Next Article in Journal
Objective Center-Finding Algorithm for Tropical Cyclones in Numerical Models
Next Article in Special Issue
Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China
Previous Article in Journal
Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison
Previous Article in Special Issue
Air Quality and Potential Health Risk Impacts of Exposure to Bacterial Aerosol in a Waste Sorting Plant Located in the Mountain Region of Southern Poland, Around Which There Are Numerous Rural Areas

Atmosphere 2019, 10(7), 375; https://doi.org/10.3390/atmos10070375

Article
Evaluation of Straw Open Burning Prohibition Effect on Provincial Air Quality during October and November 2018 in Jilin Province
1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
Division of Atmospheric Environment Management, Ecology and Environment Department of Jilin Province, Changchun 130033, China
3
Division of Atmospheric Environment, Jilin Provincial Academy of Environmental Sciences, Changchun 130012, China
4
Institute of Meteorological Science of Jilin Province, Changchun 130062, China
5
Jilin Provincial Key Laboratory of Changbai Mountain Meteorology & Climate Change, Changchun 130062, China
6
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
7
Jilin Provincial Environmental Monitoring Centre, Changchun 130011, China
*
Author to whom correspondence should be addressed.
Received: 30 May 2019 / Accepted: 2 July 2019 / Published: 5 July 2019

Abstract

:
Generally, the period (i.e., October and November) was seriously affected by frequent atmospheric pollution under concentrative seasonal crop residue burning and coal burning in Jilin Province, Northeast China. A strict straw open burning ban policy was implemented in Jilin Province during October and November 2018. However, the quantitative effect of straw fire control and its effect on air quality are still unclear. In this study, using multisource data, we evaluated the status of straw-burning control and its contribution to air quality improvement in late autumn and early winter (i.e., October and November) of 2018 at a provincial level. The results showed that the open burning of straw was effectively controlled in October and November 2018 by comparing farmland fire point data to those collected in 2015–2017. There were significant positive correlations among the fire points, aerosol optical depth (AOD), and ground-monitored air quality index (AQI) on a spatial scale. The concentration values of AQI, PM2.5, and PM10 were significantly lower than for the other three years of 2015, 2016, and 2017. Based on meteorological analysis, similar conditions were found in 2018 and 2017, which were worse than that in 2016. Combined with emissions, meteorological conditions, and source apportionment information, if the straw-burning control of 2018 had been performed in 2016 and 2017, the PM2.5 concentrations could have been reduced by at least 30.6%. These results suggest the necessity of straw burning control in the improvement of air quality during the period of late autumn and early winter. Nevertheless, the comprehensive impact of straw-burning control on air quality should be further evaluated for the whole post-harvest period (i.e., October to April of the following year) as the straw-burning period can be postponed in some cities. Furthermore, the establishment of a scientific and reasonable planned burning of straw is also crucial in gradually reducing atmospheric pollution and the actual operation of local governments in those areas where straw can be burned under certain conditions.
Keywords:
haze; PM10; PM2.5; agricultural activity; coal burning; fuel consumption; planned burning

1. Introduction

Haze weather is already one of the major regional disasters that have caused public anxiety and attracted government attention [1,2]. Studies have shown a significant increasing trend of the average number of haze days in Northeast China [3], and severe haze events occur frequently in late autumn and early winter [4,5]. The open burning of straw is an important cause of regional haze pollution during these periods [6,7,8]. In recent years, with economic development and the improvement of people’s living standards, the phenomenon of the open burning of straw has become more and more serious due to a substantial reduction in straw usage [9,10,11]. It not only pollutes the ambient environment, but also has a negative impact on traffic safety and people’s lives [12,13,14,15]. Therefore, it is of great urgency to solve the problem of the open burning of straw to meet the requirements of the public.
As one of the major agricultural provinces in China, the total planting area of crops in Jilin Province reached 5,679,200 hectares, the total output of food crops was 36,470,400 tons, and the total quantity of straw was approximately 65,029,000 tons in 2015. Due to the huge annual amounts of crop straw, it is of great strategic and practical significance to promote a straw-burning ban and comprehensive utilization to reduce air pollution and develop modern agriculture. On September 3, 2018, the Ministry of Environmental Protection of Jilin Province promulgated the “Specification for the Delimitation and Control of the Straw Burning Forbidden Area in Jilin Province (Trial)” in order to reverse the unfavorable situation of straw burning in Jilin Province and minimize the impact of straw burning on regional air quality. As a result, this required organizing the demarcation of the straw-burning-forbidden areas and limiting-condition-burning areas. Before the straw-burning ban areas were formally demarcated and approved, all local cities implemented a full straw-burning ban policy. According to the air quality monitoring data of the Jilin Provincial Environmental Monitoring Center Station, there were no moderate and heavy pollution events in October and November 2018 in the province. It is vital to evaluate the effect of straw-burning control in 2018, and its impact on provincial air quality to effectively control compound air pollution and improve regional air quality.
The objective of this study was to evaluate the status of straw-burning control and its contribution to air quality improvement in the late autumn and early winter (October and November) of 2018 at the provincial level. Using ground-based monitoring data from 2015 to 2018, meteorological data, satellite remote sensing information, and statistical data, we analyzed the spatiotemporal distribution of atmospheric pollutants, the most important meteorological indices (wind, relative humidity, and precipitation), and aerosol optical depth. Then, the contribution of the 2018 straw-burning ban to air quality improvement was quantitatively evaluated.

