Large-scale activity greenhouse gas emission tracking system

Abstract

The invention relates to the field of greenhouse gas tracking, in particular to a large-scale activity greenhouse gas emission tracking system, which comprises a route planning module for determining spatial representativeness of vehicle-mounted observation and designing and optimizing route layout according to the spatial representativeness; the vehicle-mounted observation module is used for carrying out mobile greenhouse gas emission monitoring along the detection route; the data processing module is used for processing observation data, including correction of time hysteresis, calculation of an enhancement value and elimination of too slow vehicle speed data; and the emission tracking module is used for comparing the greenhouse concentration enhancement value and the emission ratio change before and after emission reduction prevention and control. By adopting the technical scheme of the invention, the method is used for tracking the influence of greenhouse gas emission generated by the management and control scheme in the large-scale activity period.

Description

Technical Field

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

[0002] As hotspots of greenhouse gas emissions, cities account for more than 70% of global anthropogenic carbon emissions. Currently, the main means of quantifying urban carbon emissions are the IPCC inventory method and the atmospheric inversion method. Due to the spatial heterogeneity and complex source-sink distribution of cities, both methods have certain shortcomings in urban carbon emission monitoring. It is 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, the vehicle-mounted mobile measurement system has a certain degree of flexibility. It conducts cruise observations to obtain greenhouse gas concentrations at different locations, and defines the locations where the observed greenhouse gas concentrations or the enhanced values ​​relative to the background concentrations are continuously higher than the surrounding areas as emission sources, which makes it possible to track greenhouse gas emissions. However, the biggest drawback of mobile observation 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 cruise grid is not high, it is very easy to miss unknown emission sources. Therefore, the planning of the cruise route and the enhancement of the emission source signal are the key technical problems that the present invention intends to solve. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a large-scale event greenhouse gas emission tracking system for tracking the changes in greenhouse gas emissions caused by restrictions during large-scale events.

[0005] In order to achieve the above-mentioned purpose, the technical solution of the present invention is as follows: A greenhouse gas emission tracking system for large-scale activities, comprising: 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; 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.

[0006] The above scheme has the following beneficial effects: In this scheme, the gas concentration monitored by the vehicle-mounted observation module is spatially averaged for 50m, and each spatial average value is a sample. The center point of the sample is taken as the origin, and a buffer zone radius is taken every 50m 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. The buffer zone radius corresponding to the time when the correlation coefficient no longer increases with the increase of the buffer zone radius or the maximum value of the correlation coefficient is taken as the spatial representative range for the vehicle-mounted observation.

[0007] Beneficial effects: The buffer zone radius can quantify the source and 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.

[0008] In this solution, the vehicle-mounted mobile module moves along the optimized detection route before and after emission reduction and prevention, monitoring and evaluating the impact of emission reduction measures on greenhouse gas emissions. Average and compare the regional average enhancement values ​​before and after emission reduction and prevention , in order to evaluate the effectiveness 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.

[0009] Furthermore, the vehicle-mounted observation module includes a vehicle body, on which a greenhouse gas analyzer is provided, and an air inlet of the greenhouse gas analyzer extends out from the top of the vehicle body.

[0010] Beneficial effects: the vehicle body can provide a carrier for traveling along the city, the greenhouse gas analyzer can perform gas analysis while the vehicle body is moving, and the air inlet height is higher than the ground to reduce the impact of floating dust or debris on the ground on the detection.

[0011] Furthermore, the GPS locator is used to obtain time, plot observation routes, and real-time vehicle speed information.

[0012] 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, so that the detected greenhouse gas emission data can be mapped to the observation route drawn in the map.

[0013] Furthermore, based on the GPS time, the lag time of gas sampling is eliminated according to the inlet gas velocity of the greenhouse gas analyzer, the length and inner diameter of the sampling tube.

