CN109034624B - An urban air pollution exposure assessment method based on location service data - Google Patents
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Abstract
The invention belongs to the field of environmental monitoring and risk assessment, and discloses a method for assessing urban air pollution exposure based on position service data. The method comprises the following steps: (a) selecting a research area, collecting terminal signals of all communication equipment in the research area, and calculating rated OD data and travel path data in the research area; (b) obtaining spatial distribution of air pollutant concentration in a research area; (c) obtaining each residential address, office address and average pollutant concentration corresponding to each travel path by using a space overlapping method; (d) the per-person air pollution exposure in the residential and work sites was calculated. The method solves the problem of air pollution exposure evaluation in the large cities, and has the advantages of simplicity, short time consumption and low cost.
Description
Technical Field
The invention belongs to the field of environmental monitoring and risk assessment, and particularly relates to a method for assessing urban air pollution exposure based on position service data.
Background
Air pollution is one of the main environmental problems faced by China at present, and in recent years, a large-scale and long-time haze weather frequently appears in a plurality of cities, and the PM2.5 index frequency explosion table is frequently displayed. Whether air pollution is harmful to human health depends mainly on the intensity of air pollution exposure, i.e. the process of direct contact of an individual or a group with a certain concentration of pollutants during a certain period of time.
The air pollution exposure evaluation is mainly used for evaluating the intensity, frequency and duration of air pollutants contacted by a human body and analyzing the relationship between air pollution and human health. The existing air pollution exposure evaluation methods mainly comprise an individual exposure monitoring method and a biomarker monitoring method, and the methods are time-consuming, labor-consuming and high in cost, are suitable for small-quantity individual exposure evaluation in a small geographic range, and cannot be put into air pollution exposure evaluation work aiming at crowds in large cities in China. Therefore, there is a great need in our country for an air pollution exposure assessment method that can be applied in large cities.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a city air pollution exposure assessment method based on position service data, which comprises the steps of acquiring terminal signals of communication equipment of users in a research area, constructing OD data and travel path data, acquiring air pollution concentration of monitoring points in the research area, obtaining air pollution spatial distribution in the research area, acquiring average air pollution concentration of each user in a residence, a working place and a travel path through spatial overlapping, and finally obtaining per-capita air pollution exposure in the research area.
To achieve the above object, according to one aspect of the present invention, there is provided a method for assessing urban air pollution exposure based on location-based service data, the method comprising the steps of:
(a) selecting a research area, collecting terminal signals of all communication equipment users in the research area, determining the residence and office of each user according to the terminal signals, taking the residence and office as OD data in the research area, constructing the shortest path between the residence and the office of each user, taking the shortest path as a travel path of the user, and obtaining travel path data corresponding to each user;
(b) setting a plurality of monitoring points in the research area, monitoring the air pollution concentration corresponding to each monitoring point, and constructing an interpolation model between the position and the air pollution concentration according to the corresponding relation between the monitoring points and the air pollution concentration so as to obtain the spatial distribution of the air pollution concentration in the research area;
(c) respectively performing spatial superposition analysis on the OD data and the travel path data obtained in the step (a) and the spatial distribution of the air pollutant concentration obtained in the step (b) so as to obtain an average pollutant concentration corresponding to each residential address, office address and each travel path;
(d) setting the exposure time of each user in the residence, the working place and the travel route, calculating the total air pollution exposure of each user in the corresponding residence, working place and travel route according to the residence address, the office address and the average pollutant concentration corresponding to each travel route obtained in the step (c), and calculating the average air pollution exposure of each user in the residence, working place and travel route.
Further preferably, in step (a), the study area covers the residence and office of the owner of the communication device.
Further preferably, in step (b), the interpolation model preferably adopts a Kriging interpolation model Kriging, a spline curve interpolation model Splines or an inverse weight distance interpolation model IDW.
