Method for rapidly predicting stable and standard reaching of urban air quality and application thereof

Abstract

The invention discloses a method for rapidly predicting stable and standard reaching of urban air quality. The method comprises the following steps: acquiring PM2.5 monitoring concentrations and EMI indexes of a to-be-analyzed city from a plurality of historical years to the current year; whether the PM2.5 concentration of the current year under the adverse meteorological condition and the PM2.5 concentration of the current year under the average meteorological condition are smaller than or equal to the target PM2.5 concentration value of the city to be analyzed or not is predicted and judged, and if yes, it is predicted that the air quality of the city to be analyzed in the next year can stably reach the standard; according to the method, whether the air quality of a city in the next year can stably reach the standard or not can be predicted, and the influence of meteorological conditions and emission reduction measures on the air quality is comprehensively considered, so that whether the air in the future time can stably reach the standard or not can be judged, and air quality management measures can be taken conveniently.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to a method for rapidly predicting whether urban air quality is stable and meeting standards, and an application thereof. Background Art

[0002] PM 2.5 The main reason for the inability to stably meet the standards is that the local atmospheric pollutant emission base is large and is significantly affected by fluctuations in meteorological conditions: when meteorological conditions are favorable, the local environmental capacity is high and PM 2.5 The concentration can meet the standard in stages; however, once the meteorological conditions deteriorate, the air quality is prone to rebound risks. Stable air quality that meets the standard requires consideration of both meteorological factors and emission reduction factors. Since annual meteorological fluctuations may mask the effectiveness of emission reduction, it is necessary to separate the contribution of meteorological factors and emission reduction factors to the concentration reduction in historical years. In existing technologies, the contribution of meteorological factors and emission reduction factors to local air quality improvement is usually analyzed separately or by using CMAQ (multi-scale air quality model) and NAQPMS (nested grid air quality forecast model system).

[0003] However, the former of these two methods cannot conduct quantitative analysis to quantify the contributions of the two, and the qualitative description of pollution meteorological conditions lacks mathematical support, the emission inventory is difficult to update in real time, and the actual strength of emission reduction measures is difficult to quantify. The latter has the disadvantages of large workload, long time consumption, and high threshold. Therefore, a scientific, quantitative, accurate and fast analysis method is needed to describe and predict whether the city's air quality can stably meet the standards, and guide emission reduction measures based on the results of the city's stable compliance. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a method for quickly predicting whether urban air quality is stable and meets standards and its application.

[0005] A method for rapidly predicting whether urban air quality is stable and meets standards comprises the following steps:

[0006] Get the PM of the city to be analyzed from multiple historical years to the current year 2.5 Monitor concentration and EMI index;

[0007] Use formula (1) to predict the PM of the current year under adverse meteorological conditions. 2.5 Concentration C W ;

[0008] C W =S n ×C n +E i (1)

[0009] In formula (1), C WThe PM of the current year under adverse meteorological conditions 2.5 Predicted concentration, S n is the maximum value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i The emission reduction factor for PM2.5 in the current year 2.5 Contribution value of monitoring concentration;

[0010] Use formula (2) to predict the PM of the current year under average meteorological conditions. 2.5 Concentration C s ;

[0011]

[0012] In formula (2), C s is the PM of the current year under average meteorological conditions 2.5 Predicted concentration, is the average value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i The emission reduction factor for PM2.5 in the current year 2.5 Contribution value of monitoring concentration;

[0013] Judge C separately W 、C s Is it less than or equal to the target PM of the city to be analyzed? 2.5 If both are positive, it is predicted that the air quality of the city to be analyzed can be stably up to standard in the next year. If C W No, C s If yes, then it is predicted that the air quality of the city to be analyzed can meet the standard stably when the weather is stable, but cannot meet the standard stably when the weather changes suddenly. W 、C s If the answer is no to all the above, it is predicted that the air quality of the city to be analyzed will not be able to meet the standard stably in the next year.

[0014] Note: The above method can predict whether the city's air quality can stably meet the standard in the next year; by collecting historical data and using the above formula to predict the PM under adverse weather conditions and average weather conditions 2.5 This method can comprehensively consider the impact of meteorological conditions and emission reduction measures on air quality, and thus determine whether the air quality in the future can be stably up to standard, so as to facilitate air quality management measures.