2. Methodology

2.1. Study Area

Jilin Province consists of Changchun City, Jilin City, Siping City, Liaoyuan City, Tonghua City, Baishan City, Songyuan City, Baicheng City, and the Yanbian Korean Autonomous Prefecture, located at 40°52’~46°18’N, 121°38’~131°19’E (Figure 1). The terrain is high in the southeast and low in the northwest, and is bounded by the Daheishan Mountains that run through the province from north to south. It is divided into two major geomorphological units: the Changbai Mountain in the east and the Songliao Plain in the west.
As an important commodity grain production base in Jilin Province, crop straw resources are abundant. According to historical statistics, the province’s existing crop sowing area is 5.67 million hectares, of which the planting area for food crops is 5.22 million hectares per year, and the annual output of crop stalks is about 60 million tons. Judging from the variety structure, the crops planted in Jilin Province are mainly corn, rice, and soybean, accounting for 90% of the total straw in the province. The three major crops accounted for the largest proportion of corn straw, amounting to 84% of the total of the three major crops. The total proportion of rice and soybean straw is small.
The regional distribution of straw resources in Jilin Province has the following characteristics: the eastern, central, and western regions differ greatly, with more than half in the center, less than one third in the west, and about one tenth in the east. The central plain area mainly includes Changchun City, Jilin City, Liaoyuan City, and Siping City. It is the main grain production area in Jilin Province and the main production area for crop stalks. The output of crop straw accounts for 62% of the total straw in the province. The western region mainly includes Songyuan City and Baicheng City, located in the east of the Horqin Prairie, the center of the Songnen Prairie, and the western end of the Songnen Plain. There are meadows, lakes, wetlands, and sandy areas. It is a typical agro-pastoral staggered area, and the output of the main crop straw resources accounts for 33% of the total straw in the province. The eastern region mainly includes Tonghua City, Baishan City, and Yanbian Prefecture, and belongs to the temperate, humid, and semi-arid monsoon climate zone. It can be divided into the low mountainous area and the low hilly area of Changbai Mountain. The annual rainfall is 359–1000 mm. The output of crop stalks is relatively poor, accounting for only 5% of the province’s total straw production. Crop production does not occupy a dominant position in the region.

2.2. Data Sources and Processing

2.2.1. Ground Monitoring Data of Atmospheric Pollutants

Data for the multi-year ground monitoring of urban air quality index (AQI) and atmospheric pollutants (i.e., PM10, PM2.5, SO2, NO2, and CO) in Jilin Province were provided by the Environmental Monitoring Center of Jilin Province. According to the AQI class information in China, 0~50 is good, 51~100 is moderate, 101~150 is light pollution, 151~200 is moderate pollution, 201~300 is heavy pollution, and >300 is serious pollution. Using the Kriging interpolation method, the spatial distribution and inter-annual statistical information of these parameters were obtained based on the station data. The average and standard deviation data of the urban AQI and atmospheric pollutant concentration from 2015 to 2017 were calculated to represent the time change of air quality. In addition, the October and November data for each year used the average daily cumulative value to assess the differences between these years.

2.2.2. Aerosol Optimal Depth (AOD)

The AOD products we used were the daily deep blue AOD at 0.55 micron for land and ocean, which are observed and processed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) carried on Terra and Aqua satellites. The resolution of the AOD products is 3 km × 3 km. These data were retrieved from the NASA Giovanni website [16].
The MODIS AOD products have been confirmed in their ability to monitor atmospheric pollutant and quantitatively evaluate regional air quality classes. Regional AOD data were masked by the boundary of Jilin Province. For each year for the period 2015–2018, we used the average of the AOD data during October and November to analyze their differences.

