A large-scale event greenhouse gas emission tracking system

Abstract

The present invention relates to the field of greenhouse gas tracking, and particularly to a greenhouse gas emission tracking system for large-scale events. The present invention includes: a route planning module: determining the spatial representativeness of on-vehicle observations and designing and optimizing the route layout accordingly; an on-vehicle observation module: used for performing mobile greenhouse gas emission monitoring along the detection route; a data processing module: used for processing the observed data, including correcting time lag, calculating enhancement values, and eliminating data with too slow vehicle speeds; an emission tracking module: comparing the changes in greenhouse concentration enhancement values and emission ratios before and after emission reduction and prevention and control. By adopting the technical solution of the present invention, it is used to track the impact of greenhouse gas emissions generated by control plans during large-scale events.

Description

Technical Field

[0001] The present invention relates to the field of greenhouse gas tracking, and particularly to a greenhouse gas emission tracking system for large-scale events. Background Art

[0002] As a hot spot area for greenhouse gas emissions, cities account for more than 70% of global anthropogenic carbon emissions. Currently, the main methods for quantifying urban carbon emissions are the IPCC inventory method and the atmospheric inversion method. Due to the spatial heterogeneity of cities and the complex source-sink distribution, both methods have certain deficiencies in monitoring urban carbon emissions, making it difficult to accurately identify and locate emission sources, and they are not suitable for real-time tracking and monitoring of emission sources.

[0003] In contrast, vehicle-mounted mobile measurement systems have a certain degree of flexibility. By conducting mobile observations to obtain greenhouse gas concentrations at different locations, the positions where the observed greenhouse gas concentrations or the enhancement values relative to the background concentration are continuously higher than those in the surrounding areas are defined as emission sources, providing the possibility for tracking greenhouse gas emissions. However, the biggest drawback of mobile observations is that the signal strength is related to the distance from the emission source. The farther the distance and the lower the concentration, the more difficult it is to identify and locate the emission source. When the coverage rate of the mobile observation grid is not high, it is very easy to miss unknown emission sources. Therefore, the planning of the mobile observation route and the enhancement of the emission source signal are the key technical problems to be solved by the present invention. Summary of the Invention

[0004] To solve the above problems, the present invention provides a greenhouse gas emission tracking system for large-scale events, which is used to track the changes in greenhouse gas emissions generated by emission control during large-scale events.

[0005] To achieve the above object, the technical solution of the present invention is as follows: A greenhouse gas emission tracking system for large-scale events, comprising:

[0006] A route planning module: used to determine the spatial representativeness of vehicle-mounted observations, design and optimize the route layout to obtain a detection route;

[0007] A vehicle-mounted observation module: used to conduct mobile greenhouse gas emission monitoring along the detection route;

[0008] A data processing module: used to process the monitoring data, including correcting time lag, calculating enhancement values, and eliminating data with too slow vehicle speeds;

[0009] An emission tracking module: used to compare the changes in greenhouse concentration enhancement values and emission ratios before and after emission reduction and prevention and control.

[0010] Adopting the above solution has the following beneficial effects:

[0011] In this solution, the gas concentrations monitored by the vehicle-mounted observation module are spatially averaged over 50 m. Each spatial average value is taken as a sample. With the center point of the sample as the origin, a buffer radius is taken every 50 m to form several concentric circles. The correlation coefficient of the gas concentration and the impervious surface ratio of all samples is calculated within the same concentric circle range. When the correlation coefficient no longer increases with the increase of the buffer radius, or the buffer radius corresponding to the maximum value of the correlation coefficient is taken as the spatial representative range of vehicle-mounted observation.

[0012] Beneficial effects: The buffer radius can quantify the source-sink range that causes changes in gas concentration in vehicle-mounted mobile observations. Designing and optimizing the observation route based on this can ensure the spatial coverage of the observation results, improve the efficiency of the observation, and the representativeness of the data.

