous studies have shown that different power engines and different emission stages will lead to different emission factors (Fu et al., 2012, 2013; Ge et al., 2013). Therefore, to achieve higher accuracy in the calculation of emission inventories, the recommended values in the Guidelines were used in other power stages and emission stages.
75 135
75 135
4. Results and discussion 4.1. Emission factors The emission rates and net power-based emission factors of tractors and combine harvesters under different operating modes were calculated (Tables 6 and 7). On the whole, the pollutant emission rates of these two agricultural machines in working mode were significantly higher than those in idling and moving modes, and the idling mode was the lowest. This is consistent with the views proposed by Fu et al. (2013). The same is true for the net power-based emission factors calculated from the emission rates. For CO, HC, NOx, and PM emitted by the tractors, the emission factors during the working mode were 3.43, 5.86, 3.65, and 3.82 times, respectively, that of the idling mode, and 2.22, 3.15, 2.92, and 3.04 times, respectively, that of the moving mode. In addition, the emission factors were 98.2%, 84.6%, 99.6%, and 110.5 times, respectively, that of the recommended values in the Guidelines. For CO, HC, NOx, and PM emitted by the combine harvester, the emission factors of the working mode were 3.09, 4.47, 3.09, and 9.21 times, respectively, that of the idling mode, and 1.96, 2.38, 1.31, and 1.67 times, respectively, that of the moving mode. In addition, the emission factors were 103.6%, 98%, 98.7%, and 106.9 times, respectively, that of recommended value in the Guidelines. The higher emission factor of the working mode was due to the higher engine load and speed. It was not convenient to install the PPS-M on the micro-tiller due to its small size. The PM emission factor of the micro-tiller was recommended by the Guidelines for compiling an emission inventory. Table 8 shows the micro-tiller emission factors in the Stage II emission standard.The emission factors of CO, HC, and NOx were 115.8%, 107.7%, and 119.6% times, respectively, that of recommended value in the Guidelines. The emission factors of each pollutant of the three kinds of agricultural machines were not completely consistent with the recommended values in the Guidelines, but the differences were not significant. The reason is that the measured emission coefficient is for a certain agricultural machine in a certain area, and the recommended value in the Guidelines is not detailed for different regions, machine
3.2. Information on the activity levels of typical agricultural machines The activity levels of agricultural machines are closely related to the region in which they operate. Due to a lack of relevant literature on the investigation of agricultural machinery activity levels in China, a large number of investigations were conducted. By referring to the survey data and the recommended values from the Guidelines, the activity level information, including the average annual working hours, load factor, and average rated net power, were obtained. Due to the differences in ripening systems between the southern and northern planting areas in Beijing (one crop per year in the northern region and two crops per year in the southern region), the level of agricultural machinery activity was also slightly different. The tractor use times in the south and north were 600 and 400 h, respectively, and the combine harvester use times in the south and north were 300 and 150 h, respectively. There was a slight gap between the survey data and the recommended values in the Guidelines. The average annual working hours of micro-tillers all referred to the recommended values in the Guidelines. The average rated net power was obtained by dividing the total power by the retention amount. The load factor was determined to be 0.75 and was found by consulting the relevant literature and research, and the activity level of each type is shown in Tables 3−5. 4
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Table 5 Activity level information of micro-tillers in Beijing. Engine Power (kW)
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37
380
4.0
0.75
Planting areas in Beijing.
agricultural machinery showed a downward trend. Among the four pollutants, NOx accounted for the highest proportion, accounting for more than 50% of the total emissions. This was followed by CO with approximately 30%–40%, and HC and PM had the least proportion of total emissions, which was related to its emission factor. It is worth noting that after 2011, NOx and PM nearly displayed a downward trend, regardless of the increase or decrease in the amount of ownership. This is due to the increasing proportion of stage II equipment and the more stringent emission limits on pollutants, especially NOx and PM, imposed by the non-road stage II standard. This showed that the tightening of non-road emission limits was very effective in reducing pollutants, particularly NOx and PM emissions. From Fig. 2(a) and (b), it can be seen that, due to a decrease in the ownership and an increase in the proportion of stage II equipment, the total emissions of CO, HC, NOx, PM, and pollutants from tractors decreased by 69.5%, 68.67%, 76.46%, 79.41%, and 73.8%, respectively, from 2006 to 2016. It was calculated that, on the basis of a decrease in the ownership, the emission limits were tightened to reduce emissions of NOx and PM by 9.51% and 12.46%, respectively. The proportion of NOx and PM in the total pollutant emissions decreased from 5.18% to 55.32%, respectively, in 2006 to 4.07% and 49.71%, respectively, in 2016. For the same reason, emissions of CO, HC, NOx, and PM of combine harvesters decreased by 51.42%, 56.3%, 65%, and 76.7%, respectively, from 2006 to 2016. The emission limits were tightened to reduce emissions of NOx and PM by 18.2% and 28.53%, respectively. The proportion of PM and NOx in the total emissions decreased from 4.48%, and 58.66% in 2006 to 2.66% and 51.82% in 2016, respectively. As shown in Fig. 2(c), from 2006 to 2016, the ownership of microtillers kept increasing, and the emissions of pollutants and the total emissions increased. From 2006 to 2016, on the basis of a 164.79% increase in the ownership of micro-tillers, the emission limits were tightened to reduce the increase in NOx and PM emissions by 71.88% and 55.15%, respectively. PM and NOx accounted for 5.86% and 54.01%, respectively, of total emissions in 2006, and decreased to 5.52% and 46.78%, respectively, in 2016. As shown in Fig. 2(d), the total amount of typical agricultural machinery (tractors, combine harvesters, and micro-tillers) fluctuated between 30,000 and 40,000 units in Beijing from 2006 to 2016, showing a downward trend as a whole. The total amount of CO, HC, NO, PM, and total emissions decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively. The tightening of emission limits resulted in decreases in CO, HC, NO, PM, and total emissions of 2.58%, 2.31%, 13%, 14.54%, and 8.11%, respectively. PM and NOx accounted for 5.15% and 55.49%, respectively, of total emissions in 2006 and decreased to 4.16% and 49.46%, respectively, in 2016.
