CN107766296A - The method that evaluation path traffic characteristic influences on Inhaled Particulate Matters Emission concentration - Google Patents
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Abstract
The invention discloses a kind of method that evaluation path traffic characteristic influences on Inhaled Particulate Matters Emission concentration, including obtain the traffic analysis cell information of survey region；Obtain economics of population, employment, traffic and road network characteristic variable data；Obtain PM10Mean annual concentration；Utilization space interpolation method obtains the PM of each traffic analysis cell10Concentration data；Other main PM of acquisition survey region10Discharge derived data；Each variable and concentration are matched into each traffic analysis cell；Correlation analysis is carried out to screen variable；With PM10Concentration establishes linear regression model (LRM) as dependent variable；According to last model evaluation explanatory variable to PM10The influence of concentration.The inventive method selects traffic analysis cell, using linear regression model (LRM), to analyze economics of population, employment, traffic and the road network feature of traffic zone to PM as research object10The influence of concentration of emission, can be to reduce PM10Concentration and traffic sustainable development planning provide certain theoretical foundation.
Description
Technical Field
The invention relates to a method for evaluating road traffic characteristics on inhalable Particles (PM) 10 ) DischargingA method for influencing concentration belongs to the technical field of urban traffic sustainability development planning.
Background
With the rapid development of economy and urbanization, the retention amount and the utilization rate of vehicles are both increased sharply, and according to the year 2015 motor vehicle pollution prevention and control annual report published by the ministry of environmental protection in china, automobiles are main contributors of the total amount of pollutants, the direct emission of particulate matters exceeds 90%, and in addition, the main emission of nitrogen oxides (NOx) of the motor vehicles is also an indirect component formed by the particulate matters. Wherein the inhalable Particles (PM) 10 ) But also has obvious toxic action on human health, and the large amount of emission of the pesticide can undoubtedly pose a great threat to human bodies and environment. Therefore, in order to effectively reduce PM generated by motor vehicles 10 Many scholars began to study effects on PM 10 A factor of emission.
Over the years, much of the research has been directed to individual vehicles, i.e. research into individual vehicle PM 10 By exploring the vehicle type, engine characteristics, weather, road design, vehicle driving characteristics (speed, acceleration, etc.) versus PM 10 The effect of emissions. However, these methods have the disadvantages of acquiring the instantaneous speed, acceleration, etc. of the vehicle, and the acquisition of these instantaneous data is difficult and time-consuming, so these studies cannot be directly applied to the evaluation of PM in a relatively macroscopic area (traffic district, city, etc.) 10 And (4) discharging.
Disclosure of Invention
The invention aims to: aiming at the defects of the prior art, the invention aims to provide a method for evaluating road traffic characteristics on inhalable Particles (PM) 10 ) The method for analyzing the influence of emission concentration uses a traffic analysis district as a research object, and analyzes factors such as population economic characteristics, employment, traffic conditions, road network characteristics and the like on the PM of the traffic district by using a linear regression model 10 Influence of concentration, establishing traffic district PM 10 Concentration prediction model for urban traffic sustainabilityPlanning provides a certain theoretical basis.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme:
evaluation of road traffic characteristics on inhalable Particles (PM) 10 ) A method of emission concentration impact comprising the steps of:
(1) Acquiring traffic analysis cell information of a research area, visualizing the specific positions of all traffic cells in the whole research area by utilizing geographic information system software, and calculating the area of the traffic cells;
(2) Acquiring the population economy, employment, traffic conditions and road network characteristic variable data of a traffic analysis cell;
(3) PM of each air monitoring station in research area is obtained 10 The annual average concentration is obtained by utilizing geographic information system software to calibrate a monitoring station to a specific position of a research area;
(4) PM of each traffic analysis cell is obtained by using space interpolation method 10 Concentration data;
(5) Acquiring other main PM except traffic in research area 10 Emission source data;
(6) The variable data obtained in the step (2) and the traffic analysis community PM in the step (4) 10 Concentration and other major PM in step (5) 10 The emission source data is matched with each traffic analysis cell;
(7) Taking the variables obtained in the step (2) as explanatory variables, and enabling the explanatory variables to be connected with the PM one by one 10 Establishing a linear regression model for concentration, and removing PM according to significance level 10 Carrying out correlation analysis between the unremoved explanatory variables to ensure that the variables with strong correlation do not appear in the linear regression model at the same time;
(8) Analyzing cell PM with each traffic 10 Concentration is used as a dependent variable, the variable is used as an explanatory variable after being screened in the step (7) to establish a linear regression model, maximum likelihood estimation is selected to solve the model, the explanatory variables are added one by one during modeling, and fitting R is selected 2 Large and collinearity-small regression results as PM 10 Concentration prediction model；
(9) PM obtained according to step (8) 10 Interpretive variable pair PM retained in concentration prediction model evaluation model 10 The effect of concentration.
