WO2020020260A1 - 一种利用出租车进行大气监测时提高监测覆盖率的方法 - Google Patents

一种利用出租车进行大气监测时提高监测覆盖率的方法 Download PDF

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WO2020020260A1
WO2020020260A1 PCT/CN2019/097594 CN2019097594W WO2020020260A1 WO 2020020260 A1 WO2020020260 A1 WO 2020020260A1 CN 2019097594 W CN2019097594 W CN 2019097594W WO 2020020260 A1 WO2020020260 A1 WO 2020020260A1
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monitoring
sensor
unit
sub
module
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PCT/CN2019/097594
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English (en)
French (fr)
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许军
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司书春
许军
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Priority claimed from PCT/IB2018/055531 external-priority patent/WO2019150182A1/zh
Application filed by 司书春, 许军 filed Critical 司书春
Priority to GBGB2102430.2A priority Critical patent/GB202102430D0/en
Priority to CN201980043485.3A priority patent/CN112352147B/zh
Priority to NO20210246A priority patent/NO20210246A1/en
Publication of WO2020020260A1 publication Critical patent/WO2020020260A1/zh

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Definitions

  • the invention relates to a method for improving mobile monitoring coverage, and belongs to the technical field of environmental monitoring.
  • Atmospheric environmental monitoring is the process of measuring the types and concentrations of pollutants in the atmosphere, and observing their temporal and spatial distribution and changes.
  • the main pollutants monitored are sulfur dioxide, nitrogen oxides, ozone, carbon monoxide, PM 1 and PM 2.5 in the atmosphere.
  • the atmospheric environment monitoring system can collect and process the monitored data, and timely and accurately reflect the regional ambient air quality status and changes.
  • Environmental protection departments can use these data for environmental decision-making, environmental management, and pollution prevention; the public can take personal protection and rationally arrange their lives based on environmental data.
  • the current atmospheric environment monitoring equipment mainly includes fixed monitoring stations and mobile monitoring equipment.
  • the current fixed monitoring stations are mainly divided into large fixed monitoring stations (large stations) and small monitoring stations (small stations).
  • Mobile monitoring equipment mainly includes special atmospheric environmental monitoring vehicles, drones and handheld devices.
  • the large fixed monitoring station is equivalent to an independent laboratory, which monitors and analyzes the levels of multiple pollutants in the environment through expensive and sophisticated instruments.
  • Large fixed monitoring stations are characterized by a variety of pollutants and high accuracy.
  • large fixed monitoring stations have a large investment, and the general investment is in the range of one to ten million, which requires high financial support. Therefore, the number of large fixed monitoring stations will not be large and cannot be rolled out on a large scale. Therefore, only representative representatives can be selected. And feasible location for construction.
  • large fixed monitoring sites also have high requirements for site selection. The site needs a large area to accommodate large equipment, and equipment needs temperature and humidity control.
  • a large number of professional and high-quality personnel are required to use the instrument, analyze data, and maintain the instrument.
  • the data obtained from super stations can only be inferred at a single point, and it is difficult to find other nearby super stations to verify.
  • Small monitoring sites integrate grids and batches to reduce costs by integrating low-cost, miniaturized sensors.
  • the small monitoring stations also have the characteristics of convenient power consumption (can be powered by solar energy) and easy installation.
  • the accuracy and consistency of monitoring data at small stations need to be improved, and full operational guarantee is required.
  • a small monitoring site covers a wide area, it is still a fixed monitoring with limited flexibility.
  • the special atmospheric environment monitoring vehicle is a vehicle equipped with a sampling system, pollutant monitoring instruments, meteorological parameter observers, data processing devices and other auxiliary equipment. It is a mobile monitoring station and a supplement to fixed ground monitoring stations.
  • the atmospheric environmental monitoring vehicle can be driven to the scene of a pollution accident or a suspicious point to take measurements at any time in order to grasp the pollution situation in time, and its use is not restricted by time, place and season.
  • Atmospheric environmental monitoring vehicles need to be driven by full-time personnel, and professional personnel are required to operate related instruments. It is expensive and cannot be used on a large scale.
  • the measurement of particulate matter mostly uses the weighing method, micro-oscillation balance method, ⁇ -ray method; GC-FID (gas chromatography-flame ion detection) method is used for VOCs detection .
  • GC-FID gas chromatography-flame ion detection
  • Most of these precision detection instruments are large and expensive, and are not convenient for widespread spot monitoring.
  • the detection of other pollutants such as sulfur dioxide, nitrogen oxides, ozone and carbon monoxide has similar problems.
  • Similar special mobile monitoring vehicles need parking monitoring after reaching the designated location, which is equivalent to a fixed monitoring station, and cannot be moved in real time for monitoring.
  • each monitoring point requires professional installation and maintenance, and the corresponding calibration needs to be performed at intervals.
  • the sampling port of each monitoring point is generally installed at a high position, which is not conducive to monitoring ground pollution (such as road dust).
  • ground pollution such as road dust
  • roads and areas with high population density often have dense traffic flows, especially taxis. Such locations require intensive and focused monitoring.
  • urban social vehicles as mobile monitoring vehicles, equipped with atmospheric pollutant monitoring equipment and positioning equipment, combined with wireless transmission technology, can achieve large-scale near-field monitoring of air pollution.
  • the above document discloses a method for constructing an on-line monitoring system for characteristic air pollution.
  • this kind of area is covered by discrete monitoring points.
  • the number of these monitoring points depends on the pollution range that each monitoring point can effectively represent.
  • Figure 2 shows the limitations of fixed grid layout.
  • the distinctive feature of mobile monitoring is that the mobile device continuously changes the monitoring position with the movement of the moving vehicle, and basically only monitors the area along the traffic network. Its moving path is a traffic route; its coverage area is the traffic network in the entire area.
  • each monitoring device is the entire road network, which is a continuous "line” or “belt”.
  • the larger range of data distribution obtained by the "line" coverage is more representative for the overall air pollution judgment of a city.
  • the data of each monitored location comes from the same or the same group of monitoring equipment
  • the data of each monitored location comes from many different monitoring devices.
  • the pollution situation at a certain location within the road network can be measured multiple times by different mobile devices (mounted on mobile vehicles) at different time periods.
  • the reliability of the data of the monitoring equipment can be indirectly evaluated through the correlation between the monitoring data of different equipment.
  • the invention provides a method for determining the missed detection rate of mobile monitoring on the premise of ensuring the objectivity of urban air pollutant monitoring data.
  • the invention uses a large number of randomly running social vehicles equipped with hidden installation monitoring equipment as a mobile monitoring vehicle and is assisted by fixed monitoring equipment, which can realize the real-time monitoring of the distribution and change of pollutants in urban areas, as long as the mobile monitoring equipment reaches a certain level. Quantity, then the monitoring data generated by these mobile devices can objectively reflect the true situation of urban air pollution distribution and pollution level.
  • Environmental monitoring needs to ensure that the monitoring data are objective and effective. Compared with other types of pollution, environmental pollution, especially atmospheric environmental pollution, has the characteristics of great changes with time and space. Monitoring based on these characteristics is of great significance for obtaining monitoring results that accurately reflect the actual state of atmospheric pollution.
  • the spatial-temporal distribution and concentration of air pollutants are closely related to the distribution, emission volume, and topography, geomorphology, and meteorology of pollutant emission sources. Different types of pollutants, their emission patterns, and the nature of pollutants have different spatial and temporal distribution characteristics. Atmospheric pollutant levels at the same location fluctuate rapidly.
  • time resolution in air pollution monitoring which requires changes in pollutant concentration to be reflected within a specified time. For example, some acutely hazardous pollutants require a resolution of 3 minutes; some chemical aerosols, such as ozone, require a resolution of 10 minutes for the stimulation of the respiratory tract.
  • the invention deploys a large number of air pollutant monitoring equipment to monitor air pollutants on mobile monitoring vehicles, and transmits the position and monitoring data in real time, which can realize from fixed point monitoring to whole road network monitoring and extensive geographic coverage, reflecting urban pollution. The real situation of things.
  • FIG. 1 shows the system composition of the present invention.
  • the system includes a mobile monitoring vehicle equipped with monitoring equipment, a monitoring center, and a fixed monitoring station.
  • the invention proposes to reasonably increase the density of equipment to realize the coverage of motor vehicle roads in urban areas.
  • the monitoring equipment needs to transmit data in seconds in real time. Through multi-vehicle relay, 24-hour continuous monitoring can be achieved.
  • the data is processed by the monitoring center. Obtain reliable, objective and effective atmospheric environmental data. Extensive use of monitoring equipment makes up for the lack of points at each fixed monitoring site and provides data support for grid-based supervision.
  • the mobile monitoring vehicle installed with online monitoring equipment for atmospheric pollutants is constantly moving in the city, and the pollutant concentration, watering and road damage in all corners can be monitored in real time, so that the air pollution monitoring can cover every community and every road section. Avoid dead spots and blind spots.
  • the system can also access data from fixed monitoring sites, making monitoring data more complete.
  • FIG. 6 shows the change in the concentration of pollutants over time at the same location. T 0 to T 6 indicate the time from the beginning of the pollution to its dissipation. The corresponding pollutant concentration at time T 3 is the largest.
  • the equipment installation density has been greatly increased to achieve the coverage of motor vehicle roads in urban areas.
  • the monitoring equipment needs to transmit data in seconds in real time.
  • continuous monitoring can be achieved for 24 hours.
  • the heavy use of monitoring equipment makes up for the lack of fixed monitoring points at each monitoring station and provides data support for grid-based supervision.
  • the vehicles are constantly moving in the city, and the pollutant concentration, watering and road damage in all corners can be monitored in real time.
  • the road section avoids dead ends and blind spots.
