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Article

Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning

by
Luyan He
1,†,
Lingjian Duanmu
1,2,†,
Li Guo
2,
Yang Qin
3,
Bowen Shi
1,
Lin Liang
1 and
Weiwei Chen
1,*
1
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
3
Prediction and Forecast Department, Jilin Provincial Ecological Environment Monitoring Center, Changchun 130011, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(3), 279; https://doi.org/10.3390/agriculture15030279
Submission received: 17 December 2024 / Revised: 16 January 2025 / Accepted: 24 January 2025 / Published: 28 January 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

:
Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM2.5 concentration. The RSM relevance prediction model (MET-PM2.5) established a correlation equation between meteorological factors and PM2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM2.5 value remained below 75 μg/m3 when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities.

Graphical Abstract

1. Introduction

The open burning of agricultural fields is a widely practiced agricultural management technique worldwide [1]. It serves as a cost-effective method to reduce crop residue, control fungal diseases, insects, and weeds, and improve soil fertility [2,3]. However, during the process of biomass burning, various pollutants such as aerosol particles, carbon monoxide (CO), nitrogen oxides (NOx), ammonia (NH3), carbon dioxide (CO2), and volatile organic compounds (VOCs) are released into the atmosphere [4]. Currently, the open-field burning of crop residues is recognized as a significant anthropogenic source that strongly affects local air quality and contributes to regional haze pollution [5,6].
Considering the inevitable need to incinerate crop residues at the current stage [7], researchers have suggested that well-designed prescribed plans for biomass burning should be implemented [8]. Fundamentally, the prescribed burning of biomass can be viewed as a transition from unregulated and sporadic burning practices to a well-organized approach, aiming to maximize the self-purification capacity of the Earth’s atmosphere. To achieve this management objective, regulated burning has been conducted annually worldwide for decades [9,10]. For example, in the United States, approximately 1 million hectares of land, including grasslands and agricultural fields, undergo prescribed burning annually as a measure to mitigate fire risk [11]. In China, biomass burning primarily takes place in rural areas, where burning plans commonly specify the required meteorological conditions on the basis of region-specific threshold values [12]. Recent research has revealed a shift in the hotspots of open-field straw burning in China from the central and southeastern regions to the northeastern region [13]. Located in the center of Northeast China, Changchun is a megacity with a population of millions and is surrounded by extensive agricultural areas. After 2018, Changchun began to implement planned open burning to alleviate the adverse impacts of straw burning on air quality [14]. However, owing to the region’s low temperatures and long cold seasons, the window for straw burning in autumn is extremely short [15]. Consequently, despite the implementation of planned burning, a significant amount of straw is still burned by farmers in spring before the planting season, leading to severe pollution [16]. By conducting long-term observational studies, it becomes feasible to quantify the air pollution attributed to prescribed burning within regions [17]. However, studies that refine meteorological parameters through long-term observational data to develop sophisticated dynamic control plans to assess the extent of future air quality improvement are limited.
Despite the existence of various environmentally friendly methods for handling crop residues, the persistent demand for the planned burning of crop residues remains undiminished, particularly within developing nations [18]. Studies have been conducted to explore the feasibility of planned burning. Research has focused primarily on delineating the characteristic chemical element concentrations of air pollutants emitted from open-field straw burning, and has considered factors related to pollutant dispersion properties. Molecular tracers, e.g., levoglucosan and galactosan, have been utilized to quantitatively assess the impact of planned straw burning [19,20]. Furthermore, by integrating multiple observation methods, including satellites, ground monitoring, and unmanned aerial vehicles, the spatial extent of haze caused by the open burning of crop residues can be calculated [21,22]. The impacts of crop residue burning on regional atmospheric radiation, meteorology, and climate systems have also been investigated [23]. Moreover, statistical methods have been employed to consider the effects of meteorological factors (including relative humidity (RH), wind speed (WS), precipitation, etc.) on air quality during biomass burning [24]. Finally, it is estimated that the comprehensive utilization of straw residues instead of their incineration would lead to a reduction in air pollutants [25]. These studies primarily utilize numerical modeling and design fire point variations to quantitatively estimate the potential changes in air quality [26]. In general, more attention has been focused on the impact of historical biomass burning areas and burning intensities on air quality, or on the benefits of government measures to prohibit open-field straw burning [27]. However, research on the development of air pollution reduction strategies without altering the amount of biomass burning in large-scale farmlands is lacking. Studies have indicated that meteorological factors in the atmosphere play a significant role in atmospheric pollution, especially during extreme weather conditions such as atmospheric stagnation or thermal inversions. Moreover, owing to the focus on reducing burning quantities, research on harnessing meteorological parameters to formulate meteorologically sensitive control strategies is limited. The effective coordination of meteorological factors with prescribed measures holds significant potential for managing air quality by governmental authorities.
This study focused on the intensive straw burning period in Changchun, Jilin Province, from 2021–2023. The study delved into the parametric characteristics of aerosol particles, boundary layer height, and meteorological factors under the prescribed burning scenario in the region. The impacts of open-field straw burning and meteorological interactions on fine particulate matter concentrations were analyzed through single-factor analysis. Furthermore, a response surface model was employed to delineate a feasible range of meteorological factor parameters to reduce air pollution. The aim of this study is to determine the optimal combination of meteorological factors and formulate pertinent policy suggestions, with the objective of maximizing the disposal efficiency of straw and minimizing the adverse effects of air pollution.