Table 6 Tractor emission factors in the Stage II emission standard of the 37–75 kW power section. Operating modes Emission rate(g/s)
Idling Moving Working Emission factor(g/kWh) Idling Moving Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
PM
0.025 0.038 0.08 1.62 2.51 5.56 4.91 5
0.0034 0.0063 0.0198 0.22 0.41 1.29 1.10 1.3
0.034 0.042 0.123 2.21 2.76 8.06 6.97 7
0.0020 0.0025 0.0075 0.128 0.161 0.489 0.42 0.38
Table 7 Combine harvester emission factors in the Stage II emission standard of the 75–135 kW power section. Operating modes Emission rate(g/s)
Idling Moving Working Emission factor(g/kWh) Idling Moving Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
PM
0.04 0.07 0.14 1.88 2.96 5.80 5.18 5
0.006 0.011 0.027 0.25 0.47 1.117 0.98 1
0.049 0.116 0.152 2.06 4.86 6.36 5.92 6
0.0009 0.0054 0.0082 0.0375 0.2070 0.3453 0.31 0.29
Table 8 Micro-tiller emission factors in the Stage II emission standards of the 0–37 kW power section. Operating modes Emission rate(g/s) Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
0.008 7.53 6.5
0.0015 1.4 1.3
0.01 8.97 7.5
types, and other factors. The small gap with the recommended values in the Guidelines also shows that the test data in this study had a certain degree of accuracy. 4.2. Emission inventory of typical agricultural machines in Beijing from 2006 to 2016 The ownership of typical agricultural machines in Beijing from 2006 to 2016 was obtained by consulting the Yearbook of China's Agricultural Machinery Industry. By visiting agricultural machinery management departments and cooperatives, statistics of existing data, and the proportion of different power ranges of agricultural machinery in relevant references, the quantity of different emission stages and different power stages of typical agricultural machines from 2006 to 2016 was obtained (Li, 2016). By combining the activity levels and emission factors, the pollutant emissions of agricultural machinery in Beijing from 2006 to 2016 were calculated,as shown in Fig. 2. From Fig. 2, overall, the trend in pollutant emissions and total emissions from 2006 to 2016 was similar to that of the agricultural machinery ownership. The pollutant emissions of tractors and combine harvesters showed a downward trend, the pollutant emissions of microtillers showed an upward trend, and the overall pollutant emissions of
4.3. Emissions of pollutants from different agricultural machines In 2016, typical agricultural machines in Beijing emitted 855.43 tons of CO, 184.87 tons of HC, 1108.83 tons of NOx, and 93.62 tons of PM, totaling 2242.75 tons. The contribution rates of different agricultural machines to pollutant emissions were calculated using 2016 as an example. As can be seen from Fig. 3, whether for CO, HC, NO, PM, or total emissions, the proportion of tractor emissions was greater than 80%, and the contribution rate was the highest, followed very closely by combine harvesters and micro-tillers. This was due to the fact that there were far more tractors than combine harvesters, and their power 5
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Fig. 2. Pollutant emissions of agricultural machinery in Beijing from 2006 to 2016. a is tractors, b is combine harvesters, c is micro tillers, and d is the total.
limits on micro-tillers in the power range below 37 kW are not strict, which results in the poor actual emission of micro-tillers. Therefore, their emissions were close to those of the combine harvesters.
4.4. The spatial distribution of emission inventories According to statistics from the relevant agricultural departments in Beijing and a consultation with the Yearbook of China Agricultural Machinery Industry, the third-level ownership of typical agricultural machinery in various districts and counties of Beijing in 2016 was obtained. Then the pollutant emissions of each district and county were calculated according to Equation (8). The spatial distribution maps of the total amount of agricultural machinery emissions and pollutant emissions in Beijing were drawn using GIS technology, as shown in Fig. 4. On the whole, the emission intensity in the northern part of Beijing was higher than that in the southern part. The reason is that the ownership of agricultural machinery in the northern region was much higher than that in the southern region, which makes emissions in the southern region with higher activity levels lower than that in the northern region with lower activity levels. Regardless of CO, HC, NOx, PM, or total emissions, the highest contribution was from the Yanqing District, the Shunyi District, and the Miyun District, followed by the
Fig. 3. The contribution rates of pollutant emissions from different agricultural machines.
was much greater than micro-tillers. Even though the single power of a combine harvester is far greater than that of a micro-tiller, the number of micro-tillers was huge, and the implementation of stage I and II regulations primarily restricts the emission limits on combine harvesters and tractors in the power section above 37 kW. The emission 6
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Fig. 4. Spatial distribution of agricultural machinery emissions in various districts and counties of Beijing. a is CO, b is HC, c is NOx, d is PM, and e is the totality.
the emission inventory of agricultural machinery in Beijing in 2007. The reason could be that the differences in the time frames may have influenced the results. In addition, this study used a net power-based emission factor, which caused a relatively higher accuracy. It is also worth noting that for CO, HC, NO, PM, and total emissions, the total emissions in the Yanqing, Shunyi, and Miyun areas were nearly 50%. The lowest emission intensity of the Chaoyang District was
Daxing District, the Fangshan District, the Tongzhou District, and the Pinggu District. Additionally, the Changping District, the Mentougou District, the Fengtai District, the Haidian District, and the Chaoyang District had the lowest emission intensities. The reasons for the disparities were due to the ownership and activity levels. But this is slightly different from previous studies. Fan et al. (2011) found that the emission intensities of the Miyun and Pinggu areas were the highest in 7
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less than 1%. The emissions of pollutants and total emissions in the Yanqing District were more than 35 times that in the Chaoyang District, and there was a great gap between them. The most important reason was the ownership. The Chaoyang District is located in the center of the city with very few crops and a very low ownership of agricultural machinery, while the Yanqing District is located in the outskirts of the city with a large area of crop cultivation and a high ownership of agricultural machinery.
in this study. In the future, government should pay more attention to the regional impacts of high emission intensity agricultural machinery. To understand more about the actual emission levels from agricultural machinery, more experiments are needed to supplement the database of emission factors that are suitable for China. In addition, more detailed information needs to be provided for the formulation of emission inventories. This is a necessary step for China to formulate effective air pollution control policies.