[0015] Furthermore, the S n satisfy:

[0016]

[0017] In formula (3), D0 is the maximum EMI index in multiple historical years; D n It is the EMI index of the earliest year among multiple historical years.

[0018] Note: The above S n Accurately reflect the normalization of extreme and basic meteorological conditions in historical data, thereby improving the accuracy and reliability of the forecast method.

[0019] Furthermore, the E i satisfy:

[0020] E i =C i -C n ×S i (4);

[0021] In formula (4), E i A negative value indicates that the emission reduction makes PM 2.5 Monitor the concentration drop, E i A positive value indicates that the emission reduction makes PM 2.5 Monitor for elevated concentrations; C i PM for the current year 2.5 Monitor concentration; C n The PM of the earliest year among multiple historical years 2.5 Monitoring concentration, Si is the EMI index of the current year compared with the EMI index of the earliest year in history.

[0022] Note: The above formula can calculate the emission reduction factor for PM 2.5 Contribution concentration value of the monitored concentration.

[0023] The present invention also provides an application for using the prediction results in air quality management applications.

[0024] Furthermore, the application method includes: determining which of the meteorological factors and emission reduction factors is most effective for PM2.5 from multiple historical years to the current year. 2.5 The contribution of concentration is greater;

[0025] When meteorological factors affect the PM from multiple historical years to the current year 2.5 When the contribution of concentration is greater, the meteorological prediction model is used to predict whether a meteorological mutation will occur;

[0026] In the case where no sudden meteorological changes are predicted, if it is predicted that the air quality of the city to be analyzed will stably meet the standard in the next year when the weather is stable, but will not stably meet the standard in the event of sudden meteorological changes, no enhanced emission reduction measures are required; if it is predicted that the air quality of the city to be analyzed will not stably meet the standard in the next year, enhanced emission reduction measures are required;

[0027] In the event of a sudden meteorological change, when it is predicted that the air quality of the city to be analyzed can stably meet the standards when the weather is stable in the next year, but cannot stably meet the standards when the weather suddenly changes, or when it is predicted that the air quality of the city to be analyzed cannot stably meet the standards in the next year, it is necessary to strengthen emission reduction measures.

[0028] When the emission reduction factors are applied to PM from multiple historical years to the current year 2.5 When the contribution of the concentration is greater, enhanced emission reduction measures are needed.

[0029] Note: The above application method can provide an air quality management decision-making framework based on prediction results and contribution factor analysis; by judging the impact of meteorological factors and emission reduction factors on PM 2.5 By combining the contribution of different pollutants to the concentration with meteorological forecast models, this method can guide urban managers to take appropriate emission reduction measures in different situations to ensure that the air quality is stable and meets the standards; thus, it helps to improve the efficiency and effectiveness of air quality management.

[0030] Furthermore, the determination of which of the meteorological factors and emission reduction factors is most effective for PM2.5 from multiple historical years to the current year is performed. 2.5 Methods that contribute more to concentration include:

[0031] Effects of meteorological factors on PM from multiple historical years to the current year 2.5 Contribution value of concentration L i satisfy:

[0032]

[0033] In formula (5), L i A negative value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration decreased, which means that the meteorological conditions were relatively favorable. i A positive value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration increases, which means that the meteorological conditions are relatively unfavorable; D i is the EMI index of the current year, D n is the EMI index of the earliest year among multiple historical years; C n The PM of the earliest year among multiple historical years 2.5 Monitor concentrations;

[0034] Effect of emission reduction factors on PM2.5 from multiple historical years to the current year 2.5 The contribution of the concentration is the E i ;

[0035] Compare multiple historical years to multiple L in the current year i With multiple E i The absolute value of the average value, the larger the absolute value, the PM from multiple historical years to the current year. 2.5 The contribution of concentration is greater.

[0036] Note: The above method can quantify the impact of meteorological factors and emission reduction factors on PM 2.5 The size of the concentration contribution can help technicians clarify which factors have a greater impact on air quality in history, thereby providing a scientific basis for formulating more effective air quality improvement strategies. Specifically, if the above multiple L i With multiple E i The average values of the average values are all negative, then the smaller the average value is, the PM from multiple historical years to the current year 2.5 If the average values are all positive, the smaller the average value, the greater the contribution to the PM concentration from multiple historical years to the current year. 2.5 The contribution of the concentration increase is smaller; if one is positive and the other is negative, the smaller average value is the contribution of the PM2.5 concentration increase from multiple historical years to the current year. 2.5 Contributes to the decrease in concentration, and the average value is large, that is, the PM from multiple historical years to the current year 2.5 Contributes to the increase in concentration.

