Interprovincial Joint Prevention and Control of Open Straw Burning in Northeast China: Implications for Atmospheric Environment Management

Abstract

Large-scale open burning of straw residues causes seasonal and severe atmospheric pollution in Northeast China. Previous studies focused on the causes or assessment of atmospheric pollution in a single city. However, studies conducted on the interaction range, degree and policy control of pollutant transport on a large scale are still to be performed. In this study, we propose combined control of straw burning by dividing region the straw burning in Northeast China in recent 20 years, determining the transport routes between main cities, and analyzing the interaction characteristics of straw burning under different scenarios. The fire point data suggest that the most intense straw burning years in Northeast China in the past 20 years occurred in the range from 2014 to 2017, mainly after the autumn harvest (October–November) and before spring cultivation (March–April). The burning areas were concentrated in the belt of Shenyang-Changchun-Harbin, the border of the three provinces and Eastern-Inner Mongolia, and the surrounding area of Hegang and Jiamusi City. The lower number of fire points before 2013 indicates that high-intensity burning has not always been the case, while the sharp decline after 2018 is mainly due to scientific control of straw burning and increased comprehensive utilization of straw. Compared with S2, the PM_2.5 concentrations increased by 6.2% in S3 and 18.7% in S4, indicating that burning in three or four provinces at the same time will significantly increase air pollution and exert a regional transmission effect. Straw burning in Northeast China is divided into six main regions based on correlation analysis and satellite fire monitoring. Under typical S3, the case analysis results indicate that there is regional transmission interaction between different cities and provinces, focusing on multi-province border cities, and it is affected by Northwest long airflow, and Southeast and Northeast short airflow. These results provide scientific and technological support for implementing the joint prevention and control plan for straw incineration in Northeast China.

1. Introduction

Agricultural biomass burning releases numerous gases and aerosols into the atmosphere, resulting in disastrous fine particulate matter (PM_2.5) pollution, which has attracted global attention [1,2]. The air pollution in different regions is closely related to open straw burning, which significantly increases the haze pollution level [3]. Moreover, the differences between crop species, climatic conditions, farming methods, and straw utilization in different regions increase the uncertainty of air pollutant emissions from biomass burning [1,4,5]. The clarification of characteristics of agricultural burning in different regions, its impact on the atmospheric environment, as well as the impact of control measures are helpful for improving air quality.

Northeast China is among the most important grain production area in China, with a straw yield of more than 200 million tons in 2019, accounting for 23.12% of the country’s total output [6]. Open-air straw burning is the main cause of heavy pollution in Northeast China. A recent study showed that it is responsible for the emission of hundreds of thousands of tons of atmospheric pollutants [7]. The PM_2.5 emission was 173,000 tons in Northeast China, based on the analysis of the fire point data of straw burning from 2015 to 2017 [8]. A total of 80% of atmospheric pollutants released from open straw burning originate from corn straw [9]. The control of straw burning effectively mitigates air pollution, and PM_2.5 decreased by 48% after the implementation of the burning ban policy. Several areas have been effective in implementing the two-regions control (i.e., no-burning and limit-sintering) and the planned burn [10,11]. Previous studies primarily focused on the short-term variation and single-point analysis of open straw burning, while atmospheric pollutant transport routes among different provinces were less investigated.

Regional transport is another important factor affecting local air quality. The PM_2.5 emitted by intense burning not only affects the local atmosphere of burn areas but also downwind regions through atmospheric transport and circulation [12]. From a global perspective, pollutants are affected by atmospheric transport on hemispheric scales, and transcontinental air pollutant transport risks pose a major regulatory challenge to protecting public health risks [13,14,15]. A previous study documented that these meteorological factors could significantly facilitate the transport of biomass burning PM_2.5 during the post-harvest season [11]. Moreover, shorter transport distances strengthened the impact of biomass burning on downstream PM_2.5. In cross-provincial transport, 20% of PM_2.5 in Hainan originates from Guangdong, 9% from Hunan, and 7% from Hubei. The Hainan province is affected by cross-provincial PM_2.5, accounting for 72% of the total, while local PM_2.5 emissions only account for 28% [16,17]. At present, some chemical transport models are being used to analyze the impact of open biomass burning. Zhu Bin et al. [18] employed the backward trajectory model to analyze weather conditions, atmospheric boundary layer characteristics, sources, and transport paths of atmospheric pollutants influenced by straw burning in the air pollution of Nanjing and its surrounding areas. Furthermore, to evaluate the influence of the regional pollutant transport and diffusion, researchers employed the numerical model and RMPAS-Chem model and Community Multiscale Air Quality (WRF-CMAQ) [19,20,21,22,23]. To date, there are only a few studies on the mutual transmission and influence of pollutants between cities and provinces in Northeast China. Thus, it is essential to elucidate the inter-provincial air pollution transmission path and potential pollutant sources in Northeast China to establish joint prevention and control schemes of straw burning among the three provinces and realize joint control of air haze pollution.