  • This plan proposes a model of the required number of mobile monitoring vehicles equipped with on-line monitoring equipment for atmospheric pollutants, and the specific number is determined based on the average missed detection rate of monitoring.
  • the average missed detection rate represents the probability of detecting air pollution in a monitored area.
  • Method 1 Measured by the density of monitored vehicles per square kilometer
  • the method still looks at the number of monitoring devices relative to the density of an area.
  • the average missed detection rate is related to the density of monitoring equipment in cities, which is expressed as the number of vehicles per square kilometer.
  • the average number per square kilometer in the city will be very small.
  • the average missed detection rate for pollutants that dissipate quickly in a small area will be very high. reduce.
  • the number of monitoring devices released reaches a certain value, such as when the delivery density reaches n 0 , the average missed detection rate will decrease to m 0 .
  • the first method has major flaws.
  • a fixed point can only cover an area of a fixed size, while the moving point changes with time. The coverage area changes, and it can cover far beyond The spatial range of fixed points.
  • the number of mobile points required to cover the entire city is very different from the fixed points. Therefore, the estimation of the number of mobile points deployed across the city is a different problem from the fixed points. Point calculation theory to solve.
  • the air pollution monitoring national control station data volume is mainly based on one data.
  • the evaluation of urban air quality is mainly based on daily average data, while mobile monitoring stations are mostly based on minute or second data.
  • the mobile monitoring can achieve the effect of multiple fixed monitoring stations.
  • the vehicle-mounted mobile monitoring is carried out in a continuous monitoring manner, it achieves a high coverage rate.
  • the mobile monitoring equipment is equipped with a taxi and other vehicles, the location is flexible and random. It is impossible to take measures in advance to affect air quality, and the data is more convincing.
  • Method 2 Measured by the distribution of the number of monitored units of each road section on the traffic network
  • road network of an area such as the urban area of a certain city
  • a certain granularity that is, a unit of length (for example, every 100 meters or 200 meters); we will carry mobile monitoring equipment (in working state)
  • the number of times a vehicle passes through each road section unit is an important indicator.
  • timing unit In order to eliminate the short-term data repetition caused by multiple consecutive measurements, every 15 minutes or 30 minutes is used as a timing unit. In a timing unit, multiple passes of the same mobile device are counted only once. The data of different monitoring vehicles are accumulated.
  • Detection times In this article, specifically refers to the number of times a certain road section unit has been monitored by a mobile monitoring vehicle; multiple passes of the same monitoring vehicle in a continuous timing unit are counted only once.
  • the timing unit can be 15 minutes, 30 minutes, or 1 hour.
  • Counting cycle With a timing unit as the deadline, when a monitoring vehicle enters a certain road section unit, a new counting cycle is triggered; during the counting cycle, the same monitoring vehicle entering the road section unit is no longer counted in the number of detections .
  • the counting cycle ends after one timing unit is present. The same monitoring vehicle can only have at most one counting cycle in each road section unit; before the end of the previous counting cycle, a new counting cycle cannot be triggered.
  • FIG. 11 is a statistical distribution chart of the number of monitoring times (taxi) within a 24 hour period in a certain urban area by section unit according to the above method.
  • Expected index It is an index used to evaluate whether the amount of air pollution detection data collected in the road network of an urban area can reflect the overall pollution status. This indicator is characterized by two important parameters: coverage, and the number of scheduled inspections.
  • Coverage The ratio of the number of road section units that reach the predetermined number of detections to the total number of road section units.
  • the optional value range is between 50% and 90%. Generally, it is easy to select 70 to 80%.
  • Predetermined detection times When the number of detections recorded by a road segment unit within a day reaches a certain value, the road segment unit is recognized as being covered by the test; otherwise, the road segment unit is considered to be a missed road segment unit. Generally speaking, considering the length of the timing unit, the selectable number of detection times is 5-10 times.
  • Missing link unit When a certain link unit does not reach the predetermined number of detections in a day, the link unit is considered a missing link unit.
  • each of the expected indicators may be significant The difference.
  • the monitoring object is PM2.5
  • its average missed detection rate can be expressed as: m (PM2.5, 80%, 10); its meaning is: in a city area, 80% of the road section units Able to record at least 10 tests (test times) within 24 hours.
  • the invention discloses a method for determining the rated number of mobile monitoring vehicles.
  • the method focuses on the traffic road network of an urban area.
  • the method is implemented by installing air pollution detection equipment on mobile monitoring vehicles, especially taxis. Air quality monitoring of the urban area; the method includes the following steps:
  • Taxi drivers generally have a regional orientation, and the number of taxis participating in the model construction should not be too small. Generally speaking, there should be at least 50 vehicles.
  • Buses usually have a fixed route, a fixed coverage area, a fixed working time, and a fixed attendance rate. Therefore, buses are a special case of taxis. When choosing a bus line with monitoring equipment, choosing a route with a low probability of taxi will play a role in blindness. , When reaching the same missed detection rate index, it helps to reduce the total number of mobile monitoring points.
  • the amount of data monitored by one bus will have the effect of multiple taxis.
  • the calculation of the missed detection rate indicator is to meet the small probability route data It is derived from the minimum requirements, so the combination of the two will greatly reduce the total number of urban monitoring vehicles and save resources.
  • the bus usually runs back and forth along a fixed line; then the curve of the bus line is actually a smooth polyline corresponding to the model of the number of monitoring based on the section unit.
  • the bus lines B1 and B2 are evenly distributed on their respective running routes and on their respective road section units.
  • bus line B1 and the taxi cooperate to complete the monitoring of a certain pollutant, then the bus line B1 can help cover the missed road sections that are difficult for the taxi to cover.
  • bus line B1 and taxi can significantly increase the coverage of pollution detection as a whole.
  • the invention discloses a method for selecting a bus line to participate in monitoring during the coordinated monitoring of taxis and buses:
  • the optimized bus lines can partially cover the road units that are difficult for taxis to effectively cover, the number of taxis can be appropriately reduced while maintaining the overall coverage unchanged.
  • the local coverage area bi is sequentially subtracted from the coverage area f to obtain a new coverage area f ';
  • a group of vehicles with low overlap coverage should be selected for monitoring.
  • FIG. 13 is a statistical distribution diagram of a specific vehicle appearing in each grid within a week.
  • the grid marked "X" next to the picture is the main parking position of the vehicle. For a business car, this may be the location of the unit. For taxis, this may be the location of a shift, or the taxi driver's residence.
  • the invention discloses a method for improving monitoring coverage when using a taxi for atmospheric monitoring.
  • the method focuses on a traffic network in an urban area, and selects a predetermined number of groups from a group of alternative taxis.
  • a combination of vehicles is preferred, and air pollution detection equipment is installed as a mobile monitoring vehicle to monitor the air quality of the urban area; the method includes the following steps:
  • a predetermined number of preferred vehicle combinations are selected from the set of candidate taxis in a permutation and combination manner, so that the statistical distribution maps of the vehicles in the combination are superimposed on the cumulative statistical map to meet the predetermined monitoring
  • the number of link units is the largest.
  • the operating rules of different vehicles and the coverage of different road section units can be reused, so that the overall coverage rate is greatly improved.
  • the characteristics of buses are that the routes are relatively fixed, which is conducive to repeated measurements on a certain road section, and it can give more reliable and more time data.
  • the characteristics of taxis are that they have a wide distribution range and a wide time range, and can measure the places that buses cannot reach.
  • the measurement time range supplements the periods when buses are not in operation.
  • the driving route of the muck truck is often the road dust pollution section. Let such measurement focus on monitoring the dust road section, which can do more with less effort, and can also measure the dust pollution situation of your own vehicle.
  • the comprehensive data of multiple dump trucks is the background data of road dust.
  • the data of the own vehicle includes the background and the pollution of the own vehicle. Through the big data processing, the two types of data can be separated, and the road and self pollution can be evaluated separately. Facilitate control.
  • the characteristics of long-distance buses are that they can cover the blind spots of monitoring between cities, and form a wider range of monitoring.
  • the height of the taxi ceiling light is basically the same as the height of the mouth and nose of the person. It is the height at which the person mainly breathes.
  • Using a taxi equipped with air pollutant monitoring equipment to monitor the atmosphere at this height can effectively reflect the impact on people's respiratory health. The high level of air is of great significance for the governance of the atmospheric environment.
  • the invention is also beneficial in that the device uses social vehicles such as city buses, long-distance buses, taxis, and dirt trucks to carry out real-time measurement.
  • social vehicles such as city buses, long-distance buses, taxis, and dirt trucks to carry out real-time measurement.
  • Dedicated venues and professional operators are required, and low one-time investment requirements reduce the cost of measurement.
  • energy consumption and road occupation brought by special vehicles are reduced.
  • the occupation of social public resources is reduced, and the cost of air pollutant monitoring is reduced.
  • the air pollutant monitoring equipment includes a detection module, a main control module and a communication module; the detection module contains one or more air pollutant sensor units; the air pollutant sensor unit is one of the following sensors: PM 1 sensor, PM 2.5 sensor, PM 10 sensor, PM 100 sensor, NOx sensor, O 3 sensor, SO 2 sensor, VOCs sensor or TVOC sensor.
  • the main control module is connected to the power supply of the mobile monitoring vehicle.
  • the main control module provides power to the detection module and communication module on the atmospheric pollutant monitoring equipment.
  • the main control module is connected to the detection module and the communication module on the atmospheric pollutant monitoring equipment through a data interface, and performs data exchange with the detection module communication module.
