Real-time estimation method suitable for emergency pollutant emission reduction proportion in heavy pollution weather

Abstract

The invention discloses a real-time estimation method suitable for a heavy pollution weather emergency pollutant emission reduction proportion, and the method is based on a heavy pollution weather emergency emission reduction list and pollution source online data, and is suitable for the estimation of a heavy pollution weather emergency pollutant total emission reduction proportion. The method is used for estimating the emission reduction proportion of each pollutant in the heavy pollution weather emergency period in real time, reflecting the daily actual emission reduction proportion of each pollutant in the urban heavy pollution period, judging whether heavy pollution emergency management and control measures are implemented in place or not, and improving the heavy pollution weather emergency effect. The emission reduction effect of various pollution sources is enhanced, and real-time prediction and estimation can help a decision maker to timely adjust a management and control strategy and optimize emission reduction measures according to the pollutant concentration change, so that the air pollution level is effectively reduced, and the air quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental governance technology, and in particular to a real-time estimation method for emergency pollutant emission reduction ratios in heavy pollution weather. Background Art

[0002] With the acceleration of industrialization and the continuous increase in the number of motor vehicles, urban air pollution is becoming increasingly serious. Especially during heavy pollution weather conditions such as winter, pollutant concentrations rise sharply, seriously affecting public health and quality of life. To address these problems, many countries and regions have successively introduced emergency warnings and emission reduction measures for heavy pollution weather. These measures generally include restricting industrial emissions, controlling traffic flow, and adjusting energy consumption. However, how to monitor and evaluate the emission reduction effects in real time has become a key issue in current environmental governance.

[0003] However, during the emergency period of heavy pollution weather, SO2, NO x 、VOC s The system can make real-time estimates of the total social emission reduction of particulate matter to judge whether the emergency emission reduction measures for heavy pollution are in place, and make up for the defect that the emergency emission reduction list for heavy pollution can only reflect the theoretical emission reduction ratio but cannot estimate the actual emission reduction situation. Summary of the Invention

[0004] The present invention provides a real-time estimation method for the reduction ratio of pollutant emissions during heavy pollution weather emergencies, which can effectively solve the problem of the current SO2, NO x 、VOC s The system can make real-time estimates of the total social emission reduction of particulate matter, judge whether the emergency emission reduction measures for heavy pollution are in place, and make up for the defect that the emergency emission reduction list for heavy pollution can only reflect the theoretical emission reduction ratio but cannot estimate the actual emission reduction situation.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a real-time estimation method for the pollutant emission reduction ratio applicable to heavy pollution weather emergency response, based on the heavy pollution weather emergency emission reduction list and online pollution source data, to establish an estimation method for the total pollutant emission reduction ratio applicable to heavy pollution weather emergency response, which is used for real-time estimation of the emission reduction ratio of individual pollutants during heavy pollution weather emergency response, reflecting the actual daily emission reduction ratio of each pollutant during heavy pollution in the city, judging whether the heavy pollution emergency control measures are in place, and improving the effectiveness of heavy pollution weather emergency response;

[0006] The estimation method is as follows:

[0007] ΔE i %=(ΔE 工业.i +ΔE 移动源.i +ΔE 居民面源.i +ΔE 扬尘源.i+ΔE 其他源.i )×100 / (E 工业.i +E 移动源.i +E 居民面源.i +E 扬尘源.i +E 其他源.i )

[0008] Where ΔE is the reduction of pollutant emissions from various emission sources, E is the emission of various pollution sources, i is the emission of SO2, NO x 、VOC s and particulate matter;

[0009] Industrial sources:

[0010]

[0011] E 非在线.i =∑E 重点行业.i +∑E 保障类企业.i +∑E 小微企业.i +∑E 其他企业.i

[0012] Mobile Source:

[0013] E 移动源.i =E 货运车辆.i +E 非货运车辆.i +E 非道路移动源.i ;

[0014] And based on the air quality conditions and the estimated results of the emergency pollutant emission reduction ratio, it is assessed whether it is necessary to further tighten emergency control measures.