The purpose of this study is to clarify the pollution mechanism and transportation paths of straw burning among the provinces or regions in Northeast China. Based on the inter-provincial transportations of 40 typical cities in Northeast China, we selected 20 years from 2001 to 2020 and used multivariate data (i.e., meteorological data, air quality data, and fire point data) to analyze the spatial and temporal distribution characteristics of agricultural straw burning in typical periods. We further discuss the change in the regional air quality under different straw burning and meteorological condition scenarios in different provinces. Finally, we simulate the transport paths of air pollutants, possible sources, and the transport contribution of external pollution in major cities in Northeast China.

2. Materials and Method

2.1. Study Areas

Northeast China includes the Jilin Province, Liaoning Province, Heilongjiang Province, and Eastern-Inner Mongolia (HulunBuir City, Hingan League, Tongliao, and Chifeng). These areas have a temperate monsoonal climate with four distinct seasons, with long and cold winters lasting four to six months, and a minimum monthly mean temperature of −12 to −19 °C, except in mountainous areas (i.e., the Greater Khingan Mountains, Lesser Khingan Mountains, and Changbai Mountains, Figure 1). The heartland of the region is the Northeast China Plain, which is a major breadbasket region with a total area of 3.5 × 10⁵ km² [24]. As a major grain production base in China, Northeast China is one of the world’s three major black soil regions [25]. The main crops are single-season crops, such as corn, soybean, rice, and wheat, with a grain output of 174.628 million tons and accounting for 38% of China’s total output in 2020. In the past decade, crop straw is often burned in the open air, aggravating seasonal air pollution [26].

2.2. Data Sources and Processing

2.2.1. Atmospheric Pollutants and Meteorological Data

We obtained atmospheric pollutants and meteorological data for 40 typical cities in Northeast China. The local concentration of air pollutants, including PM_2.5, PM₁₀, SO₂, NO₂, O₃ (i.e., 1 h average concentration), and CO, during 2015–2020 were obtained from the China National Environmental Monitoring Center. Contemporaneous ground meteorological indexes, including the air temperature and wind speed of cities, were included in the study, as collected from the China Meteorological Administration. These data were used to characterize the spatial-temporal variation characteristics of pollutants and to explore the potential relationship between inter-city pollutant transmission. The obtained meteorological data were used to explore the role of meteorological factors in the inter-provincial transport of pollutants.

2.2.2. Fire Point Monitoring Data

The TERRA/MODIS and AQUA/MODIS fire point datasets with nominal horizontal resolutions of about 1 km² were used in this study, provided by the Fire Information for Resource Management System (FIRMS) https://firms.modaps.eosdis.nasa.gov/ (accessed on 10 October 2021). Original fire points covering areas of Northeast China were extracted for the period of 2000–2020, and the fire points in farmland were obtained by the overlay of original fire points and spatial land-use data. According to the straw burning period (i.e., March–April, October–November), the provincial initials represent a large amount of straw burning in the province; in the rest of the province are small or no incineration points, respectively. Scenario 0 (i.e., S0, small or no incineration points across sample cities in the four provinces), Scenario 1 (i.e., S1-L, S1-J, S1-H, and S1-E, straw incineration in one province), Scenario 2 (i.e., S2-LH, S2-HE, S2-JH, and S2-LJ, straw incineration in two provinces), Scenario 3 (i.e., S3-JHE, S3-LHE, S3-LJE, and S3-JLH, straw burning in three provinces), Scenario 4 (i.e., S4, sample cities in four provinces are all engaging in straw burning).

2.2.3. Air-Mass Backward Trajectories

Evaluating the backward trajectory is a common method to determine the source region and movement of pollutants within the atmosphere [27,28]. The potential source contribution factor weights (PSCF) are a gridded statistical analysis method based on the backward trajectory model that yields the distribution of the potential source regions along the trajectory to the receptor site semi-quantitatively and analyzes the pollution characteristics of different trajectories and potential source areas [29]. The long-distance pollutants transmission from other regions are generally considered to play a vital role in local haze episodes. Therefore, it is necessary to identify the long-term transport characteristics and potential emission sources of pollutants.

