CN114527235B - Real-time quantitative detection method for emission intensity - Google Patents

Real-time quantitative detection method for emission intensity Download PDF

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CN114527235B
CN114527235B CN202011322899.0A CN202011322899A CN114527235B CN 114527235 B CN114527235 B CN 114527235B CN 202011322899 A CN202011322899 A CN 202011322899A CN 114527235 B CN114527235 B CN 114527235B
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吴潇萌
牛天林
吴烨
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Abstract

The application discloses a method for real-time quantitative detection of emission intensity, which realizes accurate depiction from concentration to emission intensity. The method comprises the steps of obtaining historical pollutant monitoring data, meteorological condition data and historical emission data of a detection area by determining the detection area and the position of an emission source and a monitoring point in the detection area; and correcting the data obtained by the monitoring points through the data acquisition weather (including temperature, humidity, wind direction, wind speed and the like) correction and geographical position correction, real-time wind data correction and time correction, and calculating the real-time pollutant discharge amount of the discharge source. The tool for identifying the specific pollution source and quantifying the emission intensity in real time provided by the application excavates the application value of air quality concentration monitoring data from a deeper stratum, realizes the possibility of quantifying the emission intensity from concentration to in real time, provides a reliable tool for compiling a real-time pollutant emission list, and also provides a calculation frame for quantifying in real time of different cities.

Description

Real-time quantitative detection method for emission intensity
Technical Field
The present disclosure relates to, but is not limited to, air quality detection techniques, and more particularly, but not by way of limitation, to a method for real-time quantitative detection of emission intensity.
Background
The method for mastering the emission characteristics of the atmospheric pollutants through pollutant monitoring equipment and an air quality simulation model technology is the basis of urban air quality management and atmospheric pollution control decisions.
With the refinement and generalization of air pollution emission management and the increasing demand for precision of pollutant emission lists, the accurate identification and quantification of low-level sources (such as small-scale industrial clusters, industrial chimney emission and motor vehicle emission) gradually become one of the key points and difficulties of the work of the environmental protection department. The direct acquisition of emission values is difficult for low-height sources because there is no clear emission point and the emission process has high uncertainty. At home and abroad, emission measurement and calculation aiming at low surface sources and unorganized emission sources mostly depend on time-consuming and labor-consuming manual investigation and calculation or traditional technologies such as satellite remote sensing technology identification with higher cost and higher difficulty and the like. In the traditional method, a pollutant emission list is established on the basis of investigation or remote sensing data, then the contribution of an emission source to pollutant concentration is simulated through an air quality model, and then the comparison and verification are carried out on the pollutant emission list and monitoring station data. The process of basic data research requires a large amount of time and labor cost, and the method lacks sufficient space-time resolution and computational efficiency, cannot realize real-time quantification, and meets the requirements of real-time supervision of small-sized industries.
The existing quantitative model technology can only meet the concentration data calculation of close-range observation points, has harsh selection conditions for monitoring points and receptor points, and cannot be widely applied to widely distributed national control and economic control air quality monitoring stations or air quality micro-station data. Meanwhile, due to the lack of a refined correction parameter database, the prior art is not sensitive enough to the response of meteorological condition changes, time changes, monitoring point-emission source relative geographical position changes and other factors, so that the precision of a simulation result is deficient.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the present application.
Typical meteorological conditions are defined in this application as:
(1) typical hourly meteorological conditions: the hourly meteorological conditions (for all the calculation points) with the most severe pollution and several hourly meteorological conditions with the greatest impact on each environmental protection objective are selected.
(2) Typical solar weather conditions: the most polluting (for all calculation points) weather conditions and several weather conditions that have the greatest impact on each environmental protection objective are selected.
In this application, the background concentration value of a contaminant is defined as the historical average concentration of the contaminant excluding the contribution of the target contaminant source.
In the present application, RHC is a concentration scale, and the calculation equation for RHC reduces the effect of an abnormal event on the highest concentration by combining the maximum values of a series of measured distributions to determine the highest concentration possible, as shown in equation (2).
Figure BDA0002793476930000021
Wherein x is n The n-th highest concentration value in the concentration profile,
Figure BDA0002793476930000022
is the average of the n-1 high concentrations in all distributions; and calculating average and real-time RHC according to the read concentration data monitored in real time and by combining historical air quality characteristics.
The application discloses a device, a system and a method for identifying an air pollution source and quantitatively detecting emission intensity in real time, which realize accurate depiction from concentration to emission intensity.
