A method and system for determining the optimal control concentration threshold for air pollution based on residents' health effects.

Abstract

The embodiment of the application provides a method and system for determining an optimal control concentration threshold of atmospheric pollution based on resident health effects, and belongs to the technical field of environmental health early warning. The application first acquires baseline information of a research object; collects health and disease data and atmospheric pollution exposure data according to the baseline information; matches and integrates the baseline information, the health and disease data and the atmospheric pollution exposure data to acquire a research and analysis database; analyzes the research and analysis database to acquire feedback data under different assumed air quality standards; and obtains the optimal control concentration threshold of atmospheric pollution according to the feedback data. The application can provide technical support for the formulation of air quality standards and public health policies.

Description

Technical Field

[0001] This application relates to the field of environmental health early warning technology, and in particular to a method and system for determining the optimal control concentration threshold of air pollution based on the health effects on residents. Background Technology

[0002] Current research evidence suggests that most air pollutants exhibit an approximately linear dose-response relationship with adverse health outcomes, meaning there is no corresponding concentration threshold, making it difficult to establish corresponding concentration standards. Furthermore, existing systems suffer from deficiencies in data quality, timeliness, multi-pollutant synergistic effects, long-term health impact assessments, and regional differences, limiting the accuracy and applicability of early warning systems. High costs and complexity also restrict their widespread application in resource-constrained areas. These shortcomings affect the effectiveness of early warning systems and the impact on public health protection. Summary of the Invention

[0003] The main objective of this application is to provide a method and system for determining the optimal control concentration threshold for air pollution based on the health effects on residents.

[0004] The technical solution adopted in this invention is:

[0005] On one hand, embodiments of the present invention provide a method for determining the optimal control concentration threshold of air pollution based on the health effects on residents. The method for determining the optimal control concentration threshold of air pollution based on the health effects on residents includes the following steps:

[0006] Obtain baseline information of the research subjects;

[0007] Based on the baseline information, collect health and disease data and air pollution exposure data;

[0008] The baseline information, the health and disease data, and the air pollution exposure data are matched and integrated to obtain a research and analysis database.

[0009] The research and analysis database was analyzed to obtain feedback data under different assumed air quality standards.

[0010] Based on the feedback data, the optimal control concentration threshold for air pollution is obtained.

[0011] Furthermore, obtaining the baseline information of the research subjects includes the following steps:

[0012] Identify the research subjects;

[0013] Based on the research subjects, the baseline information of the research subjects was obtained through anonymization.

[0014] The baseline information includes lifestyle information, physical condition information, and residential address information.

[0015] Furthermore, the collection of health and disease data and air pollution exposure data based on the baseline information includes the following steps:

[0016] Based on the baseline information, lifestyle information and physical condition information are analyzed, and health and disease data are obtained; the health and disease data includes: death, chronic non-communicable diseases;

[0017] Based on the baseline information, the residential address information is analyzed, and combined with air pollution monitoring data, the long-term air pollutant exposure concentration of the research subjects is assessed to obtain air pollution exposure data.

[0018] Furthermore, the process of matching and integrating the baseline information, the health and disease data, and the air pollution exposure data to obtain a research and analysis database includes the following steps:

[0019] The baseline information, the health and disease data, and the air pollution exposure data are cleaned by removing abnormal and missing data, unifying the data format and data measurement units.

[0020] The baseline information, health and disease data, and air pollution exposure data of each research subject are matched and integrated after data cleaning to obtain the research analysis database.

[0021] Furthermore, the step of analyzing the research database to obtain feedback data under different assumed air quality standards includes the following steps:

[0022] Based on the research and analysis database, the risk ratio of long-term air pollution exposure to health outcomes was calculated using a distributed lag nonlinear model nested with a Cox proportional hazards regression model.

[0023] Calculate the absolute risk reduction value based on the risk ratio;

[0024] Based on the absolute risk reduction value, combined with the baseline mortality rate and population size, calculate the number of deaths reduced under different assumed air quality standards.