In this study, based on the characteristics of urban distribution (e.g., provincial capitals, major polluted cities, provincial border cities) and correlation analysis, seven major cities (i.e., Shenyang, Changchun, Baicheng, Harbin, Qiqihar, Jiamusi, and Tongliao) were selected for analysis. The backward trajectory model was used to calculate the backward trajectory of a typical city at a height of 500 m from 2015 to 2020 using the air quality data in Northeast China to minimize the influence of surface turbulence and determine the main backward trajectory clusters. Moreover, the parameters were set as follows, the grid cell solution was 1° × 1°, and the criterion was set to the 90% quantile of particle counts. We explored the distribution of potential sources of PM_2.5, which was considered the primary pollutant during the research period in provincial capital cities, based on the PSCF method. The potential transport path and distribution probability of air mass were calculated in Scenario 3 in 2017, combined with the pollution concentration data of a typical city (i.e., Shenyang, Changchun, Baicheng, Harbin, Qiqihar, Tieling, Hinggan League, and Tongliao), using the HYSPLIT model and PSCF method.

2.3. Statistical Analysis

The R3.6.2 software (R Development Core Team, 2009, Vienna, Austria) was used to conduct Pearson correlation analysis, and the independent sample T was used to test the significant relationship between the concentration of air pollutants in open-air straw burning in 40 major cities. The back trajectory analysis and the weight analysis of potential source contribution factors were carried out using the back trajectory function in the Open AIR package. A 54 km fishing net was established to analyze the spatial distribution characteristics of straw burning points from 2001 to 2020.

3. Results

3.1. Spatio-Temporality of Fire Points in Northeast China during 2001–2020

According to the historical trend of satellite-based fire points of straw burning, the past 20 years can be divided into three main periods (i.e., Period I: 2000–2011; Period II: 2012–2017; Period III: 2018–2020) (Figure 2). During the first period (2000–2011), the total number of fire points was relatively small, with the provinces of Liaoning, Jilin, Heilongjiang, and Eastern-Inner Mongolia accounting for 25%, 21%, 29%, and 28% of the total in the past 20 years, respectively. The second period (2012–2017) witnessed a rapid increase in the number of fire points, which increased by 53% compared with the first period, of which Liaoning, Jilin, and Heilongjiang increased by 51%, 53%, and 58%, respectively. During the third period of 2018–2020, the number of fire points fell rapidly, by 65% compared with the second period, with a maximum reduction of 79 % in Heilongjiang.

The monthly variations showed that the distribution of straw burning was bimodal; one peak was from March to April in spring, accounting for 42.5% of the whole year, while the other peak was from October to November in autumn, accounting for 41.3% of the total. The main burning period in Eastern-Inner Mongolia was from March to April, with a contribution rate of 54%, and only 17% from October to November.

The spatial distribution of fire points of straw burning every five years indicates that the number of fire points gradually increased, and the distribution area gradually shifted. During 2001–2005, about 35% of fire points were mainly concentrated in northeastern Qiqihar and Eastern Hegang. Between 2006 and 2010, 37% of the fire points were distributed in the northern Liaoning province and Baicheng and Changchun in Jilin Province, with a 46% increase compared to that in 2001–2005. In addition, eastern Hegang still had a large number of farmland burning points. From 2011 to 2015, the number of fire points increased rapidly, to 2.95 times that of the previous five years. The fire points mainly occurred in the Shenyang-Changchun-Harbin region, accounting for about 40% of the total fire points. Moreover, the number of fire points in the border area of Jilin Province, Heilongjiang, and Eastern Inner Mongolia increased by 33.4%. The total number of fire points in 2016–2020 dropped by 8.2% compared with the previous five years, as local governments gradually implemented control measures on straw burning since 2018.

3.2. Regional Air Quality under Different Straw Burning Scenarios

Compared with straw burning in Scenario 0, the concentration of PM₁₀, SO₂, NO₂, and CO increased by 22.03%, 7.81%, 20.96%, and 10.61%, respectively, while O₃ was decreased by 6.57% in Scenario 1 (

Table 1. Summary of cumulative satellite-based Fire Points (FP), mean values of atmospheric pollutants (i.e., PM_2.5, PM₁₀, SO₂, CO, NO₂, O₃) and meteorological factors (i.e., Air temperature, Wind speed) from 2015 to 2020 during different burning scenarios in four provinces/regions of Northeast China.

