CN103234883B - A kind of method based on road traffic flow real-time estimation inner city PM2.5 concentration - Google Patents

Abstract

The invention discloses a kind of based on road traffic flow real-time estimation inner city PM _2.5the method of concentration, first utilizes the road traffic flow real-time monitoring data of inner city, by discrete for traffic route pollution source be fixed intervals point pollution source; Then based on the geographical weighting of source feature proposed by the invention contiguous acceptor air pollution exposure assessment model, assessment center city PM _2.5pollution exposure Relative risk value; Last again by inner city PM _2.5the PM of position, monitoring station _2.5pollution exposure Relative risk value carries out regression modeling with observation concentration value; And the regression model constructed by utilizing estimates the real-time PM of any locus point of inner city _2.5concentration.This currently carries out PM in inner city _2.5under high density cloth point observation is difficult to carry out condition, the one of invention accurately, efficiently can estimate the real-time PM of any locus, inner city point _2.5the method of concentration.

Description

A kind of method based on road traffic flow real-time estimation inner city PM2.5 concentration

Technical field

The present invention relates to environmental monitoring and risk assessment field, particularly one is based on road traffic flow real-time estimation inner city PM _2.5the method of concentration.

Background technology

Fine particle PM _2.5(equivalent aerodynamic diameter is less than or equal to 2.5 μm) particle diameter is little, out-of-shape, specific surface area is large, and suspension time is longer in an atmosphere, and fed distance is far away, a large amount of toxic chemical can be adsorbed, because particle diameter is little, can be trapped in bronchioli terminales and alveolar, directly affects the ventilatory function of lung, make body easily be in anaerobic condition, be exposed to the PM of high concentration for a long time _2.5in environment, seriously can jeopardize health.China is global PM _2.5the Spring layer polluted, city PM _2.5concentration is higher, and the city of about 80% can not meet the new ambient air quality of country's promulgation on February 29th, 2012.Particularly since in October, 2011, comprise Beijing-Shanghai China and continued to occur haze weather manyly, had a strong impact on the daily life of the common people, caused the worry that the common people bring to health air quality problems.In inner city, due to the impact of the factors such as industry, traffic, population, PM _2.5concentration is higher, and a large amount of population concentration simultaneously lives in inner city, PM _2.5the scope of pollution effect colony larger, therefore understand PM in the inner city in city _2.5concentration space distribution has great importance.

Owing to being subject to the impact of the factors such as manpower, fund, equipment, the PM of inner city _2.5concentration monitor can only be confined to limited PM _2.5on concentration monitor website, when needing the PM obtaining any locus, whole inner city point _2.5during concentration, following method can be adopted: 1, spatial interpolation model; 2, Land_use change regression model; 3, air pollution diffusion model; 4, individual suction-type model; 5, mixture model; 6, remote sensing estimation model etc.Higher for input data dependence degree in the application process of above model, monitoring required time is longer, the retardance of monitoring is higher, require more high deficiency to the professional knowledge of researchist.One is needed efficiently, quickly and accurately to obtain inner city PM _2.5the method of concentration.

Summary of the invention

In order to disclose PM accurately and timely _2.5change in time and space process and rule, to formulate scientific and effective prevention and control measure, the invention provides one and can obtain inner city PM efficiently, quickly and accurately _2.5concentration based on road traffic flow real-time estimation inner city PM _2.5the method of concentration.

In order to realize above-mentioned technical purpose, technical scheme of the present invention is, a kind of based on road traffic flow real-time monitoring data estimation inner city PM _2.5the method of concentration, comprises the following steps:

Step 1: by discrete for traffic route pollution source in inner city be the point pollution source of fixed intervals;

Step 2: the structure carrying out geographical weighting function in whole inner city;

Step 3: according to the result of step 1, step 2, carries out the contiguous acceptor PM of the geographical weighting of source, inner city feature _2.5pollution exposure relative risk is assessed;

Step 4: the PM that step 2 is obtained _2.5pollution exposure Relative risk value and PM _2.5concentration value carries out regretional analysis, and real-time estimation inner city PM _2.5concentration.