2.2.3. Satellite-Based Fire Points

Fire point information was derived from the Fire Information for Resource Management System (FIRMS [17]), including the MODIS and VIRRS products of the Terra, Aqua, and NPP satellites. The fire point product provides the latitude and longitude of the fire point observed within a given time and the related parameters. The regional fire point information scope was defined by the boundary of Jilin Province. As the published fire point data contain information on the emission sources of the thermal abnormal points such as some industrial enterprises, the fire point information is tangential to high-resolution land use data, and the fire point information located in the farmland was retained as the straw-burning fire point information.

2.2.4. Meteorological Data

Meteorological data include daily meteorological indexes (precipitation, relative humidity, and wind speed) on the urban scale obtained from the Bureau of Meteorology of Jilin Province (2015–2018). These three meteorological indexes are considered to be important factors that affect air quality. Precipitation data were further processed into daily cumulative precipitation and cumulative precipitation days with daily values >5 mm. Relative humidity adopted daily cumulative values and high-humidity cumulative days with daily values >60%. The wind speed also adopted the daily cumulative value and low-wind-speed cumulative days with daily values <4 m/s. The relationships between the meteorological conditions and air quality were analyzed by reasonably characterizing the meteorological characteristics.

2.3. Statistical Analysis

Pearson correlations were obtained among the AQI and atmospheric pollutants during two months. The significance of the differences in AQI or atmospheric pollutant concentrations during the periods were investigated using the independent-samples t test. All of the statistical procedures and plotting were performed using the software SigmaPlot 10.0 (SPSS Inc., Chicago, IL, USA) and ArcGIS 10.2 (Esri, RedLands, CA, USA).

3. Results and Discussion

3.1. Spatial Distributions of Fire Points, AOD, AQI, and PM2.5 during October and November of 2015–2018

The number of fire points in cultivated land was closely related to the control strength of the straw-burning ban and the intensity and frequency of precipitation. According to data from MODIS, the number of fire points in October and November 2015, 2016, and 2017 were 5181, 703, and 3961, respectively (Figure 2a–d). The total number of MODIS and VIRRS fire points in October/November 2018 was 127, of which the number of MODIS fire points was only 38. According to the number of fire points, the intensity of the open burning of straw in 2015 and 2017 was the largest, with the main spatial distributions appearing in Changchun City, Jilin City, Siping City, and Baicheng City. The intensity of the open burning of straw in 2016 came second, and covered the cities of Changchun, Siping, and Baicheng. In 2018, the intensity of the open burning of straw was minimal and only sporadically appeared in individual cities. Therefore, compared with 2015–2017, the open burning of straw in Jilin Province was effectively controlled in October and November 2018. The AOD value directly reflects the spatial distribution of air quality (Figure 2e–h). Similar to the number of fire points in arable lands, the AOD value was significantly divided into three levels” 2015/2017 > 2016 > 2018. The spatial distribution of the AOD value was also very similar to the spatial distribution of the arable land fire points. In areas with dense fire points, the AOD value was relatively high.
The average AQI in October and November 2015 was 134 in Changchun City, 125 in Jilin City, and 130 in Siping City (Figure 2i–l). The center of Changchun–Jilin–Siping decreased to the surrounding cities, and the minimum value was 69 in the Yanbian Korean Autonomous Prefecture. The AQI for October and November 2016 and 2017 showed a downward trend compared to 2015. In 2016, the average AQI of Changchun–Jilin–Siping was 86, 79, and 77, respectively. The two relatively high areas appeared in Changchun City and Yushu City under their jurisdiction. In 2017, the average AQI of Changchun, Jilin, and Siping was 91, 101, and 87, respectively. The center was Yushu City in the northeast corner of Changchun City and Shulan City in the southwest corner of Jilin City, and the trend decreased to the periphery. In 2018, the average AQI of the cities in Jilin Province in October and November was significantly reduced. Five cities were excellent, and the highest value in Changchun was only 53, whereas the Yanbian Korean Autonomous Prefecture was the lowest at 44. As a result, the average AQI of Jilin Province decreased significantly in October and November 2018 when compared to 2015–2017.
In Jilin Province, there were no serious atmospheric pollution events induced by large areas of open burning straw from October to November. The cumulative average number of good and moderate days in cities across the province was 83.47%, which was an increase of 6.23% over the previous year (Table 1). The cumulative average concentration of respirable particulate matter (PM10) was 67 µg/m3, and the cumulative mean concentration of fine particulate matter (PM2.5) was 39 µg/m3, down by 15.3%. Compared with October 2015, the concentrations of PM2.5 in Changchun City, Jilin City, and Siping City decreased by 41.1%, 35.6%, and 40.5%, respectively, in October 2016. In October 2017, the PM2.5 concentrations in Changchun City, Jilin City, and Siping City decreased by 21.8%, 1.4%, and 27.8%, respectively. In October 2018, the PM2.5 concentrations in Changchun City, Jilin City, and Siping City decreased by 69.0%, 67.1%, and 68.4%, respectively. Compared with November 2015, the PM2.5 concentrations in Changchun, Jilin, and Siping decreased by 47.0%, 43.1%, and 50.8%, respectively, in November 2016. In November 2017, the PM2.5 concentrations in Changchun City, Jilin City, and Siping City decreased by 57.5%, 31.0%, and 51.6%, respectively. In November 2018, the PM2.5concentrations in Changchun City, Jilin City, and Siping City decreased by 73.1%, 71.1%, and 71.5%, respectively.