Further, in the method of the present invention, the linear regression model in step (8) is:
wherein:PM representing traffic analysis cell 10 Concentration, p i Represents the i-th class PM 10 Emission source, λ i Regression coefficients representing the I-th emission source, I representing the number of traffic analysis cells, T representing the number of explanatory variables in the model, beta 0 Constant term, x, representing the model t Represents the t-th interpretation variable; beta is a beta t Regression coefficients representing the t-th explanatory variable; epsilon represents the error.
Further, in the method of the present invention, the economic variables of the population in the step (2) include the total population number of each traffic analysis cell, the population numbers of different age groups and the number of vehicles; the employment variables comprise the number of people in each traffic analysis district at different commuting time and the number of people in different age groups; the traffic condition variables comprise the occurrence and attraction of different vehicles in each traffic analysis district and the annual average daily traffic volume of the expressway; the road network characteristic variables include a highway network density, an urban road network density and a public transport network density of each traffic analysis cell.
Further, in the method, the PM of each traffic analysis cell is obtained by using a Kriging space difference method in the step (4) 10 Concentration data.
Further, in the method of the present invention, in step (7), the variables with significance level greater than 0.1 are eliminated, so as to ensure that the variables with correlation coefficient greater than 0.5 do not appear in the model at the same time.
Further, it is possible to provideIn the method of the present invention, in step (9), the influence of different interpretation variables on the CO concentration is reflected by calculating the elasticity value corresponding to each interpretation variable, and the calculation formula of the elasticity value of each interpretation variable is:wherein E is t Elastic value, β, representing the t-th explanatory variable t Regression coefficient, x, representing the t-th explanatory variable t Denotes the t-th explanatory variable, Y t And the dependent variable value corresponding to the t-th explanation variable is shown.
Has the beneficial effects that: compared with the prior art, the traffic analysis method selects the traffic analysis cell as a research object, and analyzes the factors such as population economic characteristics, employment, traffic conditions, road network characteristics and the like to the PM of the traffic cell by using the geographic weighting regression model on a macroscopic level 10 Influence of concentration, establishing cell PM 10 And (4) a concentration prediction model. Meanwhile, the invention does not need the vehicle-related instantaneous data, so the data acquisition is relatively simple and convenient. Finally, the cell PM obtained by the method 10 Concentration prediction model for reducing PM of motor vehicle 10 Emissions and traffic sustainability planning provide certain theoretical grounds.
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FIG. 1 is a block flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples.
As shown in FIG. 1, the embodiment of the invention discloses a method for evaluating road traffic characteristics on inhalable Particles (PM) 10 ) A method of emission concentration impact comprising the steps of:
step 1) acquiring traffic analysis cell (TAZ) information of a research area: selecting a traffic analysis unit, namely a traffic analysis cell (TAZ), which is the most commonly used traffic analysis unit, as a research object, acquiring a vector map file (shapefile) corresponding to the traffic analysis cell, and obtaining basic attributes of each traffic analysis cell by utilizing ArcGIS software, such as visualizing the specific position of each traffic cell in the whole research area, calculating the area of each traffic cell, and the like;
step 2) obtaining variables such as population economy, employment, traffic conditions and road network characteristics of the traffic district: the population economic attribute variables generally comprise the total population number of each traffic cell, the population numbers of different age groups, the number of vehicles and the like; employment variables generally comprise the number of people in different commuting time of each traffic district, the number of working people in different age groups and the like; the traffic condition variables generally include the amount of occurrence and the amount of attraction of different vehicles (such as cars, public transportation, bicycles, walking, etc.) in each traffic cell (the amount of occurrence refers to the number of different vehicles generated by the traffic cell, and the amount of attraction refers to the number of different vehicles arriving at the traffic cell), the annual average daily traffic volume (AADT) of the highway, etc.; the road network characteristic variables generally comprise road network densities of different levels of a traffic community, such as highway network density, urban road network density, public traffic network density and the like;