Further preferably, in the step (d), the calculating of the total air pollution exposure of each communication equipment owner preferably adopts the following expression:
E(i)＝C(O，i)·T(O，i)+C(D，i)·T(D，i)+C(R，i)·T(R，i)
wherein i is the ith communication equipment owner, e (i) is the total air pollution exposure of the ith communication equipment owner, C (O, i), C (D, i) and C (R, i) are the average concentrations of air pollutants in the residence, the working place and the travel path corresponding to the ith communication equipment owner, respectively, and T (O, i), T (D, i) and T (R, i) are the exposure times in the residence, the working place and the travel path corresponding to the ith communication equipment owner, respectively.
Further preferably, in step (a), the determining the residence and office of each user preferably uses a method that takes the address received by each mobile device the most frequently during the working period as the working site and the address received the most frequently during the non-working period as the residence.
Further preferably, in the step (b), the air pollution concentration preferably adopts a PM2.5 concentration.
Further preferably, in step (d), the calculated average air pollution exposure is calculated by averaging the total air pollution exposure of the user.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. the method acquires the per-person air pollution exposure in the research area by acquiring the terminal information of the user communication equipment and the related air pollution concentration of the monitoring point and combining the interpolation model and the space superposition analysis method, is simple, short in time consumption, low in cost, free from the limitation of the geographical range, and suitable for air pollution exposure evaluation of each area in the large city.
2. According to the invention, the residence, the working place and the travel path of the user are identified by adopting the terminal information of the user communication equipment, so that the air pollution exposure of the user in different places is evaluated, and the operability is strong.
Drawings
FIG. 1 is a flow chart of a method for urban air pollution exposure assessment based on location based service data, constructed in accordance with a preferred embodiment of the present invention;
FIG. 2A is a schematic illustration of traffic cell data constructed in accordance with a preferred embodiment of the present invention;
FIG. 2B is a schematic illustration of urban road data constructed in accordance with a preferred embodiment of the present invention;
FIG. 3 is a map of a resident commute travel OD constructed in accordance with a preferred embodiment of the present invention;
FIG. 4 is a spatial distribution plot of PM2.5 concentration constructed in accordance with a preferred embodiment of the present invention;
fig. 5 is a PM2.5 contamination exposure evaluation constructed in accordance with a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a flowchart of a city air pollution exposure evaluation method based on location service data according to a preferred embodiment of the present invention, and as shown in fig. 1, a city air pollution exposure evaluation method based on location service data comprises the following steps:
step 1: and (6) collecting data. The method comprises the steps of collecting city basic geographic information data which mainly comprises traffic cell (TAZ) vector data and city road vector data, storing and recording the data, and collecting Location Based Service (LBS) data, wherein the content of the LBS data covers independent mobile phone terminal user Location data which appears in a research area range within a certain time range. Monitoring data of urban air pollution ground monitoring points are collected, wherein the monitoring data comprise pollutant concentration indexes and geographical position information of the monitoring points, and pollution monitoring time intervals are consistent with position service data.
Step 2: utilize LBS data to establish resident's OD picture of going on a commute, the concrete mode is: numbering each mobile phone user, counting the position service application information of each user in the non-working period of a working day according to each user, and taking the traffic cell with the most frequent occurrence as the living address (O point) of the user; and (4) counting the position service application information of the mobile phone user in the working time period of the working day, and taking the traffic cell with the most frequent occurrence frequency as the office address (point D) of the user. According to the steps, the residence places and the working places of the mobile phone terminal users are identified one by one, OD pairs are established, and a total resident commuting travel OD diagram is formed.
And step 3: based on the travel OD data constructed in step 2, a travel path between each OD pair is calculated, specifically: and taking the geometric center of the traffic cell of the residence and the working place of each mobile phone terminal user as an origin-destination point, obtaining a travel path of the OD pair by using a shortest path calculation tool, calculating the travel distance of the OD pair, and converting the travel distance into an shp file. And according to the steps, calculating one by one to obtain the trip path shp file corresponding to each OD pair.
And 4, step 4: geocoding is carried out on the air pollution ground monitoring points, common spatial interpolation models (Kriging interpolation model, spline curve interpolation model Splines and inverse weight distance interpolation model IDW) are adopted to predict the air pollution concentration of unmonitored points in the urban area, and therefore the air pollution concentration of any point in the research area is obtained, namely the air pollution concentration spatial distribution result in the research area is obtained.