Scenarios	Day	FP	Province	Atmospheric Pollutants						Meteorological
				PM2.5	PM10	SO2	NO2	CO	O3	WS	AT
S1-L	110	73	Liaoning	40	75	25	32	0.9	39	3	1.7
		78	Jilin	32	55	14	22	0.7	45	3	−2.4
		111	Heilongjiang	32	51	16	25	0.7	41	3	−4.8
		365	Eastern-Inner Mongolia	29	55	14	20	0.6	47	4	−0.9
S1-J	2	21	Liaoning	66	110	31	38	1.4	29	3	9.6
		48	Jilin	131	160	31	61	1.3	26	2	4.0
		6	Heilongjiang	126	117	21	40	1.0	26	3	1.8
		10	Eastern-Inner Mongolia	50	89	21	29	0.9	27	3	3.4
S1-H	39	156	Liaoning	60	91	27	29	1.2	49	4	4.5
		138	Jilin	75	112	21	31	0.9	51	3	0.9
		5981	Heilongjiang	53	82	15	25	0.8	46	3	0.1
		297	Eastern-Inner Mongolia	48	86	21	22	0.8	50	3	−0.3
S1-E	4	11	Liaoning	52	107	26	26	1.1	56	4	6.3
		11	Jilin	40	87	20	22	0.8	69	3	3.3
		43	Heilongjiang	36	67	20	28	0.8	62	3	0.6
		153	Eastern-Inner Mongolia	44	135	22	29	0.7	62	4	3.8
S2-LE	24	537	Liaoning	39	68	18	30	0.8	51	4	6.0
		77	Jilin	33	59	11	22	0.7	53	3	3.0
		19	Heilongjiang	32	55	14	25	0.7	49	3	0.9
		299	Eastern-Inner Mongolia	37	71	15	24	0.7	48	4	5.8
S2-LH	36	927	Liaoning	45	84	23	33	0.9	54	4	9.2
		180	Jilin	35	67	13	23	0.7	55	3	6.0
		2078	Heilongjiang	26	58	9	17	0.6	50	3	4.1
		256	Eastern-Inner Mongolia	31	63	14	19	0.6	56	3	5.2
S2-HE	23	183	Liaoning	41	85	17	24	1.0	73	4	10.5
		275	Jilin	39	86	12	22	0.7	76	3	8.5
		6730	Heilongjiang	39	74	12	24	0.7	68	4	6.9
		577	Eastern-Inner Mongolia	26	61	8	17	0.5	69	4	7.0
S2-JE	6	10	Liaoning	31	51	22	22	0.9	65	4	1.2
		669	Jilin	27	47	12	13	0.5	80	3	−1.6
		105	Heilongjiang	23	39	15	21	0.6	74	4	−5.0
		91	Eastern-Inner Mongolia	28	45	15	17	0.7	70	4	1.2
S2-JH	26	301	Liaoning	62	90	20	28	1.2	58	4	7.5
		2235	Jilin	92	133	18	33	1.0	57	2	4.1
		9005	Heilongjiang	66	95	17	27	0.8	52	3	2.6
		660	Eastern-Inner Mongolia	53	95	17	23	0.7	55	3	1.7
S2-LJ	54	955	Liaoning	39	77	20	31	0.8	44	4	4.1
		758	Jilin	35	70	16	26	0.8	45	3	1.1
		398	Heilongjiang	26	51	13	21	0.6	44	3	−1.5
		286	Eastern-Inner Mongolia	30	62	14	21	0.6	47	4	1.7
S3-JHE	73	1125	Liaoning	50	86	21	30	1.0	65	3	8.9
		8647	Jilin	58	103	16	30	1.0	64	3	6.2
		38,740	Heilongjiang	72	110	14	28	0.8	63	4	4.4
		4243	Eastern-Inner Mongolia	49	84	15	25	0.8	64	4	7.4
S3-LHF	26	603	Liaoning	46	92	20	32	0.9	64	4	8.4
		196	Jilin	39	75	13	23	0.8	63	3	5.8
		1126	Heilongjiang	35	63	12	21	0.7	58	3	4.0
		424	Eastern-Inner Mongolia	33	75	13	22	0.7	61	4	7.2
S3-LJE	56	1698	Liaoning	45	84	25	34	1.0	52	4	4.7
		1562	Jilin	31	60	13	22	0.7	54	3	1.6
		744	Heilongjiang	25	48	15	22	0.6	52	3	−0.9
		1418	Eastern-Inner Mongolia	24	48	11	18	0.6	57	4	2.5
S3-LJH	48	1703	Liaoning	51	99	20	38	1.0	51	3	10.9
		2461	Jilin	44	82	13	29	0.8	51	3	8.3
		7264	Heilongjiang	30	66	12	24	0.6	44	3	6.7
		789	Eastern-Inner Mongolia	30	66	12	23	0.6	49	3	7.4
S4	164	7294	Liaoning	56	107	23	36	1.0	71	4	9.1
		21,421	Jilin	51	96	15	27	0.8	68	3	7.0
		39,029	Heilongjiang	43	80	14	23	0.7	64	3	5.1
		11,561	Eastern-Inner Mongolia	35	75	13	22	0.6	67	4	7.9
S0	41	286	Liaoning	45	78	25	27	1.1	47	4	4.3
		55	Jilin	46	80	16	24	0.8	51	3	−0.5
		326	Heilongjiang	40	69	20	26	0.7	46	3	−2.6
		145	Eastern-Inner Mongolia	39	76	19	22	0.7	50	4	0.4