2. Materials and Methods

2.1. Study Area and Period

Changchun city is located in the central region of Jilin Province, China, and encompasses a core agricultural zone, as depicted in Figure 1. Falling within a mid-temperate zone, the region follows a seasonal agricultural cycle where crops are typically harvested once a year in October, with soil tillage and crop planting for the upcoming season commencing in April [28]. Minimal straw burning occurs during the autumn harvest due to adverse weather conditions, leading to the concentration of biomass burning activities occurring primarily in March and April, just before the onset of spring cultivation. Despite the implementation of the Straw Open Burning Prohibition Plan in 2018, it only prohibits burning within 5–10 km of urban areas and along various roads, while regulating burning in other areas on the basis of specific timeframes [29]. The extensive burning of straw, coupled with widespread farmland, significantly impacts local air quality in Jilin. The daily air quality often surpasses Chinese Grade II standards, reaching levels of severe pollution following harvest and before the spring plowing period of the subsequent year, as documented by previous studies [7]. This study focuses on the period of straw burning over the past three years (2020–2023), known as the biomass burning period (BBP), which occurs annually from March to April.

2.2. Data Sources

The distributions of active fires and cropland in Jilin Province were integrated to identify straw burning occurrences, where ignition locations within cropland were classified as open straw burning. Satellite data from the Fire Information for Resource Management System (FIRMS), including daily MODIS C6 and VIIRS V1 products [30] sourced from the Terra and Aqua satellites, were used. Ground monitoring data on the air quality index (AQI) and atmospheric pollutant concentrations (PM2.5, PM10, SO2, NO2, and CO) for each prefecture-level city were obtained from the Jilin Provincial Environmental Monitoring Centre. Additionally, meteorological condition data, such as daily mean wind speed (m/s), daily mean relative humidity (%), and daily total precipitation (mm) for each city, were provided by the Jilin Provincial Meteorological Service. These datasets were collectively utilized to assess spatial and temporal variations in air quality comprehensively.
The ground-based polarized LiDAR system (PL), specifically HKLIDAR-V, was set up at the campus of the Northeast Institute of Geography and Agroecology of the Chinese Academy of Sciences (44°00′ N, 125°25′ E) located in Changchun, China. Operating with a 532-nanometer wavelength laser emitter (Jilin Hongke Photonics Corporation, Liaoyuan, China), the system boasts a ranging resolution of 15 m and an integration time of 20 s, with a blind zone spanning 150–200 m. During its operational phase, the PL system releases polarized light into the atmosphere via a polarization beam splitter [31]. The telescope subsequently captures the backscattered light, segregating it into cross-polarized and parallel-polarized components. The Fernald algorithm [32] is then utilized to derive aerosol extinction coefficient profiles, presuming that the atmosphere comprises two constituents: aerosols and air molecules [33]. To gauge the uncertainty concerning the retrieved extinction coefficient values, the Gaussian error propagation principle was enlisted for the retrieval equation [34]. Estimating the uncertainty of the smoke extinction coefficient involves various elements, including the uncertainty linked to the smoke backward scattering coefficient calculation (ranging from 15% to 20%), the laser radar ratio (with an uncertainty of 15%), and the signal noise. These facets collectively culminate in an overall uncertainty level of approximately 27% for the estimated smoke extinction coefficient [35].
The LiDAR system receives backscattered echo power from the atmosphere at a height of r (km). To capture variations in aerosol concentration at different altitudes, the echo signals P(r)r2 are calibrated using the lidar distance squared. This calibration technique has been documented by Hooper and Eloranta [36]. In the presence of an inversion layer, aerosol particles tend to accumulate within the planetary boundary layer (PBL). As a result, the aerosol density changes between the free atmosphere and the PBL. These changes can be reflected in the variation in the gradient of P(r)r2, which is denoted as D(r). The most significant change in this gradient corresponds to the height of the PBL:
D(r) = d[P(r)r2]/dr

