A method for constructing a relative optimal air quality health index

Abstract

The application discloses a kind of relatively optimal air quality health index construction methods, as follows: obtaining air pollutant and its internal component data in the region, local population health outcome data;With pseudo Poisson distribution connection function in turn constructs air pollutant and its internal component data, corresponding daily average concentration and daily total population death number, outpatient visit number and hospitalization number between the correlation;With the most harmful effect under lag air pollutant and its component concentration and health outcome, build multi-pollutant and component bayesian model;Estimate multi-pollutant and component risk coefficient and weight, calculate excess health risk;Establish comprehensive air quality health index, according to local population different health outcome data, calculate the health risk of a variety of comprehensive air quality health index reflecting local population different health outcome data;By comparing and analyzing a variety of comprehensive air quality health index, filter out relatively optimal air quality health index.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air quality and health impact early warning and forecasting, and more particularly to a method for constructing a relatively optimal air quality health index. BACKGROUND

[0002] In recent years, China has attached more and more importance to the monitoring, early warning and improvement of air pollution levels, emphasizing the comprehensive strengthening of early warning and emergency monitoring construction, deepening the forecasting and early warning business, and promoting the fine information service of air quality forecasting and early warning.

[0003] However, as the core index of regional forecast warning system, the current air quality index (AQI) is mostly used to evaluate the air quality in China. Although AQI considers the levels of six conventional pollutants, its calculation method is a segmented linear function value obtained by comparing daily pollutant concentrations with corresponding standard nodes. Essentially, it reflects the current air quality status of the region through the maximum of the pollutants, only reflecting the risk of the highest pollutant, and cannot reflect the comprehensive impact of multiple pollutants on human health. In addition, different regions have unique regional atmospheric composite pollution characteristics, and the AQI calculated based on the general standard nodes cannot reflect the regional local pollution pattern and its population disease impact characteristics. Therefore, in view of the above problems, a Canadian research team first proposed a new index, air quality health index (AQHI), in 2008. It is an index based on the exposure-response relationship between local air pollution and population health, which can more accurately reflect and evaluate the air pollution health risk in the region and population background. However, most of the AQHI studies are based on the simple addition of the excess mortality risk of various pollutants in the single pollutant time series model. Although this calculation is relatively simple, it often overestimates the health impact of a single pollutant and the combined effect of multiple pollutants. This is mainly because, first, air pollutant concentrations are highly correlated, and the risk coefficient of a single pollutant model is only the marginal effect value of the pollutant, without considering the presence of other pollutants; second, in reality, people are exposed to a mixture of multiple air pollutants, so using a single pollutant model to estimate health effects cannot truly reflect the actual health risk; third, the internal composition of pollutants, especially particulate pollutants, is complex, and the model does not consider the impact of components; finally, using excess mortality risk as the health outcome to calculate the health index cannot reflect the sensitive changes in the health effects of short-term air pollution changes. Therefore, it is urgent to design and construct a comprehensive air quality health index (C-AQHI) to solve the above problems. Among them, the construction of a calculation model based on multiple pollutants and their components to correctly reflect the exposure-response relationship between multiple pollutants and their components and population health impact, and how to select the best health outcome are the keys to constructing a relatively optimal air quality health index. SUMMARY

[0004] The present application provides a method for constructing a relatively optimal air quality health index to solve the problems and defects of the prior art.

[0005] To achieve the above-mentioned object of the present application, the technical solutions adopted are as follows:

[0006] A construction method of a relatively optimal air quality health index, the method steps comprising the following:

[0007] S1: Obtain air pollutant and its internal component data in the region, and local population health outcome data;

[0008] S2: Adjust the problem of excessive dispersion of local population health outcome data by quasi-likelihood method, and sequentially construct air pollutant and its internal component data with pseudo-Poisson distribution connection function, analyze the correlation between daily average concentration and daily total population death, outpatient visit and hospitalization number, i.e. single pollutant time series model analysis;

[0009] S3: Based on the analysis results of single pollutant time series model, construct a multi-pollutant and component Bayesian model with the air pollutant concentration under the maximum lag and health outcome;