  • FIG. 1 is a schematic diagram of a system composition of the present invention
  • Figure 2 is a schematic diagram of a grid-fixed monitoring site layout mode
  • Figure 3 is a schematic diagram of an example monitoring platform in a city in Shandong;
  • FIG. 4 is a schematic diagram of the basic module composition of atmospheric pollutant monitoring equipment
  • Figure 5 illustrates the concealment of social vehicle monitoring
  • Figure 6 shows the characteristics of air pollution, which is time-sensitive
  • Figure 7 shows the relationship between the average missed detection rate and the monitoring equipment release density index
  • FIG. 8 is a schematic diagram of an air pollutant monitoring device including a video acquisition module
  • FIG. 9 is a schematic diagram of a distribution of detection data in a grid in a mobile monitoring mode
  • FIG. 10 is a schematic diagram of a cumulative detection distribution of a “belt” -shaped road network under a road section unit model
  • Figure 11 Statistical distribution of the number of detections (taxi) within a 24-hour period in a certain urban area by road segment unit;
  • FIG. 12 is a schematic diagram of the number of detections in the taxi and bus collaborative monitoring mode
  • FIG. 13 is a statistical distribution diagram of a specific vehicle appearing in each grid within a week.
  • Air pollutant monitoring equipment is installed on social vehicles to monitor the quality of the atmospheric environment where the vehicle is located.
  • the air pollutant monitoring equipment has an information transmission function and can return the monitored data, location data and time information wirelessly.
  • Air pollution monitoring equipment can record and record road conditions to record road pollution.
  • Air pollution monitoring equipment can transmit the collected video back to the monitoring center through wireless transmission.
  • the air pollutant monitoring equipment has the function of storing data and video data, and saves the collected data and video data locally.
  • the air pollutant monitoring equipment is also provided with a data transmission interface, which can copy the saved data and video data to the site maintenance or staff through local transmission.
  • the monitoring center receives the data returned by the atmospheric pollutant monitoring equipment, and the monitoring center stores and processes these data.
  • the monitoring center can also collect data from other types of monitoring equipment, such as collecting data from miniature fixed monitoring sites, and collecting data from fixed monitoring sites in nearby countries.
  • the monitoring center integrates the data returned by the atmospheric pollutant monitoring equipment of social vehicles, the collected data from the miniature fixed monitoring stations and the collected data from nearby nearby fixed monitoring stations, and generates data lists, data rankings, pollution cloud maps, historical playback and other data. ways of presenting. These generated data lists, data rankings, and pollution cloud maps are sent to user terminals via the network, and users can query and use them as required.
  • the monitoring center can also remotely control the operation of atmospheric pollutant monitoring equipment, such as turning on and off atmospheric pollutant monitoring equipment, turning on and off video acquisition modules, adjusting monitoring frequency, and correcting errors in atmospheric pollutant monitoring equipment.
  • the objective air pollutant monitoring data of a city can only reflect the true degree of air pollution in the city.
  • the present invention needs to set a set of the highest average miss detection rate index M 0 to represent the objectivity of the monitoring data. For example, a city needs to monitor PM 10 , and its missed detection rate is expressed as m (PM 10 ). If the city requires m (PM 10 ) ⁇ 20%, it means that the PM 10 pollution event not captured by the monitoring equipment is less than 20% of total PM 10 pollution incidents.
  • the minimum delivery density index N 0 is introduced, and N 0 represents the minimum delivery density index of the monitoring equipment required to reach M 0 .
  • the minimum delivery density of a monitoring device equipped with a PM 10 sensor to be deployed is n (PM 10 ).
  • the n (PM 10 ) needs to be calculated based on the area of the city, the number of vehicles equipped with mobile monitoring equipment, the daily mileage of vehicles equipped with mobile monitoring equipment, the driving range of vehicles equipped with mobile monitoring equipment, the type of vehicles, and the Instrument accuracy and other parameters. The larger the area of the city, the more monitoring equipment needs to be deployed; the more mileage of vehicles equipped with mobile monitoring equipment, the fewer vehicles need to be deployed; the larger the range of vehicles equipped with mobile monitoring equipment, the fewer vehicles need to be deployed .
  • the PM 10 and SO 2 of a city are monitored.
  • the highest average missed detection rate index M 0 of the city is expressed as m (PM 10 ), m (SO 2 ).
  • PM 10 the highest average missed detection rate index
  • SO 2 the highest average missed detection rate index
  • Different pollutants have different levels of pollution contribution, and cities attach different degrees of importance, so the average missed detection rate for different pollutants will have corresponding requirements.
  • cities attach less importance to monitoring SO 2 than PM 10.
  • the PM 10 and SO 2 of a city are monitored.
  • the highest average missed detection rate index M 0 of the city is expressed as m (PM 10 ) and m (SO 2 ).
  • Current monitoring equipment can also measure multiple pollutants simultaneously through the combination of internal detection modules.
  • the first launch method in this example is that all vehicles are equipped with monitoring equipment that can measure PM 10 and SO 2 simultaneously. Then the N 0 minimum monitoring equipment release density index only needs to meet the strictest highest average missed detection rate in the index.
  • the second method of launch in this example is that some vehicles are equipped with monitoring equipment that can measure PM 10 and SO 2 at the same time, and other vehicles are equipped with vehicles that can only measure PM 10 equipment.
  • the distribution density can be distinguished according to the vehicle. By mounting the PM 100 monitoring equipment on the dirt truck, the dust pollution can be more effectively monitored, and the density density of the PM 100 monitoring equipment can be achieved more effectively.
  • the PM 2.5 monitoring equipment is more Allocated to small vehicles such as taxis.
  • the basic modules of air pollutant monitoring equipment include a detection module, a main control module and a communication module.
  • the main control module is connected to the power source of the mobile monitoring vehicle, and the main control module supplies power to the detection module and the communication module on the atmospheric pollutant monitoring equipment.
  • the main control module is connected to the detection module and the communication module on the air pollutant monitoring equipment through a data interface, and performs data exchange with the detection module and the communication module. For example, the data collected by the detection module is processed by the main control module and sent to the communication module, which is then transmitted back to the monitoring center; the instructions sent by the monitoring center are received by the communication module and transmitted to the main control module.
  • the main control module adjusts the detection according to the instructions Module operation.
  • the main control module has the function of storing and exporting data and video data.
  • the main control module has a positioning function or a data interface with a positioning device, and uses GPS, Beidou and other positioning technologies to record the vehicle position in real time.
  • the basic modules of air pollutant monitoring equipment include a detection module, a main control module and a communication module.
  • the detection module detects the pollutant content of the sampled gas through the air pollutant sensor mounted on it, and obtains the concentration data of the pollutant.
  • the detection module can be equipped with a variety of air pollutant sensors, including PM 1 sensor, PM 2.5 sensor, PM 10 sensor, PM 100 sensor, nitrogen oxide sensor, ozone sensor, sulfur dioxide sensor, VOCs sensor or TVOC sensor. monitor.
  • air pollutant monitoring equipment equipped with PM 2.5 sensors and PM 10 sensors can better monitor road dust, and can timely detect road dust pollution and provide early warning.
  • the detection module can also be equipped with other types of sensors, such as wind speed sensors, wind direction sensors, temperature sensors, humidity sensors, pressure sensors, and noise sensors, to provide richer monitoring information.
  • the humidity sensor can also provide humidity correction and calibration basis for the atmospheric pollutant sensor.
  • Mobile monitoring vehicles equipped with atmospheric pollutant monitoring equipment are social vehicles.
  • Social vehicles include city buses, long-distance buses, taxis, earthmoving vehicles, municipal vehicles, official vehicles, Internet-linked vehicles, rental vehicles, shared vehicles, and vehicles with autonomous driving functions. These social vehicles do not need a dedicated site, and professional operators can perform real-time measurement of air pollution. The one-time investment is low, which reduces the energy consumption and road occupation brought by the special vehicles, and ultimately reduces the social public resources. Occupation, reducing the cost of monitoring atmospheric pollutants.
  • An air pollutant monitoring device equipped with a particulate matter sensor was installed on the bus.
  • the characteristics of buses are that the routes are relatively fixed.
  • Using one or more buses equipped with atmospheric particulate sensor monitoring equipment can monitor atmospheric particulate pollution along the entire bus line, reducing monitoring costs.
  • due to the characteristics of the bus it is possible to repeatedly measure a certain road section and give more reliable and more time data.
  • Atmospheric pollutant monitoring equipment equipped with particulate matter sensors is installed on large social vehicles such as dirt trucks, garbage disposal trucks, and long-distance vehicles. These large social vehicles often run on roads with severe dusting. Using these large social vehicles to monitor key dusting sections will do more with less. At the same time, you can measure the dust pollution of your own vehicles.
  • the data detected by these large social vehicles include the background pollution and the pollution of their own vehicles. Through the big data processing, the two types of data can be separated, and the road and self pollution can be evaluated separately to facilitate control.
  • the characteristics of long-distance buses are that they can cover the blind spots of monitoring between cities and achieve a wider range of monitoring.
  • the characteristics of taxis are that they have a wide distribution range and a wide time range, and can measure places that other social vehicles cannot reach.
  • Using social vehicles such as taxis to monitor atmospheric pollutants can more easily find areas with higher environmental health risks, because people-populated areas are hot areas, and these social vehicles, especially taxis, have a higher frequency. Repeated monitoring of these areas can obtain more accurate pollution information in densely populated areas, enabling environmental management departments to deal with pollution problems in a more targeted manner.
  • the height of the taxi ceiling light is basically the same as the height of the mouth and nose of the person. It is the height at which the person mainly breathes.
  • Using a taxi equipped with air pollutant monitoring equipment to monitor the atmosphere at this height can effectively reflect the impact on people's respiratory health.
  • the high level of air is of great significance for the governance of the atmospheric environment.
  • Figure 3 shows the monitoring results of a taxi equipped with atmospheric particulate monitoring equipment in a city in Shandong.
  • these data can automatically generate urban haze maps.
  • Technicians can further determine whether the supervision of pollution sources in the relevant area is in place and guide the precise treatment plan.
