Method and system for environmental health risk assessment based on comprehensive environmental health index

Abstract

The application discloses an environmental health risk assessment method and system based on a comprehensive environmental health index, and the method comprises the following steps: acquiring longitude and latitude information of a to-be-measured area and monitoring data of a plurality of influence factors; the influence factors comprise a temperature factor, a relative humidity factor and environmental factors; the environmental factors comprise air pollutants, noise, pollen and negative oxygen ions; exposure assessment is performed on the monitoring data of each influence factor based on the longitude and latitude information, an evaluation value corresponding to each influence factor is obtained, and the evaluation value is input into a preset multi-factor logistic regression model for calculation to obtain a comprehensive environmental health index of the to-be-measured area; the regression model comprises an interaction term related to the temperature and the environmental factors; and the environmental health risk assessment result of the to-be-measured area is determined by analyzing the comprehensive environmental health index of the to-be-measured area. The embodiment of the application can effectively improve the comprehensiveness of environmental health risk assessment and can be widely applied to the technical field of environmental health monitoring.

Description

Technical Field

[0001] This invention relates to the field of environmental health monitoring technology, and in particular to an environmental health risk assessment method and system based on a comprehensive environmental health index. Background Technology

[0002] Environmental health risk assessment refers to the qualitative or quantitative estimation of the likelihood of adverse effects on public health by biological, chemical, and physical factors in the environment. Its purpose is to identify factors in the environment that may pose a threat to public health, assess the likelihood and extent of these factors leading to health problems, provide a scientific basis for environmental management and decision-making, and prevent, control, and reduce environmental health risks to protect and promote public health.

[0003] Currently, the Air Quality Index (AQI) is widely used for environmental health risk assessment, serving as a core tool to qualitatively or quantitatively estimate the potential adverse health effects of air pollution. However, the AQI only considers the impact of air pollutants on adverse health outcomes, which limits its application in environmental health risk assessment. Summary of the Invention

[0004] In view of this, in order to solve one of the above problems, the purpose of this invention is to provide an environmental health risk assessment method and system based on a comprehensive environmental health index, which can effectively improve the comprehensiveness of environmental health risk assessment.

[0005] On one hand, embodiments of the present invention provide an environmental health risk assessment method based on a comprehensive environmental health index, comprising the following steps:

[0006] The latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors, are obtained. These influencing factors include temperature, relative humidity, and several environmental factors. The types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured.

[0007] Exposure assessment is performed on the monitoring data of each of the influencing factors based on the latitude and longitude information to obtain the assessment value corresponding to each of the influencing factors;

[0008] The evaluation value corresponding to each of the influencing factors is input into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes an interaction term related to environmental factors and temperature.

[0009] The environmental health risk assessment results of the area to be tested are determined by analyzing the comprehensive environmental health index.

[0010] Specifically, the exposure assessment of the monitoring data of each influencing factor based on the latitude and longitude information of the area to be measured, to obtain the assessment value corresponding to each influencing factor, includes:

[0011] Based on the latitude and longitude information and bilinear interpolation, a first exposure assessment is performed on the monitoring data of the temperature factor to obtain the assessment value corresponding to the temperature factor;

[0012] Based on the latitude and longitude information and bilinear interpolation, a second exposure assessment is performed on the monitoring data of the relative humidity factor to obtain the assessment value corresponding to the relative humidity factor;

[0013] Based on the latitude and longitude information and the inverse distance weighting method, a third exposure assessment is performed on the monitoring data of each of the environmental factors to obtain the assessment value corresponding to each of the environmental factors.

[0014] Specifically, the types of air pollutants to be measured are identified in the following ways:

[0015] Acquire historical data of adverse outcomes in the area to be tested during the same period and historical assessment value sets for each of the influencing factors; the historical assessment value sets of the environmental factors include historical assessment value sets for several air pollutants;

[0016] Based on the case-crossover design method, a one-way conditional logistic regression model for each of the air pollutants was constructed using the historical data of the adverse outcomes, the historical assessment value set of each of the air pollutants, and the historical assessment value set of temperature and relative humidity factors. The regression coefficient between each of the air pollutants and the adverse outcomes was then determined.

[0017] An analysis is performed based on the regression coefficients between each of the aforementioned air pollutants and the adverse outcomes, and the types of the air pollutants to be measured are determined from several of the aforementioned air pollutants based on the analysis results.