Described method, described in step 1 by discrete for traffic route pollution source in inner city be the point pollution source of fixed intervals, comprise the following steps:

Obtain the magnitude of traffic flow real-time monitoring data of inner city traffic route;

By discrete for each for inner city traffic route be the road segment segment of fixed intervals, and calculate the length of discrete rear road segment segment, middle point coordinate;

Calculate the PM of each road segment segment according to the following formula _2.5discharge capacity accounts for whole inner city PM _2.5the weights of discharge capacity:

Wherein Q _grepresent the weights that g article of road segment segment discharge capacity accounts for whole inner city discharge capacity, F _grepresent the magnitude of traffic flow that g article of road segment segment is monitored in real time, L _grepresent the length of g article of road segment segment, h represent whole inner city discrete after road hop count;

Calculate the PM of each road segment segment _2.5discharge capacity:

E _g＝E _total*Q _g

E _grepresent the PM of g article of road segment segment _2.5discharge capacity, E _totalrepresent the PM of whole inner city _2.5discharge capacity;

With point coordinate L in g article of road segment segment _g(X, Y) be the volume coordinate of fixed intervals point pollution source of traffic route pollution source and the PM of g article of road segment segment instead _2.5discharge capacity E _gthe instead PM of the fixed intervals point pollution source of traffic route pollution source _2.5discharge capacity, generates fixed intervals point pollution source layer, and is numbered the point pollution source of all generations.

Described method, the structure carrying out geographical weighting function in whole inner city described in step 2, comprises the following steps:

Build a nearly Gaussian function model;

Nearly Gaussian function model is to the distance of a right translation bandwidth b;

Wherein, i represents the point pollution source after discrete for traffic route pollution source in above-mentioned steps 1; J represents that the acceptor using these as search coverage internal contamination exposed amount spatial diversity, is called default acceptor according to the point that fixed intervals generate in survey region; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity; d _ijrepresent the distance preset between acceptor j and point pollution source i; B is the non-negative attenuation function describing funtcional relationship between weight and distance, is called bandwidth;

Select the PM of Historical Monitoring point in inner city _2.5the Relative risk value of true monitor value and the PM according to monitoring point after this geographical weighted function model calculating _2.5relative risk value, carry out the determination of bandwidth b, step is as follows:

Described method, described bandwidth b is with Akaike quantity of information for criterion is to determine the size of bandwidth b, selects the bandwidth making AIC (Akaike quantity of information) minimum to be optimum bandwidth, that is to say the b value of trying to achieve and making following formula value minimum:

The wherein number of monitoring point in n inner city; r _tcentered by the PM of t monitoring point in city _2.5the Relative risk value of true monitor value; R _tthe PM of t monitoring station after calculating according to this geographical weighted function model _2.5the Relative risk value of estimated value; Tr (s) is the mark of matrix s, that is to say matrix diagonals line element sum; e _tit is the matrix of the point emission source composition around t monitoring station; With W in up-to-date style _tbeing the matrix of t monitoring station to the geographical weighting function composition of each point emission source of surrounding, is the function of bandwidth b.

Described method, the geographical weighting of source, inner city feature of carrying out described in step 3 is close to acceptor PM _2.5pollution exposure relative risk is assessed, and comprises the following steps:

With the most southeast corner coordinate of inner city for origin coordinates, within the scope of city, create the default acceptor layer of fixed intervals, and all default acceptors are numbered;

Adopt the contiguous method calculated, in threshold range, calculate and record space length between each default acceptor and each point pollution source, and mating the PM of these point pollution sources according to the numbering of point pollution source _2.5discharge capacity, the PM of acceptor numbering, point pollution source numbering, default space length and point pollution source between acceptor and multiple point pollution source preset in generation record _2.5the record sheet of discharge capacity, the size of threshold range is relevant with the bandwidth b of geographical weighting function, is the twice of bandwidth, that is to say 2b;