Note: The scenario is divided into 5 categories, with the initials of each province indicating that the province has large-scale straw burning (i.e., Liaoning for L, Jilin for J, Heilongjiang for H, Eastern-Inner Mongolia for E). S0: Each province’s straw burning number is smaller; S1: Only one province has a large-scale straw burning scene; S2: There are two provinces at the same time with large-scale straw burning; S3: There are 3 provinces at the same time burning; S4: All provinces burn a lot of straw. WS: Wind speed, with the units of m/s; AT: Air temperature, with the units of °C.

). PM_2.5 increased the most, by about 34.41% in total. In different provinces, it increased by 9.62% (Eastern-Inner Mongolia), 54.37% (Heilongjiang), 51.09% (Jilin), and 21.1% (Liaoning). This concludes that straw burning has an evident negative effect on regional air quality. Under straw burning Scenario 4, the total number of fire points was the highest in Northeast China, and the cumulative burning time was 164 days. The concentrations of PM_2.5 in all provinces were in the following order: Jilin (56 μg m⁻³) > Liaoning (51 μg m⁻³) > Heilongjiang (43 μg m⁻³) > Eastern-Inner Mongolia (35 μg m⁻³). The maximum concentration in Heilongjiang was 310 μg m⁻³. Compared with Scenario 4, PM_2.5 experiences a decreasing trend in Scenario 2 (169 days in total) and Scenario 3 (203 days in total), with an average decrease of 18.89% (Liaoning), 15.19% (Jilin), 11.82% (Heilongjiang), and 2.62% (Eastern-Inner Mongolia) in each province, respectively. The mean concentration of PM_2.5 was 42.5 μg m⁻³ in Scenario 0 (41 days), and Scenario 2 < Scenario 3 < Scenario 4. Although adjusting the burning time of straw in a single province can reduce the concentration of air pollution, the spillover transmission risk of air pollution in other provinces can still occur. Multi-provincial joint straw burning control can help reduce the risk of regional air pollutant transport. In terms of meteorological conditions, the overall wind speed in Northeast China has little change, with an average wind speed of ~3.38 m s⁻¹. The average temperature of S0 is ~1.6 °C, and the average temperature of S4 is about 29.1 °C under different scenarios, which may be related to the seasonal temperature of straw burning in Northeast China. In the same scenario, a lower temperature leads to slower transport of atmospheric particles (e.g., the lowest mean temperature in the S3 scenario was ~7.9 °C, which is S3-LJE, and the lowest mean concentration of PM_2.5 was ~31.25 μg m⁻³). We suggest that the low temperature may be disadvantageous to the transport of atmospheric particles.

The correlation analysis of PM_2.5 among 40 major cities in Northeast China (Figure 3) shows that the air pollutant spillover between cities is mainly divided into six regions. Region I: Qiqihar, Daqing, and the surrounding areas; Region II: Changchun, Jilin, and Siping; Region III: Shenyang and Tieling; Region IV: Jiamusi, Shuangyashan, and Qitaihe; Region V: Harbin and Suihua; Region VI: Baicheng and Hinggan League. Significant associations were found between the transport of air pollutants and provincial capitals in Northeast China. The significant positive correlation (p < 0.01) coefficients were 0.975 (Harbin and Changchun), 0.940 (Harbin and Shenyang), and 0.984 (Changchun and Shenyang). Similarly, correlations were observed between pollutant transport and neighboring cities, Tieling and Siping (r = 0.952) and Baicheng and Tongliao (r = 0.952). Songyuan was also significantly correlated with Harbin (r = 0.952).