[0018] Specifically, the single-factor conditional logistic regression model for each of the aforementioned air pollutants is constructed in the following manner:

[0019] Based on the historical data of adverse outcomes, the historical assessment values ​​of the air pollutants, and the historical assessment values ​​of temperature and relative humidity, a matching group dataset is constructed; the matching groups in the matching group dataset indicate the matching relationship between the occurrence of adverse outcomes and the air pollutants, temperature, and relative humidity factors in a certain period.

[0020] Based on the first preset degree of freedom and the historical evaluation value set of the temperature factor, a first natural spline function is constructed;

[0021] Based on the second preset degree of freedom and the historical evaluation value set of the relative humidity factor, a second natural spline function is constructed;

[0022] Based on the matching group dataset, the first natural spline function, and the second natural spline function, a one-way conditional logistic regression model corresponding to the air pollutant is constructed.

[0023] Furthermore, the regression coefficients between the air pollutants and the adverse outcomes were determined in the following manner:

[0024] Based on the maximum likelihood method and the matching group dataset, the conditional likelihood function of the single-factor conditional logistic regression model corresponding to the environmental factors is constructed.

[0025] The conditional likelihood function is numerically optimized to obtain the regression coefficients, the odds ratios of the regression coefficients, and the confidence intervals, so as to determine the regression coefficients between the environmental factors and the adverse outcomes.

[0026] Specifically, the multifactor logistic regression model is constructed in the following manner:

[0027] Obtain historical data on adverse outcomes in the region under test during the same period and a set of historical evaluation values ​​for each of the influencing factors;

[0028] Based on the case-crossover design method, and using the historical data of adverse outcomes, the historical assessment value set of each environmental factor, and the historical assessment value set of temperature and relative humidity factors, a univariate conditional logistic regression model corresponding to each environmental factor is constructed, and the regression coefficient between each environmental factor and the adverse outcome is determined.

[0029] Based on the single-factor conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome, the multi-factor logistic regression model is constructed.

[0030] Furthermore, the construction of the multi-factor logistic regression model based on the univariate conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome includes:

[0031] Based on the matching group dataset corresponding to each environmental factor, determine the temperature curve in the single-factor conditional logistic regression model corresponding to each environmental factor;

[0032] The temperature curves in the single-factor conditional logistic regression model corresponding to each environmental factor are analyzed to determine the high temperature threshold, and temperature level variables and interaction terms between environmental factors and temperature levels are set according to the high temperature threshold.

[0033] Based on the second natural spline function, the single-factor conditional logistic regression model corresponding to each environmental factor, the regression coefficient between each environmental factor and the adverse outcome, the temperature level variable, and the interaction term between environmental factors and temperature level, the multi-factor logistic regression model is constructed.

[0034] Specifically, the step of inputting the evaluation value corresponding to each of the influencing factors into a preset multi-factor logistic regression model for calculation to obtain a comprehensive environmental health index includes:

[0035] The evaluation value corresponding to each of the aforementioned influencing factors is input into a preset multi-factor logistic regression model for calculation, thereby calculating the probability of adverse outcomes occurring in the region under test.

[0036] The comprehensive environmental health index of the test area is obtained by matching the probability of adverse outcomes occurring in the test area with a preset comprehensive environmental health index level range.

[0037] On the other hand, embodiments of the present invention provide an environmental health risk assessment system based on a comprehensive environmental health index, comprising:

[0038] The first module is used to acquire the latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors. The influencing factors include temperature, relative humidity, and several environmental factors. The types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured.

[0039] The second module is used to perform an exposure assessment on the monitoring data of each of the influencing factors based on the latitude and longitude information, and to obtain an assessment value corresponding to each of the influencing factors;

[0040] The third module is used to input the evaluation value corresponding to each of the influencing factors into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes an interaction term related to environmental factors and temperature.

[0041] The fourth module is used to analyze the comprehensive environmental health index and determine the environmental health risk assessment results of the area to be tested.

[0042] On the other hand, embodiments of the present invention provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the method described above.