3.2. Trends in Air Quality and Meteorological Indexes during October and November of 2015–2018

Ground monitoring data showed that the concentration values of AQI, PM2.5, and PM10in 2015 were significantly higher than in other years (i.e., 2016, 2017, and 2018), similar in 2016 and 2017, but also significantly higher than in 2018. For the SO2 cumulative values, the 4-year numerical order was: 2015 > 2016 > 2017 > 2018. The cumulative values of CO and NO2 were similar in 2015, 2016, and 2017 with no significant difference between the three years. In 2018, the cumulative values of these two pollutants were significantly lower than those of the other three years (Figure 3a–f).
According to the meteorological data, there were no significant differences in the cumulative wind speed in October and November between the four years (Figure 3g). However, there were significant differences in the cumulative value of low wind speed days, with the highest number of low wind speed days in 2017. Although the total number of low wind speed days in 2015 and 2018 were close, there were significant differences in time variations (Figure 3h). The number of days of low wind speed was minimal in 2016. Low wind speed generally represents poor diffusion (or dispersion) conditions. Therefore, based on low wind speed days, the diffusion conditions in the four years could be divided into three levels: low wind speed in 2017; medium wind speed in 2015/2018; and high wind speed in 2016. Cumulative humidity data for October and November indicated that the values in 2015 were significantly higher than in the other three years (Figure 3i). Similarly, among the high humidity days, the number of high humidity days accumulated in 2015 was nearly 70, whereas the other three years were lower than 10 days (Figure 3j). Therefore, according to the high humidity days, the four years were divided into two levels: high humidity in 2015, and low humidity for the other three years. Among the daily precipitation cumulative values, the province’s precipitation in 2016 and 2018 was similar, slightly higher than in 2017, and the precipitation in 2015 was significantly lower than in the other three years (Figure 3k). The cumulative days of significant precipitation showed that the highest year was 2016, followed by 2017 and 2018, and was the lowest in 2015 (Figure 3l).
In general, under high humidity and low wind speed, diffusion conditions are considered to be poor, whereas under low humidity and high wind speed, diffusion conditions are considered to be better [18,19]. Significant precipitation means that the number of days of straw burning can be reduced, and it is also considered to be a meteorological condition conducive to reducing emission sources. Therefore, combined with the above meteorological indexes, 2015 was characterized by medium wind speed, high humidity, and medium precipitation, and the overall meteorological conditions were the worst. 2016 featured high wind speeds, low humidity, and high precipitation, indicating optimal weather conditions. In addition, 2017 (low wind speed, low humidity, and medium precipitation) and 2018 (medium wind speed, low humidity, and medium precipitation) were estimated to be medium conditions for diffusion when compared with the other two years.