2.3. Univariate Analysis and Response Surface Methodology

To investigate the influences of the WS, planetary boundary layer height (PBLH), and RH on the PM2.5 concentration under various biomass burning scenarios, a univariate analysis was conducted by calculating the means of the fire points within the study area. Fire points exceeding or falling below this average were categorized into high- and low-burning peak period groups accordingly. Through an analysis of the three meteorological variables, potential correlations among them were investigated. Drawing from the results of the univariate analysis, experiments were devised via the Box–Behnken design principle to assess the combined effects of multiple independent variables. Response surface methodology was employed to analyze these effects, as it offers insights into the interactive impacts of variables on overall performance and provides a systematic evaluation of their respective significance levels [37]. This approach has gained widespread acceptance in various fields in recent years. In this study, the PM2.5 concentration was chosen as the primary target variable, given its significance in air quality classification in Jilin during the research period. It serves as a proxy for understanding the combined influence of straw open burning and meteorological factors on air quality. The purpose of univariate analysis is to investigate the influence of different environmental parameters under varying fire point counts. Initially, we focused on scenarios where the daily fire point count was below the regional average. In such cases, we first selected a dataset with an RH of 45%, a PBLH of 1.2 km, and a WS ranging from 0–8 m/s (with a 1 m/s interval) for fitting analysis. We subsequently varied the range of the RH from 25% to 65% (in 5% increments) while maintaining the PBLH at 1.2 km and the WS at 4 m/s, reselecting the data and performing a fitting analysis. Next, we fixed the RH at 45% and set the range of the PBLH from 0.8 km to 1.6 km (in 0.1 km increments), with the WS remaining at 4 m/s, for data selection and fitting analysis. Finally, when the daily fire point count exceeded the regional average, we employed the same parameter settings and analysis approach to comprehensively understand the impact of environmental parameters on the PM2.5 concentration distribution under different fire point count scenarios. The analysis culminated in the development of 3D response surface methodology (RSM) and corresponding mathematical models. The performance of these models was evaluated via metrics such as the coefficient of determination (R2) and root mean square error (RMSE) [38]. Furthermore, the optimal meteorological conditions were computed via these models. Both single-factor experiments and response surface modeling were conducted via SAS 9.4 software, ensuring the rigor and accuracy of the findings.

2.4. Backwards Trajectory

Evaluating backward trajectories is a commonly employed technique to pinpoint the source region and track the atmospheric movement of pollutants [39]. For this purpose, the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model was utilized to compute the transport trajectories of air pollutants during straw burning episodes [40]. Meteorological data for the model originated from the Global Data Assimilation System (GDAS), provided by the National Oceanic and Atmospheric Administration (NOAA), with a spatial resolution of 1.0° × 1.0°. In this study, we calculated the backward trajectories at a height of 500 m above the Changchun Environmental Monitoring Station and identified the primary clusters of backward trajectories for April of each year from 2021 to 2023 [41]. This approach aimed to minimize the impact of surface turbulence.

3. Results

3.1. Variations in the PM and Aerosol Extinction Coefficients

The daily average concentrations of PM2.5 and PM10 in the Changchun area from 2021–2023 are depicted in Figure 2. While most data points fall within the reference range of China’s air quality standards, a quantitative analysis reveals a significant trend: from February to April, both the PM10 and PM2.5 concentrations frequently exceeded the thresholds of 150 μg/m3 and 75 μg/m3, respectively, exacerbating particulate matter pollution. In particular, March and April emerged as the most severely polluted periods, with a substantial number of high-value points for PM10 concentrated primarily within the concentration range of 200–350 μg/m3. Notably, in April, the high-value points for PM2.5 were more prominent, with concentrations approaching those of PM10, indicating that fine particulate matter became the primary source of particulate pollution during this period.
On the basis of continuous monitoring data from LiDAR, this study conducted a detailed analysis of aerosol extinction coefficients in the altitude range of 0.2–1 km from February–April in 2023 (Figure 3). First, within the altitude range of 0.2 km, the average extinction coefficient remained stable in the range of 0.4–0.6 km−1 from February–April. This phenomenon may be closely related to the specific structure of the atmospheric boundary layer. Furthermore, with increasing altitude, the extinction coefficients within the 1 km altitude range exhibited a significant decreasing trend, further confirming the important influence of the atmospheric boundary layer structure on the distribution of aerosols. In the analysis of diurnal variation data, attention was given to the changes in the extinction coefficients at the lower atmospheric layers. The results revealed that the extinction coefficients at the lower atmospheric layers maintained relatively high levels throughout the day, especially in February, where the rate of decrease in the aerosol extinction coefficient with decreasing altitude was slow, possibly due to unfavorable diffusion conditions during February. In comparison, the extinction coefficients at the lower atmospheric layers in March and April exhibited certain differences, with the extinction coefficient in March being slightly higher than that in April and the lowest point of the extinction coefficient in March being approximately 0.5 km−1. Notably, during the planned burning period in April, the standard deviation of the extinction coefficient significantly increased, indicating a sharp increase in the aerosol concentration in the short term. Such drastic fluctuations may have a significant impact on air quality. Additionally, in the monitoring data for March, there was a high-value area of the extinction coefficient standard deviation in the altitude range of 1.6–2 km, which may be related to frequent dust events in that month.