[0013] In this solution, before and after emission reduction and prevention and control, the vehicle-mounted mobile module moves along the optimized detection route to monitor and evaluate the impact of emission reduction measures on greenhouse gas emissions. For the regional greenhouse gas concentration enhancement value is averaged, and the regional average enhancement values before and after emission reduction and prevention and control are compared to evaluate the implementation effect of the emission reduction measures. When decreases, it indicates that the emission reduction measures are effective and the regional emissions have weakened; when changes little or increases, it indicates that the emission reduction effect is not ideal.

[0014] Furthermore, the vehicle-mounted observation module includes a vehicle body, and a greenhouse gas analyzer is provided on the vehicle body. The air inlet of the greenhouse gas analyzer extends out from the top of the vehicle body.

[0015] Beneficial effects: The vehicle body can provide a carrier for traveling along the city. The greenhouse gas analyzer can perform gas analysis during the movement of the vehicle body. The height of the air inlet at a relatively high position from the ground can reduce the influence of floating dust or debris on the ground on the detection.

[0016] Furthermore, the GPS locator is used to obtain time, draw the observation route, and real-time vehicle speed information.

[0017] Beneficial effects: The GPS locator can obtain the real-time vehicle position, so that the detected greenhouse gas emission data can be bound to the position coordinates, and thus the detected greenhouse gas emission data can be mapped onto the observation route drawn on the map.

[0018] Furthermore, based on the GPS time, the lag time of gas sampling is eliminated according to the intake air flow rate of the greenhouse gas analyzer, the length and inner diameter of the sampling tube.

[0019] Beneficial effects: When gas enters from the air inlet, it takes time to reach the monitoring section of the greenhouse gas analyzer. This time consumption is related to the inlet gas flow rate and the length of the sampling tube. At the same time, monitoring requires a response time. Therefore, by obtaining the inlet gas flow rate of the greenhouse gas analyzer, the length and inner diameter of the sampling tube of the greenhouse gas analyzer to correct the lag time data of sampling, the true coordinates of the monitored data on the map can be restored, improving the monitoring accuracy.

[0020] Furthermore, the vehicle-mounted observation module is used to eliminate the monitoring data when the vehicle speed ≤ 5 m / s.

[0021] Beneficial effects: When processing data, the monitoring data when the vehicle speed ≤ 5 m / s is eliminated to reduce the influence of its own emissions on the results.

[0022] Furthermore, the difference between the vehicle-mounted mobile observation value and the background concentration is used as the enhancement value to track greenhouse gas emissions. If there is a fixed-point observation upwind in the study area, the concentration value of this observation is used as the background value of the vehicle-mounted mobile observation; otherwise, the second percentile of the vehicle-mounted mobile observation is used as the background value.

[0023] Beneficial effects: The observed concentration value not only reflects regional emissions but also the contribution of the background concentration, that is, the concentration observation value = background concentration + local enhancement. When conditions such as wind speed, wind direction, and atmospheric stability change, the background concentration will also change accordingly. Therefore, when making comparisons, the background data on the detection day is used to calculate the local enhancement value to eliminate the influence of the background concentration, making the reliability of the emission tracking results higher.

[0024] Furthermore, orthogonal regression is performed on the concentration enhancement values of two gases in a certain area, and the regression slope is regarded as the emission ratio of the emissions in this area; comparing the measured emission ratio with the emission ratio of known emission sources can determine the type of this emission source.

[0025] Beneficial effects: Different from natural ecosystems, urban atmospheres contain unique "chemical fingerprints", that is, different emission sources have special component structures. For example, the amount of CO emitted by vegetation and soil is extremely small, while fuel combustion releases a large amount of CO. In terms of fuel source types, the C isotope content in the CO 2 released by natural gas combustion (for household use) 13 is lower than that of coal combustion (in power plants) and fuel oil (in vehicles). The relative concentrations of CH 4 and N 2 O emitted by residential sources (for cooking and sewage) are higher than those of industrial emissions. Therefore, the change in the component structure of emission sources (i.e., the emission ratio) provides a new perspective for measuring emission reduction measures. Synchronously observing multiple greenhouse gases and chemical tracers and calculating the regression slope (emission ratio) of two different components can distinguish the type of this emission source.