[0037] Furthermore, the method for strengthening emission reduction measures includes:

[0038] First, calculate the compliance gap value; the compliance gap value is the PM value of the current year under adverse meteorological conditions or average meteorological conditions. 2.5 Predicted concentration and target PM2.5 in the city to be analyzed 2.5 The difference in concentration values;

[0039] Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly target value;

[0040] PM through multiple pollution sources 2.5 Monthly target values are set to strengthen emission reduction measures for multiple pollution sources.

[0041] Note: This method calculates the gap to standard and uses an emissions inventory model to break this gap down to each pollution source. This allows for specific monthly targets to be set, allowing for precise emission reduction measures to be taken for each pollution source to ensure that air quality meets predetermined standards.

[0042] Furthermore, the emission inventory model is used to quantify the contribution of each pollution source to PM 2.5 Emission contribution: the emission inventory model adopts the MEIC model inventory or localized emission inventory;

[0043] Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly targets include:

[0044] First, based on the emission inventory model, pollution sources are classified, emissions are calculated, and allocated to time and space scales;

[0045] Then, the PMF receptor model or air quality model is used to determine the effect of each pollution source on PM 2.5 The contribution ratio of the concentration is used to decompose the gap into the monthly target value for emission reduction of each pollution source based on the contribution ratio.

[0046] Note: The above method uses emission inventory models (such as MEIC model) and PMF receptor model to quantify the contribution of each pollution source to PM 2.5 The contribution of emissions to pollution sources will be analyzed, and the gap in compliance with standards will be broken down into monthly emission reduction targets for each pollution source, and precise emission reduction measures will be implemented to ensure that air quality is stable and meets standards.

[0047] Furthermore, the weather forecast model adopts an ensemble forecast system.

[0048] Description: The ensemble forecast system generates multiple forecast results by running multiple times and introducing random disturbances.

[0049] The beneficial effects of the present invention are:

[0050] The method of the present invention can predict whether the air quality of a city can stably meet the standard in the future. By collecting historical data and using the above formula to predict the PM under adverse meteorological conditions and average meteorological conditions, the 2.5 This method can comprehensively consider the impact of meteorological conditions and emission reduction measures on air quality, and thus determine whether the air quality in the future can be stably up to standard, so as to facilitate air quality management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The EMI index and PM of a city from 2016 to 2021 in the embodiment of the present invention are 2.5 Concentration variation graph;

[0052] Figure 2 This is the effect of urban meteorological conditions and emission reduction on PM2.5 in a certain city since 2017 in the embodiment of the present invention. 2.5 Contribution plot of concentration changes;

[0053] Figure 3 It is a schematic diagram of the method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to further illustrate the approach and effects achieved by the present invention, the technical solution of the present invention will be clearly and completely described below in conjunction with experiments.

[0055] According to the 2023 Ecological Environment Bulletin, in 2023, among the 339 cities in China, 203 cities met the ambient air quality standards, and the concentrations of the six pollutants involved in the evaluation met the standards, which means that the ambient air quality met the standards. 2.5 、PM 10 , SO2 and NO2 are evaluated for compliance with standards based on their annual average concentrations, and O3 and CO are evaluated for compliance with standards based on percentile concentrations. In accordance with the Technical Specifications for Ambient Air Quality Assessment (Trial) (HJ663-2013), the daily maximum 8-hour average value of O3 and the 24-hour average value of CO valid in the calendar year are sorted from small to large. The 90% position of the daily maximum 8-hour average value of O3 is compared with the national standard daily maximum 8-hour average concentration limit to determine whether O3 meets the standard; the 95% position of the 4-hour average value of CO2 is compared with the 4-hour standard concentration limit of CO2 to determine whether CO meets the standard. , accounting for 59.9%. Among them, 105 cities have fine particulate matter (PM 2.5 ) exceeded the standard, accounting for 31.0%; 79 cities exceeded the standard for ozone (O3), accounting for 23.3%; 58 cities exceeded the standard for inhalable particulate matter (PM 10 ) exceeded the standard, accounting for 17.1%; 1 city exceeded the standard for nitrogen dioxide (NO2), accounting for 0.3%; Therefore, PM 2.5 The compliance with the standards needs to be improved;