The application discloses a method for real-time quantitative detection of emission intensity, which comprises the following steps:
1) Determining a detection area and a position of an emission source and a monitoring point in the detection area, and acquiring historical pollutant monitoring data, meteorological condition data and historical emission data of the detection area;
determining the background concentration value BG of the pollutants in the detection area, the annual average concentration of the pollutants at the position of the monitoring point, the daily average emission amount of the pollutants of the emission source, the historical hour detection concentration of the monitoring point and the historical emission intensity E of the pollutants of the emission source according to the historical monitoring data of the pollutants and the meteorological condition data 0 ；
Calculating to obtain the concentration according to the historical hourly detection concentration of the monitoring pointsMeasurement scale RHC m (ii) a Collecting real-time pollutant concentration data of the position of the monitoring point, and calculating to obtain a real-time concentration measurement scale RHC of the monitoring point s ；
3) Calculating the ratio of the real-time hourly discharge amount of the real-time pollutants obtained by the monitoring points to the daily average discharge amount of the pollutants of the discharge source, and recording the ratio as C Hour ；
4) Determining the Gaussian diffusion plume in the detection area, acquiring the ratio of the pollutant concentration of the monitoring point in the Gaussian diffusion plume to the pollutant concentration of the position of the emission source, and recording the ratio as a bit value (C) met ×C Dis ) I.e. correcting for weather (including humiture, wind direction, speed, etc.) Met And geographical position correction C Dis The product of (a);
5) Drawing a wind-rose plot of the pollutant concentration with the center of the detection area as an origin by using the historical monitoring data and the meteorological condition data of the pollutant, calculating the ratio of the hourly average concentration of the pollutant at the monitoring point position to the annual average concentration of the pollutant at the monitoring point position under different wind directions and wind speeds, and establishing a database;
the real-time wind power data of the monitoring points are brought into a database, the ratio of the real-time wind power data under the condition of the closest real-time wind power data is selected and recorded as C Dir ；
The bit value (C) Met ×C Dis ) The C is Dir Said C Hour Substituting the formula (1), and calculating to obtain the real-time pollutant emission E of the emission source;
Figure BDA0002793476930000031
wherein E is the real-time pollutant emission amount of the emission source and the unit is g/km 2 /h；
E 0 The unit of the historical emission intensity of the pollutants of the emission source is g/km 2 /h；
BG is the background concentration value of the contaminant, μ g/m 3 ；
RHC m A concentration measurement scale for the concentration of said monitoring point measured in historical hours, μ g/m 3 ；
RHC s For a real-time densitometric scale of the position of the monitoring point, μ g/m 3 。
In one embodiment, the detection region in step 1) is a square or circle centred on the position of the centre of mass of the emission source position or positions;
in one embodiment, the square has a side length of 10km to 20km, and the circle has a radius of 10km to 20km;
in one embodiment, the emissions source is a low-profile emissions source.
In one embodiment, the source of the historical pollutant monitoring data is any one or more of hour monitoring data of pollutants publicly released by a national control point or a municipal control point, data provided by a supplier of a sensor monitoring micro-station or data obtained by self-building a detection network.
In one embodiment, the weather condition data is any one or more of data provided by an international weather monitoring station site, data publicly released by the U.S. national oceanic and atmospheric administration, or weather condition data collected by a detection micro-station;
in one embodiment, the historical emission data is selected from any one or more of national emission inventory public data, enterprise emission survey or emission license data, specific enterprise online emission monitoring (CEMS) data, and data derived from emission parameters;
in one embodiment, the discharge port parameters include any one or more of chimney height, discharge aperture, flue gas flow rate, and chimney floor angle.
In one embodiment, in step 3), the hourly emission variation law for the emission source is derived using the following data: any one or more of national emissions manifest published data, online emissions monitoring data, the emissions source research results, or industry average levels.
In one embodiment, in step 4), the gaussian diffusion plume in the detection region is determined as follows:
acquiring a Gaussian diffusion smoke plume pattern of a pollution source discharged in the detection area under typical meteorological conditions of 3-5 months in spring, 6-8 months in summer, 9-11 months in autumn and 12-2 months in winter to obtain a diffusion smoke plume pattern;
and the value of each bit value in the diffusion plume map is the ratio of the pollutant concentration at the position to the pollutant concentration at the position of the pollution source.
In one embodiment, obtaining the bit value of the location of the monitoring point further comprises the steps of:
a) Creating a receptor dot matrix with the resolution of 50m to 200m in the diffusion plume map, and extracting the diffusion plume value of each receptor dot position; the Gaussian diffusion smoke plume mode is to input the historical meteorological data and the historical emission data into AERMOD to carry out small-scale forward air quality simulation to obtain the Gaussian diffusion smoke plume of the pollutants under the typical meteorological conditions of the four seasons; and matching the obtained Gaussian diffusion plume with a Geographic Information System (GIS) technology to extract the diffusion condition of each receptor point in a receptor point network in the detection area.