[0010] S4: Estimate the risk coefficients and weights of multi-pollutant and component according to the multi-pollutant and component Bayesian model of S3, and further calculate the excess health risk ER t ;

[0011] S5: Based on the multi-pollutant and component Bayesian model and its excess health risk ER t , establish a comprehensive air quality health index C-AQHI,

[0012] S6: According to the different health outcome data of local population, calculate a plurality of comprehensive air quality health indexes C-AQHI reflecting the health risk of different health outcome data of local population;

[0013] S7: Through comparative analysis of a plurality of comprehensive air quality health indexes C-AQHI, screen out a relatively optimal air quality health index.

[0014] Preferably, the air pollutants include particulate matter, SO2, NO2, CO and O3; wherein the particulate matter includes PM 2.5 and PM 10 ; the internal component data corresponding to the particulate matter includes black carbon, organic matter, sulfate, nitrate and ammonium salt; and the local population health outcome data includes population death, outpatient visit and hospitalization and their cause data.

[0015] Further, the formula expression of the multi-pollutant and component Bayesian model is as follows:

[0016]

[0017] Wherein, E(Yt ) is the number of deaths Y t , or the number of hospitalizations Y t , or the number of outpatient visits Y t on day t, t represents the tth day; β is the overall effect value of the air pollutants and their internal load component mixture; X i represents the ith air pollutant other than particulate matter, where i = 1, …, p, p represents the total number of other air pollutants other than particulate matter; W i represents the coefficient of the ith air pollutant other than particulate matter; x j represents the jth internal load component of a certain particulate matter, where j = 1, …, c, c represents the total number of internal load components; W j represents the coefficient of the internal load component of a certain particulate matter; ε is the residual of the particulate matter internal load component and the particulate matter concentration, used to take into account the influence of residual difficult-to-measure or unmeasured components in the particulate matter on health; W c is the coefficient of each component residual; confounders are factors affecting the health effects of multiple air pollutants and their internal load components; intercept is the possible intercept.

[0018] Further, the best estimate of the overall health effect value of multiple pollutants is obtained by assigning the following prior uniform distribution within the range of 0 to μ to β.

[0019] Further, the best estimate of the overall health effect value of multiple pollutants is obtained by assigning the following prior uniform distribution within the range of 0 to μ to β. i and W j are assigned the following prior Dirichlet distribution such that their cumulative sums are 1, so that W i and W j are respectively equivalent to the weights of each air pollutant and each internal load component;

[0020] for i = 1…p: W i ~ Dirichlet(π i ); π i ~ Uniform(0, 1)

[0021] for j = 1…c: W j ~ Dirichlet(π j ); π j ~ Uniform(0, 1)

[0022] where Dirichlet() represents the prior Dirichlet distribution; Uniform represents the prior uniform distribution.

[0023] Further, the calculation formula of the factors confounders affecting the health effects of multiple air pollutants and their internal load components is as follows:

[0024] confounders = ns(time, 7df / year) + ns(hum, 3df) + ns(temp, 4df) + DOW + PH

[0025] where ns denotes natural cubic spline, time denotes time trend, df denotes degree of freedom, year denotes year, hum denotes humidity, temp denotes temperature, DOW denotes day of week, and PH denotes holiday effect.

[0026] Further, the excess health risk ER t , and the formula is as follows:

[0027]

[0028] Further, the comprehensive air quality health index C-AQHI is established, and the calculation formula is as follows:

[0029]

[0030] Further, according to different health outcome data of local population, a plurality of comprehensive air quality health indexes C-AQHI reflecting health risks of different health outcome data of local population are calculated respectively, and the specific process is as follows: based on the number of population deaths, the C M -AQHI reflecting the excess death risk of population related to short-term air pollution exposure is constructed, based on the population outpatient data, the C OV -AQHI reflecting the excess disease risk of population related to short-term air pollution exposure is constructed, and based on the population hospitalization data, the C HA -AQHI reflecting the excess disease risk of population related to short-term air pollution exposure is constructed.

[0031] A computer device comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the steps of the method as described above when executing the computer program.