According to the point pollution source PM that the numbering coupling of point pollution source in the record sheet generated generates according to step 1 _2.5discharge capacity;

The distance record sheet between acceptor and point pollution source is preset, by the PM of point pollution source i according to record _2.5discharge capacity carries out geographical weighting according to the nearly Gaussian function model after step 3 translation, calculates the default acceptor j exposed amount E caused by point pollution source i ⁱ _j;

E ⁱ _j＝E _i*W _ij

Wherein, E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i; E _ifor the discharge capacity of point pollution source i; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity.

Calculate and preset acceptor multiple PM suffered in threshold range _2.5the Pollution exposure Relative risk value that point pollution source causes, computing formula is as follows:

Wherein, R _jrepresent and preset acceptor j Pollution exposure Relative risk value; E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i _,preset the number of acceptor centered by m in city, l is the number of the point pollution source generated in threshold range;

Adopt the anti-distance power interpolation method in geo-statistic, to the PM of default acceptor _2.5pollution exposure Relative risk value carries out space interpolation in inner city; Generating center city PM _2.5pollution exposure risk profile figure.

Described method, the PM described in step 3 _2.5pollution exposure Relative risk value and PM _2.5regretional analysis between concentration value, and real-time estimation inner city PM _2.5concentration, comprises the following steps:

According to PM _2.5the geospatial location of monitoring station creates PM _2.5monitoring station data Layer;

According to the locus of monitoring station, the inner city PM utilizing step 3 to generate _2.5pollution exposure risk profile figure, extracts PM _2.5the PM of monitoring station position _2.5pollution exposure Relative risk value;

The PM obtained will be extracted _2.5monitoring station place PM _2.5the PM of Pollution exposure Relative risk value and monitoring station position _2.5observation concentration value carries out regretional analysis, calculates regression coefficient

Wherein, x _ifor monitoring station k place predicts the PM obtained _2.5pollution exposure Relative risk value, in inner city, all monitoring stations predict the PM obtained _2.5the mean value of Pollution exposure Relative risk value, y _kfor the PM at monitoring station k place _2.5observation concentration value, all monitoring station PM in inner city _2.5the mean value of observation concentration value, the number of monitoring station in city centered by n;

Then any locus PM in inner city is set up _2.5pollution exposure Relative risk value and PM _2.5linear regression model (LRM) between observation concentration value;

Wherein, Y is any locus PM _2.5concentration value, R is any locus PM _2.5pollution exposure Relative risk value;

The regression model obtained is applied to successively point by point any locus, inner city point PM _2.5the estimation of concentration, real-time estimation inner city PM _2.5concentration.

Compared with the conventional method, the invention has the advantages that: (1) this method utilizes road traffic flow real-time monitoring data to estimate inner city PM _2.5concentration is a kind of novel inner city PM _2.5concentration Estimation Method; (2) this method is the comparatively accurate inner city PM of one simultaneously _2.5concentration Estimation Method, with the inner city PM based on monitoring station _2.5concentration estimation is compared, and this method can estimate the PM of any locus, inner city point _2.5concentration; (3) this method or the more efficient inner city PM of one _2.5concentration Estimation Method, required inner city traffic road net vector data is relatively stable, requires lower, do not need repeated collection and arrangement to environmental variance, only needs to obtain inner city PM _2.5total emission volumn data and Real-Time Traffic Volume monitor data just can obtain inner city PM in real time _2.5concentration value.