3.2. Analysis of Fire Points and Meteorological Conditions

During the pre-sowing period from March–April of 2021–2023, large-scale concentrated straw burning incidents occurred in Jilin Province, which were mainly concentrated in late April. Although the number of fire points increased from February to March, the growth rate was relatively slow. Notably, there were significant differences in the cumulative number of fire points in different years, mainly manifested as a sharp increase in the cumulative number of straw fire points after they entered April, with the number of fire points increasing rapidly by more than 1000 within 2–4 days (Figure 4a). This rapid increase in fire points not only places enormous pressure on environmental management but could also lead to severe air pollution. Despite the implementation of planned burning policies in Jilin Province, the numerical changes in fire points in recent years have not shown a linear growth trend but rather an exponential growth trend. In comparison, the control effectiveness in 2022 and 2023 improved compared with that in 2021, with the latter reaching a cumulative number of 3000 fire points. Moreover, according to satellite monitoring data, after April, the local fire radiative power (Frp) values in the northwest region reached above 40 mW, indicating significant atmospheric pollution pressure from straw burning in the northwest region of Changchun when the wind is from the northwest (Figure 4b,c). Furthermore, based on backward trajectory analysis conducted by the Changchun Environmental Monitoring Station, it was determined that the primary origins of the airflow trajectories are the northwest and southwest areas during April of each year. This finding further substantiates the direct impact of straw burning on PM2.5 concentrations (Figure 5).
When the meteorological conditions for straw burning in the Changchun area are considered, WS is a key factor (Figure 6a). Due to the quite similar median wind speeds in March and April (3.35 m/s and 4 m/s respectively) and the generally higher wind speeds experienced during these months, both are suitable for planned burning activities. However, the relatively higher average wind speed in April favors the dispersion of pollutants, making the diffusion conditions more favorable compared to March (Figure 6a). During the study period from February to April, as the winter transitions to spring, the PBLH shows an increasing trend. Specifically, the median PBLH in February was 1.03 km, whereas in April, this value increased to 1.3 km (Figure 6b). Notably, the RH in April is relatively low, showing a significant decrease compared with that in February, with an average monthly value of approximately 40% (Figure 6c).

3.3. Interactions Between Biomass Burning and Meteorological Conditions on PM2.5

When the fire point count falls below the daily average, the influence of the WS on the PM2.5 concentration exhibits a distinct pattern. As depicted in Figure 7a, the PM2.5 concentration decreases with increasing WS when the wind speed is below 4 m/s. However, once the WS exceeds 4 m/s, the PM2.5 concentration begins to rise. Therefore, to mitigate air pollution, a WS range of 3–7 m/s is recommended for further optimization. RH also significantly influences the PM2.5 concentration. As shown in Figure 7b, when the fire point count is below the daily average and the RH is below 50%, the PM2.5 concentration decreases with increasing RH. However, once the RH exceeds 50%, the PM2.5 concentration begins to rise. On the basis of these observations, a RH range of 30–50% is chosen as the optimization target. Figure 7c illustrates the relationship between the boundary layer height and the PM2.5 concentration. When the fire point count is below the daily average, the PM2.5 concentration decreases with increasing PBLH. Considering the average variation in the boundary layer in the Changchun area, a PBLH range of 0.9–1.5 km is selected for further optimization. Figure 7d–f reveal the impact of fire points on the correlation between the PM2.5 concentration and meteorological factors. When the fire point count exceeds the daily average, the inclusion of fire points results in a decrease in the R2 value. These findings indicate that human factors significantly influence the PM2.5 concentration and consequently reduce the correlation between meteorological factors and the PM2.5 concentration. In summary, through the analysis of factors such as WS, RH, PBLH, and fire points, their influence on the PM2.5 concentration can be comprehensively understood, providing a scientific basis for subsequent air pollution control efforts.