[0014] Beneficial effect: It takes time for the gas to enter from the air inlet and reach the monitoring part of the greenhouse gas analyzer. The time is related to the intake flow rate and the length of the sampling tube. At the same time, the monitoring requires response time. Therefore, by obtaining the intake flow rate of the greenhouse gas analyzer, the length of the sampling tube of the greenhouse gas analyzer and the inner diameter to correct the sampling lag time data, the real coordinates of the monitored data on the map can be restored, thereby improving the accuracy of monitoring.

[0015] Furthermore, the vehicle-mounted observation module is used to eliminate monitoring data when the vehicle speed is ≤5m / s.

[0016] Beneficial effect: When processing data, the monitoring data when the vehicle speed is ≤5m / s will be eliminated to reduce the impact of its own emissions on the results.

[0017] Furthermore, the difference between the vehicle-mounted mobile observation value and the background concentration is taken as the enhancement value, and the enhancement value is used to track greenhouse gas emissions. 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.

[0018] Beneficial effect: The observed concentration value not only reflects regional emissions, but also the contribution of background concentration, that is, the observed concentration value = background concentration + local enhancement. When conditions such as wind speed and direction, and atmospheric stability change, the background concentration will also change. Therefore, when making a comparison, the background data of the day of detection is used to calculate the local enhancement value to eliminate the influence of background concentration and make the emission tracking results more reliable.

[0019] Furthermore, an orthogonal regression is performed on the concentration enhancement values ​​of the two gases in a certain area, and the regression slope is regarded as the emission ratio of emissions in the area; by comparing the measured emission ratio with the emission ratio of the known emission source, the type of the emission source can be determined.

[0020] Beneficial effects: Unlike natural ecosystems, urban atmospheres contain unique "chemical fingerprints", that is, different emission sources have special composition structures. For example, vegetation and soil emit very small amounts of CO, while fuel combustion releases large amounts of CO. In terms of fuel source types, natural gas combustion (domestic) releases 2.5% of CO2. 13 The C isotope content is lower than that of coal (power plants) and fuel (cars). The relative concentrations of CH4 and N2O from residential (cooking and sewage) emissions are higher than those from industrial emissions. Therefore, changes in the composition of emission sources (i.e., emission ratios) provide a new perspective for measuring emission reduction measures. By simultaneously observing multiple greenhouse gases and chemical tracers and calculating the regression slopes (emission ratios) of two different components, the type of emission source can be distinguished.

[0021] Further: For monitoring of local emission sources, a short time window is selected to capture the instantaneous changes in gas concentrations, and the concentrations of two gases within ±5s are Perform orthogonal regression; calculate the correlation coefficient R value of the orthogonal regression. 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.

[0022] Beneficial effects: In addition, since the gases from the same emission source undergo the same atmospheric transmission and diffusion process, although the gas concentration fluctuates at high frequencies due to the influence of turbulent motion, the changes in the concentrations of different gases are related, that is, the concentration ratio of these gases retains the information of the emission source and is relatively stable. Therefore, the regression slope of the two gases can be regarded as the emission ratio of the emission source. Compared with the concentration or the enhanced value of the concentration, the emission ratio does not decrease with the increase of the distance from the emission source, which can effectively enhance the signal of the emission source.

[0023] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the route distribution of an embodiment of the greenhouse gas emission tracking system for large-scale activities of the present invention; Figure 2 A schematic diagram of CO2 concentration at a background station of an embodiment of a greenhouse gas emission tracking system for large-scale activities of the present invention; Figure 3 A schematic diagram of CH4 concentration at a background station of an embodiment of a large-scale event greenhouse gas emission tracking system of the present invention; Figure 4 A schematic diagram of the △CO2 concentration navigation of an embodiment of the large-scale event greenhouse gas emission tracking system of the present invention; Figure 5 A schematic diagram of the △CO2 period of a road trip in an embodiment of the greenhouse gas emission tracking system for large-scale activities of the present invention; Figure 6 A schematic diagram of the △CH4 concentration navigation of an embodiment of the large-scale event greenhouse gas emission tracking system of the present invention; Figure 7 A schematic diagram of the road navigation △CH4 period of an embodiment of the greenhouse gas emission tracking system for large-scale activities of the present invention; Figure 8 A schematic diagram of a vehicle-mounted observation module of an embodiment of a large-scale activity greenhouse gas emission tracking system of the present invention; Fig. 9 Orthogonal regression of CO2 concentration, CH4 concentration and their enhancement values ​​observed by mobile vehicle; Fig.10is the correlation coefficient between ΔCO2 and the ratio of impervious surface under different buffer radii; Fig.11 The CH4 and CO2 observation results and CH4:CO2 emission ratio of the Xihu Tunnel in the city on January 22, 2022; Fig.12 Distribution map of CH4:CO2 emission ratios from 296 observations in multiple tunnels in the city.