  • the monitoring center also conducts statistical rankings on districts, counties, sub-district offices and road sections to provide technical means for governance assessment.
  • Air pollutant monitoring equipment can adjust the monitoring density according to the situation of pollutants. For example, when a social vehicle equipped with air pollutant monitoring equipment passes by a certain section, the air pollutant monitoring equipment detects that the pollutant concentration exceeds the upper limit of the preset value, such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (also 150 ⁇ g / m 3 , 200 ⁇ g / m 3 , 250 ⁇ g / m 3, etc.), the air pollutant monitoring equipment will increase the output frequency of air pollutant concentration detection. For example, a pollutant concentration value calculated every 3 seconds will be changed to a pollutant concentration calculated every 1 second. Value.
  • the preset value such as PM 2.5 value ⁇ 100 ⁇ g / m 3 (also 150 ⁇ g / m 3 , 200 ⁇ g / m 3 , 250 ⁇ g / m 3, etc.)
  • the pollutant that triggers an increase in the detection output frequency may be other pollutants (such as nitrogen oxides, ozone, etc.) that are monitored.
  • the air pollutant monitoring equipment reduces the detection output frequency. For example, after PM 2.5 ⁇ 50 ⁇ g / m 3 , the detection output frequency is restored to output a pollutant concentration value or longer every 3 seconds. time interval.
  • Air pollutant monitoring equipment can adjust the monitoring density and return frequency according to the designated area or road section.
  • the atmospheric pollutant monitoring equipment increases the output frequency of the corresponding atmospheric pollutant monitoring value. For example, the monitoring frequency is calculated to output a pollutant concentration value every 3 seconds. Instead, calculate and output a pollutant concentration value every 1 second.
  • the air pollutant monitoring device reduces the frequency of the corresponding air pollutant monitoring output, such as The output frequency returns to the level before entering the key area or road section.
  • the air pollutant monitoring device can also increase the frequency of transmitting the corresponding air pollutant monitoring data to the monitoring center, for example, by changing the number of data every 3 seconds. It is transmitted once every 1 second; when the mobile monitoring vehicle leaves the area or road section that needs to be monitored, the air pollutant monitoring device reduces the frequency of the corresponding air pollutant monitoring data being returned to the monitoring center, such as Transmission frequency returns to the level before entering the key area or road section.
  • This embodiment takes a section of a city with A as the starting point and B as the end point, and the name is AB as an example to describe a method for improving the monitoring coverage rate when using official vehicles for atmospheric monitoring.
  • the length of the AB road is 1000 meters, the timing unit is 15 minutes, and the set section unit length is 100 meters.
  • the starting point is A.
  • the section unit numbers are named as section unit I, section unit II, section unit III, and section unit IV, Link unit V, link unit VI, link unit VII, link unit VIII, link unit IX, link unit X.
  • the recording time is 24 hours and the number of scheduled tests is 5-10 times.
  • the official vehicle ⁇ , the official vehicle ⁇ , and the official vehicle ⁇ are arranged and combined, there are three ways of arrangement and combination. They are the combination of official vehicle ⁇ and official vehicle ⁇ ; the combination of official vehicle ⁇ and official vehicle ⁇ ; and the combination of official vehicle ⁇ and official vehicle ⁇ .

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Abstract

一种利用出租车（10）进行大气监测时提高监测覆盖率的方法，该方法以一个城市区域的交通路网为关注重点，通过在一组备选出租车（10）中选择预定数量的一组优选车辆组合，安装大气污染检测设备（60），作为移动监测车，对该城市区域的空气质量进行监测。

Description

一种利用出租车进行大气监测时提高监测覆盖率的方法 技术领域
本发明涉及提高移动监测覆盖率的方法，属于环境监测技术领域。
背景技术
经济快速发展的同时也带来了严峻的环境问题，我国大部分城市空气污染问题凸显，空气污染严重影响了城市生态景观，而且对人们的身体健康造成了严重的威胁。利用科学的环境监测技术对环境进行实时的监督和检测，可以为相关人员提供寻找解决环境问题行之有效措施和依据。大气环境监测技术可以实现空气污染现状的摸底、排查和全面分析，为治理和管控空气污染提供关键的数据和依据支持，以此达到环境保护的目的。
随着城市建设规模的不断扩大，城市功能区和产业结构布局的不断优化、调整,许多城市在城市环境、城市建设规模、人口数量及分布等方面都有了很大变化,原有的城市环境大气污染物监测设备都呈现数量上的不足或者空间分布上的不科学,不能继续满足城市环境空气监测的技术要求,从而需要增设或调整。
