1. Introduction
Crop residue burning is the process of eliminating residue left in fields by fire after crop harvesting, in which a large number of pollutants such as Carbon Monoxide (CO), Carbon Dioxide (CO
2), Nitrogen Dioxide (NO
2), Sulfur Dioxide (SO
2), Ammonia (NH
3), Methane (CH
4), Ozone (O
3), and dust are produced [
1,
2,
3,
4] and emitted into the atmosphere [
5,
6]. The practice endangers human health, affects traffic, and causes fire accidents [
7]. Also, crop residue burning severely damages soil structure and microflora for agricultural production, causing soil fertility to decline [
8,
9]. In addition to the impact of residue burning on the local environment, pollutants can spread with the wind to more distant areas and impact the air quality of surrounding areas, especially cities [
10,
11], which show a random and scattered spatial distribution. Crop residue burning in northeast China is mainly concentrated in late October (the harvest season) to May before the following year’s plowing [
12] and shows a random and scattered spatial distribution. In addition, the process is complicated by crop residue burning being significantly affected by the weather. Furthermore, farmers usually burn crop residue under good atmospheric diffusion conditions to burn it more fully and better for subsequent cultivation operations [
13]. Because of the spatial–temporal heterogeneity of crop residue burning, manual monitoring is not the best approach because it requires much time and human and financial resources.
With the development of satellite remote sensing, many research scholars began to notice the efficiency of remote sensing satellite images for residue fire points monitoring [
14,
15,
16]. These studies found that remote sensing technology can provide dynamic macro monitoring of residue fire information. Using remote sensing images with high spatio-temporal resolution, we can accurately obtain crop residue burning information such as the spatial information of residue fire points [
17] and burned area [
18]. Schroeder et al. provide a new active fire detection method for the Visible Infrared Imaging Radiometer Suite (VIIRS) that is primarily generated by 375 m thermal infrared remotely sensed data. Increased performance was achieved by utilizing the 375 m active fire data in comparison to the VIIRS 750 m baseline fire product, resulting in 3-fold and 25-fold increases in the absolute number of fire pixels recorded using daytime and nighttime data, respectively. The VIIRS 375 m fire data demonstrated much higher mapping capabilities compared to the original MODIS fire detection product [
19]. Vadrevu et al. compared the performance of several methodologies for predicting total particulate matter emissions based on the VIIRS 375 m active fire product and the MODIS 1 km fire product. Compared to the MODIS Aqua and Terra sensors, the number of fires observed by the VIIRS was 4.8 times greater. Additionally, VIIRS recorded 6.5 times as many fires as Aqua [
20].
At the same time, many scholars have also started to study the impact of crop residue burning on urban air quality. Jain et al. studied the emissions of pollutants from crop residue burning and the effect on the rate of change of air pollutant emissions [
21]. Using ground observation and MODIS active fire data, Zhuang et al. investigated the fluctuations and characteristics of PM
10 (particulate matter with an aerodynamic diameter of less than 10 μm) and PM
2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) [
22,
23] concentrations and the associations with variations in crop residue burning [
24]. Li et al. investigated crop residue open burning emissions in central China using a statistical technique. Using the VIIRS 375 m active fire product and observed emission factors, the open burning proportion of residue was utilized to improve the precision of estimated emissions [
25]. The MODIS products MOD14A1/MYD14A1 were employed to better understand the long-term spatial and temporal variations of crop residue burning in China [
26]. There have been many studies on the positive correlation between the number of residue-burning fires and air quality. However, because satellite remote sensing can only monitor residue-burning fires at the moment of transit, and not when the satellite is not in transit, there is a significant limitation in its usage. In contrast, no such limitation exists in studying the correlation between residue burned area extracted by satellite and in situ air quality.
Based on remote sensing images taken by satellites at two transit times, the time-series change in burning index is detected to extract a new residue burned area. Based on remote sensing images taken during satellite transit, a new residue burned area was extracted using time series change detection from the burning index [
26,
27]. Roteta et al. built a locally adapted multitemporal two-phase burned area method employing Sentinel-2 short- and near-infrared waveband data and MODIS active fire products [
27]. When 13 remotely sensed indices were compared based on ranked correlations, the Normalized Burn Ratio (NBR) was highest for both the post-burn and pre-/post-burn approaches. In addition, according to research by Epting et al., high correlations existed between the NBR and field-based Composite Burn Index values [
28]. These burned area extraction methods provide a solid foundation for establishing the relationship between residue burning and air quality. Nevertheless, a comparative analysis of the impact of two common residue burning parameters, the number of residue fire points, and residue burned area on urban air quality indicators has not been reported.
Yang et al. integrated satellite and in situ observations with regional spatial quality to construct a model for assessing the impact of open-air biomass burning on surface PM
2.5 concentrations in the context of severe haze in northeast China. Simulation outcomes revealed that open-air biomass burning accounted for 52.7% of PM
2.5 concentrations in northeast China [
29]. Li et al. conducted an analysis of air quality pollution characteristics, causes of haze formation, and the effects of the straw burning ban on local air quality in Suihua City, utilizing air quality data, aerosol optical depth (AOD) data, and fire point products. The findings revealed that seasonal crop residue burning was a significant contributor to air pollution in Suihua City during late autumn and early spring [
30]. Cui et al. examined the spatial and temporal patterns of straw burning in northeast China between 2013 and 2017. The research demonstrated that the gaseous emissions resulting from the combustion of straw had a noteworthy effect on the quality of air, particularly during the autumn and winter periods. A strong correlation was observed between PM
2.5 and straw burning, with a statistical significance of
p < 0.05 [
17].