[0025] The number of deaths was used as feedback data.

[0026] Furthermore, the analysis based on the aforementioned research database utilizes a distributed lag nonlinear model nested with a Cox proportional hazards regression model to calculate the hazard ratio of long-term air pollution exposure to health outcomes. The formulas used include:

[0027]

[0028]

[0029] Where h0(t) is the baseline risk function, t is the time node, L is the maximum lag time, and f(x) is the maximum risk function. t-l ,l) are cross basis functions, β l For the risk coefficient corresponding to the lag time l, HR is the hazard ratio of long-term air pollution exposure to health outcomes; f(x) 1,t-l f(x) represents the cumulative cross-base value of exposure level x1 over lag time. 0,t-l ,l) represents the cumulative cross-base value of the reference level x0 over the lag time.

[0030] Furthermore, the formula used to calculate the absolute risk reduction value based on the risk ratio includes:

[0031]

[0032] Where ARR is the absolute risk reduction value; λ0 is the baseline mortality rate; and Δc is the change in pollutant concentration per unit.

[0033] Furthermore, the formula used to calculate the reduction in mortality under different assumed air quality standards, based on the absolute risk reduction value and in conjunction with the baseline mortality rate and population size, includes:

[0034]

[0035] Where N is the population size under the current air quality standard, and t' is the time range.

[0036] On the other hand, embodiments of the present invention also provide a system for determining the optimal control concentration threshold of air pollution based on the health effects of residents, used to implement the method for determining the optimal control concentration threshold of air pollution based on the health effects of residents as described above, wherein the system for determining the optimal control concentration threshold of air pollution based on the health effects of residents includes:

[0037] The data collection module is used to collect baseline information of research subjects and, through a cohort study design, to collect health and disease data of research subjects during the follow-up period.

[0038] The data processing module is used to perform data cleaning, matching, and integration, and to establish a research and analysis database.

[0039] The data analysis and feedback module is used to assess the chronic health effects of long-term exposure to air pollutants on the population and to analyze and calculate the optimal control concentration threshold for air pollution.

[0040] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method described above for determining the optimal control concentration threshold for air pollution based on the health effects of residents.

[0041] The embodiments of this application include at least the following beneficial effects: This application provides a method and system for determining the optimal control concentration threshold for air pollution based on the health effects on residents. This invention can acquire baseline information of research subjects; collect health and disease data and air pollution exposure data based on the baseline information; match and integrate the baseline information, health and disease data, and air pollution exposure data to obtain a research analysis database; analyze the research analysis database to obtain feedback data under different assumed air quality standards; and obtain the optimal control concentration threshold for air pollution based on the feedback data. This invention can provide technical support for the formulation of air quality standards and public health policies. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a method for determining the optimal control concentration threshold of air pollution based on the health effects on residents, provided in an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of the process modules of the system for determining the optimal control concentration threshold of air pollution based on the health effects on residents, provided in an embodiment of the present invention.

[0044] Figure 3 This is a schematic diagram illustrating the reduction in the number of resident deaths that can be achieved by lowering the concentration by one unit under different potential concentration standards provided in the embodiments of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0046] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0047] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0049] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0050] 1) Cox proportional hazards regression model, a survival analysis model used to analyze the relationship between the timing of an event and predictor variables;

[0051] 2) Hazard ratio (HR) is a relative risk indicator used to measure the ratio of the risk functions of two groups of individuals;

[0052] 3) Absolute Risk Reduction (ARR) is an indicator used to measure the absolute difference in the probability of an event occurring between two groups of individuals, reflecting the actual effect of the intervention;

[0053] 4) Distributed lag nonlinear model (DLNM), a statistical model used to analyze nonlinear effects and time lag effects in exposure-response relationships.