[0026] Furthermore, for the monitoring of local emission sources, a short time window is selected to capture the instantaneous changes in gas concentrations. For the orthogonal regression of the concentrations of two gases within ±5 s is performed; the correlation coefficient R value of the orthogonal regression is calculated. When the R value exceeds 0.5, it is regarded as the signal of a certain emission source, and the regression slope is the emission ratio of this emission source; otherwise, it is the result of the combined action of multiple emission sources.

[0027] Advantageous effects: In addition, since the gases from the same emission source have experienced the same atmospheric transmission and diffusion processes, although the gas concentrations show high-frequency fluctuations due to the influence of turbulent motion, the changes in the concentrations of different gases are correlated. That is to say, the concentration ratios of these gases retain the information of the emission source and are relatively stable. Therefore, the regression slope of the two gases can be regarded as the emission ratio of this emission source. Compared with the concentration or the enhanced value of the concentration, the emission ratio does not decrease with the increase in the distance from the emission source, and can effectively enhance the signal of the emission source.

[0028] The additional aspects and advantages of the present invention will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present invention. Description of the Drawings

[0029] Figure 1 It is a schematic diagram of the route distribution of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0030] Figure 2 It is a schematic diagram of the CO 2 concentration at the background station of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0031] Figure 3 It is a schematic diagram of the CH 4 concentration at the background station of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0032] Figure 4 It is a schematic diagram of the △CO 2 concentration during vehicle traversing of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0033] Figure 5 It is a schematic diagram of the △CO 2 time period during road vehicle traversing of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0034] Figure 6 It is a schematic diagram of the △CH 4 concentration during vehicle traversing of an embodiment of the greenhouse gas emission tracking system for large-scale events of the present invention;

[0035] Figure 7 It is a schematic diagram of the road vehicle traversing △CH 4Schematic diagram of time period;

[0036] Figure 8 Schematic diagram of the vehicle-mounted observation module of the large-scale event greenhouse gas emission tracking system according to an embodiment of the present invention;

[0037] Figure 9 For vehicle-mounted mobile observation of CO 2 Concentration, CH 4 Orthogonal regression of concentration and its enhancement value;

[0038] Figure 10 For ΔCO at different buffer radii 2 And the correlation coefficient of the impervious surface ratio;

[0039] Figure 11 For CH on January 22, 2022 in the West Lake Tunnel of this city 4 And CO 2 Observation results and CH 4 :CO 2 Emission ratio;

[0040] Figure 12 For CH of 296 observations in multiple tunnels of this city 4 :CO 2 Distribution map of emission ratio.

[0041] The reference numerals in the accompanying drawings of the specification include: 1, funnel; 2, bracket; 3, Teflon tube. Detailed implementation manners

[0042] Next, the technical solutions of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0044] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0045] The following is a further detailed description through specific embodiments:

[0046] As shown in the Figures 1 - 12 appendix: A large-scale event greenhouse gas emission tracking system includes:

[0047] A route planning module: used to determine the spatial representativeness of vehicle-mounted observations, design and optimize the route layout to obtain the detection route;

[0048] A vehicle-mounted observation module: used to conduct mobile greenhouse gas emission monitoring along the detection route;

[0049] A data processing module: used to process the monitoring data, including the correction of time lag, the calculation of enhancement values, and the elimination of data with too slow vehicle speeds;

[0050] An emission tracking module: used to compare the changes in greenhouse concentration enhancement values and emission ratios before and after emission reduction and prevention and control.