step 3) obtaining the PM of each air monitoring station in the research area 10 Average annual concentration: considering each traffic cell PM 10 Emission concentration is difficult to obtain, so the method selectively obtains all PM in the research area 10 Annual average concentration data for the monitoring station. Because the data contains the position information of each monitoring station, the monitoring stations can be calibrated to the specific positions of the research area by utilizing ArcGIS software for further analysis in the step 4);
step 4) obtaining the PM of each traffic district by using a proper space interpolation method 10 Concentration data: the spatial interpolation method is to convert the measured data of discrete points into a continuous data curved surface, because the method needs to pass through the known monitoring point PM 10 Concentration obtaining PM throughout the study area 10 The concentration profile and therefore the spatial interpolation method is chosen. Performing spatial interpolation by using ArcGIS software, fitting appropriate indexes according to a trend surface: such as R 2 Selecting proper space difference methods for detection, significance F detection, prediction error related indexes and the like to obtain PM of each traffic cell 10 Concentration ofA value;
step 5) acquiring other main PM except traffic in the research area 10 Emission source data: due to each traffic cell PM 10 Emissions are not only caused by road traffic, but also include other sources (e.g., construction, manufacturing, mining, etc.). Therefore, other main PMs are obtained according to the corresponding actual situation of the research area 10 Emission source data;
step 6) mixing the variables and PM in the step 2) 10 The concentration data is matched to each traffic cell: generally, the variables of the population economy, employment and traffic conditions in the step 2) take traffic cells as units, so that the ArcGIS software can be directly utilized to select the traffic cells as connection fields, and the variables of the population economy, employment and traffic conditions are matched with the traffic cells; for the road network characteristic variable, the road network can be interrupted according to the traffic cells by utilizing ArcGIS, and then the road network length and the road network density in each traffic cell are counted. For other main PMs 10 The emission source data generally obtains the total emission amount of a certain area (such as a city and an administrative district), the method can judge the area of each traffic district according to the geographic coordinates of the traffic districts, and then other PMs are used 10 The total emission is evenly distributed according to the area of the traffic cells, and other main PMs of each traffic cell are finally obtained 10 Total amount of emission source.
Step 7) PM 10 Correlation analysis between concentration and explanatory variable: to construct the linear regression model of step 8), the explanatory variables need to be screened in advance. First for PM 10 Between the concentration and the explanatory variable, the explanatory variable is one by one compared with PM 10 Establishing a linear regression model for concentration, selecting proper significance level alpha, and removing PM 10 A variable that is not related to concentration. And secondly, performing correlation analysis between the unremoved explanatory variables, and ensuring that the variables with strong correlation do not appear in the model simultaneously in the modeling process of the linear regression model according to the correlation coefficient r. Wherein the significance level alpha is set to 0.1, the correlation coefficient r is 0.5>, 0.5 indicates a strong correlation.
Step 8) establishing a linear regression model: book (I)Method for each traffic cell PM 10 Concentration as a dependent variable and the screened variables in step 7) as explanatory variables to build a linear regression model. Considering PM per traffic cell 10 Not only caused by road traffic, but also from other sources, so the data of other emission sources of each cell acquired in step 5) is added into a model, and the specific structure of the model is as follows:
wherein:PM representing traffic analysis cell 10 Concentration, p i Represents the i-th class PM 10 Emission source, λ i Regression coefficients representing the I-th emission source, I representing the number of traffic analysis cells, beta 0 A constant term representing the model, and T represents the number of explanatory variables in the model; x is the number of t Represents the t-th interpretation variable; beta is a t Regression coefficients representing the t-th explanatory variable; ε represents the error, following a normal distribution, i.e., ε N (0,1). The left side of the equation is considered as the PM caused by road traffic 10 Concentration, in order to solve the model, the final model structure is obtained as follows:
selecting maximum likelihood estimation to solve the model, adding explanatory variables one by one during modeling, and selecting model fitting R 2 Larger and less collinearity regression results as PM 10 And (4) a concentration prediction model.
