Straw burning grading pre-reporting and monitoring method

Abstract

The application discloses a straw burning grading early warning and monitoring method, belongs to the field of atmospheric environment monitoring and agricultural pollution control, and aims to solve the problem that the prior art lacks a complete decision support system from early warning, tracing, grading to regional linkage. 2.5 The method first acquires GFS meteorological prediction data and regional emission source grid data, simulates future 72-168 hours atmospheric meteorological field, pollution field and the contribution proportion of straw burning to PM 2.5 by using a RAMS-CMAQ model (containing triple nested grid technology, and the resolution of the innermost layer is greater than or equal to 1km*1km); then the contribution proportion is traced by double-scenario simulation, and the 5-grade straw burning index (SFI) is determined in combination with the GB3095-2012 first standard (PM 2.5 ≤35μg / m³); the SFI of the region in the upwind direction is corrected, and the accuracy is improved through model verification, data assimilation and rolling update every 6-12 hours; finally, the SFI space-time distribution and visualized products of the county level and the hour level are output, precise, cooperative and controlled straw burning is realized, and the method has the values of forward-looking and business application.

Description

Technical Field

[0001] This invention relates to the field of atmospheric environmental monitoring and agricultural pollution control technology, specifically a method for graded forecasting and monitoring of straw burning. Background Technology

[0002] Straw burning is a major cause of regional air quality deterioration, especially PM2.5. 2.5 One of the major sources of the sharp increase in straw concentration. Traditional management of straw burning bans is neither effective in controlling pollution nor able to meet the actual needs of agricultural production.

[0003] Currently, some technical solutions utilize satellite remote sensing to monitor fire points, but this method is post-event monitoring, cannot provide forecast information, and is easily affected by cloud cover. Other studies attempt to use air quality models to simulate pollution diffusion, but these are usually limited to simulating pollutant concentration distribution and fail to further quantify the specific contribution of straw burning and directly link it to dynamic control measures. A complete decision support system, encompassing forecasting, source tracing, classification, and regional coordination, is lacking.

[0004] Therefore, there is an urgent need for a straw burning management system that can provide early forecasting, accurate source tracing, scientific classification, and take into account the impact of regional transmission, in order to make up for the shortcomings of existing technologies. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a graded forecasting and monitoring method for straw burning. It has the advantage of integrating meteorological forecasts, air quality models, source apportionment technologies, and environmental standards to construct an operational forecasting and early warning system, enabling graded assessment and dynamic guidance of the suitability of straw burning activities in different regions over the next few days. This solves the problem of lacking a complete decision support system from forecasting, source tracing, grading to regional coordination.

[0006] (II) Technical Solution To achieve the aforementioned objectives of scientific, precise, and tiered control over straw burning activities, this invention provides the following technical solution: A method for graded forecasting and monitoring of straw burning includes the following system: Data Acquisition and Model Simulation: GFS meteorological forecast data and regional emission source grid data were acquired. The RAMS-CMAQ regional air quality model was used as the primary simulation tool to conduct high spatiotemporal resolution numerical forecasts for the target area, obtaining spatiotemporal distribution data of atmospheric meteorological fields, atmospheric pollution fields, and the impact of straw burning on PM2.5 for the next 72-168 hours. 2.5 Data on the contribution ratio of mass concentration; Source tracing of the contribution of straw burning: Based on the atmospheric pollution field data obtained in step 1, source tracing simulations were conducted to determine the concentration of pollutants generated by straw burning, and the contribution of straw burning to PM2.5 concentrations at various points within the target area was quantitatively determined. 2.5 The actual contribution ratio of mass concentration; Incineration index classification: Based on PM2.5 levels in the Class I air quality standard of GB3095-2012 "Ambient Air Quality Standard". 2.5 The mass concentration limit of 35 μg / m³ was used as the judgment criterion, combined with the effect of straw burning on PM2. 2.5 The contribution ratio is determined according to the following rules to establish the Straw Burning Index (SFI): When the target point PM 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 1, which is judged as "suitable for combustion"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 15%-25% reduction in straw burning emissions are sufficient to reduce PM2.5 levels at that location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 2, which is judged as "moderately combustible"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 25%-55% reduction in straw burning emissions are sufficient to reduce PM2.5 at this location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 3, which is judged as "combustion control is required"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 55%-100% reduction in straw burning emissions are sufficient to reduce PM2.5 at that location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 4, which is judged as "unsuitable for combustion"; When the target point PM 2.5 The PM concentration at this location is >35 μg / m³, and after removing 100% of the contribution from straw burning, the PM2.5 concentration at this location is... 2.5 When the mass concentration is still >35μg / m³, the SFI is level 5, which is judged as "completely prohibited from combustion"; Upwind influence correction: This addresses the PM issue in step 3. 2.5 For areas with a mass concentration > 35 μg / m³, the upwind region of the area is determined by combining the wind field data obtained in step 1. The straw burning index of the upwind region is then lowered to level 2-3 to reduce the contribution of straw burning pollutants to the transmission of pollutants to the area exceeding the standard. Forecast results output: Integrate the judgment and correction results from steps 3-4 to generate a spatiotemporal distribution forecast map of straw burning index at the county / district level for the next 72-120 hours within the target area, providing precise guidance for straw burning control.