[0025] The reference numerals in the drawings of the specification include: 1. funnel; 2. bracket; 3. Teflon tube. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are 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 limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0028] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] The following is further described in detail through specific implementation methods: As attached Figure 1-Figure 12 Shown: A greenhouse gas emission tracking system for large-scale activities, comprising: 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; 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.

[0030] The route planning module uses the buffer radius to determine the spatial representativeness of vehicle-mounted observations. 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, and 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, and the route is designed and optimized accordingly.

[0031] The vehicle-mounted observation module includes a vehicle body, on which a greenhouse gas analyzer is provided, and the air inlet height of the greenhouse gas analyzer is 2.5m from the ground. The air inlet of the greenhouse gas analyzer is provided with an inverted funnel 1 and sealed with gauze. The air inlet is erected 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.

[0032] The data processing module is used to correct the time lag of the original observation data, eliminate error data and calculate the enhancement value. Based on the GPS time, 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 is obtained by using the GPS locator, and the monitoring data when the vehicle speed is ≤5m / s is eliminated. The enhanced value is obtained by subtracting the background concentration from the vehicle-mounted mobile observation value. The background concentration was collected at the benchmark climate station in the city.

[0033] Emission Tracking Module: Use emission ratios to attribute greenhouse gas emission events, and Orthogonal regression is performed, and the regression slope is regarded as the emission ratio of the area; the measured emission ratio is compared with the emission ratio of the known emission source to determine the type of emission source. For the monitoring of local emission sources, a short time window is selected to capture the instantaneous change of gas concentration. The concentration of the two gases within ±5s is 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.

[0034] The specific implementation process is as follows: The large-scale event was a large-scale sports competition in a certain city, and the hourly air pressure and wind speed data of the national benchmark climate station in the city were obtained. During the observation period before and during the sports event, the air pressure in the previous week decreased and then increased, and rebounded 1-2 days before the observation. The weather returned to sunny weather controlled by high pressure before the experiment. However, on the evening of August 13, during the experimental observation period before the sports event, it was cloudy and rainy in the experimental area, and the monitoring on that day was cancelled. During the experiment, the hourly average wind speed was basically higher during the day and lower at night. The air pressure after the sports event dropped slightly on the last day (November 1), but there was no obvious adverse weather phenomenon on the day of the experiment. During this stage of the experiment, the hourly average wind speed was lower during the day and higher at night.

[0035] like Figure 1 As shown in the figure, the route planning module selects a total of 4 routes, which basically cover the main urban area of ​​the city, including a variety of different urban functional areas and different road conditions such as bridges, viaducts, tunnels, etc. Line 1 mainly passes through riverside residential areas and scenic areas, Lines 2 and 3 are mainly in the urban area, and Line 4 is a ring expressway section, mainly in the surrounding areas and suburbs.

[0036] During sports events, the city's main urban area adopts a policy of banning out-of-town vehicles from entering and restricting the use of local vehicles with odd and even numbers, with the restricted driving period from 8:00 to 20:00.

[0037] The urban background data are provided by the city’s National Benchmark Climate Station (station number 58457, 30.22° N; 120.17° E, 43.2 m above sea level).