Accompanying drawing explanation

Fig. 1 shows based on road traffic flow real-time monitoring data estimation optional position, inner city point PM _2.5the method flow of concentration;

Fig. 2 shows discrete for the traffic route wire pollution source point pollution source flow process for fixed intervals;

Fig. 3 shows the contrast of real air contamination pattern and geographical weighting function, wherein: (a) is real air contamination pattern, (b) is geographical weighting function, (c) is geographical weighting function expression formula;

Fig. 4 shows the contiguous principle calculated in the geographical weighting of source feature contiguous acceptor air pollution exposure assessment model, wherein, (a) be calculate space length schematic diagram between the point pollution source in receptor site and threshold range, (b) contiguously calculates the record sheet presetting space length between acceptor and multiple point pollution source afterwards;

Fig. 5 shows the PM according to the embodiment of the present invention _2.5pollution exposure Relative risk value and PM _2.5regretional analysis flow process embodiment between concentration value;

Fig. 6 shows the discrete point pollution source for fixed intervals of traffic route pollution source, wherein downtown roads net centered by (a); Downtown roads net partial enlarged drawing centered by (b); Partial enlarged drawing centered by (c) after the discrete point pollution source being interval of downtown roads net.

Fig. 7 shows PM _2.5pollution exposure Relative risk value and PM _2.5observation concentration value Regression Analysis Result.

Fig. 8 shows any locus PM in inner city _2.5concentration.

Embodiment:

Here is to a preferred embodiment of the invention, the detailed description carried out by reference to the accompanying drawings.

1, traffic route pollution source are discrete is the point pollution source of fixed intervals.

As shown in Figure 2, the traffic route pollution source discrete step that the present invention adopts comprises:

First, from the Real-time Road supervisory system of traffic department, obtain the magnitude of traffic flow real-time monitoring data of inner city traffic route, Real-Time Traffic Volume monitor data due to inner city is generally issue according to road name, therefore, need according to road name, Real-Time Traffic Volume monitor data to be mated with corresponding road attribute, to obtain the Real-Time Traffic Volume on each bar road in inner city;

Secondly, by discrete for each for inner city traffic route be the road segment segment of fixed intervals, as shown in Figure 6, and calculate the length of discrete rear road segment segment, middle point coordinate; Formula is as follows respectively:

Wherein (x _a, y _a) and (x _b, y _b) be respectively the extreme coordinates of g article of road segment segment, L _g(X, Y) represents the middle point coordinate of g article of road segment segment, L _grepresent the length of g article of road segment segment.

Calculate the PM of each road segment segment according to the following formula _2.5discharge capacity accounts for whole inner city PM _2.5the weights of discharge capacity:

Q _grepresent the weights that g article of road segment segment discharge capacity accounts for whole inner city discharge capacity, F _grepresent the magnitude of traffic flow that g article of road segment segment is monitored in real time, L _grepresent the length of g article of road segment segment, h represent whole inner city discrete after road hop count;

Then, the PM of each road segment segment is calculated _2.5discharge capacity:

E _g＝E _total*Q _g

E _grepresent the PM of g article of road segment segment _2.5discharge capacity, E _totalrepresent the PM of whole inner city _2.5discharge capacity.

Finally, with L _g(X, Y) (middle point coordinate of g article of road segment segment) be the volume coordinate of the fixed intervals point pollution source of traffic route wire pollution source and E instead _g(the PM of g article of road segment segment _2.5discharge capacity) PM of the instead fixed intervals point pollution source of traffic route pollution source _2.5discharge capacity, generates fixed intervals point pollution source layer, and is numbered the point pollution source of all generations.

2, the structure of geographical weighting function.