3.4. Meteorological Control Options for Mitigating Haze Pollution from Straw Burning

On the basis of the qualitative analysis results mentioned above, the daily average values of the dominant meteorological factors were selected to establish an RSM relevance prediction model (MET-PM2.5) between the meteorological factors and the PM2.5 concentration during the straw burning period in Changchun. The experimental results from the MET-PM2.5 RSM prediction model established via SAS demonstrate that the equation meets modeling requirements and has a significant linear relationship. There are multiple influencing factors for the PM2.5 concentration, but meteorological factors play a relatively crucial role. Therefore, we considered the WS, RH, and PBLH, which strongly influence the PM2.5 concentration.
Z = 332.59 − 27.52 × X1 − 8.03 × X2 − 96.55 × X3 + 0.12 × X1 × X2 + 0.02 × X1 × X3 + 0.61 × X2 × X3 + 2.25 × X12 + 0.09 × X22 + 26.81 × X32
where Z represents the concentration of PM2.5 (μg/m3); X1 denotes the WS (m/s); X2 represents the RH (%); and X3 represents the PBLH (km).
Furthermore, to validate the reliability and sensitivity of the model, a performance evaluation was conducted via summary statistics, including the determination coefficient (R2), prediction (Pred) R-square, C.V. %, and F test, as shown in Table 1. According to the results, the reliability (R2) of the MET-PM2.5 RSM prediction model is >0.98, indicating that 98% of the response variability can be explained by these models. The Pred R-square of the model is greater than 80%, indicating that the model can explain more than 80% of the variance in the target variable. The model exhibits high accuracy, with a coefficient of variation (C.V. %) of 2.33%. The p value of the model is <0.0001, indicating that the models conform to the experimental data and are both effective and significant.
To analyze the impact of meteorological conditions during the straw burning period on air quality improvement while simultaneously excluding factors such as the spread of dust in the region, a three-year average RSM model was determined in a three-dimensional scatter plot depicting the relationships among RH, WS, PBLH, and the PM2.5 concentration (Figure 8). This model was then utilized to forecast the effects of changes in different meteorological factors on air quality during the straw burning period. While this may yield uncertain outcomes, undertaking such work is beneficial for planning burning activities from a meteorological perspective. Furthermore, through analysis via SAS software, it was deduced from the equation of the RSM model that the optimal meteorological conditions for the lowest PM2.5 concentration are as follows: the range of WS was 3.00–5.03 m/s, RH was 30.00–38.30%, and PBLH was 0.90–1.45 km. By applying the aforementioned optimized conditions for numerical selection, a predicted PM2.5 concentration below 75 μg/m3 was obtained.
On the basis of the established RSM equation, three sets of sensitivity tests were meticulously designed to elucidate the nuanced response characteristics of various meteorological factors to the PM2.5 concentration. Specifically, the average RH, WS, and PBLH during the peak months of straw burning from 2021 to 2023, notably in April, were specifically chosen as the research background to provide a robust scientific foundation for early warning systems for PM2.5 pollution. Each test set scrutinized a specific factor while holding the daily average values of the remaining influencing factors constant, ensuring the precision of the experiments. Employing the actual observed values as a benchmark, a dual test was conducted for the sensitivity factor, employing equal increments and decrements. Each alteration was precisely set at ±20%, ±40%, and ±60% relative to the observed values, forming a comprehensive array of disturbance intervals to thoroughly assess the impact of each factor on the PM2.5 concentration. Figure 9a–c clearly illustrates the variation trends of the PM2.5 concentration in the sensitivity tests. A meticulous analysis of the experimental data revealed that the average influence of each factor on the PM2.5 concentration within a 50% range follows a distinct order: RH (33.1%) emerges as the predominant influencer, followed by WS (10.8%), whereas the influence of PBLH (6.9%) remains relatively modest. Notably, during the modulation of the RH from −60% and +60% to 0% of the observed value, a pronounced downward trend in the overall PM2.5 concentration is observed, even exceeding twofold. In contrast, fluctuations in the PBLH have a comparatively mild impact on the PM2.5 concentration. Decreases in the PBLH relative to the April average prompt an increase in the PM2.5 concentration, yet its effect pales in comparison to those of RH and WS. Additionally, when the rate of change in each meteorological factor is less than 20%, the resulting variation in the PM2.5 concentration is within a 15% range.