[0038] The observation will be carried out for three days each in the three periods before, during and after sports events, which will be carried out in the city from August 11 to 13, September 25 to 27, and October 30 to November 1, 2023. The observation time will be in the morning (10:00 to 12:00) and night (20:00 to 22:00) before sports events in August, and in the morning (9:00 to 11:00), afternoon (14:00 to 16:00) and night (20:00 to 22:00) during sports events in September and after sports events in October and November.

[0039] The mean CO2 background concentration (±1 standard deviation) in the three selected time periods was as follows: the average CO2 background concentration before sports events was 435.06±13.34μmol / mol, and the average CO2 background concentration at night was 450.64±6.38μmol / mol. During sports events, the average CO2 background concentration during the day was 450.36±12.03 μmol / mol, and the average CO2 background concentration at night was 451.93±4.91 μmol / mol. After sports events, the average CO2 background concentration during the day was 445.48±15.51 μmol / mol, and the average CO2 background concentration at night was 473.39±9.15 μmol / mol. Overall, the average CO2 background concentration at night is higher than that during the daytime. The average CO2 background concentration at night shows an upward trend from before to during and after sports events. The average CO2 background concentration during the day is highest during sports events, slightly decreases after sports events, and is lowest before sports events.

[0040] The background concentration of atmospheric CH4 at urban background stations during the observation periods before, during and after sports events is as follows: Figure 2 As shown. During the observation period before the sports event, the daytime atmospheric CH4 background concentration was 2.259 ± 0.073 μmol / mol, and the nighttime atmospheric CH4 background concentration was 2.385 ± 0.102 μmol / mol. During the sports event, the average CH4 background concentration during the daytime when the traffic restriction measures were taken was 2.327 ± 0.120 μmol / mol, and the average CH4 background concentration at night was 2.306 ± 0.034 μmol / mol. After the sports event, the daytime average CH4 background concentration was 2.163 ± 0.076 μmol / mol, and the nighttime average CH4 background concentration was 2.249 ± 0.063 μmol / mol. Before and after the sports event, the nighttime average CH4 background concentration was higher than the daytime average CH4 background concentration, and the daytime average CH4 background concentration during the sports event was slightly higher than that at night. The average daytime background CH4 concentration was highest during sports events and lowest after sports events, while the average nighttime background CH4 concentration gradually decreased from before to after sports events.

[0041] The data distribution of CO2 background concentration at different stages before, during and after sports events is as follows: Figure 3As shown. The median data during the day were 430.2 μmol / mol before sports events, 448.0 μmol / mol during sports events, and 440.8 μmol / mol after sports events. The data were concentrated in the following sections: 423.6 μmol / mol before sports events, 448.0 μmol / mol during sports events, and 440.7 μmol / mol after sports events. The median data at night were 450.2 μmol / mol before sports events, 451.9 μmol / mol during sports events, and 477.8 μmol / mol after sports events. The data were concentrated in the following sections: 450.8 μmol / mol before sports events, 447.0 μmol / mol during sports events, and 463.5 μmol / mol after sports events.

[0042] The spatial distribution of the average ΔCO2 at the 100 m scale before, during and after sports events is shown in the figure below: Figure 4 As shown, (a) is the test result in the morning before the sports event, (b) is the test result in the night before the sports event, (c) is the test result in the morning during the sports event, (d) is the test result in the afternoon during the sports event, (e) is the test result in the night during the sports event, (f) is the test result in the morning after the sports event, (g) is the test result in the afternoon after the sports event, and (h) is the test result in the night after the sports event. The sections where ΔCO2 was measured to be greater than 100 μmol / mol were mostly concentrated in the central area of ​​the city and the northern section of the ring road, and the frequency of occurrence at night was much greater than that during the day during the same period. The sections with the most obvious changes were the ring road and the scenic area section where Line 1 passed. The high values ​​of these two sections during the restricted period of sports events were significantly reduced, and high values ​​appeared again at night after the restriction was lifted and after the sports events. The frequency of high values ​​during the day during sports events was significantly lower than before the sports events, and after the night restriction period ended, the average concentration in the urban area and the northern section of the ring road increased significantly.