Optionally, the resolution of the receptor point grid is determined according to research requirements, and finally, according to the extraction result of the receptor point grid, the emission of pollution sources in a target area (city, province, etc.) and the concentration relation characteristics of the receptor points in different geographic positions (relative direction, distance, etc.) are determined.
b) Setting 8 directions by taking the center of a Gaussian plume diagram as an origin, taking the range from-22.5 degrees to plus-22.5 degrees as a north direction and taking every 45 degrees as one direction, and calculating the average value of bit values of all receptor points of a fan ring where the monitoring point is located, namely the bit value of the monitoring point.
In one embodiment, the radius of the sector ring (difference between the radius of the large sector and the radius of the small sector) is 50m to 200m.
In one embodiment, the contaminant is selected from CO, SO 2 、NO 2 、PM 10 、PM 2.5 、NH 3 PN (Particle Number) and CH 4 Any one or more of.
The specific pollution source identification and emission intensity real-time quantification tool provided by the application excavates the application value of air quality concentration monitoring data from a deeper stratum, realizes the possibility of real-time quantification from concentration to emission intensity, provides a reliable tool for compiling a real-time pollutant emission list, and also provides a calculation framework for real-time quantification of different cities.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the present application. Other advantages of the present application can be realized and attained by the invention in the aspects illustrated in the description.
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The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
FIG. 1 is a technical flowchart of a construction scheme of a low-profile source identification and emission intensity real-time quantification model according to the present application;
FIG. 2 is a graph showing contaminant concentrations in an example of the present application;
FIG. 3 is a schematic view of winter discharge time correction in an embodiment of the present application;
FIG. 4 is a schematic representation of an example of an embodiment of the present application;
FIG. 5 is a Gaussian diffusion plume of pollutants in an embodiment of the present application;
FIG. 6 is a wind rose of an embodiment of the present application;
FIG. 7 shows NO calculated in examples of the present application 2 Real-time emission data;
FIG. 8 is a data comparison diagram of the calculation results and field research data in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application are described in detail below. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Example 1
The method of the application is applied to estimating the real-time emission of the key industrial park in the 12-month heavy pollution process in 2019 by the aid of the chenchen platform city in the north river, taking a certain glass enterprise park of the sand river as an example:
historical mean and background concentration calculations:
1) Determining a detection area to acquire pollutant historical monitoring data of the detection area:
and calculating monitoring data based on 3 air micro-stations distributed around the park, wherein the data comprises 2018 annual monitoring data and 2019 monthly 12 real-time monitoring data. The geographical information of the 3 monitoring sites is as follows, and the location schematic is shown in fig. 4 and table 1. The embodiment quantifies the emission intensity of the pollution sources in the area in real time by using the real-time data obtained by the monitoring point 1.
Table 1: location of the monitoring point relative to the contamination source
Monitoring point Relative pollution source position Distance interval
1 West of China 1000m to 1200m
2 South China 800m to 1000m
3 Southeast China 600m to 800m
Acquiring monitoring data of monitoring points 1 in 2018 all the year round to determine annual average concentration and historical hour monitoring concentration of pollutants at the positions of the monitoring points 1; and calculating the concentration according to the historical hourly detection concentration of the monitoring point 1 to obtain a concentration measurement scale RHC m ；
And analyzing the whole city air quality data by combining the international meteorological data of the chenchenchen table city (a site 537980), and determining the pollutant background concentration value BG of the detection region 2018 in the same period.
SO of 2019 2 、NO 2 、PM 10 And PM 2.5 Respectively at a background concentration of 15. Mu.g/m 3 ，31μg/m 3 ，58μg/m 3 And 26. Mu.g/m 3 . This example uses NO 2 For example, BG =31 μ g/m 3 。
1.1 Obtaining historical emissions and time distribution correction parameters
The annual average emission of the pollutants in a park (the detection area) is determined by national emission lists (such as MEIC, ECLIPSE, GAINS and the like) and enterprise research data. According to statistics, NO of the region 2 、SO 2 、PM 10 And PM 2.5 The annual average discharge of (c) is 3300, 2600, 1760 and 1150 tons/year respectively. And further acquiring the daily average emission amount of the pollutants of the emission source through the hourly emission change rules of the pollutants in the collection park range or related enterprises, such as national emission lists, on-line emission monitoring (CEMS) and enterprise research results.