大气环境监测是测定大气中污染物的种类及其浓度、观察其时空分布和变化规律的过程，主要监测的污染物为大气中的二氧化硫、氮氧化物、臭氧、一氧化碳、PM 1、PM 2.5、PM 10、PM 100和VOCs(挥发性有机物)或TVOC(总挥发性有机物)。大气环境监测系统可以对监测的数据进行收集和处理，并及时准确地反映区域环境空气质量状况及变化规律。环保部门可以利用这些数据进行环境决策、环境管理、污染防治；民众可以根据环境数据采取个人防护，合理安排生活。
现在的大气环境监测设备主要有固定监测站点和移动式监测设备。目前的固定监测站点主要分为大型固定监测站点(大型站)和小型监测站点(小型站)。移动式监测设备主要有专用大气环境监测车、无人机以及手持设备等。
大型固定监测站点相当于一个独立的实验室，通过昂贵精密的仪器监测分析环境中多种污染物水平。大型固定监测站点的特点是监测污染物种类多，精度高。但是大型固定监测站点投入较大，常规投入在百万至千万级别，需要高额的财政支持，因此大型固定监测站点的数量不会很多，无法大规模铺开，因此只能选择比较有代表性和可行的位置进行建设。同时大型固定监测站点对选址也有很高的要求，站点需要有大量面积容纳大型设备，设备需要温度湿度控制，同时需要大量专业高素质人员使用仪器、分析数据和对仪器的维护。此外，从超级站获得的数据只能做单点推论，很难再找邻近的其他超级站来验证。
小型监测站点通过整合低成本、小型化传感器的方法，降低成本进行网格化、批量化的布点。小型监测站点还具有用电方便(可采用太阳能供电)、易于安装等特点。但小型站监测数据的准确性和一致性有待提高，并且需要充分的运营保障。虽然小型监测站点覆盖范围较广，但仍然属于固定式监测，灵活性有限。
专用大气环境监测车是装备有采样系统、污染物监测仪器、气象参数观测仪、数据处理装置及其他辅助设备的汽车。它是一种流动监测站，是地面固定监测站点的一种补充。大气环境监测车可以随时开到发生污染事故的现场或可疑点采样测定，以便及时掌握污染情况，其使用不受时间、地点和季节的限制。大气环境监测车需要有专职人员驾驶，并且需要专业人员操作相关仪器。其价格较为昂贵，无法大规模使用。
现有的监测方式中，比如大型站点、专用移动监测车对颗粒物的测量大多采用称重法、微振荡天平法、β射线法；对VOCs检测使用GC-FID(气相色谱-火焰离子检测)方式。这些精密检测仪器大多体积很大，且十分昂贵，不便于广泛布点监测。其他污染物如二氧化硫、氮氧化物、臭氧和一氧化碳的检测也具有类似的问题。类似的专用移动监测车到达指定位置后需要停车监测，相当于一个固定监测站点，无法实时移动进行监测。
城市网格化的大气污染物监测测量投资巨大，目前的监测方式不能做到全面覆盖。每个监测点需要专业人员进行安装维护，每隔一段时间需要进行相应的校准；各监测点的采样口一般安装位置较高，不利于监测地面污染情况(如道路扬尘)。同时人口密度较大的道路和地区，往往车流尤其是出租车也较为密集，对这样的地点需要密集、着重监测。
采用城市社会车辆作为移动监测车辆，搭载大气污染物监测设备以及定位设备，结合无线传输技术，就能够实现大规模近地监测空气污染。
【现有技术1】CN106841525A
上述文件公开了一种大气特征污染在线监控系统构建的方法。
【现有技术2】《大气污染概论》，吴方正，农业出版社
该文献(见第147-149页)公开了城市大气监测的布点问题，根据规定的容许误差和置信度范围确定监测点的数量。
很明显，该文献给出了在一个监测区域内如何确定监测点的数量的启示；但是其只适用于固定点监测点。对于移动车辆这种监测模式下，其面临的问题是有显著的差异的。
固定点监测特点
首先，对于固定点监测来说，其覆盖范围是固定的，监测点的数据只能代表监测点附近的污染情况，远离监测点的地方的污染情况，只能间接推算得到；特别是监测点之间的“三不管区域”，属于明显遗漏的区域。
很明显，这种以离散的监测点来覆盖一个区域，这些监测点的数量取决于每个监测点能够有效代表的污染范围。
图2显示了固定化网格布点的局限性。
移动监测特点
移动监测的显著特征在于，移动设备随着移动车辆的运行，连续变换监测位置，基本上只在交 通路网沿线范围进行监测。其移动路径是交通路线；其覆盖范围是整个区域中的交通路网。
覆盖范围不同
固定监测每个监测设备的监测区域是固定的，离散的“点”；
移动监测每个监测设备的监测区域是整个路网，是连续的“线”或者“带”。
比起“点”状覆盖，“线”状覆盖具有显著的优势。
“线”状覆盖所获取的更大范围的数据分布，对于一个城市的整体空气污染的判断来说，更具有代表性。
监测数据来源不同
对于固定监测模式来说，每个被监测位置的数据来自于同一个或同一组监测设备；
对于移动监测模式来说，每个被监测位置的数据来自于很多不同的监测设备。
也就是说，路网范围内某个位置的污染情况可以被不同的移动设备(搭载在移动车辆上)在不同的时间段多次测量到。这样就可以通过不同设备的监测数据之间的关联，间接评价监测设备的数据可靠性。
发明内容
本发明要求以下在先申请的优先权。
【在先申请】
PCT/CN2019/074042
PCT/CN2019/074043
PCT/CN2019/074044
PCT/IB2018/055531
PCT/IB2018/055526
针对背景技术中监测方式的不足，以及城市大气环境污染监测的特点。本发明提供了一种在确保城市空气污染物监测数据客观性的前提下确定移动监测漏检率的方法。
本发明利用大量随机运行的社会车辆搭载隐藏式安装的监测设备作为移动监测车辆，辅助以固定的监测设备，能够实现对城市区域实时污染物分布和变化情况的监测，只要移动监测设备达到一定的数量，那么这些移动设备产生的监测数据就能够客观地反映城市空气污染分布和污染程度的真实情况。
环境监测需要保证监测数据的客观和有效。环境污染尤其是大气环境污染与其他类型的污染相比具有随时间、空间变化大的特点。根据这些特点进行监测对于获得正确反映大气污染实况的监测结果具有重要意义。空气污染物的时空分布及其浓度与污染物排放源的分布、排放量及地形、地貌、气象等条件密切相关。污染物的类型、排放规律及污染物的性质不同，其时空分布特 点也不同，同一地点的大气污染物水平是快速波动变化的。在大气污染监测中有时间分辨率的概念，要求在规定的时间内反映出污染物浓度变化。例如，有些急性危害的污染物，要求分辨率为3min；有些化学烟雾剂比如臭氧对呼吸道的刺激反应，要求分辨率为l0min。
本发明通过大量部署多种大气污染物监测设备于移动监测车辆上进行大气污染物监测，实时传输位置和监测数据，可以实现从固定点监测到全路网监测和广泛的地域覆盖，反映城市污染物的真实情况。
图1显示了本发明的系统组成，本系统包含搭载监测设备的移动监测车辆、监控中心、固定监测站点。本发明提出合理大量增加设备投放密度，实现城区机动车道路的覆盖，同时监测设备需要实时以秒级回传数据，通过多车接力可实现24小时连续监测，通过监控中心对数据的处理，最终得到可靠、客观和有效的大气环境数据。大量使用监测设备弥补了各固定监测站点点位数量的不足，为网格化监管提供了数据支持。通过安装了大气污染物在线监测设备的移动监测车辆不断在城市内移动，可实时监测到各个角落的污染物浓度、洒水和道路破损情况，使大气污染监测可以覆盖每个社区、每个路段，避免了死角和盲区。同时系统也可以接入固定监测站点的数据，使监测数据更加完整。
当搭载大气污染物在线监测设备的移动车辆数量较少时，采集的监测数据数量将会不足，样本不够，没有办法及时提供城市污染物的全貌信息。当监测车辆达到一定数量后，同时搭载多种污染物监测设备，才可以避免大量小范围短时间空气污染的漏检，大量搭载大气污染物在线监测设备的车辆能够得到更加完整的污染物空间和时间覆盖情况，提高监测数据的有效性。图6为同一地点的污染物浓度随时间的变化情况，T 0到T 6表示的是污染开始到消散的时间，T 3时刻所对应的污染物浓度最大，由于大气污染随时间变化较快，当投放的搭载大气污染物在线监测设备的车辆不足时，可能无法监测到同一地点T 0至T 6之间的数据，这样并不能得到此地真实的污染物情况，只有尽可能多地捕捉到T 0至T 6之间的数据才可以反映真正的污染全貌。本方案由于提出使用大量移动监测车辆搭载大气污染物监测设备进行数据采集，能够捕捉到大部分的小范围短时间的污染，可以对污染进行溯源，可以对大气污染的成因进行分析，可以为环保部门的执法和民众个人防护提供客观真实的大气污染信息。
大量增加设备安装密度，实现城区机动车道路的覆盖，同时监测设备需要实时以秒级回传数据，通过多车接力可实现24小时连续监测。大量使用监测设备弥补了各监测站固定监测点位数量的不足，为网格化监管提供了数据支持。通过大量安装了车载大气污染物在线监控系统的移动监测车辆不断在城市内移动，可实时监测到各个角落的污染物浓度、洒水和道路破损情况，使大气污染监测可以覆盖每个社区、每个路段，避免了死角和盲区。
本方案提出投放搭载大气污染物在线监测设备的移动监测车辆所需数量的模型，具体数量根据监测的平均漏检率确定。平均漏检率代表对一个监测区域的空气污染情况的检出概率。
方法一：按每平方公里的监测车辆的密度来衡量
该方法依然着眼于监测设备的数量相对于一个区域的密度。
如图7所示，平均漏检率与城市中的监测设备密度有关，监测设备密度用每平方公里的车辆数 目表示。投放的车辆总数很少时，平均到城市中每平方公里的数量将会很少，对于小范围快速消散的污染物的平均漏检率会很高，投放密度越高，平均漏检率就会降低。当投放的监测设备数量达到一定值之后，如投放密度达到n 0时，平均漏检率就会降低到m 0。
但是，方法一存在很大的缺陷。
考虑到路网的分布与网格化的区域的对应关系的不协调；移动车辆只在路网分布的范围运行，而且存在非常明显的覆盖不均现象：特别是以出租车为载体的移动监测设备在有些路段出现和逗留的概率远远大于另外一些偏远或冷清的路段。
也就是说，移动设备在持续移动中，那么就存在移动设备在一个大的区域范围内的分布是不均衡的。
如果我们按照每个网格内是否有足够的监测数据来衡量的话，就会发现，一部分网格中存在冗余的数据(移动监测车辆扎堆出现)，而另一部分网格中缺少数据(鲜有移动监测车辆出现)。
这个现象可以如图9显示，统计每个小型网格内移动监测设备测量大气污染的次数；其中每15分钟作为一个计时单元，在一个计时单元内，同一台移动设备的多次测量按一次计算。
固定点与移动点的区别在于单点的监测区域不同，固定点只能覆盖固定大小的区域，而移动点随时间的变化覆盖区域是变动的，在一段时间内(例如一天)可以覆盖远超固定点的空间范围，在满足数据量的情况下，移动点覆盖全城所需数量与固定点差别很大，因此移动点的全城部署数量的估算是与固定点不同的问题，不能套用固定点的计算理论来解决。