Step 9) obtaining PM according to step 8) 10 Interpretive variable pair PM retained in concentration prediction model evaluation model 10 The effect of concentration. Further, the regression coefficient of the explanatory variable is positive, indicating that it is positive with PM 10 The concentration is in positive correlation, the value of the explanatory variable is increased, and PM is 10 The concentration will also increase correspondingly; conversely, the regression coefficient of the explanatory variable is negative, indicating that it is negative with PM 10 The concentration is inversely related, the value of the explanatory variable is increased, PM 10 The concentration will decrease accordingly. To quantitatively reflect different interpretive variable pairs of PM 10 And calculating the corresponding elasticity value of each explanatory variable under the influence of the concentration. The elastic value represents the PM caused by 1% unit change per the explained variable 10 The concentration percentage was varied. The elastic value calculation formula of each explanatory variable is as follows:wherein E is t Elastic value, β, representing the t-th explanatory variable t Regression coefficient, x, representing the t-th explanatory variable t Denotes the t-th explanatory variable, Y t And the dependent variable value corresponding to the t-th explanation variable is shown.
The effectiveness of the process according to the invention is illustrated below with reference to specific application cases:
the present invention was tested on traffic cell PM using real data from all traffic analysis cells in los Angeles, calif 10 Performance in terms of concentration prediction. The los Angeles city has 2244 traffic cells, and the obtained data comprises the population economic characteristics (total population, population of different age groups and vehicle number) of each traffic cell in 2010, employment (population of different commuting time and working population of different age groups), traffic conditions (occurrence and attraction of different vehicles in each traffic cell, annual Average Daily Traffic (AADT) of expressway, annual average daily traffic of truck), road network characteristics (highway network density, other road network density except expressway), 10 PM 10 PM of monitoring station 10 Average concentration (ug/m 3), PM 10 Other major emission sources (manufacturing, construction, mining, power service facilities, etc.).
According to the flow chart of the invention shown in fig. 1, all data needs to be accurately matched according to traffic cells before modeling. PM with 10 monitoring points in step 4 before matching 10 Concentration according to R 2 Finally selecting kriging interpolation method to obtain each index such as value and prediction errorPM of traffic district 10 Concentration values. In step 5, according to the emission list of each region published by the U.S. environmental protection agency, the PM of the city of los Angeles 10 There are five main sources of emissions, which are: electric power service facilities such as transportation, building industry, manufacturing industry, mining industry and power stations. In order to obtain PM caused by traffic 10 Emission, according to step 5, other four sources of PM need to be obtained 10 And (4) discharging the amount. PM of different cities of los Angeles can be obtained according to official data published by the American labor office 10 The emission is obtained according to step 6) by utilizing ArcGIS software according to the area of each traffic cell to obtain other four kinds of PM sources of each cell 10 And (4) discharging the amount.
According to step 7), the variables need to be screened before the modeling analysis is performed. The significance level α was set to 0.1, yielding 23 variables and PM 10 The concentrations are significantly correlated. Furthermore, correlation analysis is performed between 23 variables, and in the modeling of step 8), the correlation coefficient r is guaranteed>, 0.5 strong correlation variables do not appear in the model at the same time.
Due to other four kinds of PM 10 Emission sources (construction, manufacturing, mining and power services) have strong correlation, so that the dimensionality reduction can be performed by using principal component analysis before modeling, and the principal component analysis results are as follows:
TABLE 1 other four PM species 10 Emission source principal component analysis results
Finally, a linear regression model is selected for modeling analysis to obtain the PM of each traffic cell 10 Concentration as a dependent variable, population economic characteristics, employment, traffic conditions, road network characteristics and other four kinds of PM 10 The emission source is used as an explanatory variable, and the final model retains the explanatory variables and the coefficient calibration results of each explanatory variable are shown in table 2.
TABLE 2 Linear regression model results
According to the model result, the number of people in the traffic cell, the number of people working at home, the traffic volume and the road network density all significantly influence the PM of the cell 10 And (4) concentration. Specifically, the larger the number of traffic cells, the larger the traffic volume, or the denser the road network, the more PM is generated 10 The greater the concentration, the greater the number of people in the home, and the less PM can be reduced 10 And (4) concentration. For further quantitative analysis of each interpretive variable pair PM 10 The influence of concentration, the corresponding elasticity value for each explanatory variable was calculated according to step 9), and the specific results are shown in table 3.
TABLE 3 elasticity values for variables
From the elastic values of the respective explanatory variables, it was found that PM was caused by 1% change in the respective explanatory variables 10 The concentration changes. If the elasticity value of the population of the traffic cell is 0.015, the PM of the cell indicates that the population of the cell increases by 1 percent 10 The concentration will increase by 0.015%. The elasticity value of the number of people working at home in the traffic community is-0.0088, which indicates that the number of people working at home in the community is increased by 1% every time 10 The concentration will decrease by 0.0088%. Thus different variable pairs of PM can be analyzed by linear regression models 10 Different effects of concentration, and specific PM reduction can be implemented according to model results 10 Concentration measures and policies, such as encouraging residents to work at home, etc. Therefore, the method can effectively evaluate PM of population economy and road traffic characteristics 10 The influence of the concentration has practical application value.