[0056] Stable compliance with air quality standards is one of the important stage goals of air pollution prevention and control in various regions. Meeting air quality standards in one year does not mean that standards will be met every year thereafter. Air quality improvement / compliance with standards is the result of the combined effects of meteorological factors and emission reduction factors. Quantifying the contribution of meteorological factors and emission reduction factors to air quality improvements in historical years and combining them with local air quality improvement goals will help to objectively analyze the gaps in local air quality stability and standards, and thus guide the formulation of local emission reduction policies.

[0057] Currently, there are two main approaches to analyzing the contribution of meteorological factors and emission reduction factors to local air quality improvements: First, analyze meteorological factors and emission reduction factors separately. For example, for meteorological factors, the pros and cons of meteorological factors on local air quality are qualitatively analyzed based on data such as wind direction, wind speed, rainfall, temperature, and humidity, combined with the emission situation at risk in the main wind direction. For emissions, the increase or decrease in local atmospheric pollutant emissions is analyzed mainly from the perspective of macroeconomic data such as fossil energy consumption, industrial output value, changes in industrial structure, and the implementation of atmospheric pollution prevention and control measures for key emission sources this year. This method then analyzes the contribution of primary atmospheric pollution source emissions to various pollutants in the ambient air. This method is simple and intuitive, and can analyze the positive or negative contribution of meteorological or emission reduction factors to air quality, but it cannot quantify the contribution of both. Another approach utilizes widely used air quality forecasting models, such as CMAQ (Multi-Scale Air Quality Model) and NAQPMS (Nested Grid Air Quality Prediction Model System), to set uncontrolled and controlled scenarios based on various emission inventories and emergency emission reduction measures. Simulating changes in ambient air pollutant concentrations within the study area under the same meteorological conditions, the effectiveness of emission reduction measures is evaluated. Simulating changes in ambient air pollutant concentrations within the study area under different meteorological conditions, using the same emission inventory, evaluates the impact of meteorological factors on air quality. Combining the simulation results of the two groups with the improvement in local air quality, the impact of meteorological and emission reduction factors on local air quality is quantified. This method has the advantage of being able to quantify the contribution of meteorological and emission reduction factors to local air quality improvements. However, it suffers from problems such as a lack of mathematical support for the qualitative description of pollutant meteorological conditions, difficulty in achieving real-time dynamic updates of emission inventories, and difficulty in quantifying the actual effectiveness of emission reduction measures. Furthermore, it suffers from the drawbacks of large air quality model simulation workload, long time consumption, high threshold, and significant computational resource consumption.

[0058] Based on the problems of existing methods that cannot be quantified or have large workload, long time consumption, high threshold, etc., the present invention establishes a method based on the environmental meteorological index EMI to quickly and quantify the impact of meteorological factors and emission reduction factors on air quality improvement, and analyzes the gap in meeting the standards in combination with regional air quality improvement goals. Figure 3 As shown, specifically as the following embodiments:

[0059] Example 1: A method for rapidly predicting whether urban air quality is stable and meets standards, comprising the following steps:

[0060] S1. Obtain the PM data of the city to be analyzed from multiple historical years to the current year. 2.5 Monitor concentration and EMI index;

[0061] Data collection: Emission reduction measures and meteorological conditions are two important factors that affect changes in the atmospheric environment; through meteorological observation data and numerical simulation, analyze the impact of meteorological conditions on changes in pollutant concentrations, and evaluate the effectiveness of emission reduction measures based on the proportion of changes in atmospheric pollutant concentrations. In recent years, the regional characteristics of atmospheric pollution have become increasingly prominent. Studying the impact of regional transport on the atmospheric environment through numerical simulation and meteorological condition analysis is of great significance for strengthening the joint prevention and control of atmospheric pollution. The China Meteorological Administration publishes the "Atmospheric Environmental Meteorological Bulletin" every year. The bulletin provides a detailed analysis of the national atmospheric environment and atmospheric pollution meteorological conditions and their changes in that year, and publishes the environmental meteorological index EMI for each region. 1 (The smaller the value, the more favorable the weather.) This index is calculated using actual meteorological data and numerical solutions to characterize the effect of meteorological conditions on PM2.5. 2.5 The concentration is affected by the change of the actual atmospheric pollutant concentration and the difference between the meteorological conditions, and the contribution of the emission reduction measures to the reduction of atmospheric pollutant concentration is analyzed. That is, the change of the EMI index can represent the effect of meteorological conditions on the ambient air PM 2.5 Quantitative contribution of concentration; the embodiment of the present invention quotes the EMI index published in the "Atmospheric Environment Meteorological Bulletin" over the years to distinguish the impact of emission reduction and meteorological conditions on changes in urban ambient air quality.