[0054] Before describing the method and system for determining the optimal control concentration threshold for air pollution based on the health effects of residents according to the present invention, it is necessary to clearly state that this application follows the principle of privacy protection when processing data. In particular, all data of research subjects involved in the present invention, including but not limited to baseline information or health and disease data collected through cohort study designs, such as gender, age, smoking, alcohol consumption, body mass index, physical activity, hypertension, coronary heart disease, stroke, etc., are strictly obtained and used with the explicit authorization and consent of the research subjects.

[0055] This invention fully utilizes population health data and environmental data to calculate the number of residents who can be reduced by one unit of concentration reduction under different assumed concentration standards. It compares the number of residents who can be reduced by one unit of concentration reduction under several assumed concentration standards, and takes into account cost and feasibility. The largest value is the recommended standard. This invention provides technical support for the formulation of air quality standards and public health policies.

[0056] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0057] On the one hand, embodiments of the present invention provide a method for determining the optimal control concentration threshold for air pollution based on the health effects on residents, referring to... Figure 1 A method for determining the optimal control concentration threshold for air pollution based on its health effects on residents includes the following steps:

[0058] S100. Obtain baseline information of the research subjects;

[0059] S200. Based on baseline information, collect health and disease data and air pollution exposure data;

[0060] S300: Match and integrate baseline information, health and disease data, and air pollution exposure data to obtain a research and analysis database;

[0061] S400. Analyze the research and analysis database to obtain feedback data under different assumed air quality standards;

[0062] S500: Based on the feedback data, the optimal control concentration threshold for air pollution is obtained.

[0063] The S100 method for obtaining baseline information of research subjects, as disclosed in this embodiment of the invention, includes the following steps:

[0064] S110. Determine the research subjects;

[0065] S120. Based on the research subjects, obtain the baseline information of the research subjects through anonymization;

[0066] S130. Baseline information includes lifestyle information, physical condition information, and residential address information.

[0067] The S200 method disclosed in this embodiment of the invention collects health and disease data and air pollution exposure data based on baseline information, including the following steps:

[0068] S210. Based on baseline information, analyze lifestyle information and physical condition information, and simultaneously acquire health and disease data; health and disease data include: mortality and chronic non-communicable diseases;

[0069] S220. Based on baseline information, analyze residential address information and combine it with air pollution monitoring data to assess the long-term air pollutant exposure concentration of the research subjects and obtain air pollution exposure data.

[0070] As an optional implementation, this embodiment of the invention employs a cohort study design, selecting a representative population as the research subjects. The inclusion criteria for the research subjects are clearly defined, such as age range, place of residence, and health status. Unqualified subjects are excluded, such as individuals with serious illnesses at baseline or those with missing data. Data sources for this embodiment include: baseline information, health and disease data of the research subjects obtained through face-to-face interviews, telephone interviews, or online questionnaires. This includes demographic information, such as gender, age, and residential address information (used to match air pollution exposure data); lifestyle information, such as smoking status (e.g., whether smoking, amount of smoking, years of smoking), alcohol consumption status (e.g., whether drinking alcohol, frequency of drinking, amount of drinking alcohol), physical activity level (e.g., exercise frequency, type of exercise); and physical condition information, such as body mass index (BMI) and past medical history (e.g., diabetes, hypertension, coronary heart disease, stroke, etc.).

[0071] The S300 method disclosed in this embodiment of the invention matches and integrates baseline information, health and disease data, and air pollution exposure data to obtain a research and analysis database, including the following steps:

[0072] S310. Remove outlier and missing data from baseline information, health and disease data, and air pollution exposure data; unify data format and data measurement units; and complete data cleaning.

[0073] S320. The baseline information, health and disease data and air pollution exposure data of each research subject after data cleaning are matched and integrated to obtain the research analysis database.