3.3. Pollutant Transportation Trajectories and Potential Sources

The backward trajectory of Northeast China from 2015 to 2020 shows that there was no significant difference in the direction of the airflow trajectory among cities in past years (Figure 4). The path and direction of the track represent the airflow through the area before it reaches the city. The moving speed of the airflow can be determined by the length of the track. Long tracks correspond to fast-moving air, and short tracks correspond to slow-moving air. According to the analysis of the direction of the airflow path between cities, the highest proportion of airflow and the longest transmission distance in Shenyang were from Hinggan League and Tongliao (cluster 3), and Changchun and Qiqihar (cluster 4). The airflow with long distances (cluster 1) stems mainly from the Chifeng direction and subsequently passes through the west of Liaoning, finally circling to the east of Dalian to reach Shenyang. The air trajectory accounts for 12.8%.

The short-distance transport flow from Changchun through Liaoyuan, which is mainly influenced by the Dandong direction (cluster 2), accounts for 19.9% of the total trajectory. The long-distance airflow transmission from Chifeng, Shenyang direction (Cluster 1), and Hinggan League, Hulun Buir direction (cluster 5), accounted for 16.7% and 16.1%, respectively, while the Harbin and Qiqihar direction accounted for 9.8% of the total trajectory. The direction of Mudanjiang and Hegang accounted for 6.2% of the total trajectory. The three categories (clusters 4, 5, and 6) all involve short-distance air mass transport in Baicheng, accounting for 21.7% of the total trajectory. The short-distance airflow in Harbin stems mainly from Baishan, Dandong direction (cluster 1), and Qitaihe, Jiamusi direction (cluster 6), accounting for 15.3% and 6.7% of the trajectory, respectively. The long-distance transmission flow comes from the Hinggan League direction (Cluster 2), accounting for 20.3% of the trajectory. The Jiamusi, Qiqihar, and Tongliao airflows have basically the same path direction, all of which are northwest long-distance transport airflows. However, Jiamusi and Qiqihar are three different types of short-distance transport airflows, while Tongliao is two types of short-distance transport airflows (clusters 5 and 6). The results of the cluster analysis show that it is mainly influenced by the northwest long airflow and southeast and northeast short airflow in Northeast China.

Based on the analysis of the potential source region of PM_2.5 in the capital cities of Northeast China (Figure 5), the potential source contribution (PSCF) of PM_2.5 was calculated, revealing that the larger values occur mainly during Period III. From 2015 to 2017, the contribution of the potential source region (WPSCF > 0.1) was mainly concentrated in the direction of Hinggan League-Baicheng-Changchun and Chifeng-Tongliao directions (Shenyang), Jiamusi-Harbin direction (Changchun), and Changchun direction (Harbin). The potential source area of PM_2.5 differs with time in Northeast China. The implementation of the straw burning policy limit in 2018 made the coverage of the WPSCF high-value area the narrowest and most scattered. However, the pollution caused by the single province straw-burning still causes the linkage effect, which leads to the large-scale haze pollution risk.

3.4. Potential Transport Trajectory Analysis of PM_2.5 during Straw Burning in Each Province under Typical Scenarios (S3) in 2017

The potential transport trajectories of PM_2.5 during straw burning were analyzed under three typical scenarios in 2017 (Figure 6), including Liaoning Province (Shenyang and Tieling, 14–16 April), Jilin Province (Changchun and Baicheng, 19–21 April), Heilongjiang Province (Harbin and Qiqihar, 1–3 October), and Eastern-Inner Mongolia (Hinggan League and Tongliao, 11–14 October). The backward trajectory shows that Shenyang is mainly affected by (PM_2.5 > 160 μg m⁻³) foreign pollution transport from the Beijing-Tianjin-Hebei region, followed by (PM_2.5 > 80 μg m⁻³) Tongliao and Changchun directions. From the direction of the Hulunbuir-Hinggan League (PM_2.5 > 60 μg m⁻³), the atmospheric particulate matter (PM_2.5 > 40 μg m⁻³) mainly affected Changchun. Changchun was also affected by (PM_2.5 > 40 μg m⁻³) Shenyang.

Harbin was less affected by external particulates, mainly from the Hulunbuir direction. The border cities in the inter-provincial transport analysis in Northeast China (Tieling, Baicheng, Qiqihar, Hinggan League, and Tongliao) show that the transport sources of inter-city transport are basically the same as those of the provinces where the cities are located. The Eastern-Inner Mongolia region was affected not only by the transmission of pollution from abroad but also by pollutants from Liaoning, Jilin, and Heilongjiang Provinces. Based on the analysis of the back-track frequency thermograms of annual sources, the Liaoning Province was mainly affected by the pollutant transport in the direction of Tongliao and Changchun, and Jilin Province was likewise influenced by the pollutant transport from Shenyang. Heilongjiang also received some pollutant transmission from Shenyang, Dalian, and elsewhere.