3.3. Contribution of Open Burning Straw Ban to Provincial Air Quality Improvement in 2018

The number of straw-burning fire points represents the intensity of the emission source, thus dividing the four years into three levels of emission intensity: weak in 2018, medium in 2016, and strong in 2015 and 2017 (Figure 4). Among the meteorological diffusion conditions, the four years were divided into three levels: poor in 2015, excellent in 2016, and medium in 2017 and 2018. As the weather conditions during October and November in 2018 were similar to 2017 and worse than 2016, the difference of premier atmospheric pollutant concentrations (i.e., PM2.5) between 2018 and 2016/2017 could indirectly reflect the contribution, assuming there was little change in the other emission sources in 2016, 2017, and 2018. Therefore, the contribution rate can be calculated by the formula (2018_PM2.5−2017_PM2.5|2016_PM2.5)/2017_PM2.5|2016_PM2.5 × 100%, which shows how much the PM2.5 concentration would be reduced by if the 2018 straw-burning prohibition had been implemented in 2016 or 2017. Based on the algorithm, the contribution rate was calculated to be 46–49%. Such a high rate suggests that the main reason for the improvement of air quality in October and November 2018 was that the straw-burning control work was fully carried out and a good control effect was achieved.
Aside from straw burning, the main emission sources in Jilin Province also include coal combustion, dust, and motor-vehicle emissions [20]. Dust is generally reflected in the concentration of coarse particulate matter (i.e., PMCoarse), and its concentration is the difference between PM10 and PM2.5. Compared with 2016 and 2017, the improvement rate of PMCoarse in 2018 was −3.14% and 14.5%, which indicates that the improvement effect was not obvious and even slightly worsened on a provincial scale. Coal combustion sources generally use SO2 as an indicator of their emission intensity [21]. Compared with 2016 and 2017, coal combustion control in 2018 improved the concentration of SO2 by 55% and 43%, respectively, suggesting that the effectiveness of the control measures for coal combustion was significant from 2016 onwards. Nitrogen dioxide is generally used as an indicator of the emission intensity of motor vehicles [22,23]. Compared with 2016 and 2017, the ratio of motor vehicle control measures to NO2 concentration improvement in 2018 was 23% and 21%, respectively, implying a significant improvement effect. However, as straw burning and industrial emissions also release SO2 and NO2, and particulate matters need to be generated by the secondary reactions of gaseous pollutants, it is difficult to attribute the improved contribution of coal combustion and motor vehicle control measures to PM2.5 reduction at the provincial level in this method. According to previous results of PM2.5 source apportionment during autumn in Jilin Province, straw burning, coal combustion, and motor vehicles contributed approximately 40%, 30%, and 10%, respectively, and the remaining 20% were other emission sources. Based on this ratio, the contribution rate of coal combustion control to PM2.5 reduction was 16.5% and12.9%, respectively, whereas motor-vehicle control was 2.3% and 2.1%, respectively. Therefore, excluding the contribution of coal combustion and motor vehicle control, the contribution of the open burning of straw to regional air quality (i.e., PM2.5) in 2018 was estimated to be 27.4–33.8%, with an average of 30.6%. That is, if the straw-burning control work in 2018 had been performed in 2016 and 2017, the PM2.5 concentrations could have been reduced by at least 30.6%. This also fully confirms the necessity of straw-burning control for the improvement of air quality during the period of late autumn and early winter.

3.4. Weaknesses and Suggestions

Although strict implementation of the straw-prohibition policy significantly improved air quality in October and November, there are still large weaknesses in the quantitative evaluation of the contribution of the straw-burning ban to air quality improvement. First, the comprehensive impact of straw-burning control on air quality should be further evaluated for the whole post-harvest period (i.e., October to April of the following year) because the straw-burning period can be postponed in some cities. Thus, it is still not clear whether the straw prohibition has actually achieved the expected results. Second, fire point information was used to evaluate the emission intensity of atmospheric pollutants from straw burning, and the straw-burning area based on high-resolution remote sensing data will be more accurate. In addition, combined with numerical simulation and statistical methods in this study, the evaluation results will show the spatial differences on a regional scale. Third, the effects of regional transport of atmospheric pollutants on other regions (e.g., Heilongjiang Province and Liaoning Province) were not considered in this study. In the same period of 2018, the prohibition work of straw open burning in the surrounding parts of Jilin Province was also relatively significant, indicating the importance of regional collaborative control. However, if a large area cannot be controlled in cooperation, the evaluation of the effect of straw-burning control in a single province will produce a large error. Finally, the weak foundation of the atmospheric pollutant emission inventory and the source apportionment of atmospheric particulate matter will create a lot of uncertainty about the contribution rate from major sources.
In recent years, through the promotion of returning straw to the field, the production of straw feed, the development of straw-curing fuel, power generation, industrial raw materials, and other methods, the comprehensive utilization of straw energy has achieved certain results where the province’s comprehensive utilization of straw resources reached 40% of the total collectable amount in Jilin Province. However, the comprehensive utilization of straw in Jilin Province is slow, the resources and commercialization of crop stalks are not high, the utilization rate of straw resources is low, the industrial chain is short and wasteful, and some related benefit mechanisms have not yet been straightened out. The value added to comprehensive straw utilization is low, and the structure is unreasonable. The comprehensive utilization of straw has not formed an advantage of scale, and the corresponding collection and transportation system has not been established. According to the present situation of the comprehensive utilization of straw and the open-burning ban policy in Jilin Province, the main suggestions are as follows. (1) Make great efforts to promote the return of corn straw to the field including the promotion of protective tillage technology and the provincial government’s research and development of key tools and extension subsidies for protected no-tillage sowing to guide the broad mass of farmers to speed up the application of protective tillage technology. (2) Establish the scientific and reasonable planned burning of straw, which is also crucial to gradually reducing atmospheric pollution, and the actual operation of local governments in those areas where straw can be burned under certain conditions. For example, at both the provincial and county-level scales, based on a high-resolution crop planting area, limited straw-burning area, and meteorological data, the straw-burning quantity was calculated. The atmospheric diffusion model can be used to predict the effects of straw burning on the ambient air quality under different meteorological conditions, and a reasonable regional and operational burning scheme can be developed. (3) Strengthen cross-provincial and regional joint defense and control mechanisms such as leadership mechanisms, organizational mechanisms, implementation mechanisms (e.g., conditions, intensity and program), oversight mechanisms, pre-assessment mechanisms, and post-assessment mechanisms.