[0043] Implementing the embodiments of the present invention has the following beneficial effects:

[0044] This embodiment provides an environmental health risk assessment method and system based on a comprehensive environmental health index. The method acquires monitoring information on temperature, relative humidity, and environmental factors in the area to be tested. Based on latitude and longitude information, it assesses the exposure level of the monitoring information to obtain an evaluation value for each factor. Then, the evaluation value of each factor is input into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index of the area to be tested. Finally, the obtained comprehensive environmental health index is analyzed to determine the environmental health risk assessment result of the area to be tested. On the one hand, this invention considers the combined effects of air pollutants, noise, pollen, negative oxygen ions, temperature, and relative humidity, etc. Constructing a comprehensive environmental health index to assess regional environmental quality can improve the comprehensiveness of environmental health risk assessment. Furthermore, this invention assesses exposure levels based on latitude and longitude information to obtain evaluation values, enabling precise assessment of an individual's exposure levels to various influencing factors, thereby improving the accuracy of environmental health risk assessment. Further, the obtained evaluation results are input into a multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index. This model includes an interaction term related to environmental factors and temperature, simultaneously considering the interactions between multiple factors and taking a more comprehensive view of the impact of various influencing factors on environmental health risk assessment, effectively improving the comprehensiveness and accuracy of environmental health risk assessment. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the steps of an environmental health risk assessment method based on a comprehensive environmental health index provided in an embodiment of the present invention.

[0046] Figure 2 This is a comparison chart of the linear relationship between different health indices and adverse health outcomes provided by an embodiment of the present invention;

[0047] Figure 3 This is a comparison chart of the interquartile range (OR) values ​​of different health indices and adverse health outcomes provided by an embodiment of the present invention;

[0048] Figure 4 This is a flowchart illustrating the steps of an environmental health risk assessment method based on a comprehensive environmental health index provided in this embodiment of the invention.

[0049] Figure 5 This is a structural block diagram of an environmental health risk assessment system based on a comprehensive environmental health index provided in an embodiment of the present invention. Detailed Implementation

[0050] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0051] AQI, Air Quality Index, is a dimensionless index that quantitatively describes air quality. It reports daily air quality parameters to describe the degree of air cleanliness or pollution and its impact on health.

[0052] CEHI, the Comprehensive Environment Health Index, is an indicator that comprehensively considers the impact of multiple environmental factors on human health. It may involve various pollution data and the health status of ecosystems. Through specific calculation methods, these factors are quantified and integrated to derive the index. This index can intuitively reflect the level of environmental health, assisting relevant departments in formulating environmental management and health intervention policies to reduce environmental risks and protect public health.

[0053] Conditional logistic regression is a statistical method used to analyze matched data (such as paired or matched designs in case-control studies). It estimates parameters using a conditional likelihood function and effectively controls for confounding factors in matched designs. This model is suitable for group-matched or individual-matched studies and can analyze the strength of associations between multiple risk factors and diseases, making it widely used in epidemiological research.

[0054] IQR, or Interquartile Range, is an important statistical indicator for measuring the dispersion of data. It is the difference between the upper quartile (Q3, the 75th percentile) and the lower quartile (Q1, the 25th percentile), i.e., IQR = Q3 - Q1. IQR reflects the distribution range of the middle 50% of the data, is insensitive to outliers, and is often used to identify outliers and compare the dispersion of different datasets.

[0055] Regression coefficient: A core parameter in regression analysis, representing the average change in the dependent variable when the independent variable changes by one unit. In a linear regression model, it reflects the strength and direction of the linear relationship between variables (positive or negative sign indicates an increasing or decreasing trend). The larger the absolute value of the coefficient, the more significant the impact.

[0056] The odds ratio (OR) reflects the strength of the association between the exposure factor and the outcome event. In some cases, the natural logarithm of the OR (ln(OR)) can be taken to obtain the logarithmic odds ratio, making the data closer to a normal distribution and facilitating statistical analysis. It is commonly used in case-control studies. OR = 1 indicates that the exposure and outcome are not related; OR > 1 suggests that the exposure increases the risk of the outcome; and OR < 1 suggests that the risk is reduced. A statistical test can then be conducted to determine whether the association is statistically significant.

[0057] Confidence Interval (CI): In statistics, a range of values ​​used to estimate a population parameter. It is calculated based on sample data and represents the probability that the true value of the parameter falls within that interval at a certain confidence level (e.g., 95%). A narrower CI indicates higher estimation accuracy and is often used to report uncertainty in the mean, proportion, or regression coefficient.

[0058] Exposure level refers to the intensity, frequency, and duration of an individual's or group's exposure to a harmful factor (such as chemicals, radiation, pathogens, etc.). Accurate assessment of exposure levels is crucial for epidemiological studies, occupational health protection, and environmental risk assessment, and is a key parameter for analyzing the causal relationships of health effects.