[0015] According to the above technical solution, the estimation method uses the basic data of the heavy pollution emergency emission reduction inventory and the online monitoring data of pollution sources;

[0016] Classify and consider emission reduction measures for enterprises that suspend production in winter, enterprises that ensure people's livelihoods, and enterprises with different performance levels in key industries;

[0017] Consider emission reduction measures for various emission sources including mobile sources, residential surface sources, and dust sources;

[0018] The unification of the base numbers of various types of data and the conservation of the total amount.

[0019] According to the above technical plan, emergency control measures for heavy pollution weather include industrial sources, mobile sources and dust sources. The emission reduction ratio is calculated by calculating the emission reduction and total emissions of various emission sources after taking emergency emission reduction measures;

[0020] During the calculation process, various pollutants are divided into two categories: static data and dynamic data. Static data uses data from the heavy pollution emergency emission reduction list, while dynamic data uses online monitoring data of pollution sources, dynamic monitoring data of freight vehicles, and enterprise electricity consumption data.

[0021] Since emergency control measures for various pollution sources are different, and emergency emission reduction measures for enterprises of different performance levels in key industries within industrial enterprises are also different, the total amount of pollutant emissions and emission reductions for each source are calculated separately;

[0022] To ensure the uniformity of the data base and the conservation of the total amount, only the static data uses the data in the heavy pollution emergency emission reduction inventory, and the others use dynamic data;

[0023] During the calculation process, dynamic data is used first, followed by static data, which is applicable to various complex situations in various cities.

[0024] According to the above technical solution, the collection of industrial sources, mobile sources and dust sources requires the collection of historical air quality data and pollution source emission data, and then the historical data is trained and verified to establish a relationship model between pollution sources and air quality changes. Then, real-time pollution source emission data and meteorological condition data are used to input the model for real-time prediction and estimation. Finally, based on the deviation between the actual monitoring data and the model prediction results, the model parameters are dynamically adjusted to optimize the estimated results of the emission reduction ratio.

[0025] According to the above technical solution, the historical air quality data mainly comes from the air quality monitoring network of government monitoring stations and local environmental protection bureaus, which regularly release air quality data from various places, including PM 2.5 、PM 10 , SO2, NO2, CO, O3 pollutant concentrations;

[0026] Cooperating with automatic monitoring equipment to collect the concentration of pollutants in the air in real time, the monitoring stations collect data at hourly or shorter time intervals to provide fine-grained air quality data. At the same time, this data is obtained through official channels or environmental monitoring platforms and provided on a monthly, quarterly, and annual basis for historical reference.

[0027] According to the above technical solution, the historical air quality data is corrected and eliminated for lost or incomplete records using interpolation, mean filling and nearest neighbor processing methods;

[0028] When the amount of data is large and the relationships are complex, the relationship between pollution sources and air quality is established, and these are input into the trained pollutant emission and air quality relationship model in real time for real-time prediction and estimation.

[0029] According to the above technical solution, the real-time pollution source emission data and meteorological condition data are obtained from online monitoring systems, traffic flow monitoring, and energy consumption data channels, including the concentration and emission of various pollutants, and combined with meteorological data temperature, humidity, wind speed and wind direction.

[0030] According to the above technical solution, the real-time pollution source emission data and meteorological condition data are cleaned, interpolated and normalized, and then input into the model for real-time prediction. The model estimates the current air quality changes and the effects of emergency emission reduction measures, predicts the concentration of pollutants, and evaluates the actual impact of emission reduction measures. Based on these real-time prediction results, decision makers can adjust emergency response measures in a timely manner. At the same time, the real-time feedback of the model also provides a basis for the evaluation and adjustment of subsequent emergency measures.