目前大气污染监测国控站数据量以1小时得出一个数据为主，评价城市空气质量多以天平均数据为主，而移动监测站多以分钟级或秒级数据为主，高频数据配以移动监测可以达到多个固定监测站点的效果，车载移动监测以连续行进监测的方式进行时更是实现高覆盖率，以出租车等车辆搭载移动监测设备时，更具有出现地点灵活、随机，无法提前采取措施影响空气质量，数据更具有说服力的效果。
方法二：按交通路网上各个路段单元被监测次数的分布来衡量
考虑到移动监测的特点，我们把关注点放到路网上。
如果把一个区域(如某城市城区范围)的路网按照一定的粒度，即长度单位(比如每100米，或者200米)划分为一个个路段单元；我们把搭载移动监测设备(处于工作状态)的车辆在经过每个路段单元的次数作为重要的衡量指标。
进一步地，为排除连续多次测量带来的短期数据重复的情形，按每15分钟或30分钟作为一个计时单元，在一个计时单元内，同一台移动设备的多次经过只按一次检测计数。不同监测车辆的数据进行累计。
如果把所有的路段单元拼接在一起，形成一个长“带”。按照一天来统计的话，我们得到图10的示意。该曲线的横坐标为各个路段单元；纵坐标为各个路段单元在一定时间内(通常选取一天)累计的检测次数(检测计数)。
由于热点路段和冷清路段的差异，以及出租车的随机性；我们基本可以判断在统计学意义上，不同路段单元上的检测次数，是按照时间不断叠加，随着数量的增加，呈现出某种半正态分布的形态。
这里需要对监测次数等术语做个定义。
检测次数：本文中，特指某个路段单元被处于监测状态的移动监测车辆经过的次数；同一辆监测车在一个连续的计时单元内的多次经过只按一次计数。
计时单元可以为15分钟、30分钟，或1个小时。
计数周期：以一个计时单元为期限，当一辆监测车辆进入某路段单元时，则触发一个新的计数周期；在计数周期内，同一辆监测车进入该路段单元都不再计入检测次数中。计数周期存续一个计时单元后结束。同一辆监测车在每个路段单元只能有最多一个计数周期；在前一个计数周期结束前，无法触发新的计数周期。
图11是依据上述方法得到的某个城市区域的24小时内监测次数(出租车)按路段单元的统计分布图。
从图中可以清晰地看到，当出租车数量只有50辆时，在24个小说内，只有大约30％的路段单元录得10次以上的检测次数。
随着安装有监测设备的出租车数量的逐步增加，越来越多的路段单元可以录得10次以上的检测次数。
如果我们以80％的路段单元在24小时内能够录得10次以上的检测次数为期望指标。那么作为示例，可以推算出安装有监测设备的出租车数量需要达到290台。
期望指标：是用来评估一个城市区域路网范围内的大气污染检测数据的采集量是否能反应整体污染状况的指标。这个指标由两个重要的参数来表征：覆盖范围、预定检测次数。
覆盖范围：达到预定检测次数的路段单元数量占路段单元总数的比例，可选的数值范围为50％～90％之间。一般选取70～80％为易。
预定检测次数：当一个路段单元在一天内录得的检测次数达到某个数值时，该路段单元被认可为得到了检测覆盖；否则，该路段单元被视为漏检路段单元。一般来说，考虑到计时单元的长度，预定检测次数可选范围为5～10次。
漏检路段单元：某一个路段单元在一天内录得的检测次数没有达到预定检测次数时，该路段单元被视为漏检路段单元。
基于路段单元的检测次数的模型下，针对某个大气污染检测对象，当我们设定一个期望指标的时候，实际上也就设定了一个平均漏检率指标m。
对于需要对多个污染物进行监测的情形，就需要对每个特定污染物设定各自的期望指标；考虑到污染危害的程度不同、治理的重点不同、时机不同，各个期望指标可能会有显著的差异。以图11为例，如果监测对象是PM2.5，其平均漏检率可以表示为：m(PM2.5，80％，10)；其含 义为：在一个城市区域内，80％的路段单元能够在24小时内录得至少10次检测(检测次数)。
本发明公开了一种确定移动监测车的额定数量的方法，所述方法以一个城市区域的交通路网为关注重点，通过给移动监测车，特别是出租车，安装空气污染检测设备，来实现对该城市区域的空气质量监测；所述方法包含以下步骤：
一)建立移动监测车检测次数曲线模型
1)针对某个城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录(检测设备编号、移动监测车进入路段单元的时间、移动监测车经过该路段单元的累计经过次数(初始值为“0”))
2)选择一部分搭载有定位系统的移动监测车(例如出租车)，跟踪记录各移动监测车在不同路段单元的经过次数(在没有安装检测设备的情况下，等同于虚拟检测次数)；考虑到出租车司机一般都有区域取向，参与模型构建的出租车的数量不宜太少，一般来说应该有至少50辆。
3)持续记录至少一周；将每天的统计数据累计后计算出日平均值；
4)以时间累计换移动监测车数量的方式，形成24小时内监测次数(出租车)按路段单元的统计分布图(如图11所示)；如果参与模型建立的出租车数量为50，则2天的累计数据(日平均值乘以2)等同于C＝100的曲线；4天的累计数据等同于C＝200的曲线；依次类推。
二)确定期望指标的两个参数(覆盖范围、预定检测次数)
三)依据检测次数曲线模型，寻找出刚好满足期望指标的曲线，得到移动监测车的额定数量(C 0)。
当出租车(移动监测车)的数量超过额定数量(C 0)时，能够达到预定检测次数的路段单元的覆盖范围会进一步提高。但是这种提高的程度是有限的，原因在于某些冷清路段、偏远路段，或者由于交通管制等原因所设计的路段，被检测到的可能性几乎为零。
另一方面，某些空气污染物(如汽车尾气)的出现场景与出租车的活跃区域是存在某些正相关的；这就意味着，出租车出没比较少的地方，往往也是某些空气污染物比较稀薄的地方；因此，在选择覆盖范围时，适当放弃纠结于部分漏检区域是合理的。
方法三、两种监测车辆(出租车、公交车)共存的情形
当公交车也参与城市空气污染的监测时，覆盖范围的问题就有了显著的变化。
公交车通常路线固定，覆盖区域固定，工作时间固定，出勤率固定，因此公交车是出租车的特例在选择安装监测设备的公交线路时，选择出租车经过概率小的路线会起到补盲作用，在达到同样的漏检率指标时有助于减少总的移动监测点布设数量。
选择出租车经过概率小的公交路线安装移动监测点时，一辆公交车所监测的数据量会起到多辆出租车的效果，而计算漏检率指标时是以满足小概率路线数据量达到最低要求而得出的，所以两者结合布设会大大减少城市监控车辆总数量，节约资源。
首先，公交车通常沿着固定的线路往返运行；那么对应于基于路段单元的监测次数模型，公交线路的曲线实际上是平稳的折线。如图12下部所示，公交线路B1和B2在其各自运行路线上，在其各自经过的路段单元上，检测次数是均匀分布的。
如果对比线路B1和线路B2，就会发现，公交线路B1中包含了一部分路段(阴影部分)，刚好落在了出租车很少出没的区域。
那么，如果公交线路B1和出租车配合完成某污染物的监测工作，那么公交线路B1就可以帮助覆盖出租车难以覆盖的漏检路段。
也就是说，公交线路B1和出租车的组合可以从整体上显著增加污染检测的覆盖范围。
而公交线路B2，由于其路径全部位于出租车的覆盖范围，所以，在B2上安装检测设备并不能从整体上显著增加污染检测的覆盖范围。
那么，在存在多条备选公交线路的情况下，如何确定哪些公交线路安装检测设备，就是一个很有意义的技术问题。
本发明公开了一种出租车和公交车协同监测时选择参与监测的公交线路的方法：
一)建立出租车检测次数曲线模型
1)针对某个城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录(检测设备编号、进入路段的时间、累计经过次数(初始值为“0”))
2)选择部分搭载有定位系统的出租车，跟踪记录各个出租车在不同路段单元的经过次数(在没有安装检测设备的情况下，等同于虚拟检测次数)；考虑到出租车司机一般都有区域取向，参与模型构建的出租车的数量不宜太少，一般来说应该有至少50辆。
3)持续记录至少一周；将每天的统计数据累计后计算出日平均值；
4)以时间累计换出租车数量的方式，形成24小时内监测次数(出租车)按路段单元的统计分布图(如图11所示)；如果参与模型建立的出租车数量为50，则2天的累计数据(日平均值乘以2)等同于C＝100的曲线；4天的累计数据等同于C＝200的曲线；依次类推。
二)建立各条公交线路检测次数曲线模型
其横轴与出租车检测次数曲线模型的横轴一致；按照公交线路的运行计划，给相应的路段单元赋予检测次数数值。
三)将各条公交线路按照其覆盖的漏检路段单元的数量进行排序
1)先选择一个覆盖范围初始值f 0(如70％或80％)；循环变量初始值i＝0
2)i＝i+1；
3)确定覆盖的漏检路段单元最多的公交线路，排第i位；
4)计算排在第i位的公交线路所覆盖的漏检路段单元所对应的局部覆盖范围b i(百分比％)；
5)将覆盖范围f (i-1)减去局部覆盖范围b i，得到新的覆盖范围f i和新的漏检路段单元；新的漏检路段单元应当扣除排在前面的公交线路已经覆盖的漏检路段单元；
6)从剩余的公交线路中继续选择覆盖的漏检路段单元最多的公交线路，排第i+1位；
7)重复步骤2)至6)，直至排序结束。
四)按步骤三)的排序选择参与协同监测的公交线路。
出租车额定数量的调整
由于优化选择的公交线路能够部分覆盖出租车难以有效覆盖的路段单元，在维持整体覆盖范围不变的前提下，可以适当减少出租车的数量。
如图12所示，由于公交线路B1能够覆盖b 1所代表的路段单元，所以，出租车的额定数量可以从原来的290辆减少到140辆。
五)确定期望指标的两个参数(覆盖范围f、预定检测次数)；
六)对于每条选定的公交线路Bi，将其局部覆盖范围bi依次从覆盖范围f中减去，得到新的覆盖范围f’；
七)以新的覆盖范围f’和预定检测次数，从出租车检测次数曲线模型，寻找出刚好满足期望指标的曲线，得到出租车车的额定数量(C 0)。
提高监测覆盖率的方法
要提高监测覆盖率，原则上就是要选择一批覆盖范围重叠度比较低的车辆来进行监测。
对每一辆特定的车而言，其活动的范围都有一定的规律。公务车，通常以所在单位为公务的出发点，沿着公务任务所经线路行驶。公交车，则有固定的运行线路。相对来说，出租车的运行要随意得多，但是每辆出租车都有其特定的交接班地点，休息地点。
因此，当选择车辆时，即使是同类型车，也应该重复考虑每辆车的运行路径规律特征。