[0059] Case-crossover design: An epidemiological method used to study the relationship between transient exposure factors and acute events. It compares exposures between cases themselves, using a specific time period before onset (the danger period) and a longer time period before onset (the control period). This method controls for individual-specific confounding factors and is suitable for studying the association between sudden exposure and disease onset, with wide applications in fields such as environmental health.

[0060] Exposure-response model: Used to describe the quantitative association between exposure factors and health effects. It uses mathematical functions (such as linear, logarithmic, sigmoid curves, etc.) to characterize the trend of health outcomes (such as morbidity risk, mortality, etc.) as exposure dose or intensity increases. This model can assess the dose-response effect of exposure, determine thresholds, and provide a scientific basis for risk assessment, setting exposure limits, and public health interventions.

[0061] Adverse Outcome: Refers to negative health outcomes that occur to an individual or group after a specific exposure or intervention. This includes clinical events such as the onset of disease (e.g., cancer, cardiovascular disease), disability, and death, and may also encompass non-fatal consequences such as functional impairment and decreased quality of life.

[0062] Bilinear Interpolation (BI) is a commonly used image or numerical interpolation method. It uses the values ​​of the four nearest pixels (or data points) around the point to be interpolated to perform linear interpolation twice, once in the horizontal direction and once in the vertical direction, thus obtaining a smooth transition result. This method is computationally efficient, preserves image details well, and reduces jagged edges.

[0063] Inverse Distance Weighting (IDW) is a spatial interpolation method based on the principle of proximity, meaning that points that are closer together are more similar in properties. It uses the distance between the interpolation point and the sample points as weights for a weighted average, with sample points closer to the interpolation point having a higher weight.

[0064] Maximum Likelihood Estimation (MLE) is a parameter estimation method that finds the parameter values ​​most likely to generate the observed data by maximizing the "likelihood function" (i.e., the probability of the data occurring). Its core idea is to select the model parameters that maximize the probability of the sample data occurring.

[0065] like Figure 1 As shown in the figure, this embodiment of the invention provides an environmental health risk assessment method based on a comprehensive environmental health index, which includes the following steps S100 to S400.

[0066] S100: Acquire the latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors; the influencing factors include temperature, relative humidity, and several environmental factors; the types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured.

[0067] Monitoring information for the target study area was obtained from emergency respiratory and circulatory system data from various hospitals within the target area, serving as the data foundation for the subsequent construction of a comprehensive environmental health index. Specifically, the monitoring data included the latitude and longitude information of the area and monitoring data for several influencing factors related to the area, such as temperature, relative humidity, noise, pollen, air pollutants, and negative oxygen ions (NAI) data.

[0068] Optionally, the latitude and longitude information can be obtained by collecting the patient's permanent address information from the emergency room records and converting the detailed structured address information into latitude and longitude coordinates through the geocoding / reverse geocoding service of the map software.

[0069] S200: Based on latitude and longitude information, the monitoring data of each influencing factor is used to conduct an exposure assessment, and the corresponding assessment value for each influencing factor is obtained.

[0070] Based on latitude and longitude information, different interpolation methods are used to assess the exposure levels of influencing factors such as temperature, relative humidity, air pollutants, noise, pollen and NAI concentration at an individual's location, thereby obtaining an individualized and accurate assessment of exposure to influencing factors.

[0071] S300: Input the assessment value corresponding to each influencing factor into the preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes the interaction terms between environmental factors and temperature.

[0072] The assessment values ​​of each influencing factor are input into a multifactor logistic regression model for calculation to obtain the probability of adverse outcomes for each influencing factor, which is then converted into an index form to finally obtain the comprehensive environmental health index.

[0073] S400: Based on the analysis of the comprehensive environmental health index, determine the environmental health risk assessment results of the area to be tested.

[0074] Based on the comprehensive environmental health analysis of the target area, such as the classification of levels, the environmental health risk assessment results of the area to be tested are obtained. Based on this data, the health risks can be interpreted and targeted travel and protection suggestions can be put forward.

[0075] Specifically, in step 100, the types of air pollutants to be measured are identified in the following way:

[0076] S110: Obtain historical data on adverse outcomes for the area under test during the same period and the historical assessment value set for each influencing factor. The historical assessment value set for environmental factors includes the historical assessment value sets for several air pollutants.