4.5. Uncertainty analysis
Declaration of interest statement
The uncertainties in this study primarily originate from key data such as actual emission factors, the level of mechanical activity, and the ownership. China has not yet established a relevant registration and filing system for agricultural machinery, and there are differences in statistics between different departments. Hence, there are some uncertainties in the amount of data. In addition, the level of mechanical activity was obtained by visiting agricultural machinery operators and agricultural machinery cooperatives, and there are some uncertainties in this data. Also, data regarding non-road mobile source emission factors are very scarce at this stage in time. In this study, only stage II machinery was measured, and the recommended values in the Guidelines are used for other emission stages of machinery. Those values are primarily based on the EU EMEP/CORINAIR data, which does not adequately reflect the emission levels of agricultural machinery under local actual working and operating conditions.
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
5. Conclusion
References
Whether it was a tractor or a combine harvester, the pollutant emission rates of the three activity modes were quite different. The working mode was the highest, and the idling mode was the lowest, which is consistent with previous studies. The net power-based emission factors also displayed the same pattern, and the emission factors of the three agricultural machines showed little differences with the recommended values in the Guidelines, which shows that the data in this study had a certain degree of authenticity and reliability. From 2006 to 2016, the total emissions of CO, HC, NOx, PM, and the total from typical agricultural machinery in Beijing decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively. The tightened emission limits reduced the emissions of CO, HC, NOx, PM, and the total by 2.58%, 2.31%, 13%, 14.54%, and 8.11%, respectively. This shows that China's various control strategies and policies effectively reduced pollutant emissions, particularly NOx and PM. In 2016, typical agricultural machinery in Beijing emitted 855.43 tons of CO, 184.87 tons of HC, 1108.83 tons of NOX, and 93.62 tons of PM, totaling 2242.75 tons. Among them, tractors contributed the most emissions, reaching more than 80%. The proportions of emissions from combine harvesters and micro-tillers were very close. It is predicted that the total emissions from micro-tillers will exceed that of combine harvesters in the future. Therefore, attention should be paid to the supervision and control of tractor emissions. In addition, the supervision of small agricultural machinery, such as micro-tillers, should be strengthened. According to the emission intensities of different districts and counties in Beijing in 2016, there was a great gap between different districts and counties in Beijing. The areas with the highest intensities were the Yanqing District, the Shunyi District, and the Miyun District. This result is slightly different from previous studies, which indicates that time may have influenced the results. Also, more accurate inventory preparation methods may also have led to a different outcome
China Vehicle Environmental Management Annual Report. Ministry of Environmental Protection of the People's Republic of China. 2017, Beijing. Fan, S.B., Nie, L., Kan, R.B., Li, X.F., Yang, T., 2011. Fuel consumption based exhaust emissions estimating from agriculture equipment in Beijing. J. Saf. Environ. 11 (1), 145–148. Fu, M.L., Ge, Y.S., Tan, J.W., Zeng, T., Liang, B., 2012. Characteristics of typical non-road machinery emissions in China by using portable emission measurement system. Sci. Total Environ. 437. Fu, M.L., Ding, Y., Yin, H., Ji, Z., Ge, Y.S., Liang, B., 2013. Characteristics of agricultural tractors emissions under real-world operating cycle. Trans. Chin. Soc. Agric. Eng. 29 (6), 42–48. Ge, Y.S., Liu, H.K., Ding, Y., Yin, H., Fu, M.L., Li, J.Q., 2013. Experimental study on characteristics of emissions and fuel consumption for combines. Trans. Chin. Soc. Agric. Eng. 29 (19), 41–47. Ji, Y.K., 2015. Development of an Air Pollutant Emission Inventory for Beijing and Preliminary Study of Haze Weather. Beijing Jiaotong University. Li, M.Y., 2016. Research of Mobile Source Inventories. Beijing institute of Technology. Lin, X.L., 2009. A study on models of NONROAD mobile source emissions. Environmental Science and Management 34 (4), 42–45. Lu, J., Huang, C., Hu, X.Y., Yang, Q., Jing, B.L., 2017. Air pollutant emission inventory of non-road machineries in typical cities in eastern China. Environ. Sci. 38 (7), 2738–2746. Ministry of Environmental Protection (MEP) of China, 2017. Measurement Method and Technical Specification for PEMS Test of Exhaust Pollutants from Heavy-Duty Diesel and Gas Fuelled Vehicles. HJ857-2017. Xie, Y.S., Yang, F., 2017. Establishment Study on emission inventory of mobile source in Nanjing city. Anhui Agricultural Science Bulletin 23 (10), 98–101. Xie, Y.S., Zheng, X.M., 2016. Atmospheric pollutant emission inventory from non-road mobile sources in Nanjing and its characteris. Pollut. Control Technol. (4), 47–51. Xu, P., 2016. The Research of an Air Pollutant Emission Inventory for Yangzhou. Yangzhou university.2016. Xu, J.C., Li, M.L., Xu, D., Su, M.H., 2006. Study on portable emission measurement system. Automot. Eng. (3), 30–33. Zhang, L.J., Zheng, J.Y., Yin, S.S., Peng, K., Zhong, L.J., 2010. Development of non-road mobile source emission inventory for the Pearl River Delta region. Environ. Sci. 31 (4), 886–891. Zhang, S.C., Li, Z.Y., Dao, K.Y., Nan, Y., Shi, X., Liu, H.B., 2016. Correlation analysis of GPS water vapor and hazy in Beijing. Sci. Surv. Mapp. 41 (08), 43–47. Zhang, Y., Andre, Michel., Li, D., Zhang, X., Wu, L., 2017. Development of a non-road mobile source emissions inventory for Tianjin. Environ. Sci. 11, 4447–4453.
Acknowledgments This study was supported by the Key National R & D Program of China (Grant No. 2017YFD0700300). We thank Bo Zhang, Nianjie Ma, Junnan Cheng, Shushan Shao, and Dongxu Zhang for their contributions to this research. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116903.