Calculating the ratio of the real-time pollutant hourly discharge amount obtained by the monitoring point 1 to the pollutant daily average discharge amount of the discharge source, as shown in fig. 3, that is, the ratio is the time correction C in the formula (1) Hour 。
1.2 ) obtain geographic correction parameters
Firstly, a gaussian diffusion plume (as shown in fig. 5) in a region 6km × 6km (as shown in fig. 4) where a surface source is located is calculated by using an aeronet (open source software) meteorological processing module aeronet, and a diffusion plume pattern of a pollution surface source emission in a research region under typical meteorological conditions of four seasons is obtained. And the value of each bit value in the diffusion plume graph is the ratio of the pollutant concentration at the position to the pollutant concentration at the position of the pollution source.
Determining concentration diffusion correction under typical meteorological conditions and geographic positions according to the concentration diffusion smoke plume in the detection range, and the specific process is as follows: a) A receptor point matrix with the resolution of 200m as in fig. 4 is created, and the diffusion plume value of each receptor point position is extracted. b) The average of all the values of the receptor points in each orientation, as shown in the shaded area of fig. 5, every 200m (for example, a sector area with a radius of 200m, a sector-shaped area with a radius of 200-400m, a sector-shaped area with a radius of 400-600m, etc.) is calculated from eight orientations (-north orientation at-22.5 ° to +22.5 °, 1 orientation every 45 °). The average value of the receptor sites in the area of the monitoring point 1 is recorded as the bit value.
1.3 Obtaining real-time wind correction parameters
A pollutant concentration wind rose plot (shown in figure 6) is drawn by taking the center of a detection area as an origin, the ratio of the hour average concentration to the year average concentration of pollutants at the monitoring point 1 in a specific wind speed interval (0-2 m/s,2-4m/s,4-8m/s, more than 8 m/s) and wind direction (8 directions, north direction is-22.5 degrees) mode is calculated, and a database is established;
the real-time wind power data of the monitoring point 1 is brought into a database, and the ratio under the condition that the real-time wind power data are the closest is selected as C Dir 。
And if the monitoring point is located in a certain area, the average value of the area is the comprehensive correction of the meteorological (including average temperature, humidity, wind direction and wind speed) and geographic position of the detected concentration of the monitoring point. For example, the monitoring point 1 is positioned on the west side and is 1000-1200m away from the emission source, the time is between 00 and 23 days in 12 and 23 months in 2019, the wind speed is 2m/s, and NO is calculated by the formula 2 History RHC of m The concentration was 22.9. Mu.g/m 3 1NO, monitoring point 2 Real-time RHC of s The concentration was 58. Mu.g/m 3 (ii) a And E is 0 ＝1800μg/m 2 /h、BG＝26μg/m 3 、C met ×C Dis ＝2.38、C Dir ＝0.8、C Hour =0.92 band inIn the formula, real-time emission E =4406 μ g/m is obtained 2 /h。
Figure BDA0002793476930000081
Comparative example: emission quantification and validation
Based on the calculated real-time emissions of example 1, NO calculated for a specific time period was selected 2 The results are shown in FIG. 7.
In order to verify the accuracy of the method, the contemporaneous surface source emission is obtained through field research and compared with a calculation simulation result. In the embodiment 1, the emission sources in the detection area all issue partial CEMS monitoring data, and relevant parameters of the discharge outlet (chimney) can be acquired through a satellite map, so that the real-time emission of the detection area in the embodiment 1 is estimated.
The comparison result is shown in fig. 8, the method for real-time quantitative detection of emission intensity provided by the application has a high real emission reduction degree, and can replace an emission acquisition means with low efficiency and high cost to carry out emission quantification.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.