[0077] The study aims to obtain emergency information on respiratory and circulatory systems in hospitals within the target area over a historical period to determine the occurrence and data of adverse outcomes during that period. On the other hand, it also aims to obtain the exposure levels of each influencing factor related to the target area during the historical period, i.e., the historical assessment value set for each influencing factor. This data serves as the foundation for constructing a multifactor logistic regression model. The historical assessment value set for air pollutants is obtained in the same way as that for environmental factors.

[0078] The types of air pollutants can include PM2.5. 2.5 PM 10 NO2, SO2, CO, O3, etc.

[0079] S120: Based on the case-crossover design method, a single-factor conditional logistic regression model was constructed for each air pollutant, using historical data of adverse outcomes, historical assessment values ​​for each air pollutant, and historical assessment values ​​for temperature and relative humidity. The regression coefficients between each air pollutant and the adverse outcome were then determined.

[0080] Based on a case-crossover design, conditional logistic regression was performed to construct a one-way conditional logistic regression model for each air pollutant, and the regression coefficients between each air pollutant and adverse outcomes were determined based on the constructed models.

[0081] S130: Based on the regression coefficients between each air pollutant and adverse outcomes, the types of air pollutants to be measured are determined from several types of air pollutants according to the analysis results.

[0082] A one-way logistic regression model was constructed for each type of air pollutant, and the regression coefficients between each air pollutant and adverse outcomes were obtained. These coefficients were used to determine the appropriate types of pollutants to calculate the comprehensive environmental health index of the area to be tested.

[0083] Environmental factors include air pollutants, noise, pollen, and negative oxygen ions (NAI); the data processing methods for different types of environmental factors are the same in the embodiments of this invention; the data processing methods for air pollutants described below are also applicable to environmental factors such as noise, pollen, and NAI.

[0084] Specifically, in step S120, the single-factor conditional logistic regression model for each air pollutant is constructed in the following manner:

[0085] S121: Construct a matching group dataset based on historical data of adverse outcomes, historical assessment sets of air pollutants, and historical assessment sets of temperature and relative humidity. Matching groups in the matching group dataset indicate the matching relationship between the occurrence of adverse outcomes and air pollutants, temperature, and relative humidity factors at a given time period.

[0086] Based on historical data of adverse outcomes and historical assessment values ​​of corresponding types of air pollutants, temperature, and relative humidity, a matching dataset is constructed. The matching dataset can indicate the historical assessment values ​​of the relative factors (air pollutants, temperature, and relative humidity) corresponding to when the adverse outcome occurs (adverse outcome occurs or adverse outcome does not occur (control group)). Further calculations can obtain the probability that a case belongs to a certain time point when the historical assessment values ​​of the corresponding air pollutants, temperature, and relative humidity are determined.

[0087] S122: Construct a first natural spline function based on the historical evaluation set of the first preset degree of freedom and the temperature factor. Construct a second natural spline function based on the historical evaluation set of the second preset degree of freedom and the relative humidity factor.

[0088] Based on the historical evaluation set of preset degrees of freedom, temperature factors, and relative humidity, a corresponding natural spline function can be constructed, which can be used to smoothly fit nonlinear variables in regression models.

[0089] S123: Construct a single-factor conditional logistic regression model for air pollutants based on the matched group dataset, the first natural spline function, and the second natural spline function.

[0090] Specifically, the model formula for the single-factor conditional logistic regression model corresponding to each air pollutant is shown in equation (1): logit(P(case=1 in stratum i|x j ,tm,rh))=α stratum i +β j ×x j +ns(tm,4)+ns(rh,4) (1)

[0091] Where, stratum represents stratification based on patient number, consisting of the patient's onset time (case=1) and its control time (case=0); α represents a constant or intercept for the i-th stratum; β j The regression coefficient representing the environmental factor (in this embodiment, air pollutants) represents the impact of each factor on health outcomes; tm is temperature; rh is relative humidity; ns() represents the natural spline function, which is used to control the nonlinear effects, with a degree of freedom set to 4; P represents the conditional probability that the corresponding values ​​of each environmental factor (in this embodiment, air pollutants), temperature, and relative humidity belong to a case at a certain time point in a specified stratum; logit is the logit transformation, which refers to converting the probability P into a log-dominance ratio through a logarithmic function, thereby establishing a linear relationship in logistic regression.