8
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Emission inventory research of typical agricultural machinery in Beijing, China Xiuning Hou, Jinling Tian, Changbo Song, Jie Wang, Jiyun Zhao, Xuemin Zhang
T
⁎
College of Engineering, China Agricultural University, Beijing, 100083, China
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords: PEMS Agricultural machinery Emission factor Emission inventory
Non-road mobile machinery, especially agricultural machinery, has increasingly become an important source of air pollution in China. Hence, it is important to study these emissions. In this study, Beijing is used as an example, and the emission characteristics of 22 types of agricultural machinery (tractors, combine harvesters, and micro-tillers) in idling mode, moving mode, and working mode were tested using a portable emission measurement system (PEMS). The results indicate that the net power-based emission factor in working mode was larger than that in the idling and moving modes. Based on information from actual emission factors, ownership and activity levels, a typical agricultural machinery emission inventory for Beijing from 2006 to 2016 is established using the complex method from the Technical Guidelines for Compiling Non-Road Mobile Source Emission Inventory. In addition, the spatial distribution of the emission inventory is analyzed using Geographic Information System (GIS). Typical agricultural machinery in Beijing emitted carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), and the total, and these decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively, from 2006 to 2016, which proved the validity of emission control technologies and relevant regulations in China. In addition, the emission intensities of the different agricultural machines and different regions differed significantly. In 2016, the overall contribution of tractor emissions accounted for more than 80%. In the Yanqing District, Shunyi District, and Miyun District, the total emissions reached nearly 50%. The Chaoyang District was less than 1%. Therefore, in the future, government should pay more attention to high emission intensity agricultural machinery and its regional impacts.
⁎
Corresponding author. E-mail address: [email protected] (X. Zhang).
https://doi.org/10.1016/j.atmosenv.2019.116903 Received 4 March 2019; Received in revised form 30 July 2019; Accepted 17 August 2019 Available online 19 August 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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1. Introduction
example for other provinces and municipalities. In this study, eleven tractors, three combine harvesters, and eight micro-tillers are used as the research objects. Their emission characteristics under different working modes are measured using PEMS. Then the emission factors based on net power are calculated and compared. Due to this investigation, the three-level ownership and activity level of agricultural machinery in Beijing are obtained. Finally, according to the complex methods outlined in the Guidelines, an emission inventory of typical agricultural machinery in Beijing from 2006 to 2016 is obtained. The emission inventory shows the differences in emission intensities during different years, using different agricultural machinery, and in different regions of Beijing. This emission inventory of typical agricultural machinery in Beijing is of great significance for monitoring the atmospheric environment and controlling air pollution in Beijing.
According to the China Vehicle Environmental Management Annual Report (2017), non-road mobile sources emitted 704,000 tons of hydrocarbons (HC), 5,639,000 tons of nitrogen oxides (NOx), and 472,000 tons of particulate matter (PM) in 2015. Among these figures, agricultural machinery was responsible for emitting 352,000 tons of HC, 2,105,000 tons of NOX, and 210,000 tons of PM, accounting for 50.0%, 37.3%, and 44.4% of total emissions, respectively. Therefore, agricultural machinery is an important source of pollution from non-road mobile machinery. Hence, more in-depth research is needed regarding the emissions from these vehicles. The portable emission measurement system (PEMS) is a device that is directly placed in a driving machine to collect the pollutant emission rate. This device is widely used to study the regular pattern of emission changes, and it can obtain a sensitive response, is anti-vibrational and stable, and can achieve real-time data collection (Xu et al., 2006). Currently, PEMS is widely used to test motor vehicles emissions in China, and PEMS is also being used to test agricultural machinery. Some scholars have studied the emission characteristics of tractors and combine harvesters under actual operating modes using PEMS (SEMTECH-DS and MAHA-MPM4), which provides a basis and reference for the research of agricultural machinery emissions (Fu et al., 2013; Ge et al., 2013). An emission inventory can show the temporal and spatial distributions and changes in pollutants in a certain area over a period of time. Currently, the global level of non-road machinery activities and emission data are difficult to obtain, and the development of emission models and inventories is still being undertaken. Only the NONROAD model, developed by the U.S. Environmental Protection Agency (EPA), has been widely used (Lin, 2009). For a long time, the NONROAD model has been used for the research and compilation of non-road emission inventories in China. On December 31, 2014, the Ministry of Ecology and Environment of the People's Republic of China issued the Technical Guidelines for the Compilation of Emission Inventories of Nonroad Mobile Pollution Sources (Trial Implementation), hereinafter referred to as the Guidelines. Since then, a method for compiling air pollutant emission inventories of non-road mobile sources has been formally proposed, and standardization for the compilation of the emission inventories from non-road mobile sources has also been realized. Based on the current situation of non-road mobile sources in China, these Guidelines provide basic emission factors and establish calculation methods for major pollutant emissions from vehicles including construction machinery, agricultural machinery, small general machinery, diesel generating units, ships, railway diesel locomotives, and civil aviation aircraft. According to the accuracy of the calculation results, the inventory can be divided into complex methods based on net power or time, general methods based on mileage, or simple methods based on fuel consumption. Currently, some experts and scholars have established pollutant emission inventories in different regions, such as Beijing, Nanchang, Tianjin, Chengdu, Nanjing, the Yangtze River Delta, and the Pearl River Delta (Lu et al., 2017; Ji, 2015; Zhang et al., 2010, 2017; Xu., 2016; Xie and Yang, 2017; Xie and Zheng, 2016). Most of them have adopted the least accurate method in the Guidelines, estimations based on fuel consumption. They have also drawn primarily on the non-road mobile machinery emission factors provided by the United States given in the NONROAD model, or they have used the recommended values in the Guidelines. This has resulted in greater uncertainty in emission inventories. National Aeronautics and Space Administration(NASA)released a global air quality map in September 2010, showing that China has one of the highest concentration of fine particulate matter in the world (Zhang et al., 2016). In recent years, fog and haze have frequently occurred in Beijing, which has become one of the most serious areas for fog and haze pollution in China. Therefore, Beijing needs to formulate stricter local emission standards than the national standards and set an