[0051] The route planning module uses the buffer radius to determine the spatial representativeness of vehicle-mounted observations. By performing a 50m spatial average on the gas concentrations monitored by the vehicle-mounted observation module, each spatial average value is a sample. Taking the center point of the sample as the origin, a buffer radius is taken every 50m to form several concentric circles. Calculate the correlation coefficient between the gas concentration and the impervious surface ratio of all samples within the same concentric circle range. When the correlation coefficient no longer increases with the increase of the buffer radius, or the buffer radius corresponding to the maximum value of the correlation coefficient, is used as the spatial representative range of vehicle-mounted observations, and the route is designed and optimized accordingly.

[0052] The vehicle-mounted observation module includes a vehicle body, on which a greenhouse gas analyzer is provided. The intake port height of the greenhouse gas analyzer is 2.5m above the ground. The intake port of the greenhouse gas analyzer is provided with an inverted funnel 1 and sealed with gauze. The intake port is installed on the top of the vehicle body through a bracket 2 and extends into the vehicle body through a Teflon tube 3 to communicate with the monitoring part of the greenhouse gas analyzer. A GPS locator and a driving recorder are provided on the vehicle body. The GPS locator is used to draw the observation route; the driving recorder is used to record the road and traffic flow conditions.

[0053] The data processing module is used to correct the time lag of the original observed data, eliminate error data and calculate the enhancement value. Taking GPS time as the standard, the sampling lag time is corrected according to the intake flow rate of the greenhouse gas analyzer, the length and inner diameter of the sampling tube. The real-time vehicle speed of the vehicle body is obtained by using a GPS locator, and the monitoring data when the vehicle speed ≤ 5 m / s is eliminated. The enhanced value is obtained by subtracting the background concentration from the on-vehicle mobile observation value. The background concentration in this case is collected at the city's reference climate station.

[0054] Emission tracking module: Attribution of greenhouse gas emission events is carried out using emissions. For two gases in a certain area, orthogonal regression is performed, and the regression slope is regarded as the emission ratio of the emissions in this area; the measured emission ratio is compared with the emission ratio of known emission sources to determine the type of emission source. For the monitoring of local emission sources, a short time window is selected to capture the instantaneous changes in gas concentration. For the orthogonal regression of the concentrations of two gases within ±5 s, the correlation coefficient R value of the orthogonal regression is calculated. When the R value exceeds 0.5, it is regarded as the signal of a certain emission source, and the regression slope is the emission ratio of this emission source; otherwise, it is the result of the combined action of multiple emission sources.

[0055] The specific implementation process is as follows:

[0056] The large-scale event is a large-scale sports competition in a certain city. Hourly air pressure and wind speed data of the city's national reference climate station are obtained. Before and during the sports event, the air pressure decreased and then increased in the previous week, and rebounded 1-2 days before the observation, and the weather returned to sunny weather under high-pressure control before the experiment. However, on the evening of August 13 during the experimental observation before the sports event, it rained in the experimental area, and the monitoring on that day was cancelled. The hourly average wind speed during the experiment was generally higher during the day and lower at night. The air pressure after the sports event decreased slightly on the last day (November 1), but there were no obvious adverse weather phenomena on the experimental day. During this stage, the hourly average wind speed during the experiment was lower during the day and higher at night.

[0057] As Figure 1 shown, the route planning module selects a total of 4 routes, which basically cover the main urban area of the city, including various different urban functional areas and different road conditions such as bridges, viaducts, and tunnels. Route 1 mainly passes through the residential areas along the river and scenic sections, Routes 2 and 3 are mainly in the urban area, and Route 4 is the ring-road highway section, mainly surrounding the suburbs.

[0058] During the sports event, the policy of banning foreign vehicles and restricting local vehicles to odd-even license plates is adopted in the main urban area of the city. The restricted time period is from 8:00 to 20:00.

[0059] The urban background data is provided by the city's national reference climate station (station number 58457, 30.22° N; 120.17° E, altitude 43.2 m).