[0031] Compared with the existing technology, the beneficial effects of the present invention are: the structure of the present invention is scientific and reasonable, and it is safe and convenient to use. It improves the implementation efficiency of emergency control measures for heavy pollution weather and enhances the emission reduction effect of various pollution sources. Real-time prediction and estimation can help decision makers adjust control strategies in time according to changes in pollutant concentrations and optimize emission reduction measures, thereby effectively reducing air pollution levels and improving air quality. Real-time feedback and optimization mechanisms make emergency management more scientific and accurate, provide strong support for urban environmental governance, improve the intelligence level and operability of air quality management, and ultimately achieve the optimal effect of emergency control of heavy pollution weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0033] In the attached figure:

[0034] Figure 1 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0036] Example: Figure 1 As shown, the present invention provides a technical solution, a real-time estimation method for the proportion of pollutant emission reductions applicable to heavy pollution weather emergencies, characterized by: based on the heavy pollution weather emergency emission reduction list and based on online data of pollution sources, an estimation method for the total pollutant emission reduction ratio applicable to heavy pollution weather emergencies is established, which is used for real-time estimation of the emission reduction ratio of individual pollutants during the heavy pollution weather emergency, reflecting the actual daily emission reduction ratio of each pollutant during the heavy pollution period in the city, judging whether the heavy pollution emergency control measures are in place, and improving the effectiveness of heavy pollution weather emergency response;

[0037] The estimation method is as follows:

[0038] ΔE i %=(ΔE 工业.i +ΔE移动源.i +ΔE 居民面源.i +ΔE 扬尘源.i +ΔE 其他源.i )×100 / (E 工业.i +E 移动源.i +E 居民面源.i +E 扬尘源.i +E 其他源.i )

[0039] Where ΔE is the pollutant emission reduction of various emission sources, E is the emission of various pollution sources, and i is SO2, NOx, VOCs and particulate matter;

[0040] Industrial sources:

[0041]

[0042] E 非在线.i =∑E 重点行业.i +∑E 保障类企业.i +∑E 小微企业.i +∑E 其他企业.i

[0043] Mobile Source:

[0044] E 移动源.i =E 货运车辆.i +E 非货运车辆.i +E 非道路移动源.i ;

[0045] And based on the air quality conditions and the estimated results of the emergency pollutant emission reduction ratio, it is assessed whether it is necessary to further tighten emergency control measures.

[0046] According to the above technical solution, the estimation method uses basic data such as the heavy pollution emergency emission reduction inventory and pollution source online monitoring data;

[0047] Classify and consider emission reduction measures for enterprises that suspend production in winter, enterprises that ensure people's livelihoods, and enterprises with different performance levels in key industries;

[0048] Consider emission reduction measures for various emission sources including mobile sources, residential surface sources, and dust sources;

[0049] The unification of the base numbers of various data and the conservation of the total amount will ensure that the estimation results will not deviate significantly.

[0050] According to the above technical plan, emergency control measures for heavy pollution weather mainly include industrial sources, mobile sources and dust sources. The emission reduction ratio is calculated by calculating the emission reduction and total emissions of various emission sources after taking emergency emission reduction measures;

[0051] In actual work, due to the following reasons, the theoretical emission reduction ratio calculated based on the heavy pollution emergency emission reduction inventory is far from the actual situation. It cannot reflect the actual emission reduction situation, cannot effectively support heavy pollution emergency work, and will also lead to large deviations in the evaluation results of heavy pollution emergency response:

[0052] (1) The base of the heavy pollution emergency emission reduction list is based on the pollution source emission list of the previous year, which has a large deviation from the actual emissions;

[0053] (2) The mobile source emission base is calculated based on the number of motor vehicles in the previous year, but the majority of actual freight vehicles are non-local vehicles;

[0054] 3) Some enterprises may not have fully implemented emergency emission reduction measures for heavy pollution; (4) The tightening control measures during heavy pollution periods cannot be reflected;

[0055] To address these issues, we accurately estimate the actual emission reduction ratio of each pollutant during the heavy pollution emergency in real time. During the calculation process, we divide each type of pollutant into two categories: static data and dynamic data. The static data uses the data in the heavy pollution emergency emission reduction list, and the dynamic data uses the online monitoring data of pollution sources, dynamic monitoring data of freight vehicles, and enterprise electricity consumption data.