4. Discussion

Several studies have demonstrated that from February to April, as the PBLH increases, the near-surface extinction coefficient exhibits a downward trend, indicating a gradual decrease in atmospheric aerosol content during this period. This decline positively correlates with the PBLH [33,42,43]. However, our study observed a contrasting pattern in Changchun (Figure 3), where the near-surface particulate matter concentrations rose in tandem with the PBLH. This anomaly is attributed to the large-scale open burning of crop residues in the region from March to April. To effectively alleviate the adverse effects of crop residue burning on air quality, this study developed a method to identify the optimal combination of meteorological factors in typical agricultural cities in Northeast China. This screening scheme was crafted through single-factor experiments and 3D response surface methodology, which comprehensively analyzes meteorological conditions and offers performance evaluations to guarantee the validity of the results. Consequently, the method is capable of thoroughly dissecting the influence of both meteorological conditions and straw burning on air pollutants, thereby providing a scientific rationale for the formulation of prescribed straw burning policies in the area.
The results of the data analysis revealed that during straw burning periods, the primary meteorological factors comprehensively influence atmospheric pollution. On the basis of a univariate analysis, this study definitively pinpointed the notable effects of WS, RH, and PBLH on PM2.5 concentrations, especially when the number of fire points was below the daily average. These findings align with previous research conducted by Cai and Yu [44] and Meng et al. [45]. Specifically, at low WS levels (below 4 m/s), the variable of PM2.5 decreased as WS increased, suggesting that particulate matter diffusion was impeded by relatively stagnant weather conditions [46]. Conversely, at high WS ranges (above 4 m/s), the opposite trend was observed due to potential regional pollutant transport [47]. Yin et al. [48] conducted a study in Beijing and discovered a similar relationship between the parameters of PM2.5 and meteorology. Moreover, we found that when the RH was less than 50%, the PM2.5 concentration decreased as the RH increased. This trend might be attributed to dust transport from arid western regions. However, when the RH exceeded 50%, the opposite trend was observed. This latter trend was likely due to the hygroscopic growth effect of aerosol particles [49]. An increase in the PBLH resulted in lower PM2.5 values, as higher boundary layers provided better diffusion conditions [50]. When the number of fire points surpassed the daily average, the inclusion of these fire points weakened the correlation between weather and PM2.5. This discovery underscores the importance of considering the impact of straw burning when selecting meteorological scenarios. However, it is often the result of multiple factors acting in concert that influence the meteorological impact on air quality. For example, He et al. [51] reported that low WS and PBLH can facilitate the accumulation of near-surface PM2.5, whereas Li et al. [52] reported that lower WS, higher RH, and the absence of precipitation were closely associated with more PM2.5 collection in autumn and winter in Jilin. Therefore, this study introduced the MET-PM2.5 equation to analyze the mathematical relationships between these three key meteorological factors and the PM2.5 concentration.
Mathematical models can be employed to delve into the correlations between multiple meteorological parameters and atmospheric pollutants. On the basis of sensitivity tests, a screening method was developed to identify the optimal weather combinations. In this study, a robust MET-PM2.5 RSM prediction model was formulated, with excellent fitting results. This underscores the suitability of the response surface model for optimizing meteorological scenarios, a technique that has already been applied in diverse fields, such as process improvement and technical refinement [53,54]. Using this equation, we derived optimal ranges for RH (30.00–38.30%), WS (3.00–5.03 m/s), and PBLH (0.90–1.45 km) to maintain low PM2.5 levels during the straw burning season. Similarly, Zhou et al. [55] used different ranges of RH and WS to forecast the scope of PM2.5. Furthermore, a sensitivity test was adopted to screen for the factors with the greatest impact on pollution in this research, a method validated in previous research. For example, Wen et al. [14] conducted