4. Conclusions

Due to the conventional cultivation method and the time of straw residue burning, the late autumn and early winter are the most serious periods of atmospheric pollution in Northeast China on an annual scale over the past five years (i.e., 2013–2017). A strict straw open-burning ban implementation in 2018 significantly improved the air quality during the studied periods in Jilin Province. The meteorological conditions were similar between 2018 and 2017, worse than 2016, and better than 2015. Based on a comprehensive consideration of the emissions, meteorological conditions, and source apportionment information, if straw-burning control in 2018 had been performed in 2016 and 2017, the PM2.5 concentrations could have been reduced by at least 30.6%. Thus, it is necessary to control straw burning for the improvement of air quality during these periods. Future research should focus on the evaluation of the whole post-harvest period (i.e., October to April of the following year) and the scientific and reasonable planned burning practice of straw.

Author Contributions

Formal analysis, W.C.; Writing—original draft preparation, W.C.; Supervision, W.C.; Funding acquisition, W.C; Project administration, J.L., Q.B., Z.G., T.C., Y.Y.

Funding

This research was funded under the auspices of the National Key R & D Program of China (No. 2017YFC0212303, 2016YFA0602304), the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (No. QYZDB-SSW-DQC045), the National Natural Science Foundation of China (No. 41775116), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2017275), Northeast Institute of Geography and Agroecology, CAS (No. IGA-135-05), Science and Technology Development Project in Jilin Province (No. 20180520095JH).