[0056] Since emergency control measures for various pollution sources are different, and emergency emission reduction measures for enterprises of different performance levels in key industries within industrial enterprises are also different, the total amount of pollutant emissions and emission reductions for each source are calculated separately;

[0057] To ensure the uniformity of the data base and the conservation of the total amount, only the static data uses the data in the heavy pollution emergency emission reduction inventory, and the others use dynamic data;

[0058] During the calculation process, dynamic data is used first, followed by static data, which is applicable to various complex situations in various cities.

[0059] According to the above technical solution, the collection of industrial sources, mobile sources and dust sources requires the collection of historical air quality data and pollution source emission data, and then the historical data is trained and verified to establish a relationship model between pollution sources and air quality changes. Then, real-time pollution source emission data and meteorological conditions data are used to input the model for real-time prediction and estimation. Finally, based on the deviation between the actual monitoring data and the model prediction results, the model parameters are dynamically adjusted to optimize the estimated results of the emission reduction ratio.

[0060] According to the above technical solution, historical air quality data mainly comes from the air quality monitoring network of government monitoring stations and local environmental protection bureaus, which regularly release air quality data from various places, including PM 2.5 、PM 10 , SO2, NO2, CO, O3 pollutant concentrations;

[0061] Cooperating with automatic monitoring equipment to collect the concentration of pollutants in the air in real time, the monitoring stations collect data at hourly or shorter time intervals to provide fine-grained air quality data. At the same time, this data is obtained through official channels or environmental monitoring platforms and provided on a monthly, quarterly, and annual basis for historical reference.

[0062] According to the above technical solution, historical air quality data can be corrected and eliminated for lost or incomplete records using interpolation, mean filling, and nearest neighbor processing methods;

[0063] When the amount of data is large and the relationships are complex, the relationship between pollution sources and air quality is established, and these are input into the trained pollutant emission and air quality relationship model in real time for real-time prediction and estimation.

[0064] According to the above technical solution, real-time pollution source emission data and meteorological condition data are obtained from online monitoring systems, traffic flow monitoring, and energy consumption data channels. Real-time pollution source emission data, including the concentration and emission of various pollutants, are combined with meteorological data such as temperature, humidity, wind speed, and wind direction.

[0065] According to the above technical solution, real-time pollution source emission data and meteorological condition data are input into the model for real-time prediction after data cleaning, interpolation and normalization. The model can estimate the current air quality changes and the effectiveness of emergency emission reduction measures, predict the concentration of pollutants, and evaluate the actual impact of emission reduction measures. Based on these real-time prediction results, decision makers can adjust emergency response measures in a timely manner. At the same time, the real-time feedback of the model also provides a basis for the evaluation and adjustment of subsequent emergency measures.

[0066] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A real-time estimation method for the proportion of pollutant emission reductions in emergency response to heavy pollution weather, characterized by: Based on the heavy pollution weather emergency emission reduction list and online pollution source data, a method for estimating the total pollutant emission reduction ratio applicable to heavy pollution weather emergencies is established. This method is used to estimate the emission reduction ratio of individual pollutants in real time during heavy pollution weather emergencies, reflect the actual daily emission reduction ratio of each pollutant during heavy pollution in cities, judge whether heavy pollution emergency control measures are in place, and improve the effectiveness of heavy pollution weather emergencies. The estimation method is as follows: ΔE i %=(ΔE 工业.i +ΔE 移动源.i +ΔE 居民面源.i +ΔE 扬尘源.i +ΔE 其他源.i )×100 / (E 工业.i +E 移动源.i +E 居民面源.i +E 扬尘源.i +E 其他源.i ) Where ΔE is the pollutant emission reduction of various emission sources, E is the emission of various pollution sources, and i is SO2, NOx, VOCs and particulate matter; Industrial sources: AND 非在线.i =∑E 重点行业.i +∑E 保障类企业.i +∑E 小微企业.i +∑E 其他企业.i Mobile Source: AND 移动源.i =And 货运车辆.i +E 非货运车辆.i +E 非道路移动源.i ; And based on the air quality conditions and the estimated results of the emergency pollutant emission reduction ratio, it is assessed whether it is necessary to further tighten emergency control measures.

2. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 1, characterized in that: The estimation method uses the basic data of the heavy pollution emergency emission reduction inventory and pollution source online monitoring data; Classify and consider emission reduction measures for enterprises that suspend production in winter, enterprises that ensure people's livelihoods, and enterprises with different performance levels in key industries; Consider emission reduction measures for various emission sources including mobile sources, residential surface sources, and dust sources; The unification of the base numbers of various types of data and the conservation of the total amount.

3. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 2, characterized in that: Heavy pollution weather emergency control measures include industrial sources, mobile sources and dust sources. The emission reduction ratio is calculated by calculating the emission reduction and total emissions of each type of emission source after taking emergency emission reduction measures; During the calculation process, various pollutants are divided into two categories: static data and dynamic data. Static data uses data from the heavy pollution emergency emission reduction list, while dynamic data uses online monitoring data of pollution sources, dynamic monitoring data of freight vehicles, and enterprise electricity consumption data. Since emergency control measures for various pollution sources are different, and emergency emission reduction measures for enterprises of different performance levels in key industries within industrial enterprises are also different, the total amount of pollutant emissions and emission reductions for each source are calculated separately; To ensure the uniformity of the data base and the conservation of the total amount, only the static data uses the data in the heavy pollution emergency emission reduction inventory, and the others use dynamic data; During the calculation process, dynamic data is used first, followed by static data, which is applicable to various complex situations in various cities.

4. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 3, characterized in that: The collection of industrial sources, mobile sources and dust sources requires the collection of historical air quality data and pollution source emission data, and then training and verifying the historical data to establish a relationship model between pollution sources and air quality changes. Then, real-time pollution source emission data and meteorological conditions data are used to input the model for real-time prediction and estimation. Finally, based on the deviation between the actual monitoring data and the model prediction results, the model parameters are dynamically adjusted to optimize the estimated results of the emission reduction ratio.

5. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 4, characterized in that: The historical air quality data are mainly from the air quality monitoring network of government monitoring stations and local environmental protection bureaus, which regularly release air quality data for various places, including PM 2.5 、PM 10 , SO2, NO2, CO, O3 pollutant concentrations; Cooperating with automatic monitoring equipment to collect the concentration of pollutants in the air in real time, the monitoring stations collect data at hourly or shorter time intervals to provide fine-grained air quality data. At the same time, this data is obtained through official channels or environmental monitoring platforms and provided on a monthly, quarterly, and annual basis for historical reference.

6. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 4, characterized in that: The historical air quality data is corrected and eliminated for lost or incomplete records using interpolation, mean filling, and nearest neighbor processing methods; When the amount of data is large and the relationships are complex, the relationship between pollution sources and air quality is established, and these are input into the trained pollutant emission and air quality relationship model in real time for real-time prediction and estimation.

7. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 4, characterized in that: The real-time pollution source emission data and meteorological condition data are obtained from online monitoring systems, traffic flow monitoring, and energy consumption data channels to obtain real-time pollution source emission data, including the concentration and emission of various pollutants, and are combined with meteorological data such as temperature, humidity, wind speed, and wind direction.

8. A real-time estimation method for pollutant emission reduction ratio applicable to heavy pollution weather emergencies according to claim 4, characterized in that: After data cleaning, interpolation and normalization, the real-time pollution source emission data and meteorological condition data are input into the model for real-time prediction. The model estimates the current air quality changes and the effectiveness of emergency emission reduction measures, predicts the concentration of pollutants, and evaluates the actual impact of emission reduction measures. Based on these real-time prediction results, decision makers can adjust emergency response measures in a timely manner. At the same time, the real-time feedback of the model also provides a basis for the evaluation and adjustment of subsequent emergency measures.

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