[0074] As an optional implementation, this invention standardizes data formats and units of measurement (e.g., age is standardized to years, and smoking volume to "packs / year"), checks data integrity and consistency, excludes outliers (e.g., age exceeding a reasonable range), and fills in or excludes missing data to ensure data quality. Personal information of research subjects is anonymized to ensure data security. The obtained baseline information is matched and integrated with health outcome data and air pollution exposure data to form a structured research and analysis database, facilitating subsequent data analysis and modeling.

[0075] The S400 method disclosed in this embodiment of the invention analyzes a research and analysis database to obtain feedback data under different assumed air quality standards, including the following steps:

[0076] S410. Based on the research and analysis database, use a distributed lag nonlinear model nested with a Cox proportional hazards regression model to calculate the risk ratio of long-term air pollution exposure to health outcomes.

[0077] S420. Calculate the absolute risk reduction value based on the risk ratio;

[0078] S430. Based on the absolute risk reduction value, combined with the baseline mortality rate and population size, calculate the number of deaths reduced under different assumed air quality standards.

[0079] S440, using the number of deaths as feedback data.

[0080] As an optional implementation, embodiments of the present invention use other health outcome data as feedback, such as disease incidence, hospitalization rate, and medical expenses, to reflect the impact of air pollution on health. The relevant method steps are as follows:

[0081] We used a Cox proportional hazards regression model to analyze the relationship between long-term air pollution exposure and various health outcomes (such as respiratory diseases, cardiovascular and cerebrovascular diseases, hospitalization events, etc.).

[0082] The absolute risk reduction under different assumed air quality standards was calculated based on the hazard ratio (HR) and the baseline health outcome incidence.

[0083] Based on the absolute risk reduction (ARR), combined with the baseline incidence of health outcomes and population size, calculate the health benefits (such as a reduction in the number of respiratory diseases, a reduction in the number of hospitalizations, etc.) under different assumed air quality standards;

[0084] The calculated health benefit data are used as feedback data to evaluate the health benefits of different assumed air quality standards, such as the reduction in respiratory diseases (e.g., asthma, chronic obstructive pulmonary disease), the reduction in cardiovascular diseases (e.g., myocardial infarction, stroke), the reduction in hospitalizations (reflecting the impact of air pollution on the burden on medical resources), and the reduction in medical expenses (estimated based on the reduction in the number of diseases and the reduction in hospitalizations).

[0085] The embodiment of this invention discloses S410, which analyzes a research database and uses a distributed lag nonlinear model nested with a Cox proportional hazards regression model to calculate the risk ratio of long-term air pollution exposure to health outcomes. The formula used includes:

[0086]

[0087]

[0088] Where h0(t) is the baseline risk function, t is the time node, L is the maximum lag time, and f(x) is the maximum risk function.t-l ,l) are cross basis functions, β l For the risk coefficient corresponding to the lag time l, HR is the hazard ratio of long-term air pollution exposure to health outcomes; f(x) 1,t-l f(x) represents the cumulative cross-base value of exposure level x1 over lag time. 0,t-l ,l) represents the cumulative cross-base value of the reference level x0 over the lag time.

[0089] The S420 method disclosed in this embodiment of the invention calculates the absolute risk reduction value based on the risk ratio, and the formula used includes:

[0090]

[0091] Where ARR is the absolute risk reduction value; λ0 is the baseline mortality rate; and Δc is the change in pollutant concentration per unit.

[0092] The S430 method disclosed in this embodiment calculates the reduction in mortality under different assumed air quality standards based on the absolute risk reduction value, combined with the baseline mortality rate and population size. The formula used includes:

[0093]

[0094] Where N is the population size under the current air quality standard, and t' is the time range.

[0095] On the other hand, embodiments of the present invention also provide a system for determining the optimal control concentration threshold of air pollution based on the health effects of residents, for implementing the aforementioned method for determining the optimal control concentration threshold of air pollution based on the health effects of residents. The system for determining the optimal control concentration threshold of air pollution based on the health effects of residents includes:

[0096] The data collection module is used to collect baseline information of research subjects and, through a cohort study design, to collect health and disease data of research subjects during the follow-up period.