[0060] Three days of observations were conducted in each of the three periods before, during, and after the sports event, which were carried out in this city from August 11 - 13, 2023, September 25 - 27, and October 30 - November 1, 2023. The observation times were in the morning (10:00 to 12:00) and at night (20:00 to 22:00) before the sports event in August, and in the morning (9:00 to 11:00), afternoon (14:00 to 16:00), and at night (20:00 to 22:00) during the sports event in September and after the sports event from October to November.

[0061] During the observations in the three selected periods, CO 2 The mean background concentration (±1 standard deviation) is as follows: The average daytime CO 2 background concentration before the sports event was 435.06 ± 13.34 μmol / mol, and the average nighttime CO 2 background concentration was 450.64 ± 6.38 μmol / mol. During the sports event, the average daytime CO 2 background concentration was 450.36 ± 12.03 μmol / mol, and the average nighttime CO 2 background concentration was 451.93 ± 4.91 μmol / mol. After the sports event, the average daytime CO 2 background concentration was 445.48 ± 15.51 μmol / mol, and the average nighttime CO 2 background concentration was 473.39 ± 9.15 μmol / mol. Overall, the average nighttime CO 2 background concentration was higher than the daytime during the same period. From before the sports event to during and after the sports event, the average nighttime CO 2 background concentration showed an upward trend. The average daytime CO 2 background concentration was the highest during the sports event, slightly decreased after the sports event, and was the lowest in the period before the sports event.

[0062] The atmospheric CH background concentrations at the urban background station during each observation period before, during, and after the sports event 4 are as Figure 2 shown. During the observation period before the sports event, the average daytime atmospheric CH 4 background concentration was 2.259 ± 0.073 μmol / mol, and the average nighttime atmospheric CH 4 background concentration was 2.385 ± 0.102 μmol / mol. During the sports event, with traffic restrictions in place, the average daytime CH 4 background concentration was 2.327 ± 0.120 μmol / mol, and the average nighttime CH 4The background concentration was 2.306 ± 0.034 μmol / mol. The average daytime CH after the sports event 4 The background concentration was 2.163 ± 0.076 μmol / mol, and the average nighttime CH 4 The background concentration was 2.249 ± 0.063 μmol / mol. Before and after the sports event, the average nighttime CH 4 The background concentration was higher than the average daytime CH 4 The background concentration, the average daytime CH during the sports event 4 The background concentration was slightly higher than that at night. The average daytime CH 4 The background concentration was the highest during the sports event and the lowest after the sports event. The average nighttime CH 4 The background concentration gradually decreased from before the sports event to after the sports event.

[0063] Before, during, and after the sports event, for CO 2 The data distribution of the background concentration is as Figure 3 shown. The median values of the daytime data were 430.2 μmol / mol before the sports event, 448.0 μmol / mol during the sports event, and 440.8 μmol / mol after the sports event. The data were concentrated in the following ranges: 423.6 μmol / mol before the sports event, 448.0 μmol / mol during the sports event, and 440.7 μmol / mol after the sports event. The median values of the nighttime data were 450.2 μmol / mol before the sports event, 451.9 μmol / mol during the sports event, and 477.8 μmol / mol after the sports event. The data were concentrated in the following ranges: 450.8 μmol / mol before the sports event, 447.0 μmol / mol during the sports event, and 463.5 μmol / mol after the sports event.

[0064] The 100-m scale average ΔCO during different time periods before, during, and after the sports event 2 The spatial distribution is as Figure 4 shown, where (a) is the detection result in the morning before the sports event, (b) is the detection result at night before the sports event, (c) is the detection result in the morning during the sports event, (d) is the detection result in the afternoon during the sports event, (e) is the detection result at night during the sports event, (f) is the detection result in the morning after the sports event, (g) is the detection result in the afternoon after the sports event, and (h) is the detection result at night after the sports event. The measured ΔCO 2Segments with concentrations greater than 100 μmol / mol are mostly concentrated in the urban central area and some sections of the northern part of the ring expressway, and their occurrence frequency at night is much higher than that during the day in the same period. The segments with the most obvious changes are the ring expressway and the scenic area segments along Line 1. The high values at these two segments significantly decrease during the traffic restrictions for sports events, and then reappear in large numbers at night after the lifting of the restrictions and after the sports events. The occurrence frequency of high values during the day of the sports events is significantly lower than that before the sports events, while after the end of the night traffic restriction period, the average concentration in the urban area and the northern part of the ring expressway increases significantly.