图13是某特定车辆在一周内出现在各个网格的统计分布图。
图中旁边标注“X”的网格是该车辆的主要停靠位置。对于公务车来说，这可能是单位所在地。对于出租车而言，这可能是交接班的位置，或者出租车司机住宅。
本发明公开了一种利用出租车进行大气监测时提高监测覆盖率的方法，所述方法以一个城市区域的交通路网为关注重点，通过在一组备选出租车中选择预定数量的一组优选车辆组合，安装大气污染检测设备，作为移动监测车，对该城市区域的空气质量进行监测；所述方法包含如下步骤：
1)针对所述城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录；
2)对所述备选出租车在一段时间内的运行路径进行统计，跟踪记录各备选出租车在不同路段单元的经过次数；同一辆备选出租车在一个连续的计时单元内的多次经过只按一次计数；得到备选出租车出现在各个路段单元的统计分布图；
3)通过排列组合的方式，从所述一组备选出租车中选取预定数量的一组优选车辆组合，使得该组合中各车辆的统计分布图叠加在一起的累计统计图中，满足预定监测次数的路段单元的数量最大。
多种车辆的协同
采用不同种类车辆的组合，可以重复利用不同车辆的运行规律和不同的路段单元覆盖范围，使总体覆盖率得到大幅度提高。
使用社会车辆还有其他如下特点：公交车的特点是路线比较固定，利于对某一路段反复多次测量，能够给出更可靠更多时段的数据，公交车发送班次车辆比较多，间隔时间比较均匀，班次多的时候往往是交通高峰，也是颗粒物污染较严重的时段。出租车的特点是分布范围比较广，时间范围广，可以测量到公交车不能到达的地方，测量时间范围补充公交车不运营的时段。渣土车的行驶路线往往是道路扬尘污染严重的路段，让这样的测量来重点监控扬尘路段，事半功倍，还可以测量自身车辆的扬尘污染情况。多个渣土车的综合数据是道路扬尘背景数据，自身车辆的数据包含背景和自身车辆的污染，通过大数据处理，可以将两种数据分开，从而对道路和自身污染分别给出评价，以利于管控。长途车的特点是可以覆盖城市之间的监测盲点，形成更大范围的监测。
使用出租车等社会车辆进行大气污染物监测，更能找到环境健康风险更高的地区，因为人多的地方是热点领域，也是这些社会车出现频率更高的地区。对这些地区进行多次重复地监测可以获得人流密集地区更加准确的污染信息，使得环境管理部门可以更有针对性地处理污染问题。同时出租车顶灯的高度基本与人员口鼻高度相当，是人员主要进行呼吸的高度，采用出租车搭载大气污染物监测设备对这一高度的大气进行监测，可以有效的反映影响人们呼吸健康这一高度的空气，对大气环境治理有重要意义。
环境监测尤其是网格化监测成本较高，该发明的有益之处还在于该装置利用市内公交车、长途车、出租车、渣土车等社会车辆搭载大气污染物传感器进行实时测量，不需要专用的场地、专业操作人员，对一次性投入要求较低从而降低了测量的成本。同时降低了专用车辆带来的能耗、道路占用。最终减少了对社会公共资源的占用，降低了大气污染物监测成本。
大气污染物监测设备包括检测模块、主控模块和通讯模块；检测模块包含一种或多种大气污染物传感器单元；大气污染物传感器单元为以下传感器之一：PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、O 3传感器、SO 2传感器、VOCs传感器或TVOC传感器。
主控模块与移动监测车辆的电源进行连接，主控模块为大气污染物监测设备上的检测模块和通 讯模块进行供电。主控模块与大气污染物监测设备上的检测模块和通讯模块还通过数据接口连接，与检测模块通讯模块进行数据交换。
附图说明
图1为本发明系统组成示意图；
图2是网格化固定监测站点布点模式示意图；；
图3是山东某市实例的监控平台示意图；
图4是大气污染物监测设备基本模块组成示意图；
图5示意了利用社会车辆监测的隐蔽性；
图6显示的是大气污染的特性，大气污染具有时效性；
图7显示的是平均漏检率与监测设备投放密度指标的关系；
图8是大气污染物监测设备包含视频采集模块的示意图；
图9是移动监测模式下检测数据在网格中的分布示意图；
图10是“带”状路网在基于路段单元模型下的累计检测分布示意图；
图11某个城市区域的24小时内检测次数(出租车)按路段单元的统计分布图；
图12是出租车和公交车协同监测模式下检测次数的曲线示意图；
图13是某特定车辆在一周内出现在各个网格的统计分布图。
图中，10-出租车，20-公交车，30-监控中心，40-固定监测站点，50-用户终端，70-其他社会车辆，60-大气污染物监测设备，601-检测模块，602-视频采集模块，603-通讯模块，604-主控模块；
图中，C-出租车(监测车)数量，C 0-出租车(监测车)额定数量，b 1-公交线路B1漏检路段单元所对应的局部覆盖范围(百分比)；f 0-覆盖范围初始值，f 1-新的覆盖范围(考虑b 1的情况下)。
具体实施方式
实施例1：
一种提高大气污染物监测数据客观性的方法及系统，利用社会车辆搭载大气污染物监测设备，包括大气污染物监测设备、监控中心、固定监测站点和用户终端，如图1所示。
大气污染物监测设备安装在社会车辆上，用于监测车辆所处位置的大气环境质量，大气污染物监测设备具备信息传输功能，可以将监测到的数据、位置数据和时间信息通过无线的方式回传至监控中心。大气污染物监测设备可以对道路情况进行视频录制采集，记录道路污染情况，大气污染物监测设备可以通过无线传输的方式将采集的视频回传至监控中心。大气污染物监测设备具有数据、视频资料的储存功能，将采集的数据、视频资料保存在本地。大气污染物监测设备还设有数据传输接口，可以通过本地传输的方式将保存的数据和视频资料拷贝至现场的维修或工作人员。
监控中心接收大气污染物监测设备回传的数据，监控中心对这些数据进行储存、处理。监控中心还可以收集其他类型监测设备的数据，如收集微型固定监测站点数据、收集附近国家固定监测站点的数据等。监控中心综合社会车辆的大气污染物监测设备回传的数据、收集到的微型固 定监测站点数据和收集到的附近国家固定监测站点的数据，生成数据列表、数据排名和污染云图、历史回放等数据呈现方式。这些生成的数据列表、数据排名和污染云图等处理结果通过网络的方式发送至用户终端，用户可以根据需求查询和使用。监控中心还可以远程控制大气污染物监测设备的运行，如开启关闭大气污染物监测设备、开启关闭视频采集模块、调整监测频率、修正大气污染物监测设备误差等。
实施例2：
得到一个城市客观的大气污染物监测数据才可以反映这个城市真实的大气污染程度，本发明需要设定一组最高平均漏检率指标M 0代表监测数据的客观性。例如一个城市需要对PM 10进行监测，其漏检率表示为m(PM 10)，若这个城市要求m(PM 10)<20％，即表示没有被监测设备捕捉到的PM 10污染事件要小于总PM 10污染事件的20％。
为了达到m(PM 10)<20％，引入最低投放密度指标N 0，N 0表示要达到M 0所需要监测设备的最小投放密度指标。在本例子中，为了达到m(PM 10)，所需投放的搭载PM 10传感器的监测设备最低投放密度为n(PM 10)。该n(PM 10)需要进行测算，测算的依据是城市的面积、搭载移动监测设备车辆的数目、搭载移动监测设备车辆的日行驶里程、搭载移动监测设备车辆的行驶范围、车辆类型、搭载的仪器精度等参数。城市面积越大，所需要投放的监测设备越多；搭载移动监测设备的车辆日行驶里程越多，所需投放车辆越少；搭载移动监测设备的车辆行使范围越大，所需投放车辆越少。
实施例3：
对一个城市的PM 10和SO 2进行监测，在本发明中这个城市的最高平均漏检率指标M 0表示为m(PM 10)、m(SO 2)。不同污染物的污染贡献水平会有不同、城市的重视程度也不同，所以不同污染物的平均漏检率会有对应的要求。通常城市对SO 2的监测重视程度低于PM 10，则本例中PM 10和SO 2的最高平均漏检率数值设定为m(PM 10)＝20％、m(SO 2)＝30％。
当m(PM 10)＝20％、m(SO 2)＝30％，仅使用出租车搭载监测设备，每个设备仅能监测PM 10或SO 2的情况下，监测设备的最小投放密度n(PM 10)将大于n(SO 2)，即搭载PM 10监测设备的出租车投放量要多于搭载SO 2监测设备的出租车。
实施例4：
对一个城市的PM 10和SO 2进行监测，在本发明中这个城市的最高平均漏检率指标M 0表示为m(PM 10)、m(SO 2)，本例中PM 10和SO 2的最高平均漏检率数值设定为m(PM 10)＝20％、m(SO 2)＝30％。目前的监测设备还可以通过内部检测模块的组合，同时测量多种污染物。
本例中第一种投放方式是所有车辆都搭载可以同时测量PM 10和SO 2的监测设备。那么N 0最小监测设备投放密度指标只需满足指标中最严格的那个最高平均漏检率，第一种方式的n(PM 10)＝n(SO 2)，具体n(PM 10)值通过城市的面积以及实施例二中所述投放密度指标参数关系计算得出。
本例中第二种投放方式是一部分车辆都搭载可以同时测量PM 10和SO 2的监测设备,另一部分车 辆搭载仅可以测量PM 10设备的车辆。此时n(PM 10)>n(SO 2)，具体n(PM 10)和n(SO 2)需要经过测算并最终满足m(PM 10)＝20％、m(SO 2)＝30％。
实施例5：
城市中的扬尘污染主要体现在PM 100数值上，渣土车是扬尘污染的一种主要贡献。城市如果需要对PM 2.5和PM 100进行监测，最高平均漏检率指标要求为m(PM 10)＝m(PM 100)＝20％。投放密度可以根据车辆进行区分，将PM 100监测设备跟多的搭载于渣土车上，可以更有效的监测扬尘污染，更有效的达到PM 100监测设备投放密度指标；PM 2.5监测设备则更多的分配给出租车等小型车辆。
实施例6：
大气污染物监测设备基本模块包括检测模块、主控模块和通讯模块。其中主控模块与移动监测车辆的电源进行连接，主控模块为大气污染物监测设备上的检测模块和通讯模块进行供电。主控模块与大气污染物监测设备上的检测模块和通讯模块还通过数据接口连接，与检测模块、通讯模块进行数据交换。例如检测模块采集的数据经主控模块处理后发送至通讯模块，再由通讯模块回传至监控中心；监控中心发送的指令由通讯模块接收后传输至主控模块，主控模块根据指令调整检测模块的运行。主控模块具备数据和视频资料的储存和导出功能。主控模块具备定位功能或与定位设备的数据接口，利用GPS、北斗等定位技术实时记录车辆位置。
实施例7