In real air pollution dispersal pattern, with a certain distance from the position of pollution source, pollutant levels are the highest, with this position for axis of symmetry, lower apart from the larger concentration of this axis of symmetry distance.The geographical weighting of source feature proposed by the invention contiguous acceptor air pollution exposure assessment model is simulated real air pollution dispersal pattern by geographical weighting function, and concrete building process, adopts following steps:

First, a nearly Gaussian function model is built;

Secondly, nearly Gaussian function model is to the distance of a right translation bandwidth b;

Wherein, i represents the point pollution source after discrete for traffic route pollution source in above-mentioned steps 1; J represents that the acceptor using these as search coverage internal contamination exposed amount spatial diversity, is called default acceptor according to the point that artificial definition fixed intervals generate in survey region; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity; d _ijrepresent the distance preset between acceptor j and point pollution source i; B is the non-negative attenuation function describing funtcional relationship between weight and distance, is called bandwidth;

Select the PM of Historical Monitoring point in inner city _2.5the Relative risk value of true monitor value and the PM according to monitoring point after this geographical weighted function model calculating _2.5relative risk value, carry out the determination of bandwidth b, step is as follows:

With Akaike quantity of information for criterion determines the size of bandwidth b, select the bandwidth making AIC (Akaike quantity of information) minimum to be optimum bandwidth, that is to say the b value of trying to achieve and making following formula value minimum:

The wherein number of monitoring point in n inner city; r _tcentered by the PM of t monitoring point in city _2.5the Relative risk value of true monitor value; R _tthe PM of t monitoring station after calculating according to this geographical weighted function model _2.5the Relative risk value of estimated value; Tr (s) is the mark of matrix s, that is to say matrix diagonals line element sum; e _tit is the matrix of the point emission source composition around t monitoring station; With W in up-to-date style _tbeing the matrix of t monitoring station to the geographical weighting function composition of each point emission source of surrounding, is the function of bandwidth b.

Finally, the personnel for the ease of association area understand the thought of the geographical weighting adopted in the present invention, and Fig. 3 shows the contrast of geographical weighting function and real air pollution dispersal pattern.

3, the contiguous acceptor PM of the geographical weighting of source, inner city feature _2.5pollution exposure relative risk is assessed, and the step of employing comprises:

First, according to the scope of inner city, create the default acceptor layer at internal fixtion interval, inner city, and all default acceptors are numbered;

Secondly, adopt the contiguous method calculated, in threshold range, calculate and record space length between each default acceptor and each point pollution source, the record sheet of acceptor numbering, point pollution source numbering, default space length between acceptor and multiple point pollution source preset in generation record, the size of threshold range is relevant with the bandwidth b of geographical weighting function, is the twice of bandwidth, that is to say 2b; Fig. 4 shows the contiguous principle calculated, and wherein between each point pollution source and certain threshold range each default acceptor interior, the computing formula of space length is as follows:

Wherein d _ijbe the space length between i-th default acceptor and a jth point pollution source, 2d is PM _2.5the threshold range of impact;

Again, record the space length between multiple point pollution source in each default acceptor and threshold range, and mate the PM of these point pollution sources according to the numbering of point pollution source _2.5discharge capacity, the PM of acceptor numbering, point pollution source numbering, default space length and point pollution source between acceptor and multiple point pollution source preset in generation record _2.5the record sheet of discharge capacity, as shown in table 1;

Then, according to the point pollution source PM that the numbering coupling of point pollution source in the record sheet generated generates according to step 1 _2.5discharge capacity; The distance record sheet between acceptor and multiple point pollution source is preset, by the PM of point pollution source i according to record _2.5discharge capacity carries out geographical weighting according to the nearly Gaussian function model after step 2 translation, calculates the default acceptor j exposed amount E caused by point pollution source i ⁱ _j;

E ⁱ _j＝E _i*W _ij

Wherein, E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i; E _ifor the discharge capacity of point pollution source i; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity.