[0032] The beneficial effects of the present application are as follows:

[0033] Compared with the traditional AQI and AQHI established by only considering single pollutant or even considering multiple pollutants without considering the internal component data of air pollutants, the influencing factors considered in the present application are more comprehensive. Meanwhile, the air pollutant concentration with the maximum lagging effect and the best death outcome are used to construct the multi-pollutant and component Bayesian model, and the risk coefficients and weights of the multi-pollutant and component are estimated, and the excess health risk caused by the combination is further calculated, and then the comprehensive air quality health index C-AQHI is established by combining the multi-pollutant and component Bayesian model and the excess health risk. The comprehensive air quality health index C-AQHI established in the present application has better effect than the AQI established by the traditional segmented linear function and the AQHI established by the traditional generalized additive model. In addition, in order to select the best, the relatively optimal air quality health index is screened out by comparing and analyzing the comprehensive air quality health index C-AQHI corresponding to various health outcomes, and the relatively most sensitive and effective C-AQHI in the region matching various health effects is finally obtained. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a flowchart of a method for constructing a relatively optimal air quality health index. DETAILED DESCRIPTION

[0035] The embodiments of the present application will be described below with reference to the drawings and preferred embodiments, and other advantages and effects of the present application can be easily understood by those skilled in the art from the disclosure herein. The present application can also be implemented or applied by means of other different specific embodiments, and various modifications or changes can be made to the details in the specification based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, and are not intended to limit the protection scope of the present application.

[0036] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and only the components related to the present application are shown in the diagrams, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be arbitrarily changed in type, number and proportion, and the layout pattern of the components may also be more complex.

[0037] Embodiment 1

[0038] As shown in Figure 1 , a method for constructing a relatively optimal air quality health index, the method steps include the following:

[0039] S1: Obtain the air pollutant and internal component data in the region and the local population health outcome data. The air pollutants include particulate matter, SO2, NO2, CO and O3; wherein the particulate matter includes PM2.5 , PM 10 ; particulate matter PM 2.5 The internal load component data of PM 2.5 includes black carbon, organic matter, sulfate, nitrate, ammonium salt; and the local population health outcome data includes population death, outpatient visit and hospitalization and their cause data.

[0040] In this embodiment, the air pollutant data mainly comes from the daily monitoring data of air pollution monitoring stations in the urban area of a city from 2016 to 2019, mainly including temperature, humidity and the following six kinds of air pollutants: PM 10 , PM 2.5 , SO2, NO2, CO and O3. The PM 2.5 component data comes from the daily component provincial database of the city from 2016 to 2019.

[0041] Among them, the concentrations of PM 10 , PM 2.5 , SO2, NO2 and CO are daily 24-hour average concentrations, and the concentration of O3 is daily maximum 8-hour average concentration. The concentrations of the four pollutants are daily ground concentrations based on the latitude and longitude of individual households, and the results are shown in Table 1.

[0042] Table 1 Air pollution and meteorological conditions in a city from 2016 to 2019

[0043]

[0044]

[0045] The population death data comes from the daily population death data of the city from 2017 to 2019, and the fields mainly include age, gender, death time, death place, pre-death permanent address, primary cause of death, and primary cause of death ICD 10 t coding (The International Statistical Classification of Diseases and Related Health Problems 10th Revision). There are a total of 156,000 entries, i.e. death data, in three years.

[0046] The population outpatient visit data and the number of hospitalizations come from the daily outpatient and hospitalization data of 299 hospitals in the city from 2016 to 2019, including the age, gender, disease ICD-10 code and other information of the patients. The total number of outpatient visits in three years is 18,424,345, and the total number of hospitalizations is 4,656,065.

[0047] Table 2 Daily population death, outpatient visit and hospitalization in a city

[0048]

[0049] S2: Adjust the over-dispersion problem of local population health outcome data by quasi-likelihood method, and sequentially build air pollutants and their internal component data with pseudo-Poisson distribution connection function, to correspond to the correlation between daily average concentration and daily total population death, outpatient visits and hospitalization, that is, single pollutant time series model analysis.