[0097] The data processing module is used to perform data cleaning, matching, and integration, and to establish a research and analysis database.

[0098] The data analysis and feedback module is used to assess the chronic health effects of long-term exposure to air pollutants on the population and to analyze and calculate the optimal control concentration threshold for air pollution.

[0099] As an optional implementation, this embodiment of the invention utilizes a cohort study design. First, a data collection module gathers baseline information of the study subjects. Based on their residential addresses, the annual average air pollution exposure concentration for each subject is assessed. Health and disease information of the subjects is recorded during follow-up. In the data analysis and feedback module, the chronic health effects of long-term air pollution exposure on the population are evaluated. Simultaneously, based on different assumed concentration thresholds, the number of deaths that can be reduced by one unit decrease in air pollution concentration is calculated. By comparing these values, the largest value is determined as the optimal air pollution control concentration threshold.

[0100] As an optional implementation method, refer to Figure 2 The embodiments of the present invention provide a data collection module, a data processing module, and a data analysis and feedback module;

[0101] In the data collection module, baseline information data of the study subjects is collected, and health and disease data of the study subjects are collected during the follow-up period through a cohort study design;

[0102] The residential address information of the study subjects in the baseline information is used to assess the long-term air pollutant exposure data of each study subject and to calculate the annual average air pollutant exposure concentration.

[0103] The data processing module cleans, matches, and integrates the collected data to establish a research and analysis database.

[0104] The data analysis and feedback module assesses the chronic health effects of long-term exposure to air pollutants on the population and calculates the optimal control concentration threshold for air pollution.

[0105] Optionally, baseline information data of the study participants includes: gender, age, smoking, alcohol consumption, body mass index, and physical activity.

[0106] Health and disease data include: mortality, diabetes, hypertension, coronary heart disease, stroke, etc.

[0107] Air pollution exposure data includes: fine particulate matter (PM2.5) 2.5 .

[0108] Optionally, the data in the data collection module is cleaned to remove abnormal and missing data, and to standardize the data format and data measurement units.

[0109] The data from the data collection module is matched and integrated, and the baseline information, health and disease data, and air pollution exposure data of each research subject are matched and integrated to form a structured research analysis database for data analysis.

[0110] Optionally, a data analysis and feedback module is provided to assess the chronic health effects of long-term exposure to air pollution on the population.

[0111] The hazard ratio (HR) and 95% confidence interval for long-term air pollution exposure to health outcomes were calculated using a distributed lag nonlinear model (DLNM) nested with a Cox proportional hazards regression model. The HR calculations for different concentrations using the DLNM nested with a Cox proportional hazards regression model are as follows:

[0112]

[0113] Where h0(t) is the baseline risk function, t is the time node, L is the maximum lag time, and f(x) is the maximum risk function. t-l ,l) are cross basis functions, β l is the coefficient corresponding to the lag time l.

[0114]

[0115] Where L is the maximum lag time, f(x) 1,t-l f(x) represents the cumulative cross-base value of exposure level x1 over lag time. 0,t-l ,l) represents the cumulative cross-base value of the reference level x0 over the lag time.

[0116] Optionally, the data analysis and feedback module includes calculating the absolute risk reduction (ARR) using the derived formula:

[0117]

[0118] Where λ0 is the baseline mortality rate, β l The risk coefficient is lagged by 1 day, and Δc is the change in pollutant concentration per unit.

[0119] Optionally, the data analysis and feedback module includes further calculation of the reduction in deaths using the derived formula:

[0120]

[0121] Where λ0 is the baseline mortality rate under a specific air quality standard, N is the population size under the current air quality standard, t' is the time range, and β l The risk coefficient is lagged by 1 day, and Δc is the change in pollutant concentration per unit.