[0065] Analyze and statistically process the 1 Hz road mobile monitoring data after removing the tunnel sections, and take ΔCO above 100 μmol / mol 2 as extremely high values, and count the proportion of the occurrence of extremely high values. During the day, the occurrence frequency of the ΔCO 2 extremely high value before the sports events is 71.0 ‰; during the sports events is 27.6 ‰; after the sports events is 89.3 ‰. At night, the occurrence frequency of the ΔCO 2 extremely high value before the sports events is 70.1 ‰; during the sports events is 85.0 ‰; after the sports events is 101.9 ‰.

[0066] Analyze the occurrence rates of the high values of ΔCO 2 for different routes respectively. The results show that the occurrence frequencies of the high values of the routes passing through different regions show different changing trends. During the sports events, the occurrence frequencies of the high values in the morning and at night of Line 1 both decrease. However, the occurrence frequencies of the high values of Line 2 and Line 3 only decrease during the morning traffic restriction period. After the lifting of the restriction at night, the occurrence rates of the high values of ΔCO 2 for these two lines both increase significantly compared with before the sports events. The occurrence rate of the high values of ΔCO 2 for Line 4 shows a significant increase after the sports events, and the changes during and before the sports events are far less than the increase amplitude after the sports events.

[0067] Figure 5 For different stages before, during, and after the sports events, the data distribution of ΔCO 2 is as follows. The median values of the data during the day are 34.36 μmol / mol before the sports events, 25.94 μmol / mol during the sports events, and 42.56 μmol / mol after the sports events. The average value of the daytime enhancement of CO 2 decreases from before the sports events to during the sports events, and then increases after the sports events. Its average value at night is the highest during the sports events and then decreases after the sports events. During the three-day observations before, during, and after the sports events, the average enhanced concentration of CO 2 (± 1 standard deviation) is as follows: the daytime ΔCO 2was 42.43 ± 37.81 μmol / mol, and the nocturnal ΔCO 2 was 44.58 ± 37.19 μmol / mol. During the sports event, with traffic restrictions in place, the diurnal ΔCO 2 was 32.15 ± 30.23 μmol / mol, and the nocturnal ΔCO 2 was 48.44 ± 37.77 μmol / mol. After the sports event, the diurnal ΔCO 2 was 51.56 ± 49.27 μmol / mol, and the nocturnal ΔCO 2 was 52.44 ± 40.98 μmol / mol. The mean change trend of ΔCO 2 was that overall, it was less during the day than at night. During the day, it was sports event period < before the sports event < after the sports event, and at night, it was before the sports event < during the sports event < after the sports event.

[0068] Figure 6 For the ΔCH during different time periods of before, during, and after the sports event for the 100m average 4 driving survey map. Among them, (a) shows the detection results in the morning before the sports event, (b) shows the detection results at night before the sports event, (c) shows the detection results in the morning during the sports event, (d) shows the detection results in the afternoon during the sports event, (e) shows the detection results at night during the sports event, (f) shows the detection results in the morning after the sports event, (g) shows the detection results in the afternoon after the sports event, and (h) shows the detection results at night after the sports event. In the figure, the measured ΔCH 4 spatiotemporal distribution basic law was similar to that of ΔCO 2 Similar. The nocturnal mean value was higher than the diurnal one. During the sports event, especially during the day, the sections with high values decreased significantly compared to before and after the sports event, and the high-value distribution was mainly in the urban area. After the sports event, the sections with relatively high ΔCH 4 were significantly more than before the sports event. The sections with high values above 1 μmol / mol were basically concentrated near landfills and urban intersections, and the distribution was more concentrated and regular compared to the high-value area of ΔCO 2 During the sports event, the mean value of ΔCH 4 in the northern section of the ring expressway at night was higher than that before the sports event, and there were more high-value hotspots.