[0050] S3: Based on the results of single pollutant time series model analysis, in this example, the pollutant concentration with the largest hazard effect, that is, the pollutant concentration under lag0, is used to build a multi-pollutant and component Bayesian model with health outcomes.

[0051] The formula expression of the multi-pollutant and component Bayesian model is as follows:

[0052]

[0053] Wherein, E(Y t ) is the expectation of death Y t , or hospitalization Y t , or outpatient visits Y t on the tth day, t represents the tth day; β is the overall effect value of air pollutants and their internal component mixture, which is determined as 0.004 by training data in 2016 and 2017 in this example; X i represents the i th air pollutant other than particulate matter, wherein i = 1,..., p, p represents the total number of other air pollutants other than particulate matter, which is 4 in this example; W i represents the coefficient of the i th air pollutant other than particulate matter; x j represents the j th internal component of a certain particulate matter, wherein j = 1,..., c, c represents the total number of internal components, which is 5 in this example; W j represents the coefficient of PM 2.5 internal component; ε is the residual of internal component and PM 2.5 concentration, which is used to include the influence of residual or unmeasured components in PM 2.5 on health; W c is the coefficient of each component residual; confounders are factors affecting health of multiple air pollutants and their internal components; intercept is the possible intercept.

[0054] In this embodiment, by specifying the following prior uniform distribution of β within the range of 0 to μ, the best estimation value of the overall health effect value of multiple pollutants and components β is 0.004, which is obtained by using the data in 2016 and 2017 as training data.

[0055] Specifically, the overall health effect value of the plurality of pollutants can be made to obtain the best estimated value applied to the current data scenario within a reasonable range based on expert knowledge and existing evidence, as follows:

[0056] β ~ Uniform (0, μ)

[0057] where Uniform represents a prior uniform distribution.

[0058] The present example also makes use of the W i and W j The following prior Dirichlet distribution is specified so that the cumulative sum of each is 1, so that W i and W j are equivalent to the weights of each air pollutant and each internal load component, respectively;

[0059] for i = 1... p: W i ~ Dirichlet (π i ); π i ~ Uniform (0, 1)

[0060] for j = 1... c: W j ~ Dirichlet (π j ); π j ~ Uniform (0, 1)

[0061] where Dirichlet () represents a prior Dirichlet distribution; Uniform represents a prior uniform distribution.

[0062] In the present embodiment, the formula for calculating the confounders of the plurality of air pollutants and their internal load components for health impact is as follows:

[0063] confounders = ns (time, 7 df / year) + ns (hum, 3 df) + ns (temp, 4 df) + DOW + PH

[0064] where ns represents a natural cubic spline, time represents a time trend, df represents a degree of freedom, which is taken as 7 in the present example, year represents a year, hum represents humidity, temp represents temperature, DOW represents a day of the week; and PH represents a holiday effect.

[0065] The formula for calculating the confounders is shown below: ns is a natural spline, which controls the natural fluctuations (such as seasonal effects) of the mortality / hospitalization / clinic visits by incorporating 7 degrees of freedom df per year time; in addition, cubic spline terms of humidity hum and temperature temp are incorporated to control their nonlinear effects on health outcomes; finally, the effects of day of the week DOW and holiday effect PH are also excluded by incorporating them to affect the association between air pollution and health outcomes.

[0066] S4: According to the multi-pollutant and component Bayesian model of S3, the risk coefficients and weights of air multi-pollutants and components in 2018 are estimated, and the excess health risk ER caused by the comprehensive effect is further calculated. t .

[0067] wherein the excess health risk ER t is calculated according to the following formula:

[0068]

[0069] S5: Based on the multi-pollutant and component Bayesian model and the excess health risk ER t , the comprehensive air quality health index C-AQHI is calculated; the calculation formula is as follows:

[0070]

[0071] S6: According to the different health outcome data of the local population, a plurality of comprehensive air quality health indexes C-AQHI reflecting the health risk of the local population are calculated respectively;

[0072] Specifically, based on the number of deaths of the population, the C M -AQHI reflecting the excess mortality risk of the population related to short-term air pollution exposure is constructed, based on the clinic visit data of the population, the C OV -AQHI reflecting the excess morbidity of the population related to short-term air pollution exposure is constructed, and based on the hospitalization data of the population, the C HA -AQHI reflecting the excess morbidity risk of the population related to short-term air pollution exposure is constructed.