2. Experimental method 2.1. Portable emission measurement system The PEMS is composed of the Testo 350 flue gas analyzer, the Sailsors TF100 thermal mass flowmeter, and the Pegasor PPS-M fast particle sensor. Among these, the Testo 350 flue gas analyzer is used to measure the volume concentration of CO, NOx, and HC. The TF100 thermal mass flowmeter is used to measure the mass flow of exhaust gas. It is suitable for high temperature, high humidity, and dirty gas emissions. It is stable and reliable and has been widely used in vehicle exhaust gas testing. The Pegasor PPS-M type fast particle sensor is used for instantaneous and long-term monitoring of ultrafine particles. It has high resolution and sensitivity and can measure instantaneous mass concentration and quantity concentration of particles. 2.2. Agricultural equipment and test site selection In the selection process of the testing machinery, three key factors were considered: (1) The ownership and use of agricultural machinery were considered synthetically. (2) The tested agricultural machinery should cover typical power ranges. (3) Stage II equipment with the largest number was chosen. In this study, twenty-two agricultural machines were tested, including eleven tractors, three combine harvesters, and eight micro-tillers. The engine power range of the tractor was 40.4–75 kW, the engine power of the combine was 86 kW, and the engine power range of the micro-tiller was 3.7–4.1 kW. According to the divisions of power ranges for agricultural machinery engines in the Guidelines, these engine powers fell into the following standardized power ranges: 37–75, 75–135, and 0–37 kW. Testing machines were leased from the Beijing Pinggu Agricultural Machinery Industry Association, which includes both foreign brands (John Deere) and domestic brands (YTO, LOVOL, YUEDA, and DUOLI). The experiment lasted from April to May, 2018. The test site was leased farmland in the Pinggu District, Beijing. The experimental process did not affect the agricultural activities of nearby farmland. The diesel oil used in the tests came from a gas station in the Pinggu District, and the sulfur content was less than 50 ppm. Table 1 presents the specifications of the tested agricultural machinery. 2.3. Test operating modes Previous studies have shown that the emission rates of pollutants from agricultural machinery vary greatly under different operating conditions (Fu et al., 2013; Ge et al., 2013). Therefore, in this study, the idling, moving, and working modes were selected to test. The operating modes are described in Table 2. The activity type distributions come from statistics obtained from the actual work of the agricultural machines in the field, an investigation of senior agricultural machinery operators, and references from relevant literature (Fu et al., 2013). 2
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Table 1 Specifications of the tested agricultural machinery. ID
Equipment type
Manufacturer
Mode year
Power (kW)
Emission standard
1 2 3 4 5 6 7 8 9 10 11 12
Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Tractor Combine harvester Combine harvester Combine harvester Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller Micro-tiller
YTO JOHN DEERE JOHN DEERE JOHN DEERE JOHN DEERE LOVOL YUEDA YTO JOHN DEERE JOHN DEERE JOHN DEERE JOHN DEERE
2013 2012 2015 2011 2013 2015 2012 2012 2016 2014 2012 2013
40.4 55.1 55.1 55.1 55.1 55.1 59 62.5 75 75 75 86
Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage Stage
JOHN DEERE
2011
86
Stage II
JOHN DEERE
2011
86
Stage II
DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI DUOLI
2013 2014 2014 2013 2012 2014 2016 2014
3.7 3.7 4.1 4.1 4.1 4.1 4.1 4.1
Stage Stage Stage Stage Stage Stage Stage Stage
13 14 15 16 17 18 19 20 21 22
II II II II II II II II II II II II
Fig. 1. The general view of the PEMS installation.
quantity concentration of PM. Because the measurement time of each instrument was different, in order to synchronize time, it was necessary to correct the test data before calculating the instantaneous emission rate. 2.5.2. Calculation of the emission factor For gaseous pollutants, since the measured tail gas concentration is the dry basis concentration, and the tail gas in the actual discharge that contains water, it was necessary to convert the concentration under a dry base to that under a wet base. Equation (1)−(3) show the calculation process:
II II II II II II II II
Ci, w = Ci, d × K,
(1)
where i is the type of pollutant; Ci, w is the wet base concentration of i; Ci, d is the dry base concentration of i; and K is the conversion coefficient of the dry and wet bases.
2.4. Test procedure
K= Typically, the entire test procedure took 4–5 h for each device and generally included the following four steps: (1) PEMS Installation. The PEMS installation followed two principles: a. The instrument must be firmly and reliably installed to ensure continuous data recording during the testing process; b. The installation of the instrument must not affect the normal work of the testing machinery. Considering the complex structure of agricultural machinery and the small cab space, the test system was fixed on the top of the test machine. Alternately, the test system was connected with the machine to be tested and placed in a vehicle to collect the data during the agricultural operation. The installation process took approximately 40–60 min. Fig. 1 shows the general view of the PEMS installation. (2) PEMS warm up. The Testo 350 and PPS-M needed to warm up for 30 min each. (3) Emission tests were performed for at least 20 min per mode. (4) The PEMS was removed.
1 , 1 + 0.5 × (CCO, d + CCO2, d ) × y − CH2, d
(2)
where y is the atomic ratio of hydrogen to carbon in fuel. For diesel, it is usually 1.85.
CH2, d =
0.5 × y × CCO, d × (CCO, d + CCO2, d ) , CCO, d + 3 × CCO2, d
(3)
The instantaneous mass emission rate (g/s) is calculated according to Equation (4):
ERi = Ci, w × Vstd × ρi, std ,
(4)
where ERi is the instantaneous mass emission rate of i; g/s, Vstd is the standard volume flowrate of the tail gas, m3/min; and ρi, std is the standard density of the pollutant i, g/m3. For the PM, the data measured using PPS-M was the mass concentration (mg/m3). The instantaneous mass emission rate (g/s) was calculated according to Equation (5):
2.5. Data processing
ERPM g s = CPM × Vstd,
2.5.1. Time synchronized treatment of pollutants In this study, the original data measured using PEMS were the volume concentration of gaseous pollutants (CO, HC, NOx), the instantaneous mass flow of the tail gas, the mass concentration and the
where ERPM is the instantaneous mass emission rate of PM, g/s; and Vstd is the standard volume flowrate of the tail gas, m3/min. The way to calculate Vstd refers to HJ857-2017 issued by Ministry of Environmental Protection (MEP) of China 2017. The Vstd is
( )
(5)
Table 2 Operating modes and the activity type distribution. Equipment type
Operating modes
Equipment activities
Distributions of activity types
Tractor
Idling
5%
Combine harvester
Moving Working Idling
The engine keeps running at a low speed (600–800 rpm), but the tested equipment is not moving or working The tractor is moving, the working mechanism is in a static state, and there is no power output. The tractor provides power for the operation mechanism (rotary tillage) while moving on the farmland. The engine keeps running at a low speed (600–800 rpm), but the tested equipment is not moving or working The combine harvester is moving, the working mechanism is in a static state, and there is no power output. The combine harvester does crop harvesting while moving in the field. Rotary tillage with micro-tiller while moving in the farmland
Moving
Micro-tiller
Working Working
3
15% 80% 5% 15% 80% 100%
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calculated by dividing the mass flow by the standard density, and the standard density of the tail gas is 1.293 kg/m3. The basic emission factor was calculated based on Equation (6):
Table 3 Activity level information of tractors in each power range in Beijing.