[0069] Analyze and statistically process the 1 Hz road driving survey data of the road without tunnels. Taking ΔCH 4 above 1 μmol / mol as extremely high values, the proportion of extremely high values was statistically analyzed, and the results are as follows. During the day, the frequency of extremely high values of ΔCH 4 was 1.2 ‰ before the sports event; 1.0 ‰ during the sports event; 0.9 ‰ after the sports event. At night, the ΔCH 4The extremely high value appears with a frequency of 8.0 ‰ before the sports event; 8.0 ‰ during the sports event; and 8.5 ‰ after the sports event.

[0070] From the high-value frequency statistics of the sub-lines, Line 1 shows a gradually increasing trend in the high-value appearance frequency from before the sports event to after the sports event, and the frequency increase at night after the sports event is nearly four times. In contrast, for Line 2, the high-value occurrence rate gradually decreases from before the sports event to after the sports event. No high value was detected in the morning period after the sports event, and the decline from during the sports event at night to after the sports event exceeds half. For Line 3 in the same urban area, the highest high-value appearance frequency in the morning is during the sports event, while the high-value appearance frequency at night after the sports event nearly doubles. For Line 4, almost no high value appears in the morning period, and the period with the most high values at night is during the sports event.

[0071] CH 4 The enhanced concentration mean (±1 standard deviation) is as follows: ΔCH during the day before the sports event 4 is -0.033 ± 0.139 μmol / mol, and ΔCH at night 4 is 0.013 ± 0.217 μmol / mol. During the sports event, with traffic restrictions in place, ΔCH during the day 4 is -0.048 ± 0.146 μmol / mol, and ΔCH at night 4 is 0.015 ± 0.235 μmol / mol. After the sports event, ΔCH during the day 4 is -0.007 ± 0.125 μmol / mol, and ΔCH4 at night is 0.099 ± 0.293 μmol / mol. The mean change trend of ΔCH 4 is similar to that of ΔCO 2 During the day, the overall value is less than at night, and during the day, it is during the sports event < before the sports event < after the sports event, while at night, it is before the sports event < during the sports event < after the sports event.

[0072] Figure 7 For different stages before, during, and after the sports event, ΔCH 4Data distribution. The median values of the data during the day were -0.037 μmol / mol before the sports event, -0.051 μmol / mol during the sports event, and -0.011 μmol / mol after the sports event. The data were relatively concentrated in the following ranges: -0.031 μmol / mol before the sports event, -0.038 μmol / mol during the sports event, and 0.038 μmol / mol after the sports event. The median values of the data at night were -0.012 μmol / mol before the sports event, -0.019 μmol / mol during the sports event, and 0.038 μmol / mol after the sports event.

[0073] When the mobile monitoring vehicle travels within the plume range of a certain emission source, the readings of the greenhouse gas analyzer will increase due to this emission source, and each observed peak is caused by one or more emission sources. For CO within ±5 s 2 and CH 4 orthogonal regression was performed. As Figure 9 shown, the emission ratios with R > 0.5 were screened out, and the median value was 0.00167. Since tunnel observations showed ( Figure 11 and Figure 12 ), the CH 4 :CO 2 emission ratio of traffic emissions in this city was 0.0005, which was much smaller than the emission ratio observed on the street. This means that there are stronger CH 4 emission sources on this street.

[0074] Figure 10 are the correlation coefficients between ΔCO 2 and the impervious surface ratio at different buffer radii on the elevated roads and streets in this city, where (a) is the elevated road and (b) is the street. The results show that when the buffer radius is greater than 1.5 km, the change in the correlation coefficient (r) between ΔCO 2 and the impervious surface ratio on the elevated road gradually slows down, while on the street this distance is about 1 km. Therefore, the spatial representative range of on-vehicle observations on the elevated road is defined as 1.5 km, and the spatial representative range of street observations is defined as 1 km.