[0092] Specifically, the process of constructing a single-factor conditional logistic regression model for each type of air pollutant described above is also applicable to environmental factors such as noise, pollen, and NAI.

[0093] Specifically, in step 120, the regression coefficients between air pollutants and adverse outcomes are determined as follows:

[0094] S124: Based on the maximum likelihood method and the matched group dataset, construct the conditional likelihood function of the single-factor conditional logistic regression model corresponding to air pollutants.

[0095] Given parameters, the conditional likelihood function of the single-factor conditional logistic regression model corresponding to this type of air pollutant is constructed based on the probability of occurrence of the observed data in the matching group dataset.

[0096] S125: Numerical optimization is performed on the conditional likelihood function to obtain the regression coefficients, the odds ratio of the regression coefficients, and the confidence interval, so as to determine the regression coefficients between air pollutants and adverse outcomes.

[0097] The constructed conditional likelihood function is logarithmically transformed and maximized to achieve numerical optimization, yielding the regression coefficients for each type of air pollutant, representing their impact on health outcomes. Furthermore, based on these regression coefficients, the corresponding odds ratios and confidence intervals are calculated as criteria for determining the validity of the regression coefficients.

[0098] Specifically, the data processing procedure described above for determining the regression coefficient between a certain air pollutant and adverse outcomes is also applicable to environmental factors such as noise, pollen, and NAI.

[0099] In one embodiment, the regression coefficients for various air pollutants can be calculated as follows: β CO =1.63×10 -4 The concentration units are all μg / m³ 3 .

[0100] In a one-way conditional logistic regression model, the regression coefficient β of NAI is... NAI -7.76×10 -3 Its concentration unit is 10. 8 pcs / m 3 The results show that NAI has a protective effect on human health; while the relationship between noise, pollen and human health is not significant.

[0101] Since the unit of NAI concentration is different from that of pollutants, the IQR (interquartile range) is used as the standardized unit to calculate the effect value. This can better explain the health impact of different pollutant exposure assessment values. The OR values ​​for each interquartile range of different pollutants in this example are shown in Table 1 below:

[0102] Table 1

[0103]

[0104]

[0105] Based on the analysis of Table 1 and the actual historical environmental data of the target area, PM2.5 is selected in this embodiment. 2.5O3, NO2, NAI, temperature, and relative humidity are incorporated into the final model calculations.

[0106] Specifically, in step S200, an exposure assessment is performed on the monitoring data of each influencing factor based on the latitude and longitude information of the area to be measured, to obtain the assessment value corresponding to each influencing factor, including:

[0107] S210: Based on latitude and longitude information and bilinear interpolation, a first exposure assessment is performed on the monitoring data of temperature factor to obtain the corresponding assessment value of temperature factor; based on latitude and longitude information and bilinear interpolation, a second exposure assessment is performed on the monitoring data of relative humidity factor to obtain the corresponding assessment value of relative humidity factor.

[0108] Based on the grid data of temperature and dew point temperature of the target area collected by the local weather forecast center, a bilinear interpolation exposure assessment is performed on the temperature and dew point temperature of the day. The principle is to estimate the exposure level of any location in two-dimensional space based on the known grid data. By using the values ​​of four grid points around the observation point, linear interpolation is performed in both the horizontal (x-direction) and vertical (y-direction) directions. Based on the above variables of temperature and dew point temperature, relative humidity is calculated to obtain a more accurate temperature and relative humidity exposure assessment.

[0109] S220: Based on latitude and longitude information and the inverse distance weighting method, a third exposure assessment is performed on the monitoring data of each environmental factor to obtain the assessment value corresponding to each environmental factor.

[0110] Based on the obtained latitude and longitude information, an inverse distance-weighted exposure assessment is performed on the concentrations of air pollutants, pollen, noise, and NAI to be measured on that day. The principle is to estimate the exposure level at any location based on environmental exposure data from multiple monitoring points. This method assumes that the closer the observation point is to the target location, the greater its exposure value will have an impact on that location, and the estimated value is calculated by using the inverse distance-weighted average.

[0111] Specifically, the multifactor logistic regression model in step S300 is constructed in the following way:

[0112] S310: Obtain historical data on adverse outcomes and historical assessment values ​​for each influencing factor for the region under test during the same period.