[0007] Preferably, the "atmospheric meteorological field spatiotemporal distribution data" mentioned in step 1 specifically includes hourly spatiotemporal distribution data of near-surface temperature, relative humidity, wind direction of the horizontal wind field, wind speed of the horizontal wind field, and air pressure within the target area.

[0008] Preferably, the "emission source grid data" mentioned in step 1 includes pollutants SO2 and NO corresponding to industrial emission sources, domestic emission sources, agricultural non-burning emission sources, and potential straw burning emission sources within the target area. x VOCs s Equal to PM aerosols 2.5 PM 10 Gridded emission intensity data, etc.

[0009] Preferably, the "district / county-level spatial resolution" in step 5 specifically means: using the administrative districts / counties within the target area as the basic spatial unit, outputting the straw burning index for each district / county per hour for the next 72-168 hours; the "hourly time period" specifically means: the time interval between the forecast results is 1 hour.

[0010] Preferably, it also includes a "model validation and data assimilation step": before the RAMS-CMAQ model is officially put into operation, PM2.5 data are collected from multiple national-level air quality monitoring stations in the target area. 2.5 Hourly observation data, with a valid data station ratio of no less than 90%, will be used to simulate PM in the model. 2.5 The mass concentration was compared with the observed data, and the rationality of the model was verified by calculating the "monthly average deviation between simulated and observed values" and the "correlation coefficient". When the correlation coefficient is not lower than 0.5-0.7, the model can be confirmed to be used for subsequent forecasts. The observed data was fed back into the model using data assimilation technology to correct the initial field and boundary conditions and improve the forecast accuracy.

[0011] Preferably, the specific rule for "lowering the straw burning index in the upwind area to level 2-3" in step 4 is as follows: if the initial judgment index of the upwind area is level 1, then adjust it to level 2; if the initial judgment index is level 2-3, then maintain the original level; if the initial judgment index is level 4-5, then lower it to level 3.

[0012] Preferably, the specific implementation of "pollutant concentration source tracing simulation" in step 2 is as follows: using the "source apportionment module" or "sensitivity analysis tool" of the RAMS-CMAQ mode, calculate PM2.5 concentrations under two scenarios: "including all emission sources" and "excluding straw burning sources". 2.5 Mass concentration, the difference between the two, and PM under the "including all emission sources" scenario. 2.5 The ratio of mass concentration to PM2.5 is the effect of straw burning on PM2.5. 2.5 The proportion of contribution.

[0013] Preferably, the numerical forecast in step 1 uses a triple-nested grid technique, with the innermost grid having a resolution of no less than 3km × 3km, to accurately simulate meteorological and pollution processes within the region.

[0014] Preferably, the forecast result output step further includes: generating a visualization graphic product, including a spatial distribution map of the straw burning index, a time variation curve, and a pie chart of grade percentage statistics, and releasing it to management departments and the public through a WebGIS platform or mobile application.

[0015] Preferably, it also includes "real-time rolling forecast and update steps": every 6-12 hours, the RAMS-CMAQ model is driven by the latest GFS meteorological data to make a rolling forecast for the next 72-168 hours, and the forecast results are dynamically corrected based on the latest monitoring data to form an operational closed loop.

[0016] (III) Beneficial Effects Compared with existing technologies, the present invention provides a method for graded forecasting and monitoring of straw burning, which has the following beneficial effects: 1. The straw burning graded forecasting and monitoring method is forward-looking and accurate: it uses numerical models to make forward-looking forecasts for the next 168 hours and accurately quantifies the contribution of straw burning through source apportionment technology, which changes the passive situation of traditional post-event supervision.