Acknowledgments

We would also like to thank the students involved in the work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ren, J.; Li, B.; Yu, D.; Liu, J.; Ma, Z.S. Approaches to prevent the patients with chronic airway diseases from exacerbation in the haze weather. J. Thorac. Dis. 2016, 8, E1. [Google Scholar]
  2. Zheng, G.J.; Duan, F.K.; Su, H.; Ma, Y.L.; Cheng, Y.; Zheng, B.; Zhang, Q.; Huang, T.; Kimoto, T.; Chang, D.; et al. Exploring the severe winter haze in Beijing: The impact of synoptic weather, regional transport and heterogeneous reactions. Atmos. Chem. Phys. 2015, 15, 2969–2983. [Google Scholar] [CrossRef]
  3. Cui, Y.; Zhao, C.Y.; Zhou, X.Y.; Ao, X.; Wang, T.; Li, Q.; Liu, M.Y.; Ma, F.S. Climatic characteristics of haze days in Northeast of China over the past 50 years. China Environ. Sci. 2016, 36, 1630–1637. [Google Scholar]
  4. Chen, W.W.; Zhang, S.C.; Tong, Q.S.; Zhang, X.L.; Zhao, H.M.; Ma, S.Q.; Xiu, A.J.; He, Y.X. Regional Characteristics and Causes of Haze Events in Northeast China. Chin. Geogr. Sci. 2018, 28, 112–126. [Google Scholar] [CrossRef]
  5. Ma, S.Q.; Chen, W.W.; Zhang, S.C.; Tong, Q.S.; Bao, Q.Y.; Gao, Z.T. Characteristics and Cause Analysis ofHeavy Haze in Changchun City in Northeast China. Chin. Geogr. Sci. 2017, 27, 989–1002. [Google Scholar] [CrossRef]
  6. Zhao, H.M.; Zhang, X.L.; Zhang, S.C.; Chen, W.W.; Tong, D.Q.; Xiu, A.J. Effects of Agricultural Biomass Burning on Regional Haze in China: A Review. Atmosphere 2017, 8, 88. [Google Scholar] [CrossRef]
  7. Zhang, G.; Li, J.; Li, X.D.; Xu, Y.; Guo, L.L.; Tang, J.H.; Lee, C.; Liu, X.; Chen, Y.J. Impact of anthropogenic emissions and open biomass burning on regional carbonaceous aerosols in South China. Environ. Pollut. 2010, 158, 3392–3400. [Google Scholar] [CrossRef]
  8. Qin, Y.; Xie, S.D. Historical estimation of carbonaceous aerosol emissions from biomass open burning in China for the period 1990–2005. Environ. Pollut. 2011, 159, 3316–3323. [Google Scholar] [CrossRef]
  9. Huang, X.; Li, M.; Li, J.; Song, Y. A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products. Atmos. Environ. 2012, 50, 9–15. [Google Scholar] [CrossRef]
  10. Mendoza, T.C.; Mendoza, B.C. A review of sustainability challenges of biomass for energy: Focus in the Philippines. Agric. Technol. 2016, 12, 281–310. [Google Scholar]
  11. Lee, S.C.; Wang, B. Characteristics of emissions of air pollutants from burning of incense in a large environmental chamber. Atmos. Environ. 2004, 38, 941–951. [Google Scholar] [CrossRef]
  12. Li, W.J.; Shao, L.Y.; Buseck, P.R. Haze types in Beijing and the influence of agricultural biomass burning. Atmos. Chem. Phys. 2010, 10, 8119–8130. [Google Scholar]
  13. Keywood, M.; Kanakidou, M.; Stohl, A.; Dentener, F.; Grassi, G.; Meyer, C.P.; Torseth, K.; Edwards, D.; Thompson, A.M.; Lohmann, U.; et al. Fire in the Air: Biomass Burning Impacts in a Changing Climate. Crit. Rev. Environ. Sci. Technol. 2013, 43, 40–83. [Google Scholar] [CrossRef]
  14. Hayashi, K.; Ono, K.; Kajiura, M.; Sudo, S.; Yonemura, S.; Fushimi, A.; Saitoh, K.; Fujitani, Y.; Tanabe, K. Trace gas and particle emissions from open burning of three cereal crop residues: Increase in residue moistness enhances emissions of carbon monoxide, methane, and particulate organic carbon. Atmos. Environ. 2014, 95, 36–44. [Google Scholar] [CrossRef]
  15. Chen, H.; Yin, S.; Li, X.; Wang, J.; Zhang, R. Analyses of biomass burning contribu-tion to aerosol in Zhengzhou during wheat harvest season in 2015. Atmos. Res. 2018, 207, 62–73. [Google Scholar] [CrossRef]
  16. Giovanni. Available online: http://giovanni.sci.gsfc.nasa.gov/giovanni (accessed on 30 May 2019).
  17. Fire Information for Resource Management System. Available online: https://firms.modaps.eosdis.nasa.gov/ (accessed on 30 May 2019).
  18. Zeng, S.; Zhang, Y. The Effect of Meteorological Elements on Continuing Heavy Air Pollution: A Case Study in the Chengdu Area during the 2014 Spring Festival. Atmosphere 2017, 8, 71. [Google Scholar] [CrossRef]
  19. Jones, A.M.; Harrison, R.M.; Baker, J. The wind speed dependence of the concentrations of airborne particulate matter and NOx. Atmos. Environ. 2010, 44, 1682–1690. [Google Scholar] [CrossRef]
  20. Fang, C.; Zhang, Z.; Jin, M.; Zou, P.; Wang, J. Pollution Characteristics of PM2.5 Aerosol during Haze Periods in Changchun, China. Aerosol Air Qual. Res. 2017, 17, 888–895. [Google Scholar] [CrossRef]
  21. Chen, L.W.A.; Chow, J.C.; Doddridge, B.G.; Dickerson, R.R.; Ryan, W.F.; Mueller, P.K. Analysis of summertime PM2.5 and haze episode in the Mid-Atlantic region. J. Air Waste Manag. Assoc. 2003, 53, 946–956. [Google Scholar] [CrossRef] [PubMed]
  22. Tonne, C.; Beevers, S.; Armstrong, B.; Kelly, F.; Wilkinson, P. Air pollution and mortality benefits of the London Congestion Charge: Spatial and socioeconomic inequalities. Occup. Environ. Med. 2008, 65, 620–627. [Google Scholar] [CrossRef] [PubMed]
  23. Pattenden, S.; Hoek, G.; Braun-Fahrlander, C.; Forastiere, F.; Kosheleva, A.; Neuberger, M.; Fletcher, T. NO2 and children’s respiratory symptoms in the PATY study. Occup. Environ. Med. 2006, 63, 828–835. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Provincial topography and atmospheric environmental monitoring sites in Jilin Province. The blue solid triangles represent the sampling locations in nine prefecture-level cities; the black solid lines are the boundary of each prefecture-level city; the green solid circle is the capital (i.e., Changchun City) of Jilin Province.