[0062] Specifically, the EMI calculation method refers to the PM 2.5 Meteorological Conditions Evaluation Index (EMI)" (QX / T479-2019); the statistical method refers to the "Service Specifications for the Assessment of the Impact of Meteorological Conditions on the Effectiveness of Air Pollution Prevention and Control (Interim)" (Qijian Letter

[2019] No. 68);

[0063] Unfavorable meteorological year: The year with the maximum EMI index is the unfavorable meteorological year;

[0064] Meteorological average year: the EMI index of each year in recent years is taken as the average value, which is the EMI index of the meteorological average year.

[0065] For example, the EMI index and PM of a city published in the Atmospheric Environment Meteorological Bulletin are collected. 2.5 Data on annual averages (actual conditions) (third column of Table 1);

[0066] S2. Use formula (1) to predict the PM of the current year under adverse meteorological conditions. 2.5 Concentration C W ;

[0067] C W =S n ×C n +E i (1)

[0068] In formula (1), C WThe PM of the current year under adverse meteorological conditions 2.5 Predicted concentration, S n is the maximum value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i The emission reduction factor for PM2.5 in the current year 2.5 Contribution value of monitoring concentration;

[0069] S3. Use formula (2) to predict the PM of the current year under average meteorological conditions. 2.5 Concentration C s ;

[0070]

[0071] In formula (2), C s is the PM of the current year under average meteorological conditions 2.5 Predicted concentration, is the average value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i For the reduction factor of PM 2.5 Contribution value of monitoring concentration;

[0072] For example, a city in southern China is selected as a specific implementation case for illustration, where the base year is 2016 and the current year is 2021. Specifically, the EMI index and PM2.5 of a city published in the Atmospheric Environment Meteorological Bulletin are collected. 2.5 The annual average (actual) data (the third column of Table 1); the EMI index is normalized as shown in the second column of Table 1; according to the above method, the PM2.5 value of the city is calculated under the influence of meteorological conditions only and without taking any emission reduction measures. 2.5 Concentration, that is, the index of each year in the second column × 40.79 (base year PM 2.5 concentration, the third column is the value of 2016) to get the fourth column. Calculate the effect of emission reduction on PM 2.5 Concentration contribution is the value of each year in the third column minus the data of the corresponding year in the fourth column to get the fifth column. 2.5 Concentration contribution, that is, the value of each year in the fourth column minus 40.79 (base year PM 2.5 concentration, the third column is the value of 2016), and the sixth column is obtained to calculate the impact of emission reduction and meteorological conditions on PM2.5 in the region in historical years. 2.5 The contribution of concentration is calculated in the fifth and sixth columns.

[0073] Table 1 EMI index and PM of a city from 2016 to 20212.5 concentration

[0074]

[0075] In order to analyze the causes of air quality improvement more intuitively, the EMI index and PM 2.5 The actual concentration is normalized with 2016 as the base year, and a trend chart is drawn, as shown in the figure below. Figure 1 The area between the two lines represents the contribution to emission reduction, PM 2.5 The concentration curve below the EMI index curve indicates that the emission reduction has made the PM 2.5 The distance between the EMI index and the vertical axis value 1 represents the contribution of meteorology. Above the vertical axis value 1, it means that the meteorological conditions are more unfavorable than the base year, which has a great impact on PM2.5. 2.5 The contribution of PM in this area is positive, which makes the PM 2.5 The concentration increases, and vice versa.