k
EFi =
∑n = j ERi, n × 3600 Pn
,
∑ EFi,m × FCm
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37 37 ≤ G < 75 ≤ G < G ≥ 135 G < 37 37 ≤ G < 75 ≤ G < G ≥ 135
400 400 400 400 600 600 600 600
22.07 50.7 89.74 152.42 22.07 50.7 89.74 152.42
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
Planting areas in northern Beijing such as Yanqing, Shunyi, Miyun, etc. Planting areas in southern Beijing such as Daxing, Fangshan, Tongzhou, etc.
(6)
where EF (g/kWh) is a net power-based emission factor; i is a certain pollutant; n (s) is the duration of a certain operating mode; J (s) and K (s) are the start and end times of the operating mode, respectively; ER (g/s) is the instantaneous emission rate of a pollutant at a certain operating mode; and P is the engine power at that operating mode (kWh). The total emission factors were calculated according to Equation (7):
TEFi =
Engine Power (kW)
(7)
75 135
75 135
Table 4 Activity level information of combine harvesters in each power range in Beijing.
where TEF is the total emission factor; m is the typical operating mode (such as idling, moving, working); and FC is the weight factors of the operating mode.
Engine Power (kW)
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37 37 ≤ G < 75 ≤ G < G ≥ 135 G < 37 37 ≤ G < 75 ≤ G < G ≥ 135
150 150 150 150 300 300 300 300
22.06 66 91.47 208.68 22.06 66 91.47 208.68
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
Planting areas in northern Beijing such as Yanqing, Shunyi, Miyun, etc. Planting areas in southern Beijing such as Daxing, Fangshan, Tongzhou, etc.
3. Method of establishing the emission inventory 3.1. Emission calculation method According to the method with the highest accuracy in the Guidelines, the pollutant discharge was calculated according to the Equation (8):
E=
∑ ∑ ∑ (Pj,k,n × Gj.k .n × LFj,k,n × hrj.k .n × EFj,k,n) × 10−6, j
k
n
(8)
where E (t) is the CO, HC, NOx, and PM emissions of a non-road mobile machine; j is the category of the non-road movable machine; k is the emission standard; n is the engine power range; P is the machine ownership; G is the average rated net power; LF is the load factor; hr (h) is the number of hours used in the year; and EF (g/kWh) is the pollutant emission factor. It is worthy to note that the emission factors were measured only for tractors, combine harvesters, and micro-tillers in stage II with power ranges of 37–75, 75–130, and less than 37 kW, respectively. Previous studies have shown that different power engines and different emission stages will lead to different emission factors (Fu et al., 2012, 2013; Ge et al., 2013). Therefore, to achieve higher accuracy in the calculation of emission inventories, the recommended values in the Guidelines were used in other power stages and emission stages.
75 135
75 135
4. Results and discussion 4.1. Emission factors The emission rates and net power-based emission factors of tractors and combine harvesters under different operating modes were calculated (Tables 6 and 7). On the whole, the pollutant emission rates of these two agricultural machines in working mode were significantly higher than those in idling and moving modes, and the idling mode was the lowest. This is consistent with the views proposed by Fu et al. (2013). The same is true for the net power-based emission factors calculated from the emission rates. For CO, HC, NOx, and PM emitted by the tractors, the emission factors during the working mode were 3.43, 5.86, 3.65, and 3.82 times, respectively, that of the idling mode, and 2.22, 3.15, 2.92, and 3.04 times, respectively, that of the moving mode. In addition, the emission factors were 98.2%, 84.6%, 99.6%, and 110.5 times, respectively, that of the recommended values in the Guidelines. For CO, HC, NOx, and PM emitted by the combine harvester, the emission factors of the working mode were 3.09, 4.47, 3.09, and 9.21 times, respectively, that of the idling mode, and 1.96, 2.38, 1.31, and 1.67 times, respectively, that of the moving mode. In addition, the emission factors were 103.6%, 98%, 98.7%, and 106.9 times, respectively, that of recommended value in the Guidelines. The higher emission factor of the working mode was due to the higher engine load and speed. It was not convenient to install the PPS-M on the micro-tiller due to its small size. The PM emission factor of the micro-tiller was recommended by the Guidelines for compiling an emission inventory. Table 8 shows the micro-tiller emission factors in the Stage II emission standard.The emission factors of CO, HC, and NOx were 115.8%, 107.7%, and 119.6% times, respectively, that of recommended value in the Guidelines. The emission factors of each pollutant of the three kinds of agricultural machines were not completely consistent with the recommended values in the Guidelines, but the differences were not significant. The reason is that the measured emission coefficient is for a certain agricultural machine in a certain area, and the recommended value in the Guidelines is not detailed for different regions, machine
3.2. Information on the activity levels of typical agricultural machines The activity levels of agricultural machines are closely related to the region in which they operate. Due to a lack of relevant literature on the investigation of agricultural machinery activity levels in China, a large number of investigations were conducted. By referring to the survey data and the recommended values from the Guidelines, the activity level information, including the average annual working hours, load factor, and average rated net power, were obtained. Due to the differences in ripening systems between the southern and northern planting areas in Beijing (one crop per year in the northern region and two crops per year in the southern region), the level of agricultural machinery activity was also slightly different. The tractor use times in the south and north were 600 and 400 h, respectively, and the combine harvester use times in the south and north were 300 and 150 h, respectively. There was a slight gap between the survey data and the recommended values in the Guidelines. The average annual working hours of micro-tillers all referred to the recommended values in the Guidelines. The average rated net power was obtained by dividing the total power by the retention amount. The load factor was determined to be 0.75 and was found by consulting the relevant literature and research, and the activity level of each type is shown in Tables 3−5. 4
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Table 5 Activity level information of micro-tillers in Beijing. Engine Power (kW)
Average annual working hours (h)
Average rated net power (kW)
Load factor
Zone of application
G < 37
380
4.0
0.75
Planting areas in Beijing.