[0075] Obviously, the above embodiments are only examples for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of this invention.

Claims

1. A greenhouse gas emission tracking system for large-scale activities, characterized in that: include: Route planning module: used to determine the spatial representativeness of vehicle-mounted observations, design and optimize the route layout to obtain the inspection route; Vehicle-mounted observation module: used for mobile greenhouse gas emission monitoring along the detection route; The gas concentration monitored by the vehicle-mounted observation module is spatially averaged over 50 m. Each spatial average value is a sample. The center point of the sample is taken as the origin. A buffer radius is taken every 50 m to form several concentric circles. The correlation coefficient between the gas concentration and the impervious surface ratio of all samples is calculated within the same concentric circle range. The buffer radius corresponding to the time when the correlation coefficient no longer increases with the increase of the buffer radius or the maximum value of the correlation coefficient is taken as the spatial representative range of the vehicle-mounted observation; In the route planning module, the planned route is optimized based on the buffer radius; Data processing module: used for processing monitoring data, including time lag correction, enhancement value calculation and elimination of data with too slow speed; Emission tracking module: used to compare the changes in greenhouse concentration enhancement values ​​and emission ratios before and after emission reduction and prevention.

2. The greenhouse gas emission tracking system for large-scale activities according to claim 1, characterized in that: The vehicle-mounted observation module includes a vehicle body, on which are installed a greenhouse gas analyzer, a GPS locator and a driving recorder. The air inlet of the greenhouse gas analyzer extends from the top of the vehicle body; the GPS locator is used to obtain time, draw observation routes, and real-time vehicle speed information; the driving recorder is used to record road and traffic conditions.

3. The greenhouse gas emission tracking system for large-scale activities according to claim 1, characterized in that: Based on GPS time, the lag time of gas sampling is eliminated according to the inlet flow rate of the greenhouse gas analyzer, the length and inner diameter of the sampling tube.

4. The greenhouse gas emission tracking system for large-scale activities according to claim 1, characterized in that: When using enhanced value calculations to track greenhouse gas emissions, the enhanced value is the difference between the vehicle-mounted mobile observation value and the background concentration. If there is a fixed-point observation upwind of the study area, the concentration value of the observation is used as the background value of the vehicle-mounted mobile observation; otherwise, the second percentile of the vehicle-mounted mobile observation is used as the background value.

5. The greenhouse gas emission tracking system for large-scale activities according to claim 1, characterized in that: Eliminate monitoring data when the vehicle speed is ≤5m / s.

6. The greenhouse gas emission tracking system for large-scale activities according to claim 1, characterized in that: Enhanced values ​​for regional greenhouse gas concentrations Average and compare the regional average enhancement values ​​before and after emission reduction and prevention , in order to evaluate the effectiveness of the implementation of emission reduction measures; When it decreases, it means that the emission reduction measures are effective and regional emissions have been reduced; When the change is small or increases, it means that the emission reduction effect is not ideal.

7. The greenhouse gas emission tracking system for large-scale activities according to claim 6, characterized in that: Using emission ratios to attribute greenhouse gas emission events, When performing orthogonal regression, the regression slope is regarded as the emission ratio of emissions in the area; the measured emission ratio is compared with the emission ratio of emission sources of known types to determine the type of the emission source.

8. The greenhouse gas emission tracking system for large-scale activities according to claim 6, characterized in that: For monitoring of local emission sources, a short time window is selected to capture the instantaneous changes in gas concentrations. Orthogonal regression is performed and the correlation coefficient R value of the orthogonal regression is calculated. When the R value exceeds 0.5, it is regarded as a signal of a certain emission source, and the regression slope is the emission ratio of the emission source; otherwise, it is the result of the joint action of multiple emission sources.

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