Subsequently, utilize the geographical weighting of source feature contiguous acceptor air pollution exposure assessment model, calculate and preset acceptor multiple PM suffered in threshold range _2.5the Pollution exposure Relative risk value that point pollution source causes.Computing formula is as follows:

Wherein, R _jrepresent and preset acceptor j Pollution exposure Relative risk value; E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i _,preset the number of acceptor centered by m in city, l is the number of the point pollution source generated in threshold range;

Finally, the anti-distance power interpolation method in geo-statistic is adopted, to the PM of default acceptor _2.5pollution exposure Relative risk value carries out space interpolation in inner city; Generating center city PM _2.5pollution exposure risk profile figure.The formula of space interpolation is as follows:

Wherein, the PM of arbitrfary point, city centered by R _2.5relative exposure, R _jfor presetting the PM of acceptor j in neighborhood _2.5pollution exposure Relative risk value, d _jfor the distance of arbitrfary point and default acceptor j, the default receptor site number of city internal reference interpolation centered by l, the number of the point pollution source also namely generated in threshold range;

4, PM _2.5pollution exposure Relative risk value and PM _2.5regretional analysis between concentration value, and real-time estimation inner city PM _2.5concentration

As shown in Figure 5, the present invention adopts separate regression steps to comprise:

First, according to PM _2.5the geospatial location of monitoring station creates PM _2.5monitoring station data Layer;

Secondly, according to the locus of monitoring station, the inner city PM utilizing step 4 to generate _2.5pollution exposure risk profile figure, extracts PM _2.5the PM of monitoring station position _2.5pollution exposure Relative risk value;

Then, the PM obtained will be extracted _2.5monitoring station place PM _2.5the PM of Pollution exposure Relative risk value and monitoring station position _2.5observation concentration value carries out regretional analysis, calculates regression coefficient as shown in Figure 7,

Wherein, x _kfor monitoring station k place predicts the PM obtained _2.5pollution exposure Relative risk value, in inner city, all monitoring stations predict the PM obtained _2.5the mean value of Pollution exposure Relative risk value, y _kfor the PM at monitoring station i place _2.5observation concentration value, all monitoring station PM in inner city _2.5the mean value of observation concentration value, the number of monitoring station in city centered by n;

Subsequently, any locus PM in inner city is set up _2.5pollution exposure Relative risk value and PM _2.5linear regression model (LRM) between observation concentration value;

Y＝1.0194*R–0.0761

Wherein, Y is any locus PM _2.5concentration value, R is any locus PM _2.5pollution exposure Relative risk value;

Finally, the regression model obtained is applied to successively point by point any locus, inner city point PM _2.5the estimation of concentration, real-time estimation inner city PM _2.5concentration, as shown in Figure 8.

Above in detailed introduction of the present invention, apply specific case and set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping.Meanwhile, for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.

Claims (1)

1. estimate inner city PM based on road traffic flow real-time monitoring data for one kind _2.5the method of concentration, is characterized in that, comprises the following steps:

Step 1: by discrete for traffic route pollution source in inner city be the point pollution source of fixed intervals, comprise the following steps:

Obtain the magnitude of traffic flow real-time monitoring data of inner city traffic route;

By discrete for each for inner city traffic route be the road segment segment of fixed intervals, and calculate the length of discrete rear road segment segment, middle point coordinate;

Calculate the PM of each road segment segment according to the following formula _2.5discharge capacity accounts for whole inner city PM _2.5the weights of discharge capacity:

$Q_g=\frac{L_g*F_g}{\underset{g=1}{\overset{h}{\Σ}}{L}_g*F_g}$

Wherein Q _grepresent the weights that g article of road segment segment discharge capacity accounts for whole inner city discharge capacity, F _grepresent the magnitude of traffic flow that g article of road segment segment is monitored in real time, L _grepresent the length of g article of road segment segment, h represent whole inner city discrete after road hop count;

Calculate the PM of each road segment segment _2.5discharge capacity:

E _g＝E _total*Q _g

E _grepresent the PM of g article of road segment segment _2.5discharge capacity, E _totalrepresent the PM of whole inner city _2.5discharge capacity;

With point coordinate L in g article of road segment segment _g(X, Y) be the volume coordinate of fixed intervals point pollution source of traffic route pollution source and the PM of g article of road segment segment instead _2.5discharge capacity E _gthe instead PM of the fixed intervals point pollution source of traffic route pollution source _2.5discharge capacity, generates fixed intervals point pollution source layer, and is numbered the point pollution source of all generations;