大气污染物监测设备基本模块包括检测模块、主控模块和通讯模块。其中检测模块通过搭载的大气污染物传感器对采样气体的污染物含量进行检测，得到污染物的浓度数据。检测模块可以搭载多种大气污染物传感器，包括PM 1传感器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、臭氧传感器、二氧化硫传感器、VOCs传感器或TVOC传感器，用于污染物的监测。例如搭载PM 2.5传感器和PM 10传感器的大气污染物监测设备可以较好的对道路扬尘进行监测，可以及时发现道路扬尘污染并进行预警。
检测模块还可以搭载其他类型的传感器，例如风速传感器、风向传感器、温度传感器、湿度传感器、压强传感器、噪音传感器，提供更丰富的监测信息。并且例如湿度传感器还可以为大气污染物传感器提供湿度修正和校准依据。
实施例8：
搭载大气污染物监测设备的移动监测车辆是社会车辆。社会车辆包括市内公交车、长途车、出租车、渣土车、市政车辆、公务车辆、网约车、租赁车辆、共享汽车，以及具有自动驾驶功能的车辆。这些社会车辆不需要专用的场地、专业操作人员便可以对大气污染的情况进行实时测量，一次性投入较低，降低了专用车辆带来的能耗、道路占用，最终减少了对社会公共资源的占用，降低了大气污染物监测成本。
实施例9：
将搭载颗粒物传感器的大气污染物监测设备安装于公交车上。公交车的特点是路线比较固定，利用一台或者几台安装了大气颗粒物传感器监测设备的公交车便可以对整条公交线路的沿线进行大气颗粒物污染监控，降低了监测成本。同时由于公交车行使的特点，可以对某一路段反复多次测量，能够给出更可靠更多时段的数据。公交车发送班次车辆比较多，间隔时间比较均匀，班次多的时候往往是交通高峰，也是颗粒物污染较严重的时段。
实施例10：
将搭载颗粒物传感器的大气污染物监测设备安装于渣土车、垃圾处理车、长途车等大型社会车辆上。这些大型社会车辆行驶的路线往往是道路扬尘严重的路段，利用这些大型社会车辆监控重点扬尘路段事半功倍，同时还可以测量自身车辆的扬尘污染情况。这些大型社会车辆所检测的数据包含背景污染情况和自身车辆的污染情况，通过大数据处理，可以将两种数据分开，从而对道路和自身污染分别给出评价，以利于管控。长途车的特点是可以覆盖城市之间的监测盲点，达成更大范围的监测。
实施例11：
将搭载颗粒物传感器的大气污染物监测设备安装于出租车上。出租车的特点是分布范围比较广，时间范围广，可以测量到其他社会车辆不能到达的地方。使用出租车等社会车辆进行大气污染物监测，更能找到环境健康风险更高的地区，因为人多的地方是热点领域，也是这些社会车辆尤其是出租车出现频率更高的地区。对这些地区进行多次重复地监测可以获得人流密集地区更加准确的污染信息，使得环境管理部门可以更有针对性地处理污染问题。同时出租车顶灯的高度基本与人员口鼻高度相当，是人员主要进行呼吸的高度，采用出租车搭载大气污染物监测设备对这一高度的大气进行监测，可以有效的反映影响人们呼吸健康这一高度的空气，对大气环境治理有重要意义。
图3为山东某城市搭载大气颗粒物监测设备的出租车监测结果。共约100辆车，每天合计行程超过2.3万公里，可产生120万组数据。通过监控中心的大数据处理平台，这些数据可自动生成城市霾图。技术人员可进一步判断相关区域污染源监管是否到位，指导精准治理的方案。监控中心还对区县、街道办及路段进行统计排名，为治理考核提供技术手段。
实施例12：
大气污染物监测设备可以根据污染物情况调节监控密度。如当搭载大气污染物监测设备的社会车辆路过某路段，大气污染物监测设备检测到污染物浓度超过预设值上限之后，如PM 2.5值≥100μg/m 3(也可以是150μg/m 3、200μg/m 3、250μg/m 3等)后，大气污染物监测设备提高大气污染物浓度检测输出频率，例如由每3秒计算输出一个污染物浓度数值改为每1秒计算输出一个污染物浓度数值。触发提高检测输出频率的污染物可以是监测的其他污染物(如氮氧化物、臭氧等)。当污染物浓度低于设定值下限后，大气污染物监测设备降低检测输出频率，如PM 2.5≤50μg/m 3后，检测输出频率恢复为每3秒输出一个污染物浓度数值或更长的时间间隔。
实施例13：
大气污染物监测设备可以根据指定的区域或路段调节监控密度和回传频率。
当搭载大气污染物监测设备的社会车辆进入需要重点监控的区域或路段时，大气污染物监测设备提高相应大气污染物的监测数值输出频率，例如监测频率由每3秒计算输出一个污染物浓度数值改为每1秒计算输出一个污染物浓度数值；当所述移动监测车辆离开需要重点监控的区域或路段时，所述大气污染物监测设备降低相应大气污染物的监测输出频率，例如将污染物输出频率恢复到进入重点区域或路段之前的水平。
当移动监测车辆路过需要重点监控的区域或路段时，所述大气污染物监测设备还可以提高相应大气污染物的监测数据向监控中心回传的频率，例如由每3秒回传一次个数值改为每1秒回传一次；当所述移动监测车辆离开需要重点监控的区域或路段时，所述大气污染物监测设备降低相应大气污染物的监测数据向监控中心回传的频率，例如将回传频率恢复到进入重点区域或路段之前的水平。
实施例14：
本实施例以某城市一段以A为起点B为终点，名称为AB的道路为例，说明利用公务车辆进行大气监测时提高监测覆盖率的方法。
AB道路长度为1000米，计时单元为15分钟，设定的路段单元长度为100米则以A为起点，路段单元编号分别命名为路段单元Ⅰ、路段单元Ⅱ、路段单元Ⅲ、路段单元Ⅳ、路段单元Ⅴ、路段单元Ⅵ、路段单元Ⅶ、路段单元Ⅷ、路段单元Ⅸ、路段单元Ⅹ。有三辆公务车辆，分别命名为公务车辆α、公务车辆β、公务车辆γ。记录时间为24小时，预定检测数量5-10次。
三辆公务车辆在24小时累计经过次数表
Figure PCTCN2019097594-appb-000001
如果对公务车辆α、公务车辆β、公务车辆γ进行排列组合，有三种排列组合方式。分别为公务车辆α、公务车辆β组合；公务车辆α、公务车辆γ组合，和公务车辆β、公务车辆γ组合。
公务车辆α、公务车辆β组合在24小时累计经过次数表
Figure PCTCN2019097594-appb-000002
公务车辆α、公务车辆β组合在24小时中，10个路段单元中达到预定检测数量的路段单元为4 个，认为覆盖范围为40％。
公务车辆α、公务车辆γ组合在24小时累计经过次数表
Figure PCTCN2019097594-appb-000003
公务车辆α、公务车辆γ组合在24小时中，10个路段单元中达到预定检测数量的路段单元为8个，认为覆盖范围为80％。
公务车辆β、公务车辆γ组合在24小时累计经过次数表
Figure PCTCN2019097594-appb-000004
公务车辆β、公务车辆γ组合在24小时中，10个路网单元中达到预定检测数量的路段单元为7个，认为覆盖范围为70％。
根据以上结果，我们认为公务车辆α、公务车辆γ的组合达到了80％的覆盖范围，在所有组合中覆盖范围最大，因此可以将公务车辆α、公务车辆γ作为优选的组合，其监测数据用于AB路段的监测数据的参考。

Claims (10)

  1. 一种利用出租车进行大气监测时提高监测覆盖率的方法，所述方法以一个城市区域的交通路网为关注重点，通过在一组备选出租车中选择预定数量的一组优选车辆组合，安装大气污染检测设备，作为移动监测车，对该城市区域的空气质量进行监测；所述方法包含如下步骤：
    1)针对所述城市区域，将交通路网以路段单元为单位进行分解；建立并初始化各个路段单元的数据库；该数据库包含路段单元编号、路段单元位置信息、路段单元检测记录；
    2)对所述备选出租车在一段时间内的运行路径进行统计，跟踪记录各备选出租车在不同路段单元的经过次数；同一辆备选出租车在一个连续的计时单元内的多次经过只按一次计数；得到备选出租车出现在各个路段单元的统计分布图；
    3)通过排列组合的方式，从所述一组备选出租车中选取预定数量的一组优选车辆组合，使得该组合中各车辆的统计分布图叠加在一起的累计统计图中，满足预定监测次数的路段单元的数量最大。
  2. 如权利要求1所述的方法，其特征在于，所述的路段单元检测记录包含检测设备编号、移动监测车进入路段单元的时间、移动监测车经过该路段单元的累计经过次数；所述累计经过次数的初始值为“0”。
  3. 如权利要求1所述的方法，其特征在于，所述的路段单元的长度为100米或200米。
  4. 如权利要求1所述的方法，其特征在于，所述的计时单元为15分钟、30分钟，或1个小时。
  5. 如权利要求1所述的方法，其特征在于，所述的预定检测次数为5～10次。
  6. 如权利要求1至5之一所述的方法，其特征在于，所述的大气污染检测设备包括检测模块、主控模块和通讯模块；所述检测模块包含一种或多种大气污染物传感器单元；所述大气污染物传感器单元为以下传感器之一：PM 1传感 器、PM 2.5传感器、PM 10传感器、PM 100传感器、氮氧化物传感器、O 3传感器、SO 2传感器、VOCs传感器或TVOC传感器。
  7. 如权利要求6所述的方法，其特征在于，所述检测模块包含至少两个同类子传感器单元组成传感器模组；所述子传感器单元工作在正常的工作频率；所述检测模块还包含至少一个与传感器模组同类的子传感器单元组成的低频校准模组；低频校准模组内的子传感器单元的工作频率远低于传感器模组内子传感器单元的工作频率。
  8. 如权利要求7所述的方法，其特征在于，所述传感器模组的工作频率与低频校准模组的工作频率的比率为：2:1，3:1，4:1，5:1，6:1，7:1，8:1，9:1，10:1，15:1，或者20:1。
  9. 如权利要求6所述的方法，其特征在于，当所述主控模块发现所述传感器模组中一个子传感器单元出现疑似异常，并判断该疑似异常子传感器为异常子传感器后，对所述异常子传感器进行隔离，所述异常子传感器归入隔离区，多核传感器模组降级后继续正常工作；进入隔离区的子传感器如无法自愈则停止工作；如可以自愈则做降频工作处理，但是子传感器输出的数据不参与主控模块输出数据的计算；主控模块监测进入隔离区的子传感器输出的数据，判断其是否达到恢复条件；将达到恢复条件的子传感器调离隔离区，恢复工作。
  10. 如权利要求7所述的方法，其特征在于，所述子传感器异常的判定标准为下列几种异常之一：
    1)子传感器异常波动；
    2)子传感器异常飘移；
    3)子传感器相关性异常。
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