4. Discussion and Policy Implications

4.1. Has There Been a Long History of Massive Burning?

These results indicate that the large quantities of straw burning thought to have a long time series did not persist in Northeast China. The historical trend of satellite fire points in the past 20 years shows that straw burning was relatively low in the first period (2000–2011). However, during the second period (2012–2017), the number of fires increased rapidly, by 53% compared with the first period. The burning regions are mainly in the central urban areas, which are the main crop distribution areas in Northeast China. These findings might be attributed to the total straw yield reaching 628.7 million tons, out of which 10.9 million tons were openly burned during the five-year period (2013–2017) [8]. The open straw burning has seasonal and cyclical variations, and severe haze events have been often observed during and after fall harvesting and before spring plowing in recent years [11,30].

As a result of socio-economic development, agricultural production expands, which exacerbates environmental risks [31]. Burning large volumes of straw in open fields releases the particulate matter and deteriorates air quality, affecting the air of adjacent towns/cities by diffusion in the following days [32]. Previous studies have shown that meteorological factors, such as weak wind, high relative humidity, and no precipitation, are strongly correlated with high PM_2.5 levels in Jilin in the autumn and winter [33]. In this study, the lower the temperature, the slower the transport of atmospheric particles in the same scenario. Furthermore, the interregional level of transmission is more complex.

4.2. Are There Significant Interregional Interactions and Complex Transport of Air Pollutants?

The results indicate that the transport distance significantly affects the elimination and diffusion of particles. Six main combustion regions were identified in this study, of which Shenyang, Changchun, and Harbin were the main burning cities. In a megalopolis, the long airflow from the northwest and short airflow from the southeast and northeast were the main influences, and the spillover (transmission) of regional air pollution affects urban air quality, directly or indirectly [19]. The pollutant sources in different provinces were simulated and compared in combination with the classification of straw burning fire points set by scenarios. By considering the regional transmission (S2, S3, S4), the air quality between cities was observed to have evident mutual influence, especially in Scenario 4 (S4). The regional transport of biomass burning PM_2.5 was considerably enhanced with the increase in emission intensity [28].

However, in S0 and S1, the transmission effect between regions was not fully reflected, and unilateral and unorganized straw burning may have caused more serious air quality deterioration. Notably, this study did not take the transport of other air pollutants into account in the scenario simulation, even though they have been established as factors affecting regional air quality and human health in recent studies [34,35,36]. On the one hand, PM_2.5, as the primary air pollutant directly exposed to the air, efficiently represents the scale of air pollution in cities [37]. On the other hand, the transport of atmospheric particles is not only affected by the meteorological conditions between cities in the province but also by the inter-provincial transport [38]. The selective exploration of a single pollutant has precision and comprehensiveness. In summary, the transport distance, emission intensity, and meteorological factors play an important role in the regional transport of PM_2.5 from burning biomass.

4.3. Can PSCF Simulation Be Combined with Scenario Analysis to Accurately Identify Potential Sources of Pollutants across Provinces?

PSCF simulation was combined with scenario analysis, which is a systematic method to evaluate the air quality transfer characteristics of field straw burning and is convenient and practical for assessing the environmental performance and challenges of interprovincial straw burning joint prevention and control measures in Northeast China [8,29]. The simulation of air pollutant transport in Northeast China is an effective addition to the study of inter-provincial Pollutant Transport Law. Using the Scenario 3 analysis, the value of the simulation results was enhanced by the passive effects of air pollution on interprovincial transmission. According to the simulation results, the foreign pollutants in Liaoning Province mainly originate from the Eastern-Inner Mongolia region and the direction of Beijing-Tianjin-Hebei. Pollutants in Jilin Province mainly originate from Liaoning Province, Qiqihar, and Hinggan League. The Heilongjiang Province is mainly affected by the transmission of pollutants in the Eastern-Inner Mongolia region. Similar to PM_2.5 emitted by biomass burning will be transported to downwind cities [19].