Step 2: the structure carrying out geographical weighting function in whole inner city;

Step 3: according to the result of step 1, step 2, carries out the contiguous acceptor PM of the geographical weighting of source, inner city feature _2.5pollution exposure relative risk is assessed;

Step 4: the PM that step 3 is obtained _2.5pollution exposure Relative risk value and PM _2.5concentration value carries out regretional analysis, and real-time estimation inner city PM _2.5concentration;

The structure carrying out geographical weighting function in whole inner city described in step 2, comprises the following steps:

Build a nearly Gaussian function model;

$W_{ij}=\left\{\begin{array}{cc}{\[1-{\left(\frac{d_{ij}}{b}\right)}^2\]}^2& d_{ij}\≤b\\ 0& d_{ij}>b\end{array}\right.$

Nearly Gaussian function model is to the distance of a right translation bandwidth b;

$W_{ij}=\left\{\begin{array}{cc}{\[1-{\left(\frac{d_{ij}-b}{b}\right)}^2\]}^2& d_{ij}\≤2b\\ 0& d_{ij}>2b\end{array}\right.$

Wherein, i represents the point pollution source after discrete for traffic route pollution source in above-mentioned steps 1; J represents that the acceptor using these as search coverage internal contamination exposed amount spatial diversity, is called default acceptor according to the point that fixed intervals generate in survey region; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity; d _ijrepresent the distance preset between acceptor j and point pollution source i; B is the non-negative attenuation function describing funtcional relationship between weight and distance, is called bandwidth;

Select the PM of Historical Monitoring point in inner city _2.5the Relative risk value of true monitor value and the PM according to monitoring point after this geographical weighted function model calculating _2.5relative risk value, carries out the determination of bandwidth b;

Described bandwidth b is with Akaike quantity of information for criterion is to determine the size of bandwidth b, selects the bandwidth making AIC and Akaike quantity of information minimum to be optimum bandwidth, that is to say the b value of trying to achieve and making following formula value minimum:

$AIC=2n*ln\left(\widehat{\mathrm{\σ}}\right)+n*ln\left(2\mathrm{\π}\right)+n*\[\frac{n+tr\left(s\right)}{n-2-tr\left(s\right)}\]$

$\widehat{\mathrm{\σ}}=\frac{\underset{t=1}{\overset{n}{\Σ}}{(r_t-R_t)}^2}{n-tr\left(s\right)};$

$s=\left[\begin{array}{c}{E}_1{\left({e_1}^{\′}{W}_1{e}_1\right)}^{-1}{e_1}^{\′}{W}_1\\ E_2{\left({e_2}^{\′}{W}_2{e}_2\right)}^{-1}{e_2}^{\′}{W}_2\\ ...\\ E_t{\left({e_t}^{\′}{W}_t{e}_t\right)}^{-1}{e_t}^{\′}{W}_t\end{array}\right]$

The wherein number of monitoring point in n inner city; r _tcentered by the PM of t monitoring point in city _2.5the Relative risk value of true monitor value; R _tthe PM of t monitoring station after calculating according to this geographical weighted function model _2.5the Relative risk value of estimated value; Tr (s) is the mark of matrix s, that is to say matrix diagonals line element sum; e _tit is the matrix of the point emission source composition around t monitoring station; With W in up-to-date style _tbeing the matrix of t monitoring station to the geographical weighting function composition of each point emission source of surrounding, is the function of bandwidth b;

The geographical weighting of source, inner city feature of carrying out described in step 3 is close to acceptor PM _2.5pollution exposure relative risk is assessed, and comprises the following steps:

With the most southeast corner coordinate of inner city for origin coordinates, within the scope of city, create the default acceptor layer of fixed intervals, and all default acceptors are numbered;