agricultural machinery showed a downward trend. Among the four pollutants, NOx accounted for the highest proportion, accounting for more than 50% of the total emissions. This was followed by CO with approximately 30%–40%, and HC and PM had the least proportion of total emissions, which was related to its emission factor. It is worth noting that after 2011, NOx and PM nearly displayed a downward trend, regardless of the increase or decrease in the amount of ownership. This is due to the increasing proportion of stage II equipment and the more stringent emission limits on pollutants, especially NOx and PM, imposed by the non-road stage II standard. This showed that the tightening of non-road emission limits was very effective in reducing pollutants, particularly NOx and PM emissions. From Fig. 2(a) and (b), it can be seen that, due to a decrease in the ownership and an increase in the proportion of stage II equipment, the total emissions of CO, HC, NOx, PM, and pollutants from tractors decreased by 69.5%, 68.67%, 76.46%, 79.41%, and 73.8%, respectively, from 2006 to 2016. It was calculated that, on the basis of a decrease in the ownership, the emission limits were tightened to reduce emissions of NOx and PM by 9.51% and 12.46%, respectively. The proportion of NOx and PM in the total pollutant emissions decreased from 5.18% to 55.32%, respectively, in 2006 to 4.07% and 49.71%, respectively, in 2016. For the same reason, emissions of CO, HC, NOx, and PM of combine harvesters decreased by 51.42%, 56.3%, 65%, and 76.7%, respectively, from 2006 to 2016. The emission limits were tightened to reduce emissions of NOx and PM by 18.2% and 28.53%, respectively. The proportion of PM and NOx in the total emissions decreased from 4.48%, and 58.66% in 2006 to 2.66% and 51.82% in 2016, respectively. As shown in Fig. 2(c), from 2006 to 2016, the ownership of microtillers kept increasing, and the emissions of pollutants and the total emissions increased. From 2006 to 2016, on the basis of a 164.79% increase in the ownership of micro-tillers, the emission limits were tightened to reduce the increase in NOx and PM emissions by 71.88% and 55.15%, respectively. PM and NOx accounted for 5.86% and 54.01%, respectively, of total emissions in 2006, and decreased to 5.52% and 46.78%, respectively, in 2016. As shown in Fig. 2(d), the total amount of typical agricultural machinery (tractors, combine harvesters, and micro-tillers) fluctuated between 30,000 and 40,000 units in Beijing from 2006 to 2016, showing a downward trend as a whole. The total amount of CO, HC, NO, PM, and total emissions decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively. The tightening of emission limits resulted in decreases in CO, HC, NO, PM, and total emissions of 2.58%, 2.31%, 13%, 14.54%, and 8.11%, respectively. PM and NOx accounted for 5.15% and 55.49%, respectively, of total emissions in 2006 and decreased to 4.16% and 49.46%, respectively, in 2016.
Table 6 Tractor emission factors in the Stage II emission standard of the 37–75 kW power section. Operating modes Emission rate(g/s)
Idling Moving Working Emission factor(g/kWh) Idling Moving Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
PM
0.025 0.038 0.08 1.62 2.51 5.56 4.91 5
0.0034 0.0063 0.0198 0.22 0.41 1.29 1.10 1.3
0.034 0.042 0.123 2.21 2.76 8.06 6.97 7
0.0020 0.0025 0.0075 0.128 0.161 0.489 0.42 0.38
Table 7 Combine harvester emission factors in the Stage II emission standard of the 75–135 kW power section. Operating modes Emission rate(g/s)
Idling Moving Working Emission factor(g/kWh) Idling Moving Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
PM
0.04 0.07 0.14 1.88 2.96 5.80 5.18 5
0.006 0.011 0.027 0.25 0.47 1.117 0.98 1
0.049 0.116 0.152 2.06 4.86 6.36 5.92 6
0.0009 0.0054 0.0082 0.0375 0.2070 0.3453 0.31 0.29
Table 8 Micro-tiller emission factors in the Stage II emission standards of the 0–37 kW power section. Operating modes Emission rate(g/s) Working Total emission factor(g/kWh) Recommended values of the Guidelines
CO
HC
NOx
0.008 7.53 6.5
0.0015 1.4 1.3
0.01 8.97 7.5
types, and other factors. The small gap with the recommended values in the Guidelines also shows that the test data in this study had a certain degree of accuracy. 4.2. Emission inventory of typical agricultural machines in Beijing from 2006 to 2016 The ownership of typical agricultural machines in Beijing from 2006 to 2016 was obtained by consulting the Yearbook of China's Agricultural Machinery Industry. By visiting agricultural machinery management departments and cooperatives, statistics of existing data, and the proportion of different power ranges of agricultural machinery in relevant references, the quantity of different emission stages and different power stages of typical agricultural machines from 2006 to 2016 was obtained (Li, 2016). By combining the activity levels and emission factors, the pollutant emissions of agricultural machinery in Beijing from 2006 to 2016 were calculated,as shown in Fig. 2. From Fig. 2, overall, the trend in pollutant emissions and total emissions from 2006 to 2016 was similar to that of the agricultural machinery ownership. The pollutant emissions of tractors and combine harvesters showed a downward trend, the pollutant emissions of microtillers showed an upward trend, and the overall pollutant emissions of
4.3. Emissions of pollutants from different agricultural machines In 2016, typical agricultural machines in Beijing emitted 855.43 tons of CO, 184.87 tons of HC, 1108.83 tons of NOx, and 93.62 tons of PM, totaling 2242.75 tons. The contribution rates of different agricultural machines to pollutant emissions were calculated using 2016 as an example. As can be seen from Fig. 3, whether for CO, HC, NO, PM, or total emissions, the proportion of tractor emissions was greater than 80%, and the contribution rate was the highest, followed very closely by combine harvesters and micro-tillers. This was due to the fact that there were far more tractors than combine harvesters, and their power 5
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Fig. 2. Pollutant emissions of agricultural machinery in Beijing from 2006 to 2016. a is tractors, b is combine harvesters, c is micro tillers, and d is the total.
limits on micro-tillers in the power range below 37 kW are not strict, which results in the poor actual emission of micro-tillers. Therefore, their emissions were close to those of the combine harvesters.