[0122] Optionally, the data analysis and feedback module includes using the obtained formula to determine the reduction in the number of deaths when the concentration change Δc is set to 1:

[0123]

[0124] Where λ0 is the baseline mortality rate under a specific air quality standard, N is the population size under the current air quality standard, t' is the time range, and βl The risk coefficient is calculated with a lag of 1 day.

[0125] Optionally, the data analysis and feedback module uses the obtained formula to calculate the number of residents who can be reduced by one unit of concentration under different assumed air quality standards. The value that is the largest is determined to be the air pollutant concentration threshold, which is the most practical air quality standard.

[0126] As an optional implementation, this embodiment of the invention collects health information of residents in Hebei, Hunan, Zhejiang, Jiangxi, and Gansu provinces starting from the data collection module, while simultaneously assessing long-term exposure to air pollution concentrations to obtain the annual average PM2.5 concentration. 2.5 Concentration; the data processing module is further used to clean, match, and integrate the collected data to establish the dataset used in this embodiment; the baseline information data of the research subjects in this embodiment includes: gender and age; the health outcome specifically refers to death; the air pollution specifically refers to the annual average PM2.5 concentration. 2.5 Concentration. In the data analysis and feedback module of this embodiment, mortality data from various provinces and specific PM levels are used. 2.5 The proportion of the population exposed to the concentration, combined with the obtained HR value, is used to calculate the number of residents who will die less per unit decrease in concentration.

[0127] At present, the PM2.5 standard stipulated in my country's ambient air quality standards is... 2.5 The secondary concentration limit is 35 μg / m³. 3 Based on this, this embodiment assumes PM 2.5 The secondary concentration standards were reduced by 1 μg / m³ in sequence. 3 That is, from 35 μg / m 3 Up to 20 μg / m 3 Fifteen concentrations were set. Furthermore, long-term PM2.5 levels were assessed using data from the selected study subjects. 2.5 The impact of exposure on mortality risk in the population was investigated by calculating the hazard ratio (HR) at the assumed standard concentration. Further calculations were made to determine the reduction in the number of deaths per unit decrease in concentration at different standard concentrations. The results are as follows: Figure 3 As shown.

[0128] As an optional implementation method, such as Figure 3 As shown, this embodiment of the invention compares the number of resident deaths reduced by one unit decrease in concentration under different assumed standard concentrations, and determines the standard concentration corresponding to the largest value as the PM2.5 concentration for the province. 2.5 Long-term exposure concentration threshold. In some embodiments, the maximum reduction in the number of resident deaths per unit decrease in concentration across different provinces is 25 μg / m³. 3 or 26μg / m 3 Among them, Gansu, Hebei, and Zhejiang had a concentration of 26 μg / m³. 3This can avoid the maximum human value; Hunan and Jiangxi are at 25 μg / m³. 3 To avoid maximizing the human value, and considering the different situations in various provinces, this embodiment determines 26 μg / m³. 3 This represents the long-term exposure concentration threshold.

[0129] This invention fully utilizes population health information and air pollution data, taking into account cost and feasibility, to accurately determine the potential concentration thresholds of air pollutants. The invention includes calculating the number of resident deaths reduced by one unit decrease in concentration under different assumed concentration standards, comparing the number of residents prevented from dying under several assumed concentration standards, and determining the highest value, considering cost and feasibility, represents the optimal health benefit and is thus the recommended standard. This invention provides technical support for the formulation of air quality standards and public health policies.

[0130] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method described above for determining the optimal control concentration threshold for air pollution based on the health effects on residents.

[0131] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0132] On the other hand, embodiments of the present invention also provide an apparatus for determining the optimal control concentration threshold of air pollution based on the health effects on residents, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described above for determining the optimal control concentration threshold of air pollution based on the health effects on residents.