[0017] 2. This straw burning classification forecasting and monitoring method enables scientific classification and management: It innovatively proposes a five-level burning index (SFI) system based on environmental capacity and emission reduction potential, realizing the transformation from "one-size-fits-all" to "classified and precise management", making management measures more scientific and humane.

[0018] 3. The straw burning graded forecasting and monitoring method and regional collaborative management: The "upwind direction influence correction" mechanism is introduced to dynamically link the air quality of downwind areas with the management level of upwind areas, realize cross-regional collaborative governance, and improve the overall management efficiency.

[0019] 4. This straw burning graded forecasting and monitoring method is operational and visualized: the entire method process can form an automated business operation system, the output results are intuitive and easy to understand, and they are published through an information platform, which greatly improves the efficiency and capability of decision support and public services. Attached Figure Description

[0020] Figure 1 This is the overall flowchart of the straw burning graded forecasting and monitoring method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Example: Taking Guangxi Zhuang Autonomous Region of China as an example, the method of the present invention is applied to the graded forecasting of straw burning.

[0024] First, data acquisition and model simulation were performed. Global weather forecast data (GFS) from the US National Center for Environmental and Environmental Protection (NCEP) were obtained as the initial meteorological field and boundary conditions. An emission source inventory of Guangxi and surrounding areas was collected, including industrial point sources, transportation line sources, agricultural non-point sources (including fertilizer application, livestock farming, etc.), and potential emission sources from straw burning estimated based on crop yield. All source data were processed into a gridded format. The RAMS-CMAQ model was configured using a triple-nested grid (D01: 27km, D02: 9km, D03: 3km), with the innermost layer D03 covering the entire Guangxi region. The model simulated atmospheric environmental conditions for the next 168 hours.

[0025] Before the model was run, model verification and data assimilation were performed. PM data were collected from over 50 national monitoring stations in Guangxi. 2.5 Hourly observation data was used, and data from the past month was compared with the model simulation results. The calculated correlation coefficient was 0.65, which meets the preset reasonableness requirement of 0.5-0.7, thus confirming that the model can be used for subsequent forecasts. Subsequently, a three-dimensional variational assimilation method was used to assimilate the current observation data into the model. This technique feeds the observation data back into the model to correct the initial concentration field of the model, thereby improving the forecast accuracy.

[0026] After the model runs, it outputs hourly meteorological fields (near-surface wind, temperature, pressure, humidity) and pollution fields (PM). 2.5 Concentrations of SO2, NO2, etc.

[0027] Next, the contribution of straw burning to emission sources was traced. Source apportionment tools for the CMAQ model (such as ISAM or PSAT) were used, and the "Source Apportionment Module" or "Sensitivity Analysis Tool" in the RAMS-CMAQ mode was employed to run two simulations: one for the baseline scenario (including all emission sources) and the other for the control scenario (excluding straw burning emission sources). PM2.5 at each grid point was calculated under both scenarios. 2.5The difference in concentration, and the ratio of this difference to the baseline concentration, represents the contribution of straw burning. This method quantitatively determines the impact of straw burning on PM2.5 concentrations at various points within the target area. 2.5 The actual contribution ratio of mass concentration. For example, the baseline scenario of PM at a certain location. 2.5 If the concentration is 50 μg / m³ and the control scenario is 40 μg / m³, then the contribution ratio is (50-40) / 50 = 20%.

[0028] Then, an incineration index classification is performed. This is combined with the above contribution ratios and PM2.5 levels. 2.5 Concentration, based on the PM10 concentration of the Class I air quality standard in GB3095-2012 "Ambient Air Quality Standards". 2.5 The mass concentration limit of 35 μg / m³ is used as the judgment criterion, and the judgment is made according to preset rules. For example:

[0029] If the current PM in a certain county A 2.5 If the value is 30 μg / m³ (≤35 μg / m³), then its straw burning index (SFI) is directly determined to be Level 1, which is considered "suitable for combustion"; If the current PM in a certain county B 2.5 The concentration is 60 μg / m³, of which straw contributes 20 μg / m³ (33.3%). To reduce the concentration to 35 μg / m³, a reduction of 25 μg / m³ is needed. This 25 μg / m³ reduction must be achieved by reducing straw emissions. Calculations show that the required reduction in straw emissions is 25 / 20 = 125%, which exceeds 100%. This means that even if 100% of the contribution from straw burning is removed, PM2.5 will still be significantly reduced. 2.5 The concentration could only be reduced to 40 μg / m³ (60-20=40), which is still higher than the standard of 35 μg / m³. Therefore, the county B was judged to be at level 5 of the straw burning index (SFI) and was judged to be "completely prohibited from burning".