Adopt the contiguous method calculated, in threshold range, calculate and record space length between each default acceptor and each point pollution source, and mating the PM of these point pollution sources according to the numbering of point pollution source _2.5discharge capacity, the PM of acceptor numbering, point pollution source numbering, default space length and point pollution source between acceptor and multiple point pollution source preset in generation record _2.5the record sheet of discharge capacity, the size of threshold range is relevant with the bandwidth b of geographical weighting function, is the twice of bandwidth, that is to say 2b;

According to the point pollution source PM that the numbering coupling of point pollution source in the record sheet generated generates according to step 1 _2.5discharge capacity;

The distance record sheet between acceptor and point pollution source is preset, by the PM of point pollution source i according to record _2.5discharge capacity carries out geographical weighting according to the nearly Gaussian function model after step 3 translation, calculates the default acceptor j exposed amount E caused by point pollution source i ⁱ _j;

E ⁱ _j＝E _i*W _ij

Wherein, E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i; E _ifor the discharge capacity of point pollution source i; W _ijrepresent that the exposed amount discharging the default acceptor j caused by point pollution source i accounts for the weight of point pollution source i discharge capacity;

Calculate and preset acceptor multiple PM suffered in threshold range _2.5the Pollution exposure Relative risk value that point pollution source causes, computing formula is as follows:

$R_j=l*\underset{i=1}{\overset{l}{\Σ}}{E}_j^i/\underset{i=1}{\overset{l}{\Σ}}\underset{j=1}{\overset{m}{\Σ}}{E}_j^i$

Wherein, R _jrepresent and preset acceptor j Pollution exposure Relative risk value; E ⁱ _jrepresent the exposed amount preset acceptor j and caused by point pollution source i, preset the number of acceptor centered by m in city, l is the number of the point pollution source generated in threshold range;

Adopt the anti-distance power interpolation method in geo-statistic, to the PM of default acceptor _2.5pollution exposure Relative risk value carries out space interpolation in inner city; Generating center city PM _2.5pollution exposure risk profile figure;

PM described in step 3 _2.5pollution exposure Relative risk value and PM _2.5regretional analysis between concentration value, and real-time estimation inner city PM _2.5concentration, comprises the following steps:

According to PM _2.5the geospatial location of monitoring station creates PM _2.5monitoring station data Layer;

According to the locus of monitoring station, the inner city PM utilizing step 3 to generate _2.5pollution exposure risk profile figure, extracts PM _2.5the PM of monitoring station position _2.5pollution exposure Relative risk value;

The PM obtained will be extracted _2.5monitoring station place PM _2.5the PM of Pollution exposure Relative risk value and monitoring station position _2.5observation concentration value carries out regretional analysis, calculates regression coefficient

$\left\{\begin{array}{c}\hat{b}=\frac{\underset{k=1}{\overset{n}{\Σ}}(x_k-\stackrel{\‾}{x})(y_k-\stackrel{\‾}{y})}{\underset{k=1}{\overset{n}{\Σ}}(x_k-\stackrel{\‾}{x})}\\ \hat{a}=\stackrel{\‾}{y}-\hat{b}\stackrel{\‾}{x}\end{array}\right.$

Wherein, x _kfor monitoring station k place predicts the PM obtained _2.5pollution exposure Relative risk value, in inner city, all monitoring stations predict the PM obtained _2.5the mean value of Pollution exposure Relative risk value, y _kfor the PM at monitoring station k place _2.5observation concentration value, all monitoring station PM in inner city _2.5the mean value of observation concentration value, the number of monitoring station in city centered by n;

Then any locus PM in inner city is set up _2.5pollution exposure Relative risk value and PM _2.5linear regression model (LRM) between observation concentration value;

$Y=\hat{a}*R+\hat{b}$

Wherein, Y is any locus PM _2.5concentration value, R is any locus PM _2.5pollution exposure Relative risk value;

The regression model obtained is applied to successively point by point any locus, inner city point PM _2.5the estimation of concentration, real-time estimation inner city PM _2.5concentration.