Figure 1. Provincial topography and atmospheric environmental monitoring sites in Jilin Province. The blue solid triangles represent the sampling locations in nine prefecture-level cities; the black solid lines are the boundary of each prefecture-level city; the green solid circle is the capital (i.e., Changchun City) of Jilin Province.
Atmosphere 10 00375 g001
Figure 2. Spatial distribution of field fire points (ad), aerosol optical thickness (AOD) (eh) and air quality index (AQI) (il) during October and November from 2015 to 2018.
Figure 2. Spatial distribution of field fire points (ad), aerosol optical thickness (AOD) (eh) and air quality index (AQI) (il) during October and November from 2015 to 2018.
Atmosphere 10 00375 g002
Figure 3. Air quality index and atmospheric pollutant (PM10, PM2.5, SO2, CO, and NO2) concentrations (af), wind speed (g,h), relative humidity (i,j) and precipitation (k,l) during October and November from 2015 to 2018. The cumulative values of AQI, the concentration of pollutants, the average wind speed, and the average relative humidity in Jilin Province were calculated by using the daily average of the monitoring data of the nine cities and autonomous prefecture for daily accumulation. The cumulative value of daily precipitation used the sum of the nine cities and autonomous prefecture for daily accumulation. The number of low wind speed days accumulated on a daily basis using the number of days with a wind speed of less than 4m/s in the nine cities and autonomous prefecture. The accumulation of high humidity days used the number of days in which the relative humidity of the nine cities and autonomous prefecture was higher than 60% for daily accumulation. The cumulative days of obvious precipitation were accumulated daily by the days when the precipitation exceeded 5mm in the nine cities and autonomous prefecture.
Figure 3. Air quality index and atmospheric pollutant (PM10, PM2.5, SO2, CO, and NO2) concentrations (af), wind speed (g,h), relative humidity (i,j) and precipitation (k,l) during October and November from 2015 to 2018. The cumulative values of AQI, the concentration of pollutants, the average wind speed, and the average relative humidity in Jilin Province were calculated by using the daily average of the monitoring data of the nine cities and autonomous prefecture for daily accumulation. The cumulative value of daily precipitation used the sum of the nine cities and autonomous prefecture for daily accumulation. The number of low wind speed days accumulated on a daily basis using the number of days with a wind speed of less than 4m/s in the nine cities and autonomous prefecture. The accumulation of high humidity days used the number of days in which the relative humidity of the nine cities and autonomous prefecture was higher than 60% for daily accumulation. The cumulative days of obvious precipitation were accumulated daily by the days when the precipitation exceeded 5mm in the nine cities and autonomous prefecture.
Atmosphere 10 00375 g003
Figure 4. Qualitative relationships between the AQI, the intensity of the source of straw burning, and the meteorological conditions in October and November 2018. Note: The intensity of the emission source is mainly divided into three levels according to the number of fire points; meteorological diffusion conditions are mainly divided into three levels based on wind speed, humidity, and precipitation data.
Figure 4. Qualitative relationships between the AQI, the intensity of the source of straw burning, and the meteorological conditions in October and November 2018. Note: The intensity of the emission source is mainly divided into three levels according to the number of fire points; meteorological diffusion conditions are mainly divided into three levels based on wind speed, humidity, and precipitation data.
Atmosphere 10 00375 g004
Table 1. Air quality-related indexes for the major polluted cities during October and November of the period from 2015 to 2018.
Table 1. Air quality-related indexes for the major polluted cities during October and November of the period from 2015 to 2018.
CityChangchunJilinSiping
IndexPM2.5
Conc.
NGM/
Ratio
NH/
Ratio
PM2.5
Conc.
NGM/
Ratio
NH/
Ratio
PM2.5
Conc.
NGM/
Ratio
NH/
Ratio
2015/1087 ± 6215/48.45/16.173 ± 5518/58.11/3.279 ± 6215/48.44/12.9
2016/1051 ± 2823/74.20/047 ± 2427/87.10/047 ± 2228/90.30/0
2017/1068 ± 4625/713/9.772 ± 4117/54.81/3.257 ± 3123/74.20/0
2018/1027 ± 1531/1000/024 ± 1031/1000/025 ± 1531/1000/0
2015/11134 ± 11612/406/20116 ± 9511/36.76/20124 ± 11112/406/20
2016/1171 ± 5622/73.32/6.766 ± 4220/66.72/6.761 ± 4021/701/3.3
2017/1157 ± 3323/76.71/3.380 ± 12324/80.03/1060± 3221/700/0
2018/1136 ± 1629/96.70/033 ± 1430/1000/035 ± 1429/96.70/0
NGM: number of good and moderate days (AQI < 100); NH: number of heavy polluted days (AQI ≥ 200). Ratio: the ratios of NGM or NH to the whole month. The unit of ratio is in %.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top