And 5: performing space superposition analysis on shp data (namely OD image data) of the traffic cells and the space distribution result of the air pollutant concentration obtained in the step (4) by using a space superposition analysis method, and calculating to obtain an average pollutant concentration value of each traffic cell, wherein the traffic cells comprise residential areas and working areas; and (4) carrying out space superposition analysis on the path shp file obtained in the step (3) and the air pollutant concentration space distribution result obtained in the step (4), and calculating the pollutant average concentration value on each path of the OD pairs.
Step 6: the air pollution exposure for all users was calculated. Firstly, air pollution exposure E (O, i), E (D, i) and E (R, i) of a user i on a residence place, a working place and a commuting travel path of the user i are respectively calculated, and then the total exposure E (i) is calculated, wherein the calculation mode is as follows:
E(O，i)＝C(O，i)·T(O，i)
E(D，i)＝C(D，i)·T(D，i)
E(R，i)＝C(R，i)·T(R，i)
T(R，i)＝2·Distance·Speed
E(i)＝E(O，i)+E(D，i)+E(R，i))
wherein i is the ith communication equipment owner, e (i) is the total air pollution exposure of the ith communication equipment owner, C (O, i), C (D, i) and C (R, i) are the average concentrations of air pollutants in the residence, the working place and the travel path corresponding to the ith communication equipment owner, respectively, and T (O, i), T (D, i) and T (R, i) are the exposure times in the residence, the working place and the travel path corresponding to the ith communication equipment owner, respectively. For computational convenience, the exposure times of all users may be taken to the same value. The exposure time T (R, i) on the commute path is calculated as: and (4) for users with travel origin-destination points in different traffic cells, dividing the travel distance obtained in the step (3) by the average speed of the city, and taking a value which is 2 times as large as the round-trip time of the user on duty and off duty. And assigning values to the exposure time according to the same time for users with travel origin-destination points in the same traffic cell.
And 7: and (6) respectively calculating the per-person air pollution exposure of each traffic district by taking the residence place where the user is located as a statistical unit according to the air pollution exposure of all the users obtained in the step 6. The calculation method is that the sum of the air pollution exposure of all users who are identified to live in the community in the traffic community is calculated, and the sum is divided by the total number of people who live in the community to obtain the per-person air pollution exposure.
The following is a detailed description of a preferred embodiment of the invention, taken in conjunction with the accompanying drawings.
Step 1: selecting a wuhan city urban development area as a preferred embodiment, fig. 2A is a schematic diagram of traffic cell data constructed according to the preferred embodiment of the present invention, and fig. 2B is a schematic diagram of urban road data constructed according to the preferred embodiment of the present invention, as shown in fig. 2A and 2B, collecting traffic cell shp data and urban road shp data, and converting them into a unified geospatial coordinate system. LBS data comes from a certain mobile application cloud service and big data platform provider, and the content covers independent mobile phone terminal user position data which appears in the Wuhan city development area within a week time range. The LBS data acquisition mechanism comprises the following steps: 1) periodically updating data, namely reporting GPS position data of a user to a background SDK platform at a frequency with the particle precision of 1 hour/time after the user opens an APP (including WeChat, QQ, Unionpay and the like) for calling position information; 2) event trigger data, i.e. events such as user logging in, searching, sending and receiving information and pushing at any APP mentioned above, will also form instant location data.
Step 2: the method for identifying the residence and the working place of each mobile phone user by using the LBS data comprises the following steps: according to the position service application information of the working day non-working time period (21: 00-07: 00), taking the traffic cell with the most frequent occurrence as the residence address (O point) of the user; and (3) counting the position service application information of the mobile phone user in the working time period (10: 00-17: 00) of the working day, and taking the traffic cell with the most frequent occurrence frequency as the office address (point D) of the user. Fig. 3 is a graph of resident commuting travel ODs constructed according to a preferred embodiment of the present invention, as shown in fig. 3, according to the above method, the present embodiment identifies 386 ten thousand OD pairs, wherein there are 4 gray segments representing OD line segments, respectively, which represent different numbers of travel people, the darker the color represents the larger the number of people, as can be seen from the graph, most of the commuting travel ODs are concentrated in the main urban area, the shorter distance commuting travel OD pairs and the medium distance commuting travel ODs have the largest number of pairs, and the longer distance commuting travel ODs have the smaller number of pairs.