[0073] In this example, the excess risk estimation of air pollution and component data on different health outcomes and the calculation results of the comprehensive air quality health index are shown in Tables 3 and 4.

[0074] Table 3 Excess risk of different indexes related to different health outcomes of the total population per IQR change

[0075]

[0076] Note: AQHI is the air quality health index calculated by the traditional single pollutant model; AQI is the air quality index; C-AQHI is the comprehensive air quality health index calculated by the multi-pollutant and component Bayesian model; wherein, a C-AQHI represents C M -AQHI, which is a comprehensive air quality health index reflecting the excess mortality risk of the population related to short-term air pollution exposure based on population mortality data; b C-AQHI represents C HA -AQHI, which is a comprehensive air quality health index reflecting the excess morbidity risk of the population related to short-term air pollution exposure based on population hospitalization data; c C-AQHI represents C OV -AQHI, which is a comprehensive air quality health index reflecting the excess morbidity of the population related to short-term air pollution exposure based on population outpatient visit data; bold indicates significant correlation (P<0.05);

[0077] Table 4 Comparison of different air quality indices in 2016-2018

[0078]

[0079] Note: AQHI and C OV -AQHI are calculated based on outpatient data.

[0080] S7: By comparing and analyzing the various comprehensive air quality health indices C-AQHI, the relatively optimal air quality health index is selected.

[0081] In this embodiment, the comprehensive air quality health index C-AQHI is established based on the multi-pollutant Bayesian model and the excess health risk ER t , which is superior to the traditional AQI established based on the segmented linear function considering only a single pollutant or the air quality health index AQHI established based on the generalized additive model considering multiple pollutants. In this embodiment, the multi-pollutant Bayesian model and the component parameters are considered, and the AQHI index of each health outcome is the relatively optimal under the health outcome. Therefore, the C M -AQHI, C OV -AQHI, and C HA -AQHI are all optimal.

[0082] For example, as shown in Tables 3 and 4, in the health risk estimation of each health outcome constructed in this embodiment, the risk estimation value of C-AQHI corresponding to the multi-pollutant and component Bayesian model is the highest, which is superior to the traditional air quality index AQI and the air quality health index AQHI established based on the single pollutant generalized additive model.

[0083] However, the present embodiment is to select the best from the best, and the C M -AQHI, C OV -AQHI, C HA -AQHI three comprehensive air quality health indexes are compared, as shown in Table 3, and the C-AQHI (C M -AQHI, C OV -AQHI, C HA -AQHI) are compared, and it is determined that the C-AQHI (i.e., C OV -AQHI) based on the Bayesian model constructed based on outpatient data and multiple pollutants and their internal components can best reflect, reveal and warn the short-term health risks related to air pollution and its component changes. Therefore, the present embodiment selects the C OV -AQHI as the relatively optimal AQHI index.

[0084] Embodiment 2

[0085] A computer device includes a memory and a processor, and the memory stores a computer program capable of running on the processor, characterized in that the processor implements the steps of the construction method of the relatively optimal air quality health index according to the embodiment 1 when executing the computer program.

[0086] The memory and the processor are connected in a bus mode, and the bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage stabilizers, and power management circuits, which are well known in the art, and therefore, they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements such as multiple receivers and transmitters, which provide a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna, and further, the antenna also receives data and transmits the data to the processor.

[0087] In another specific embodiment, a computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the construction method of the relatively optimal air quality health index as described above:

[0088] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a program stored in a storage medium, including a plurality of instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0089] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation modes of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the claims of the present application.