sensitivity experiments to simulate the effectiveness of various straw control measures in enhancing air quality, and Xu et al. [56] used this approach to examine the influences of weather factors and human activities on PM2.5 pollution from 2016–2020. The results revealed that RH (33.1%) had the most significant impact on the PM2.5 concentration, followed by WS (10.8%) and PBLH (6.9%), which aligns with past findings [57]. Notably, when the RH exceeded 60%, there was a trend toward more than a twofold increase in the total value of PM2.5. This discovery is linked to the potential air current sources in spring in Northeast China [12]. Specifically, at lower RH, pollutants are transported from arid northwest regions [58], whereas at higher RH, the increase in local emissions from incomplete straw burning and the transport effect of southeast sea breezes exacerbate the situation [59]. Therefore, we can determine appropriate meteorological combinations on the basis of the numerical ranges and significance of various weather factors, which is crucial for mitigating the intensity of air pollution during agricultural residue burn-off periods in Northeast China.
On the basis of the optimal meteorological conditions identified through the MET-PM2.5 model, it is crucial to devise tailored prescribed straw burning programs. Various countries (e.g., the United States, the United Kingdom, and Canada) have already implemented planned burning initiatives. For example, the United States advocates the adoption of smoke management plans (SMPs) by states and tribes, aiming to minimize the impact of air pollutant emissions on air quality while recognizing open burning as a legitimate agricultural management practice to sustain production and safeguard public health [16]. Northeast China has also witnessed notable advancements in planned crop residue burn-off [60], exemplified by the ongoing enforcement of the two-region control policy, which comprises no-burning zones and limit-sintering zones [12,61]. Additionally, Wen et al. [62] proposed sophisticated management recommendations for the region through meticulous multifactor impact analysis. These recommendations include considering the meteorological conditions and transportation routes of different cities, determining control spatial scales on the basis of various transportation distances, and establishing burning intervals and restrictions on burning areas. These plans comprehensively consider current straw burning practices, weather dispersion factors, and air pollution levels, utilizing models to pinpoint the most suitable burning times and locations to achieve air quality improvement objectives [29,63,64]. Therefore, this study leverages the MET-PM2.5 equation derived from the RSM model to quantify the optimal range of meteorological factors conducive to burning and to explore regulatory suggestions for biomass incineration in the region, encompassing burn-off times and batches.
Although the model developed in this study is capable of identifying the optimal meteorological factor combination, uncertainties persist. First, the ground-based monitoring approach primarily captures the impact within urban sites, neglecting the exploration of spatial pollutant distributions and regional transport. Research has demonstrated that cross-regional transport can substantially contribute to local pollutant levels [39,65]. Second, variations in wind speed across different altitudes also influence the transportation and deposition of particulate matter. Yang et al. [66] reported that air currents at various heights (500 m, 1500 m, and 3000 m) have different impacts on haze weather, with the air current at 500 m making the most significant contribution to air pollution. Moreover, this model fails to account for the physical and chemical reactions of atmospheric pollutants, which can influence the formation of fine particulate matter. These processes include aerosol-cloud-radiation feedback, the production of secondary organic aerosols (SOAs), the increase in organic aerosols (OAs) from volatile basis sets (VBSs), and others, all of which affect PM2.5 concentrations [67,68]. Therefore, further research, such as the integration of numerical models (e.g., WRF-Chem and WRF-CMAQ) to predict and assess the spatiotemporal distribution of PM2.5 under various meteorological scenarios, is imperative [69,70]. Ultimately, the analysis methodology for assessing the influence of weather conditions on pollutant values during straw burning should be refined to provide more precise information for decision-making in Northeast China.