[0076] Therefore, by Figure 1 It can be seen that compared with 2016, from 2017 to 2021, only 2017 was a meteorologically unfavorable year (i.e., EMI was greater than 1), and the other years were meteorologically favorable years to varying degrees. 2.5 The decrease in the EMI index is greater than that in the EMI index, indicating that the reduction in local pollutant emissions has a significant impact on PM 2.5 have different degrees of contribution.

[0077] Furthermore, the effects of emission reduction and meteorological conditions on PM 2.5 The contribution of Figure 2 Compared with 2016, with the gradual advancement of air pollution prevention and control work, emission reduction has a significant impact on PM 2.5 The contribution of climate change has increased year by year; 2017 was the most unfavorable year for meteorology, and 2020 was the most favorable year for meteorology.

[0078] From the above analysis, we can see that if the most unfavorable meteorological conditions in recent years (i.e. the meteorological conditions in 2017) are met, the PM2.5 level in this city will be 2.5 The annual average concentration has dropped to 35 μg / m 3 Below, it meets the national secondary standard; if it is average meteorological conditions, it has met the national secondary standard since 2019.

[0079] With the continuous improvement of air quality, the country has set air quality assessment targets for various regions. The air quality assessment target for this region is 29.5μg / m 3To stably achieve this air quality target, the standard would be reached under both the most unfavorable and average meteorological conditions, with the gaps being 8.39% and 1.47% respectively. Under unfavorable meteorological conditions, there is greater pressure to reduce emissions.

[0080] S4, judge C respectively W 、C s Is it less than or equal to the target PM of the city to be analyzed? 2.5 If both are positive, it is predicted that the air quality of the city to be analyzed can be stably up to standard in the next year. If C W No, C s If yes, then it is predicted that the air quality of the city to be analyzed can meet the standard stably when the weather is stable, but cannot meet the standard stably when the weather changes suddenly. W 、C s If the answer is no, it is predicted that the air quality of the city to be analyzed will not be able to meet the standard stably in the next year.

[0081] S n satisfy:

[0082]

[0083] In formula (3), D0 is the maximum EMI index in multiple historical years; D n It is the EMI index of the earliest year among multiple historical years.

[0084] E i satisfy:

[0085] E i =C i -C n ×S i (4);

[0086] In formula (4), E i When it is a negative value, it means that the emission reduction has caused the PM2.5 monitoring concentration to decrease. i A positive value indicates that emission reduction has led to an increase in PM2.5 monitoring concentration; C i is the PM2.5 monitoring concentration in the current year; C n is the PM2.5 monitoring concentration of the earliest year among multiple historical years, and Si is the EMI index of the current year compared with the EMI index of the earliest year among multiple historical years.

[0087] The prediction results are used in the application of air quality management; the application method includes: determining which of the meteorological factors and emission reduction factors is most important for the PM2.5 level from multiple historical years to the current year. 2.5 The contribution of concentration is greater;

[0088] When meteorological factors affect the PM from multiple historical years to the current year 2.5When the contribution of concentration is greater, the meteorological prediction model is used to predict whether a meteorological mutation will occur;

[0089] In the case where no sudden meteorological changes are predicted, if it is predicted that the air quality of the city to be analyzed will stably meet the standard in the next year when the weather is stable, but will not stably meet the standard in the event of sudden meteorological changes, no enhanced emission reduction measures are required; if it is predicted that the air quality of the city to be analyzed will not stably meet the standard in the next year, enhanced emission reduction measures are required;

[0090] In the event of a sudden meteorological change, when it is predicted that the air quality of the city to be analyzed can stably meet the standards when the weather is stable in the next year, but cannot stably meet the standards when the weather suddenly changes, or when it is predicted that the air quality of the city to be analyzed cannot stably meet the standards in the next year, it is necessary to strengthen emission reduction measures.

[0091] When the emission reduction factors are applied to PM from multiple historical years to the current year 2.5 When the contribution of the concentration is greater, enhanced emission reduction measures are needed.