[0043] The 1 Hz road navigation data excluding tunnels were analyzed and statistically analyzed, with ΔCO2 above 100 μmol / mol as the extremely high value, and the proportion of extremely high values ​​was counted. During the day, the frequency of extremely high ΔCO2 values ​​before sports events was 71.0 ‰; during sports events, the frequency was 27.6 ‰; and after sports events, the frequency was 89.3 ‰. At night, the frequency of extremely high ΔCO2 values ​​before sports events was 70.1 ‰; during sports events, the frequency was 85.0 ‰; and after sports events, the frequency was 101.9 ‰.

[0044] The occurrence rates of ΔCO2 high values ​​on different routes were analyzed separately. The results showed that the frequency of high values ​​on routes passing through different areas showed different trends. During sports events, the frequency of high values ​​on Line 1 decreased in the morning and at night. However, the frequency of high values ​​on Lines 2 and 3 decreased only in the morning when the restrictions were lifted. At night, the occurrence rates of ΔCO2 high values ​​on these two lines increased significantly compared to before the sports events. The occurrence rate of ΔCO2 high values ​​on Line 4 in all periods showed a significant increase after the sports events, and the changes during and before the sports events were far from comparable to the increase after the sports events.

[0045] Figure 5 The data distribution of ΔCO2 in different stages before, during and after sports events. The median of the daytime data was 34.36 μmol / mol before sports events, 25.94 μmol / mol during sports events, and 42.56 μmol / mol after sports events. The mean value of the daytime enhancement value of CO2 decreased from before to during sports events, and increased after sports events. Its nighttime mean value was the highest during sports events and fell after sports events. In the three-day observations before, during and after sports events, the mean value of the enhanced CO2 concentration (± 1 standard deviation) was as follows: ΔCO2 was 42.43 ± 37.81 μmol / mol during the day before sports events, and ΔCO2 was 44.58 ± 37.19 μmol / mol at night. During sports events, the ΔCO2 during the day when traffic restrictions were implemented was 32.15±30.23 μmol / mol, and the ΔCO2 at night was 48.44±37.77μmol / mol. After sports events, the daytime ΔCO2 was 51.56±49.27 μmol / mol, and the nighttime ΔCO2 was 52.44±40.98 μmol / mol. The average ΔCO2 trend was that the daytime was generally smaller than the nighttime, with the daytime being during sports events < before sports events < after sports events, and the nighttime being before sports events < during sports events < after sports events.

[0046] Figure 6The ΔCH4 navigation chart of the 100m average before, during and after sports events. (a) is the detection result of the morning before the sports event, (b) is the detection result of the night before the sports event, (c) is the detection result of the morning during the sports event, (d) is the detection result of the afternoon during the sports event, (e) is the detection result of the night during the sports event, (f) is the detection result of the morning after the sports event, (g) is the detection result of the afternoon after the sports event, and (h) is the detection result of the night after the sports event. In the figure, the basic temporal and spatial distribution of ΔCH4 is similar to that of ΔCO2. The average value at night is higher than that during the day. The sections with high values ​​during sports events, especially during the day, are significantly reduced compared with before and after sports events, and the high value distribution is also mainly in urban areas. The sections with higher ΔCH4 after sports events are significantly more than before sports events. The sections with high values ​​above 1 μmol / mol are basically concentrated near landfills and urban intersections, and the distribution is more concentrated and regular than the high value area of ​​ΔCO2. The average ΔCH4 concentration in the northern section of the ring road at night during sports events was higher than before the sports events, and more high-value hotspots appeared.

[0047] The 1 Hz road navigation data excluding the tunnels were analyzed and counted, with ΔCH4 above 1 μmol / mol as the extreme high value, and the proportion of the occurrence of the extreme high value was counted, and the results are as follows. During the day, the frequency of the extremely high ΔCH4 value before sports events was 1.2 ‰; during sports events, it was 1.0 ‰; and after sports events, it was 0.9 ‰. At night, the frequency of the extremely high ΔCH4 value before sports events was 8.0 ‰; during sports events, it was 8.0 ‰; and after sports events, it was 8.5 ‰.