[0133] The processor and memory can be connected via a bus or other means. Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0134] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining an optimal control concentration threshold of atmospheric pollution based on health effects of residents, characterized in that, The method for determining the optimal control concentration threshold of atmospheric pollution based on the health effect of residents comprises the following steps: Obtaining baseline information of the research subjects, including: Determining the research subjects; Obtaining baseline information of the research subjects through anonymization processing according to the research subjects; The baseline information includes lifestyle information, physical condition information, and residence address information; According to the baseline information, collecting health and disease data and atmospheric pollution exposure data, including: According to the baseline information, analyzing the lifestyle information and the physical condition information, and simultaneously obtaining health and disease data; the health and disease data include death and chronic non-communicable diseases; According to the baseline information, analyzing the residence address information, combining atmospheric pollution monitoring data, and evaluating the long-term atmospheric pollution exposure concentration of the research subjects to obtain atmospheric pollution exposure data; Matching and integrating the baseline information, the health and disease data, and the atmospheric pollution exposure data to obtain a research analysis database, including: Removing abnormal data and missing data, unifying data formats and data measurement units, and completing data cleaning of the baseline information, the health and disease data, and the atmospheric pollution exposure data; Matching and integrating the baseline information, the health and disease data, and the atmospheric pollution exposure data of each research subject after data cleaning to obtain a research analysis database; According to the research analysis database, analyzing and obtaining feedback data under different assumed air quality standards, including: According to the research analysis database, using a distributed lag non-linear model nested Cox proportional hazards regression model to calculate the risk ratio of long-term atmospheric pollution exposure to health outcomes; According to the risk ratio, calculating the absolute risk reduction value; According to the absolute risk reduction value, combining the baseline mortality rate and the population size to calculate the number of deaths reduced under different assumed air quality standards; Taking the number of deaths reduced as the feedback data; According to the feedback data, obtaining the optimal control concentration threshold of atmospheric pollution.

2. The method of determining optimal control concentration threshold of atmospheric pollution based on resident health effect according to claim 1, wherein, The formula used for calculating the risk ratio of long-term atmospheric pollution exposure to health outcomes according to the research analysis database by using a distributed lag non-linear model nested Cox proportional hazards regression model includes: ； ； in, For the baseline risk function, As a time node, L It is the maximum lag time. For cross basis functions, To correspond to the lag time The risk factor, The risk ratio of long-term exposure to air pollution to health outcomes; For exposure level The cumulative cross-base value over the lag time, For reference level The cumulative cross-base value over the lag time.

3. The method of determining optimal control concentration threshold of atmospheric pollution based on resident health effect according to claim 1, characterized in that, The formula used for calculating the absolute risk reduction value according to the risk ratio includes: ； wherein, is the absolute risk reduction; is the baseline mortality, is the contaminant per unit concentration change.

4. The method of determining optimal control concentration threshold of atmospheric pollution based on resident health effect according to claim 1, wherein, The formula used for calculating the number of deaths reduced under different assumed air quality standards according to the absolute risk reduction value, combining the baseline mortality rate and the population size includes: ； wherein, is the population size under current air quality standards, is the time horizon.

5. A system for determining optimal control concentration thresholds of atmospheric pollution based on health effects of residents for implementing the method of determining optimal control concentration thresholds of atmospheric pollution based on health effects of residents according to any one of claims 1 to 4, characterized in that, The system for determining the optimal control concentration threshold of atmospheric pollution based on the health effect of residents comprises: A data collection module for collecting baseline information of research subjects through cohort study design and collecting health and disease data of research subjects during follow-up; A data processing module for performing data cleaning, matching, and integration to establish a research analysis database; A data analysis and feedback module for evaluating the chronic health effects of long-term exposure to atmospheric pollutants on the population and analyzing and calculating the optimal control concentration threshold of atmospheric pollution.

6. A computer readable storage medium characterized by, The computer readable storage medium stores computer executable instructions for causing a computer to perform the method for determining the optimal control concentration threshold of atmospheric pollution based on the health effect of residents according to any one of claims 1 to 4.

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