[0092] The determination of which of the meteorological factors and emission reduction factors is most important for PM from multiple historical years to the current year 2.5 Methods that contribute more to concentration include:

[0093] Effects of meteorological factors on PM from multiple historical years to the current year 2.5 Contribution value of concentration L i satisfy:

[0094]

[0095] In formula (5), L i A negative value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration decreased, which means that the meteorological conditions were relatively favorable. i A positive value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration increases, which means that the meteorological conditions are relatively unfavorable; D i is the EMI index of the current year, D n is the EMI index of the earliest year among multiple historical years; C n The PM of the earliest year among multiple historical years 2.5 Monitor concentrations;

[0096] Effect of emission reduction factors on PM2.5 from multiple historical years to the current year 2.5 The contribution of the concentration is the E i ;

[0097] Compare multiple historical years to multiple L in the current year i With multiple E i The absolute value of the average value, the larger the absolute value, the PM from multiple historical years to the current year. 2.5 The contribution of concentration is greater;

[0098] Specifically, if the average values are all negative, the smaller the average value is, the PM from multiple historical years to the current year. 2.5 If the average values are all positive, the smaller the average value, the greater the contribution to the PM concentration from multiple historical years to the current year. 2.5 The contribution of the concentration increase is smaller; if one is positive and the other is negative, the smaller average value is the contribution of the PM2.5 concentration increase from multiple historical years to the current year. 2.5 Contributes to the decrease in concentration, and the average value is large, that is, the PM from multiple historical years to the current year 2.5 Contributes to the increase in concentration.

[0099] The methods for implementing enhanced emission reduction measures include:

[0100] First, calculate the compliance gap value; the compliance gap value is the PM value of the current year under adverse meteorological conditions or average meteorological conditions. 2.5 Predicted concentration and target PM2.5 in the city to be analyzed 2.5 The difference in concentration values; the difference in reaching the standard = 1-air quality target value / PM in an unfavorable meteorological year or an average meteorological year 2.5 concentration;

[0101] Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly target value;

[0102] The emission inventory model is used to quantify the contribution of each pollution source to PM 2.5 Emission contribution: the emission inventory model adopts the MEIC model inventory or localized emission inventory;

[0103] Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly targets include:

[0104] First, based on the emission inventory model, pollution sources are classified, emissions are calculated, and allocated to time and space scales;

[0105] Then, the PMF receptor model or air quality model is used to determine the effect of each pollution source on PM 2.5 The contribution ratio of the concentration, based on which the gap in compliance with the standards is decomposed into monthly emission reduction targets for each pollution source;

[0106] PM through multiple pollution sources 2.5Monthly target values are set, and emission reduction measures are strengthened for multiple pollution sources, such as strengthening end-of-pipe treatment of industrial coal-fired boilers, increasing road sprinklers to control road dust, and strengthening management and control of construction sites. The weather forecast model uses an ensemble forecast system;

[0107] For example, using the MEIC model list and PMF receptor model, the PM 2.5 The concentration is 70 μg / m 3 The target value is 35 μg / m 3 , the gap to meeting the target requires a 50% reduction in emissions.

Claims

1. A method for rapidly predicting whether urban air quality is stable and meets standards, characterized in that: The following steps are involved: Get the PM of the city to be analyzed from multiple historical years to the current year 2.5 Monitor concentration and EMI index; Use formula (1) to predict the PM of the current year under adverse meteorological conditions. 2.5 Concentration C W ; C W =S n ×C n +E i (1) In formula (1), C W The PM of the current year under adverse meteorological conditions 2.5 Predicted concentration, S n is the maximum value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i The emission reduction factor for PM2.5 in the current year 2.5 Contribution value of monitoring concentration; Use formula (2) to predict the PM of the current year under average meteorological conditions. 2.5 Concentration C s ; In formula (2), C s is the PM of the current year under average meteorological conditions 2.5 Predicted concentration, is the average value of the EMI index after normalization in multiple historical years, C n The PM of the earliest year among multiple historical years 2.5 Monitor concentration, E i The emission reduction factor for PM2.5 in the current year 2.5 Contribution value of monitoring concentration; Judge C separately W 、C s Is it less than or equal to the target PM of the city to be analyzed? 2.5 If both are positive, it is predicted that the air quality of the city to be analyzed can be stably up to standard in the next year. If C W No, C s If yes, then it is predicted that the air quality of the city to be analyzed can meet the standard stably when the weather is stable, but cannot meet the standard stably when the weather changes suddenly. W 、C s If the answer is no to all the above, it is predicted that the air quality of the city to be analyzed will not be able to meet the standard stably in the next year.

2. A method for rapidly predicting whether urban air quality is stable and up to standard according to claim 1, characterized in that: The S n satisfy: In formula (3), D0 is the maximum EMI index in multiple historical years; D n It is the EMI index of the earliest year among multiple historical years.