Claims (13)

1. A method for real-time quantitative detection of emission intensity comprises the following steps:
1) Determining a detection area and a position of an emission source and a monitoring point in the detection area, and acquiring historical pollutant monitoring data, meteorological condition data and historical emission data of the detection area;
determining the detection area through the historical monitoring data of the pollutants and the meteorological condition dataThe background concentration value BG of the pollutant, the annual average concentration of the pollutant at the monitoring point, the daily average emission amount of the pollutant of the emission source, the historical hour detection concentration of the monitoring point and the historical emission intensity E of the pollutant of the emission source 0 ；
Calculating to obtain a concentration measurement scale RHC through the historical hourly detection concentration of the monitoring points m (ii) a Collecting real-time pollutant concentration data of the position of the monitoring point, and calculating to obtain a real-time concentration measurement scale RHC of the monitoring point s ；
2) Calculating the ratio of the real-time hourly discharge amount of the real-time pollutants obtained by the monitoring points to the daily average discharge amount of the pollutants of the discharge source, and recording the ratio as C Hour ；
3) Determining the Gaussian diffusion plume in the detection area, acquiring the ratio of the pollutant concentration of the monitoring point in the Gaussian diffusion plume to the pollutant concentration of the position of the emission source, and recording the ratio as a bit value (C) met ×C Dis )；
4) Drawing a wind-rose chart of the pollutant concentration by using the historical monitoring data and the meteorological condition data of the pollutant and taking the center of the detection area as an origin, calculating the ratio of the hourly average concentration of the pollutant at the monitoring point position to the annual average concentration of the pollutant at the monitoring point position under different wind directions and wind speeds, and establishing a database;
the real-time wind power data of the monitoring points are brought into a database, the ratio of the real-time wind power data under the condition of the closest real-time wind power data is selected and recorded as C Dir ；
The bit value (C) Met ×C Dis ) Said C Dir The C is Hour Substituting the formula (1), and calculating to obtain the real-time pollutant emission E of the emission source;
Figure FDA0003843185580000011
wherein E is the real-time pollutant emission amount of the emission source and the unit is g/km 2 /h；
E 0 The unit of the historical emission intensity of pollutants of an emission source is g/km 2 /h；
BG is the background concentration value of the contaminant, μ g/m 3 ；
RHC m A concentration measurement scale of the concentration of the monitoring point in the historical hour 3 ；
RHC s For a real-time densitometric scale of the position of the monitoring point, μ g/m 3 。
2. A method of real-time quantitative detection of emission intensity as claimed in claim 1, wherein the detection region in step 1) is a square or circle centered at the location of the centroid of the emission source location or locations.
3. The method for real-time quantitative detection of emission intensity according to claim 2, wherein the sides of the square are 10km to 20km, and the radius of the circle is 10km to 20km.
4. The method for real-time quantitative detection of emission intensity of claim 2, wherein the emission source is a low-profile emission source.
5. The method for real-time quantitative detection of emission intensity according to claim 1, wherein in step 2), the hourly emission variation law of the emission source is derived by using the following data: any one or more of national emissions manifest published data, online emissions monitoring data, the emissions source research results, or industry average levels.
6. The method for real-time quantitative detection of emission intensity according to claim 1, wherein in step 3), the gaussian diffusion plume in the detection region is determined to be:
acquiring a Gaussian diffusion smoke plume pattern of pollution sources discharged in the detection area under typical meteorological conditions of 3-5 months in spring, 6-8 months in summer, 9-11 months in autumn and 12-2 months in winter to obtain a diffusion smoke plume pattern;
and the value of each bit value in the diffusion plume map is the ratio of the pollutant concentration at the position to the pollutant concentration at the position of the pollution source.
7. The method for real-time quantitative detection of emission intensity of claim 6, wherein obtaining the bit value for the monitoring point location further comprises the steps of:
a) Creating a receptor dot matrix with the resolution of 50m to 200m in the diffusion plume map, and extracting the diffusion plume value of each receptor dot position;
b) Setting 8 directions by taking the center of a Gaussian plume diagram as an origin, taking the range from-22.5 degrees to plus-22.5 degrees as a north direction and taking every 45 degrees as one direction, and calculating the average value of bit values of all receptor points of a fan ring where the monitoring point is located, namely the bit value of the monitoring point.
8. The method for real-time quantitative detection of emission intensity according to claim 7, wherein the radius of the fan ring is 50m to 200m.
9. The method for real-time quantitative detection of emission intensity according to any one of claims 1 to 8, wherein the source of the historical monitoring data of pollutants is any one or more of hour monitoring data of pollutants publicly released by national control points or municipal control points, data provided by suppliers of sensor monitoring micro-stations or data obtained by self-building detection networks.
10. The method for real-time quantitative detection of emission intensity of any one of claims 1 to 8, wherein the meteorological condition data is any one or more of data provided by an international meteorological monitoring station site, data publicly released by the U.S. national oceanic and atmospheric administration, or meteorological condition data collected by a detection micro-station.
11. A method of real-time quantitative detection of emission intensity as claimed in any one of claims 1 to 8, wherein the historical emission data is selected from any one or more of national emission checklist published data, enterprise emission survey or emission permit data, enterprise-specific online emission monitoring (CEMS) data, and data derived from emission parameters.
12. The method for real-time quantitative detection of emission intensity according to claim 11, wherein the discharge port parameters include any one or more of chimney height, discharge caliber, flue gas flow rate and chimney floor angle.
13. The method for real-time quantitative detection of emission intensity according to any one of claims 1 to 8, wherein the pollutant is selected from CO, SO 2 、NO 2 、PM 10 、PM 2.5 、NH 3 PN and CH 4 Any one or more of.
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