And step 3: urban road data is input and a road network is established, and the distance is used as road impedance. And aiming at each OD pair, calculating the distance of the shortest path by using a shortest path calculation tool and taking the central point of the residential area corresponding to the traffic cell as a starting point and the central point of the working area corresponding to the traffic cell as a terminating point, and storing the path of the shortest path as an shp file.
And 4, step 4: and downloading the PM2.5 day-average concentration data of the air pollution ground monitoring points for one week according to the same time range as the LBS data, and calculating the one-week average concentration of each station. Geocoding is carried out according to the geographic position of the monitoring point, PM2.5 concentration spatial interpolation is carried out by adopting a Kriging method to obtain PM2.5 concentration distribution data (grid format) in the range of the embodiment, FIG. 4 is a PM2.5 concentration spatial distribution diagram constructed according to the preferred embodiment of the invention, as shown in FIG. 4, the gray colors of different degrees of each cell in the diagram correspond to the PM2.5 concentration, and the brighter the color shows that the PM2.5 concentration is higher. As can be seen, the concentration of PM2.5 is highest in urban centers, the concentration of PM2.5 is relatively low in suburban areas, and the concentration of PM2.5 is at a moderate level in suburban areas.
And 5: the method for acquiring the PM2.5 concentration indexes on each traffic cell and travel path by using the GIS space superposition method comprises the following specific steps: carrying out spatial superposition on the traffic cell data and the PM2.5 concentration spatial distribution data, and counting the average PM2.5 concentration value of each traffic cell; and performing spatial superposition on the travel path data of each OD pair and PM2.5 concentration spatial distribution data, and calculating to obtain an average PM2.5 concentration value on the travel path corresponding to each OD pair.
The method is specifically carried out in the following way: taking PM2.5 concentration spatial distribution data as an overlapped base map, taking traffic cell data as an overlapped overlying map layer, and counting the average PM2.5 concentration value of each traffic cell; and taking the PM2.5 concentration spatial distribution data as an overlapped base map, taking the travel path data of each OD pair as an overlapped overlying map layer, and calculating to obtain an average PM2.5 concentration value on the travel path corresponding to each 0D pair.
Step 6: the air pollution exposure for all users was calculated.
(1) Calculating the air pollution exposure E (O, i) and E (D, i) of the user i at the residence and the work place in the following way:
E(O，i)＝C(O，i)·T(O，i)
E(D，i)＝C(D，i)·T(D，i)
here, for the sake of calculation convenience, the embodiment sets the residence and working place exposure times of all users to 10 hours and 9 hours, respectively.
(2) Calculating the air pollution exposure E (R, i) of the user i on the commuting travel path in the following way:
E(R，i)＝C(R，i)·T(R，i)
T(R，i)＝2·Distance(i)·Speed
the distance (i) is divided by the average city speed (20 km/h in the embodiment), and multiplied by 2 to represent the round trip time to and from work. For users with travel origin-destination in the same traffic cell, the embodiment sets the exposure time to 0.5 hours.
(3) Calculating the sum E (i) of the air pollution exposures of the user i on the residence, workplace and commuting travel path:
E(i)＝E(O，i)+E(D，i)+E(R，i)
(4) according to the above procedure, the air pollution exposure of all users is calculated.
And 7: according to the air pollution exposure of all users obtained in the step 6, taking the residence places where the users are located as statistical units, respectively counting the per-person air pollution exposure of each traffic cell to obtain a spatial distribution result of the air pollution exposure, wherein fig. 5 is a PM2.5 pollution exposure evaluation result constructed according to the preferred embodiment of the invention, as shown in fig. 5, the colors of different degrees of gray levels of each unit in the graph represent the per-person PM2.5 pollution exposure of the unit, the darker the color represents the higher the exposure, as can be seen from the graph, the pollution exposure of the central area, the north area and the west area of the city is higher, the pollution exposure of the south area is lower, and finally shp data is output to complete the evaluation process.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
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