3. The method for rapidly predicting whether urban air quality is stable and up to standard according to claim 1, characterized in that: The E i satisfy: E i =C i -C n ×S i (4); In formula (4), E i A negative value indicates that the emission reduction makes PM 2.5 Monitor the concentration drop, E i A positive value indicates that the emission reduction makes PM 2.5 Monitor for elevated concentrations; C i PM for the current year 2.5 Monitor concentration; C n The PM of the earliest year among multiple historical years 2.5 Monitoring concentration, Si is the EMI index of the current year compared with the EMI index of the earliest year in history.

4. The use of the method according to claim 1, characterized in that The prediction results are used in air quality management applications.

5. Application of the method according to claim 4, characterized in that The application method includes: determining which of meteorological factors and emission reduction factors has an impact on PM2.5 from multiple historical years to the current year. 2.5 The contribution of concentration is greater; When meteorological factors affect the PM from multiple historical years to the current year 2.5 When the contribution of concentration is greater, the meteorological prediction model is used to predict whether a meteorological mutation will occur; In the case where no sudden meteorological changes are predicted, if it is predicted that the air quality of the city to be analyzed will stably meet the standard in the next year when the weather is stable, but will not stably meet the standard in the event of sudden meteorological changes, no enhanced emission reduction measures are required; if it is predicted that the air quality of the city to be analyzed will not stably meet the standard in the next year, enhanced emission reduction measures are required; In the event of a sudden meteorological change, when it is predicted that the air quality of the city to be analyzed can stably meet the standards when the weather is stable in the next year, but cannot stably meet the standards when the weather suddenly changes, or when it is predicted that the air quality of the city to be analyzed cannot stably meet the standards in the next year, it is necessary to strengthen emission reduction measures. When the emission reduction factors are applied to PM from multiple historical years to the current year 2.5 When the contribution of the concentration is greater, enhanced emission reduction measures are needed.

6. Application of the method according to claim 5, characterized in that The determination of which of the meteorological factors and emission reduction factors is most important for PM from multiple historical years to the current year 2.5 Methods that contribute more to concentration include: Effects of meteorological factors on PM from multiple historical years to the current year 2.5 Contribution value of concentration L i satisfy: In formula (5), L i A negative value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration decreased, which means that the meteorological conditions were relatively favorable. i A positive value indicates that the current year's meteorological conditions make PM2.5 higher than the meteorological conditions in the earliest year in history. 2.5 The monitoring concentration increases, which means that the meteorological conditions are relatively unfavorable; D i is the EMI index of the current year, D n is the EMI index of the earliest year among multiple historical years; C n The PM of the earliest year among multiple historical years 2.5 Monitor concentrations; Effect of emission reduction factors on PM2.5 from multiple historical years to the current year 2.5 The contribution of the concentration is the E i ; Compare multiple historical years to multiple L in the current year i With multiple E i The absolute value of the average value, the larger the absolute value, the PM from multiple historical years to the current year. 2.5 The contribution of concentration is greater.

7. Use of the method according to claim 5, characterized in that The methods for implementing enhanced emission reduction measures include: First, calculate the compliance gap value; the compliance gap value is the PM value of the current year under adverse meteorological conditions or average meteorological conditions. 2.5 Predicted concentration and target PM2.5 in the city to be analyzed 2.5 The difference in concentration values; Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly target value; PM through multiple pollution sources 2.5 Monthly target values are set to strengthen emission reduction measures for multiple pollution sources.

8. Use of the method according to claim 7, characterized in that The emission inventory model is used to quantify the contribution of each pollution source to PM 2.5 Emission contribution: the emission inventory model adopts the MEIC model inventory or localized emission inventory; Based on the emission inventory model, the compliance gap value is decomposed into multiple pollution sources to obtain the PM 2.5 Monthly targets include: First, based on the emission inventory model, pollution sources are classified, emissions are calculated, and allocated to time and space scales; Then, the PMF receptor model or air quality model is used to determine the effect of each pollution source on PM 2.5 The contribution ratio of the concentration is used to decompose the gap into monthly target values for emission reduction of each pollution source based on the contribution ratio.

9. Use of the method according to claim 5, characterized in that The weather forecast model adopts an ensemble forecast system.

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