5. Conclusions

On the basis of a comprehensive analysis for surface observations (including PM2.5, WS, RH, and PBLH) and satellite fire points data from Changchun spanning the years 2021 to 2023, this study formulated an MET-PM2.5 model to quantify the influence of meteorological factors on PM2.5 concentrations. Employing this model, we determined that the optimal combination of meteorological conditions for minimizing the level of PM2.5 is characterized by a RH between 30.00% and 38.30%, a WS ranging from 3.00 to 5.03 m/s, and a PBLH of 0.90 to 1.45 km. Sensitivity tests further revealed that to uphold air quality standards (level II, PM2.5: 75 μg/m3), the acceptable variation range for each of these three meteorological indicators should not exceed 20%. Ultimately, this methodology pinpoints the ideal weather parameter combination, paving the way for enhanced management strategies that are pivotal for mitigating air pollution during scheduled agricultural residue burning seasons. Nonetheless, this approach still encounters uncertainties, particularly those pertaining to pollutant spatial distribution, regional transport, and the physicochemical reactions of atmospheric pollutants. Hence, potential refinements include incorporating numerical simulations to refine the calculation process and accounting for the physicochemical behaviors of aerosols.

Author Contributions

Conceptualization, L.H., L.D. and W.C.; writing—original draft preparation, L.H. and L.D.; writing—review and editing, L.H., L.D., W.C., L.G. and L.L.; methodology, L.H. and L.D.; software, L.D.; validation, Y.Q. and L.G.; Investigation, B.S.; visualization, L.H., B.S. and L.D.; supervision, Y.Q., W.C., L.L. and L.G.; data curation, Y.Q. and B.S.; project administration, W.C.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Joint Funds of the National Key Research and Development Program of China (2023YFF0807202) and the Ecology and Environment Department of Jilin Province (2024-05).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this paper are included in this article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Locations of (a) Northeast China and (b) Changchun city in Jilin Province, China. Note: the red-lined area indicates Changchun city; red dots represent environmental monitoring stations; orange triangles denote meteorological stations; and black flags signify ground-based polarized LiDAR systems.
Figure 1. Locations of (a) Northeast China and (b) Changchun city in Jilin Province, China. Note: the red-lined area indicates Changchun city; red dots represent environmental monitoring stations; orange triangles denote meteorological stations; and black flags signify ground-based polarized LiDAR systems.
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Figure 2. Distribution of daily PM2.5 and PM10 concentrations in Changchun during (a) February, (b) March, and (c) April from 2021–2023 (the red area represents the reference range of China’s air quality standards, the orange circle depicts the primary high-concentration area for PM10, whereas the gray circle indicates the main high-concentration area for PM2.5).
Figure 2. Distribution of daily PM2.5 and PM10 concentrations in Changchun during (a) February, (b) March, and (c) April from 2021–2023 (the red area represents the reference range of China’s air quality standards, the orange circle depicts the primary high-concentration area for PM10, whereas the gray circle indicates the main high-concentration area for PM2.5).
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Figure 3. Vertical structure and diurnal variation in the aerosol optical extinction coefficient in (a,b) February, (c,d) March, and (e,f) April 2023 in Changchun city.
Figure 3. Vertical structure and diurnal variation in the aerosol optical extinction coefficient in (a,b) February, (c,d) March, and (e,f) April 2023 in Changchun city.
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Figure 4. Variations in (a) the number of fire points in Changchun from February to April 2021–2023 and the special distributions of fire radiative power (Frp) in (b) March and (c) April.
Figure 4. Variations in (a) the number of fire points in Changchun from February to April 2021–2023 and the special distributions of fire radiative power (Frp) in (b) March and (c) April.
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Figure 5. Backward trajectory analysis (6 clusters) was conducted for the Changchun Environmental Monitoring Station in April of each year during 2021 (a), 2022 (b), and 2023 (c). The numbers on each trajectory indicate the frequency of occurrence throughout the month.
Figure 5. Backward trajectory analysis (6 clusters) was conducted for the Changchun Environmental Monitoring Station in April of each year during 2021 (a), 2022 (b), and 2023 (c). The numbers on each trajectory indicate the frequency of occurrence throughout the month.
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Figure 6. Monthly variations in (a) wind speed (WS), (b) planetary boundary layer height (PBLH), and (c) relative humidity (RH) in Changchun from February to April 2021–2023.
Figure 6. Monthly variations in (a) wind speed (WS), (b) planetary boundary layer height (PBLH), and (c) relative humidity (RH) in Changchun from February to April 2021–2023.
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Figure 7. Correlations between various factors (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)) and the PM2.5 concentration during straw burning periods when the fire points are below (a–c) and above (d–f) the average level.
Figure 7. Correlations between various factors (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)) and the PM2.5 concentration during straw burning periods when the fire points are below (a–c) and above (d–f) the average level.
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Figure 8. Interactive effects of (a–c) meteorological conditions (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)) on PM2.5 concentrations in response surface methodology (RSM) in Changchun city from February, March, and April 2021–2023.
Figure 8. Interactive effects of (a–c) meteorological conditions (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)) on PM2.5 concentrations in response surface methodology (RSM) in Changchun city from February, March, and April 2021–2023.
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Figure 9. (a–c) Sensitivity test of daily average PM2.5 concentrations to changes in dominant meteorological factors (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)).
Figure 9. (a–c) Sensitivity test of daily average PM2.5 concentrations to changes in dominant meteorological factors (i.e., wind speed (WS), relative humidity (RH), and planetary boundary layer height (PBLH)).
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Table 1. Performance evaluation of the response surface methodology (RSM) model for the PM2.5 equation via summary statistics.
Table 1. Performance evaluation of the response surface methodology (RSM) model for the PM2.5 equation via summary statistics.
F Value (Model)p Value (Model)C.V. %R-SquaredAdj R-SquaredPred R-Squared
71.18<0.00012.330.980.970.86
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MDPI and ACS Style

He, L.; Duanmu, L.; Guo, L.; Qin, Y.; Shi, B.; Liang, L.; Chen, W. Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning. Agriculture 2025, 15, 279. https://doi.org/10.3390/agriculture15030279

AMA Style

He L, Duanmu L, Guo L, Qin Y, Shi B, Liang L, Chen W. Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning. Agriculture. 2025; 15(3):279. https://doi.org/10.3390/agriculture15030279

Chicago/Turabian Style

He, Luyan, Lingjian Duanmu, Li Guo, Yang Qin, Bowen Shi, Lin Liang, and Weiwei Chen. 2025. "Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning" Agriculture 15, no. 3: 279. https://doi.org/10.3390/agriculture15030279

APA Style

He, L., Duanmu, L., Guo, L., Qin, Y., Shi, B., Liang, L., & Chen, W. (2025). Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning. Agriculture, 15(3), 279. https://doi.org/10.3390/agriculture15030279

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