Claims

1. A method for constructing a relative optimal air quality health index, characterized in that: The method steps are as follows: S1: Obtain the data of air pollutants and their internal components in the region and the health outcome data of the local population; the air pollutants include particulate matter, SO2, NO2, CO, and O3; the internal component data corresponding to particulate matter includes black carbon, organic matter, sulfate, nitrate, and ammonium salt; S2: Adjust the overdispersion problem of the health outcome data of the local population by quasi-likelihood method, and sequentially build the air pollutants and their internal component data with pseudo-Poisson distribution connection function, to analyze the correlation between the daily average concentration and the daily total number of deaths, outpatient visits, and hospitalizations of the population, i.e., single-pollutant time series model analysis; S3: Based on the analysis results of the single-pollutant time series model, build a multi-pollutant and component Bayesian model with the air pollutant concentration at the lag with the largest hazard effect and the health outcome; The formula expression of the multi-pollutant and component Bayesian model is as follows: in, E(Y t ) For the first t Daily death toll Y t or number of hospitalized patients Y t or number of outpatients Y t Expectations Indicates the first t sky; This represents the overall effect value of the mixture of air pollutants and their internal components; X i Indicates the first category excluding particulate matter. i One air pollutant, among which i =1,…, p , p This indicates the total number of air pollutants other than particulate matter; W i Indicates the first category excluding particulate matter. i coefficients of air pollutants; x j The first particulate matter j One internally loaded component, of which j =1,…, c , c Indicates the total number of internally loaded components; The coefficient representing the internal load of a certain particulate matter; ε is the residual between the internal load of the particulate matter and the particulate matter concentration, used to include the health effects of residual, difficult-to-measure or unmeasured components in the particulate matter. These are the coefficients of the residuals of each component; confounders Factors affecting health include various air pollutants and their internal components; The intercept; S4: Estimate the multi-pollutant and component Bayesian model risk coefficients and weights, and further calculate the excess health risk caused by the combination of the above ER t ; S5: Based on the multi-pollutant and component Bayesian model and its excess health risk ER t , the comprehensive air quality health index C-AQHI is established, S6: According to the different health outcome data of the local population, calculate a plurality of comprehensive air quality health indexes C-AQHI reflecting the health risk of the different health outcome data of the local population; S7: By comparing and analyzing the plurality of comprehensive air quality health indexes C-AQHI, screen out a relatively optimal air quality health index.

2. The method of constructing a relative optimal air quality health index according to claim 1, wherein: The particulate matter includes PM 2.5 , PM 10 ; and the local population health outcome data includes population mortality, outpatient visits and hospitalizations, and their cause data.

3. The method of constructing a relative optimal air quality health index according to claim 1, wherein: By applying the method to a priori uniform distribution in the range 0 to a best estimate of the overall health effect value for a variety of pollutants is obtained.

4. The method of constructing a relative optimal air quality health index according to claim 1, wherein: By applying the following priors Dirichlet distributions such that their cumulative sums are 1, respectively, W i and are assigned such that W i and are equivalent to the weights of each air pollutant and each internally carried component, respectively; wherein, represents a prior Dirichlet distribution; represents a prior uniform distribution.

5. The method of constructing a relative optimal air quality health index according to claim 1, wherein: Factors affecting the health impact of multiple air pollutants and their internal load components confounders The calculation formula is as follows: wherein, represents a natural cubic spline, represents a time trend, represents a degree of freedom, represents a year, represents humidity, represents temperature, represents a day of the week; represents a holiday effect.

6. The method of constructing a relative optimal air quality health index according to claim 1, wherein: the excess health risk ER t , as follows: 。 7. The method of constructing a relative optimal air quality health index according to claim 1, wherein: The comprehensive air quality health index C-AQHI is established, and the calculation formula is as follows:

8. The method of constructing a relative optimal air quality health index according to claim 7, wherein: According to the different health outcome data of the local population, a plurality of comprehensive air quality health indexes C-AQHI reflecting the health risks of the different health outcome data of the local population are calculated, specifically as follows: based on the number of population deaths, a C M -AQHI reflecting the excess mortality risk of the population related to short-term air pollution exposure is constructed, based on the population outpatient visit data, a C OV -AQHI reflecting the excess morbidity risk of the population related to short-term air pollution exposure is constructed, and based on the population hospitalization data, a C HA -AQHI reflecting the excess morbidity risk of the population related to short-term air pollution exposure is constructed. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the computer device is characterized in that, The processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

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