4.4. The spatial distribution of emission inventories According to statistics from the relevant agricultural departments in Beijing and a consultation with the Yearbook of China Agricultural Machinery Industry, the third-level ownership of typical agricultural machinery in various districts and counties of Beijing in 2016 was obtained. Then the pollutant emissions of each district and county were calculated according to Equation (8). The spatial distribution maps of the total amount of agricultural machinery emissions and pollutant emissions in Beijing were drawn using GIS technology, as shown in Fig. 4. On the whole, the emission intensity in the northern part of Beijing was higher than that in the southern part. The reason is that the ownership of agricultural machinery in the northern region was much higher than that in the southern region, which makes emissions in the southern region with higher activity levels lower than that in the northern region with lower activity levels. Regardless of CO, HC, NOx, PM, or total emissions, the highest contribution was from the Yanqing District, the Shunyi District, and the Miyun District, followed by the
Fig. 3. The contribution rates of pollutant emissions from different agricultural machines.
was much greater than micro-tillers. Even though the single power of a combine harvester is far greater than that of a micro-tiller, the number of micro-tillers was huge, and the implementation of stage I and II regulations primarily restricts the emission limits on combine harvesters and tractors in the power section above 37 kW. The emission 6
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Fig. 4. Spatial distribution of agricultural machinery emissions in various districts and counties of Beijing. a is CO, b is HC, c is NOx, d is PM, and e is the totality.
the emission inventory of agricultural machinery in Beijing in 2007. The reason could be that the differences in the time frames may have influenced the results. In addition, this study used a net power-based emission factor, which caused a relatively higher accuracy. It is also worth noting that for CO, HC, NO, PM, and total emissions, the total emissions in the Yanqing, Shunyi, and Miyun areas were nearly 50%. The lowest emission intensity of the Chaoyang District was
Daxing District, the Fangshan District, the Tongzhou District, and the Pinggu District. Additionally, the Changping District, the Mentougou District, the Fengtai District, the Haidian District, and the Chaoyang District had the lowest emission intensities. The reasons for the disparities were due to the ownership and activity levels. But this is slightly different from previous studies. Fan et al. (2011) found that the emission intensities of the Miyun and Pinggu areas were the highest in 7
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less than 1%. The emissions of pollutants and total emissions in the Yanqing District were more than 35 times that in the Chaoyang District, and there was a great gap between them. The most important reason was the ownership. The Chaoyang District is located in the center of the city with very few crops and a very low ownership of agricultural machinery, while the Yanqing District is located in the outskirts of the city with a large area of crop cultivation and a high ownership of agricultural machinery.
in this study. In the future, government should pay more attention to the regional impacts of high emission intensity agricultural machinery. To understand more about the actual emission levels from agricultural machinery, more experiments are needed to supplement the database of emission factors that are suitable for China. In addition, more detailed information needs to be provided for the formulation of emission inventories. This is a necessary step for China to formulate effective air pollution control policies.
4.5. Uncertainty analysis
Declaration of interest statement
The uncertainties in this study primarily originate from key data such as actual emission factors, the level of mechanical activity, and the ownership. China has not yet established a relevant registration and filing system for agricultural machinery, and there are differences in statistics between different departments. Hence, there are some uncertainties in the amount of data. In addition, the level of mechanical activity was obtained by visiting agricultural machinery operators and agricultural machinery cooperatives, and there are some uncertainties in this data. Also, data regarding non-road mobile source emission factors are very scarce at this stage in time. In this study, only stage II machinery was measured, and the recommended values in the Guidelines are used for other emission stages of machinery. Those values are primarily based on the EU EMEP/CORINAIR data, which does not adequately reflect the emission levels of agricultural machinery under local actual working and operating conditions.
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
5. Conclusion
References
Whether it was a tractor or a combine harvester, the pollutant emission rates of the three activity modes were quite different. The working mode was the highest, and the idling mode was the lowest, which is consistent with previous studies. The net power-based emission factors also displayed the same pattern, and the emission factors of the three agricultural machines showed little differences with the recommended values in the Guidelines, which shows that the data in this study had a certain degree of authenticity and reliability. From 2006 to 2016, the total emissions of CO, HC, NOx, PM, and the total from typical agricultural machinery in Beijing decreased by 63.11%, 62.93%, 72.07%, 74.67%, and 68.66%, respectively. The tightened emission limits reduced the emissions of CO, HC, NOx, PM, and the total by 2.58%, 2.31%, 13%, 14.54%, and 8.11%, respectively. This shows that China's various control strategies and policies effectively reduced pollutant emissions, particularly NOx and PM. In 2016, typical agricultural machinery in Beijing emitted 855.43 tons of CO, 184.87 tons of HC, 1108.83 tons of NOX, and 93.62 tons of PM, totaling 2242.75 tons. Among them, tractors contributed the most emissions, reaching more than 80%. The proportions of emissions from combine harvesters and micro-tillers were very close. It is predicted that the total emissions from micro-tillers will exceed that of combine harvesters in the future. Therefore, attention should be paid to the supervision and control of tractor emissions. In addition, the supervision of small agricultural machinery, such as micro-tillers, should be strengthened. According to the emission intensities of different districts and counties in Beijing in 2016, there was a great gap between different districts and counties in Beijing. The areas with the highest intensities were the Yanqing District, the Shunyi District, and the Miyun District. This result is slightly different from previous studies, which indicates that time may have influenced the results. Also, more accurate inventory preparation methods may also have led to a different outcome
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Acknowledgments This study was supported by the Key National R & D Program of China (Grant No. 2017YFD0700300). We thank Bo Zhang, Nianjie Ma, Junnan Cheng, Shushan Shao, and Dongxu Zhang for their contributions to this research. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116903.
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