However, the satellite-based fire counts have some degree of uncertainty and are thus underestimated due to the satellite overpass time not fully covering straw burning time and the disturbance of cloud cover and smoke plumes to thermal detections [8]. For example, the influence of cloud coverage would cause uncertainties in the MODIS-detected fire spots, and uncertainties in the land use data from remote sensing would cause uncertainties in the straw burning areas. From another perspective, there are numerous other factors, such as the cause of farmland fires, which may lead to differences in the amount of recorded open straw burning [39]. Despite these limitations in the fire detections, the use of MODIS active fire data is by far the latest and most comprehensive active fire data and is widely used in numerous fields, such as climate change and fire risk assessment [39].

4.4. Is the Implementation of Straw Control Policy Effective?

To improve regional air quality in the northeast and reduce the transfer of air pollutants between cities, Jilin and Heilongjiang implemented control policies and achieved remarkable results. A measure to control pollutants called the Straw Open Burning Prohibition Plan (SOBPP) [40]. It was thus implemented from October 2018 to March 2019 in the Jilin Province. The environmental regulation of the Straw Open Burning Prohibition Plan was implemented by the Ecology and Environment Department of the province in the 2018 post-harvest season [10,11]. The results indicate that the environmental regulation of the Straw Open Burning Prohibition Plan adopted by Jilin Province effectively controlled the intensity of straw open burning during the 2018 post-harvest season. This directly contributed to the improvement in ambient air quality, and the degree of improvement was closely related to the level of stagnation in terms of meteorological conditions [38]. The Straw Open Burning Prohibition Plan contains a series of control measures for the open field burning of straw, including the demarcation of areas where it was banned and areas where it was limited, plans for burning straw in different sub-regions in areas where it was limited at different times, building systems of responsibility for prohibitions on burning, and implementing all-day inspections [41]. This was implemented in Jilin and Heilongjiang Provinces according to provincial conditions. These measures confirm that the implementation of straw policy is conducive to improving air quality and effectively reducing the regional pollutant transmission risk.

In view of the agricultural production in Northeast China, options to reduce straw burning and increase the straw recovery rate within a short time are limited. At present, while developing new methods and measures to solve the problem of agricultural straw, the joint prevention and control of air pollution caused by straw burning must be actively pursued. Regional air pollution control must include early warning and forecasting, heavy pollution weather emergency response, and other aspects of the joint operation forms. The Straw Open Burning Prohibition Plan helps improve air quality not only because it reduces the total amount of straw burning but also because it renders the process more organized to avoid simultaneous burning in multiple areas. Based on historical and forecasted meteorological conditions for different regions, developing scientific environmental regulations in terms of the Straw Open Burning Prohibition Plan can help improve its effects. Moreover, combined with the characteristics of local characteristics (i.e., meteorological, planting structure, feeding), it is suggested to promote fertilizer and feed usage, supplemented by energy and other technology for straw comprehensive utilization in Northeast China. Fertilizer mainly promotes the technology of direct/indirect straw returning to the field, which takes conservation tillage as the core; Feed to promote the “straw into meat” project, which realizes the integration of planting and breeding; Low calorific value and instability should be improved in biofuels/energy technology.

5. Conclusions

Agricultural straw has not sustained high-intensity burning in the past 20 years. The results of this study showed that the most intense straw burning years in Northeast China in the past 20 years were from 2014 to 2017, mainly after autumn harvest (October–November) and before Spring cultivation (March–April). Six main combustion regions were identified in this study, among which Shenyang, Changchun, and Harbin were the main cities. In this study, satellite data and atmosphere monitoring data were combined with HYSPLIT backward trajectory to innovatively determine the impact of straw burning on pollutant transportation in urban areas under different scenarios. Compared with straw burning in S0, the concentration of PM_2.5, PM₁₀, SO₂, NO₂, and CO increased by 34.41%, 22.03%, 7.81%, 20.96%, and 10.61%, respectively, while O₃ was decreased by 6.57% in Scenario 1. The results from S2 to S4 show a gradual increase in the pollutants, with an average growth rate of 12.45% for PM_2.5, indicating that the air quality between cities has evident mutual influence, especially in S4. We suggest that low temperatures may be disadvantageous to the transport of atmospheric particles. In the megalopolis, we found that the long airflow from the northwest and short airflow from the southeast and northeast were the main influences, and the spillover (transmission) of regional air pollution directly or indirectly affects urban air quality.

Apart from local emission control of biomass burning, this study indicates that more attention must be paid to the influence of cross-regional transport from the transport path, transport distance, and meteorological factors so as to motivate the creation of new policies to further reduce pollution. While encouraging and supporting the comprehensive utilization of straw, it is also necessary to rationally plan straw burning in the region. At present, most new regulations have been focused on coal consumption and industrial emissions. Further research must focus on the health and social costs of straw burning.