[0048] From the statistics of high-value frequencies of sub-lines, Line 1 shows a trend of gradually increasing high-value frequency from before to after sports events, and the frequency increase at night after sports events is nearly four times. On the contrary, Line 2 has a gradually decreasing high-value occurrence rate from before to after sports events. No high values ​​were detected in the morning after sports events, and the drop from sports events to after sports events at night was more than half. Line 3, which is also in the urban area, has the highest frequency of high values ​​in the morning during sports events, while the frequency of high values ​​at night after sports events nearly doubles. There are almost no high values ​​in the morning on Line 4, and the highest number of high values ​​at night is between sports events.

[0049] The mean values ​​of CH4 enhanced concentration (±1 standard deviation) are as follows: ΔCH4 before sports events was -0.033 ± 0.139 μmol / mol during the day, and ΔCH4 was 0.013 ± 0.217 μmol / mol at night. During sports events, the ΔCH4 during the day when traffic restrictions were implemented was -0.048 ± 0.146 μmol / mol, and ΔCH4 was 0.015 ± 0.235 μmol / mol at night. After sports events, ΔCH4 was -0.007 ± 0.125 μmol / mol during the day, and ΔCH4 was 0.099 ± 0.293 μmol / mol at night. The mean change trend of ΔCH4 is similar to that of ΔCO2, with the daytime being generally smaller than the nighttime, with the daytime being during sports events < before sports events < after sports events, and the nighttime being before sports events < during sports events < after sports events.

[0050] Figure 7 The data distribution of ΔCH4 in different stages before, during and after sports events. The median data during the day were -0.037 μmol / mol before sports events, -0.051 μmol / mol during sports events, and -0.011 μmol / mol after sports events. The data were concentrated in the following sections: -0.031 μmol / mol before sports events, -0.038 μmol / mol during sports events, and 0.038 μmol / mol after sports events. The median data at night were -0.012 μmol / mol before sports events, -0.019 μmol / mol during sports events, and 0.038 μmol / mol after sports events.

[0051] When the vehicle travels into the plume range of a certain emission source, the reading of the greenhouse gas analyzer will increase due to the emission source, and each observed peak is caused by one or more emission sources. Orthogonal regression is performed on CO2 and CH4 within ±5s, such as Fig. 9 As shown, the emission ratios with R>0.5 were screened out, and the median was 0.00167. Fig.11 and 12 ), the CH4:CO2 emission ratio of the city's traffic emissions is 0.0005, which is much smaller than the emission ratio observed on the street. This means that there is a stronger CH4 emission source on this street.

[0052] Fig.10The correlation coefficient between ΔCO2 and the ratio of impervious surfaces at different buffer radii on the elevated and streets of the city, where (a) is the elevated bridge 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 ΔCO2 and the ratio of impervious surfaces on the elevated road gradually slows down, while on the street this distance is about 1 km. Therefore, the spatial representative range of vehicle-mounted observations on the elevated road is defined as 1.5 km, and the spatial representative range of street observations is defined as 1 km.

[0053] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled 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 the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the 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; 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 gas concentration monitored by the vehicle-mounted observation module is spatially averaged over 50 m, and each spatial average value is a sample. The center point of the sample is taken as the origin, and a buffer zone 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 zone radius corresponding to the time when the correlation coefficient no longer increases with the increase of the buffer zone radius or the maximum value of the correlation coefficient is taken as the spatial representative range of the vehicle-mounted observation.

3. The greenhouse gas emission tracking system for large-scale activities according to claim 2, characterized in that: In the route planning module, the planned route is optimized based on the buffer radius.

4. 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.

5. 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.

6. 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.

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

8. 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.

9. The greenhouse gas emission tracking system for large-scale activities according to claim 8, 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.

10. The greenhouse gas emission tracking system for large-scale activities according to claim 8, 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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