To address the problems raised in previous studies on the correlation between fire points and urban air quality, the objectives of the present research were (i) to analyze the correlation among the number of residue fire points, residue burned area, air quality index (AQI), and the concentration of each pollutant (PM2.5, PM10, SO2, CO, NO2, and O3) using the fire point product of VIIRS/MODIS and the area burned product of MOD64; and (ii) to discuss the effects of crop residue burning on the air quality of typical industrial cities in northeast China using wind direction data.
4. Discussion
Yang et al. compared the effects of geographical, pollution, and meteorological conditions on the spatial and temporal distribution of PM
2.5 concentrations over China [
44]. In response to the results above, we added wind direction to analyze its effect on the number of residue fire points, residue burn area, and AQI. The main effect of wind direction on air pollution is the direction of horizontal transport of pollutants, where the direction downwind of the pollution source is the pollution area and the wind direction and wind speed determine the extent and intensity of the appearance of air pollution [
44]. To discuss the role of wind direction on atmospheric pollutant transport, in this study, we divided the wind direction into 16 directions, namely, N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, and NNW.
Figure 5 shows the wind direction distribution in Daqing City from 1 to 8 March 2019, where the circle’s radius represents the wind speed magnitude. We found that the wind in Daqing is mainly from the west–northwest, west, and southwest, with an average wind speed of 6–9 m/s.
The concentration distributions of PM
2.5, PM
10, CO, NO
2, O
3, and SO
2 in each wind direction are shown in
Figure 6, which were generated in Origin 2018 using meteorological data and air pollution monitoring data [
45]. The distribution of atmospheric pollutant concentrations showed an almost identical consistent spatial distribution of each pollutant, but there were extreme values of pollutant concentrations in particular wind directions. The distributions of PM
2.5 and PM
10 were similar, with the highest concentrations occurring in the wind direction of WNW, followed by higher concentrations in the wind directions of SE and SSW. NO
2 and CO also showed concentration maxima in the WNW wind direction, followed by higher concentrations in the SE wind direction, which may be related to the diffusion of pollutants in the surrounding areas.
As a primary driver of air pollutant dispersion, it is widely accepted that wind direction can strongly influence the direction of air pollutant dispersion. Different wind directions can lead to different urban areas being polluted by crop residue burning. In this study, the effect of wind direction on AQI was considered. We conducted correlation analyses using the number of residue fire points and burned area in the upwind buffer zone with urban AQI, respectively, as shown in
Table 5. Comparing
Table 3 and
Table 6, we found that the correlation between the number of fire points and AQI in each buffer zone upwind of Daqing was higher than that without considering the wind correlation, and the correlation coefficients were all above 0.6 (0.66, 0.77, 0.66, and 0.64 at 25, 50, 75, and 100 km buffer, respectively). The correlation coefficient between the burned area and AQI in the adjoining wind direction also improved, reaching over 0.8 except for the 100 km buffer, with the 50 km buffer having the highest correlation coefficient of 0.88. These results may be due to only areas in the downwind path of the burned area being affected by crop residue burning. In contrast, urban areas in the other directions are rarely affected. Similar to
Section 3.2, the correlation coefficient between the residue burned area and the air quality index in each buffer zone upwind of Daqing was higher than that between the number of fire points and the air quality index, as shown in
Table 4 and
Table 5. Nonetheless, there are numerous causes for their correlation not reaching higher levels.
As an industrial city, industrial emissions in Daqing also account for a significant proportion of all atmospheric pollutants, resulting in air pollution from industrial emissions as well as residue burning. In addition, meteorological conditions including wind speed, temperature, humidity, rainfall, and air pressure all affect air pollution caused by residue burning [
46,
47,
48]. According to Zhang et al., a reduction in PM
10 was found to occur under stronger wind and higher precipitation conditions [
49]. In addition, emissions from residue burning vary depending on the residue type and moisture content, and the phase of burning [
50]. The effect of various combinations of meteorological conditions on air pollution is complex, so the impact of residue burning on air quality in industrial cities needs further analysis.
5. Conclusions
In this study, the correlation between different residue burning parameters on air quality in Daqing City was investigated comparatively using the VIIRS active fire point product, the MODIS burned area product, and buffer zone analysis. The association between MODIS burned area products and AQI was found to be around 0.8, with a maximum of 0.82 at a buffer zone radius of 50 km. Meanwhile, it was found that the correlation between the number of residue fire points extracted from VIIRS active fire products and air quality was above 0.6, again with a maximum of 0.75 at a buffer radius of 50 km. Within other levels of buffer zones, the correlation between residue burned area and AQI was consistently higher than that between residue fire points and AQI.
By comparing the correlation between VIIRS fire points, MODIS burned area, and AQI and the concentration of each pollutant, we found that the correlation between residue burned area and AQI and the concentration of each pollutant is higher than that between the number of residue fire points and AQI and the concentration of each pollutant. MODIS burned area monitoring, on the other hand, detects changes in the time series of images taken by satellite at two transit moments to obtain the new burned area and cumulative burned area during this period, allowing the monitoring of fire traces caused by fire points at non-transit moments.
From analyzing the correlation between residue fire points, residue burned area, and the concentration of each pollutant (PM2.5, PM10, CO, NO2, SO2, and O3), we found significant correlations between crop residue burning and PM2.5, PM10, CO, and NO2 concentrations, with the highest correlation seen with PM2.5 at an R2 of 0.81. Moreover, the correlation between residue burned area and PM2.5, PM10, CO, and NO2 concentrations was significantly higher than the correlation between the number of residue fire points and their concentrations.
In this study, the correlation between straw fire point and burned area and AQI are mainly determined by using the fire point product of VIIRS/MODIS and the area burned product of MOD64. However, in the future, there is a desire to use geostationary satellites to establish the correlation between straw fire points, burned area, and AQI at a lower temporal resolution to find further better indicators to characterize the impact of straw burning on the surrounding regional air quality.