Claims (6)
1. Evaluation of road traffic characteristics on inhalable Particles (PM) 10 ) A method of emission concentration influence, comprising the steps of:
(1) Acquiring traffic analysis cell information of a research area, visualizing the specific positions of all traffic cells in the whole research area by utilizing geographic information system software, and calculating the area of the traffic cells;
(2) Acquiring the population economy, employment, traffic conditions and road network characteristic variable data of a traffic analysis cell;
(3) PM of each air monitoring station in research area is obtained 10 The annual average concentration is obtained by utilizing geographic information system software to calibrate a monitoring station to a specific position of a research area;
(4) PM of each traffic analysis cell is obtained by using space interpolation method 10 Concentration data;
(5) Acquiring other main PM except traffic in research area 10 Emission source data;
(6) The variable data obtained in the step (2) and the traffic analysis cell PM in the step (4) are processed 10 Concentration and other major PM in step (5) 10 The emission source data is matched with each traffic analysis cell;
(7) Taking the variables obtained in the step (2) as explanatory variables, and enabling the explanatory variables to be respectively connected with PM 10 Establishing a linear regression model for concentration, and removing PM according to significance level 10 Carrying out correlation analysis between the unremoved explanatory variables to ensure that the variables with strong correlation do not appear in the linear regression model at the same time;
(8) Analyzing cell PM with each traffic 10 Taking the concentration as a dependent variable, taking the variable screened in the step (7) as an explanatory variable to establish a linear regression model, selecting maximum likelihood estimation to solve the model, adding the explanatory variables one by one during modeling, and selecting fitting R 2 Large and collinearity-small regression results as PM 10 A concentration prediction model;
(9) PM obtained according to step (8) 10 Interpretive variable pair PM retained in concentration prediction model evaluation model 10 The effect of concentration.
2. The method for evaluating the influence of road traffic characteristics on the inhalable particle emission concentration as claimed in claim 1, wherein the linear regression model in the step (8) is as follows:
wherein:PM representing traffic analysis cell 10 Concentration, p i Represents the i-th class PM 10 Emission source, λ i Regression coefficients representing the I-th emission source, I representing the number of traffic analysis cells, T representing the number of explanatory variables in the model, β 0 Constant term, x, representing the model t Represents the t-th interpretation variable; beta is a t Regression coefficients representing the tth explanatory variable; epsilon represents the error.
3. The method of claim 1, wherein the demographic variables of step (2) include total population, population of different age groups and number of vehicles per traffic analysis cell; the employment variables comprise the number of people in each traffic analysis district at different commuting time and the number of people in different age groups; the traffic condition variables comprise the occurrence and attraction of different vehicles in each traffic analysis district and the annual average daily traffic volume of the expressway; the road network characteristic variables include a highway network density, an urban road network density and a public transport network density of each traffic analysis cell.
4. The method for evaluating the influence of road traffic characteristics on the inhalable particle emission concentration according to claim 1, wherein the step (4) is implemented by using a kriging spatial difference method to obtain the PM of each traffic analysis cell 10 And (4) concentration data.
5. The method for evaluating the influence of road traffic characteristics on the inhalable particulate matter emission concentration according to claim 1, wherein in the step (7), the variables with the significance level larger than 0.1 are rejected, so that the variables with the correlation coefficient larger than 0.5 are ensured not to simultaneously appear in the model.
6. The method for evaluating the influence of road traffic characteristics on the emission concentration of inhalable particles as claimed in claim 1, wherein in step (9), the influence of different explanatory variables on the CO concentration is reflected by calculating the elasticity value corresponding to each explanatory variable, and the elasticity value of each explanatory variable is calculated by the formula:wherein, E t Elastic value, β, representing the t-th explanatory variable t Regression coefficient, x, representing the t-th explanatory variable t Denotes the t-th explanatory variable, Y t The dependent variable value corresponding to the t-th interpretation variable is shown.
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| CN111798932A (en) * | 2020-07-03 | 2020-10-20 | 中科三清科技有限公司 | Pollutant monitoring data processing method and device, storage medium and terminal |
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