[0030] If the current PM in a certain county C 2.5 The total emissions are 50 μg / m³, of which straw contributes 30 μg / m³. A reduction of 15 μg / m³ is needed to reach the standard of 35 μg / m³. The required reduction in straw emissions is 15 / 30 = 50%, which falls within the range of 25%-55%. Therefore, it is classified as a Straw Burning Index (SFI) Level 3, indicating a need for "controlled burning".

[0031] Next, the upwind influence correction is implemented. Assume that areas with a Straw Burning Index (SFI) of Level 4 are consistently observed in Nanning city (downstream). Based on the simulated prevailing southeast wind field (from the wind field data obtained in step 1), the upwind area of ​​this exceeding-standard region is determined to be a portion of Guigang city. These areas were originally classified as Straw Burning Index (SFI) Level 1 (suitable for burning). According to the correction rule of "if the initial SFI of the upwind area is Level 1, adjust to Level 2; if the initial SFI is Level 2-3, maintain the original level; if the initial SFI is Level 4-5, downgrade to Level 3," the level of these upwind areas is downgraded to Straw Burning Index (SFI) Level 2, classified as "moderately combustible," to recommend that the area reduce burning and thus mitigate its contribution to pollution transmission to Nanning city.

[0032] Finally, the forecast results are output. All the above results are integrated to generate a dynamic forecast map of straw burning index for Guangxi over the next 168 hours, hourly, and at the county level. The "district / county-level spatial resolution" uses administrative districts and counties within the Guangxi Zhuang Autonomous Region as the basic spatial unit, outputting the hourly straw burning index for each district / county over the next 168 hours. The "hourly time interval" refers to a 1-hour time interval between forecast results. The product is released through the decision support platform of the provincial Department of Ecology and Environment. Managers at all levels can view the recommended burning level for any district / county at any time within the next few days through the platform, thereby formulating differentiated inspection and control plans. Simultaneously, visual graphic products are generated, including a spatial distribution map of the straw burning index, a time-varying curve, and a pie chart showing the percentage of different burning levels, facilitating more intuitive access to forecast information for management departments.

[0033] In addition, the system initiates a real-time rolling forecast and update every 24 hours. Every 24 hours, it uses the latest GFS meteorological data to drive the RAMS-CMAQ model to make a rolling forecast for the next 168 hours, and dynamically corrects the forecast results based on the latest monitoring data, forming an operational closed loop to ensure the timeliness and accuracy of guidance information.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for graded forecasting and monitoring of straw burning, characterized in that, Including the following systems: Data Acquisition and Model Simulation: GFS meteorological forecast data and regional emission source grid data were acquired. The RAMS-CMAQ regional air quality model was used as the primary simulation tool to conduct high spatiotemporal resolution numerical forecasts for the target area, obtaining spatiotemporal distribution data of atmospheric meteorological fields, atmospheric pollution fields, and the impact of straw burning on PM2.5 for the next 72-168 hours. 2.5 Data on the contribution ratio of mass concentration; Source tracing of the contribution of straw burning: Based on the atmospheric pollution field data obtained in step 1, source tracing simulations were conducted to determine the concentration of pollutants generated by straw burning, and the contribution of straw burning to PM2.5 concentrations at various points within the target area was quantitatively determined. 2.5 The actual contribution ratio of mass concentration; Incineration index classification: Based on PM2.5 levels in the Class I air quality standard of GB3095-2012 "Ambient Air Quality Standard". 2.5 The mass concentration limit of 35 μg / m³ was used as the judgment criterion, combined with the effect of straw burning on PM2. 2.5 The contribution ratio is determined according to the following rules to establish the Straw Burning Index (SFI): When the target point PM 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 1, which is judged as "suitable for combustion"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 15%-25% reduction in straw burning emissions are sufficient to reduce PM2.5 levels at that location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 2, which is judged as "moderately combustible"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 25%-55% reduction in straw burning emissions are sufficient to reduce PM2.5 at this location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 3, which is judged as "combustion control is required"; When the target point PM 2.5 A concentration greater than 35 μg / m³ and a 55%-100% reduction in straw burning emissions are sufficient to reduce PM2.5 at that location. 2.5 When the mass concentration is ≤35μg / m³, the SFI is level 4, which is judged as "unsuitable for combustion"; When the target point PM 2.5 The PM concentration at this location is >35 μg / m³, and after removing 100% of the contribution from straw burning, the PM2.5 concentration at this location is... 2.5 When the mass concentration is still >35μg / m³, the SFI is level 5, which is judged as "completely prohibited from combustion"; Upwind influence correction: This addresses the PM issue in step 3. 2.5 For areas with a mass concentration > 35 μg / m³, the upwind region of the area is determined by combining the wind field data obtained in step 1. The straw burning index of the upwind region is then lowered to level 2-3 to reduce the contribution of straw burning pollutants to the transmission of pollutants to the area exceeding the standard. Forecast results output: Integrate the judgment and correction results from steps 3-4 to generate a spatiotemporal distribution forecast map of straw burning index at the county / district level for the next 72-120 hours within the target area, providing precise guidance for straw burning control.

2. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The "atmospheric meteorological field spatiotemporal distribution data" mentioned in step 1 specifically includes hourly spatiotemporal distribution data of near-surface temperature, relative humidity, wind direction of the horizontal wind field, wind speed of the horizontal wind field, and air pressure within the target area.

3. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The "emission source grid data" mentioned in step 1 includes pollutants SO2 and NO corresponding to industrial emission sources, residential emission sources, agricultural non-burning emission sources, and potential straw burning emission sources within the target area. x VOCs s Equal to PM aerosols 2.5 PM 10 Gridded emission intensity data, etc.

4. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The "district / county-level spatial resolution" mentioned in step 5 specifically refers to: using the administrative districts / counties within the target area as the basic spatial unit, outputting the straw burning index for each district / county per hour for the next 72-120 hours; the "hour-level time period" specifically refers to: the time interval between forecast results is 1 hour.

5. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, It also includes the "model validation and data assimilation steps": before the RAMS-CMAQ model is officially put into operation, PM2.5 data are collected from multiple national-level air quality monitoring stations in the target area. 2.5 Hourly observation data, with a valid data station ratio of no less than 90%, will be used to simulate PM in the model. 2.5 The mass concentration was compared with the observed data, and the rationality of the model was verified by calculating the "monthly average deviation between simulated and observed values" and the "correlation coefficient". When the correlation coefficient is not lower than 0.5-0.7, the model can be confirmed to be used for subsequent forecasts. The observed data was fed back into the model using data assimilation technology to correct the initial field and boundary conditions and improve the forecast accuracy.

6. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The specific rules for "lowering the straw burning index in the upwind area to level 2-3" in step 4 are as follows: if the initial judgment index of the upwind area is level 1, then adjust it to level 2; if the initial judgment index is level 2-3, then maintain the original level; if the initial judgment index is level 4-5, then lower it to level 3.

7. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The specific implementation method of "pollutant concentration source tracing simulation" in step 2 is as follows: using the "source apportionment module" or "sensitivity analysis tool" of the RAMS-CMAQ model, calculate PM2.5 concentrations under the two scenarios of "including all emission sources" and "excluding straw burning sources". 2.5 Mass concentration, the difference between the two and PM under the "including all emission sources" scenario. 2.5 The ratio of mass concentration to PM2.5 is the effect of straw burning on PM2.

5. 2.5 The proportion of contribution.

8. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The numerical forecasting in step 1 employs a triple-nested grid technique, with the innermost grid having a resolution of no less than 1km × 1km, used to accurately simulate meteorological and pollution processes within the region.

9. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, The forecast result output step also includes: generating visual graphic products, including a spatial distribution map of the straw burning index, a time variation curve, and a pie chart of grade percentage statistics, and releasing them to management departments and the public through a WebGIS platform or mobile application.

10. The method for graded forecasting and monitoring of straw burning according to claim 1, characterized in that, It also includes "real-time rolling forecast and update steps": every 6-12 hours, the latest GFS meteorological data is used to drive the RAMS-CMAQ model to make a rolling forecast for the next 72-120 hours, and the forecast results are dynamically corrected based on the latest monitoring data to form an operational closed loop.

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