[0113] The study aims to obtain emergency information on respiratory and circulatory systems in hospitals within the target area over a historical period to determine the occurrence and data of adverse outcomes during that period. It also aims to obtain the exposure levels of each influencing factor related to the target area during the historical period, i.e., the historical assessment values ​​of each influencing factor. The obtained data will serve as the basis for constructing a multivariate logistic regression model.

[0114] S320: Based on the case-crossover design method, construct a univariate conditional logistic regression model for each environmental factor based on historical data of adverse outcomes, historical assessment sets of each environmental factor, and historical assessment sets of temperature and relative humidity factors, and determine the regression coefficient between each environmental factor and the adverse outcome.

[0115] Similar to the method used to construct the single-factor conditional logistic regression model for the aforementioned air pollutants, a case-cross design was used to perform conditional logistic regression to construct a single-factor conditional logistic regression model for each environmental factor. The regression coefficient between each environmental factor and the adverse outcome was then determined based on the constructed model.

[0116] S330: Construct a multifactor logistic regression model based on the single-factor conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome.

[0117] Based on the single-factor conditional logistic regression model corresponding to each environmental factor, the regression coefficient between each environmental factor and the adverse outcome is determined, the impact of different environmental factors on health outcomes is determined, and the interaction or combined effect between various influencing factors is further constructed, and finally a multi-factor logistic regression model is constructed.

[0118] Specifically, in step S330, based on the univariate conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome, a multivariate logistic regression model is constructed, including:

[0119] S331: Based on the matching group dataset corresponding to each environmental factor, determine the temperature curve in the single-factor conditional logistic regression model corresponding to each environmental factor.

[0120] The matching group dataset is used to construct a single-factor logistic regression model for each environmental factor, and the temperature curve in the constructed single-factor logistic regression model for each environmental factor is determined for subsequent determination of the temperature threshold.

[0121] S332: Analyze the temperature curves in the single-factor conditional logistic regression model corresponding to each environmental factor, determine the high temperature threshold, and set temperature level variables and interaction terms between environmental factors and temperature levels based on the high temperature threshold.

[0122] Based on the temperature curves in each single-factor model, a high-temperature threshold is selected to set the temperature level variable. Temperatures below the threshold are classified as low, and those above the threshold are classified as high. An interaction term is introduced into the multi-factor logistic regression model to explore the interaction between environmental factors (air pollutants, NAI, noise, or pollen) and temperature levels. This allows for a comprehensive consideration of the combined effects of various environmental factors and temperature factors, thereby improving the comprehensiveness of the model evaluation.

[0123] S333: Based on the second natural spline function, the single-factor conditional logistic regression model corresponding to each environmental factor, the regression coefficient between each environmental factor and the adverse outcome, the temperature level variable, and the interaction term between environmental factors and temperature level, a multi-factor logistic regression model is constructed.

[0124] After introducing the interaction terms between various environmental factors and temperature levels, a natural spline function of relative humidity is introduced to control the nonlinear variables of the model. Finally, based on the single-factor conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome, a multi-factor logistic regression model is constructed.

[0125] Specifically, the model formula for the multifactor logistic regression model is shown in equation (2) below:

[0126] logit(P) = ∑ j β j ×c j +∑ j γ j ×(c j :TM)+β k ×TM+ns(rh,4)+α (2)

[0127] Where, β j Regression coefficients representing environmental factors (air pollutants, noise, pollen, or NAI need to be measured) represent the impact of that factor on health outcomes; β k The regression coefficients for temperature level TM represent the primary impact of temperature on health outcomes; γ j The regression coefficient is the interaction coefficient between environmental factors (air pollutants, noise, pollen or NAI)j and temperature level TM, which measures the health impact of the two factors working together. The remaining parameters are consistent with the univariate regression logistic model of each environmental factor in equation (1).

[0128] Furthermore, the regression coefficients of the multifactor logistic regression model are also estimated using the maximum likelihood method.

[0129] In one embodiment, PM is selected. 2.5O3, NO2, NAI, temperature, and relative humidity are used in the final multifactor logistic regression model calculation. The model coefficients are as follows:

[0130] Regression coefficients for each air pollutant to be measured: The concentration units for all air pollutants are μg / m³. 3 The regression coefficients indicate that all air pollutants have harmful effects on human health.

[0131] Regression coefficient of NAI: β NAI = -2.40 × 10 -3 Its concentration unit is 10. 8 pcs / m 3 This indicates that NAI has a protective effect on population health.

[0132] Regression coefficient for temperature level: β TM =1.96×10 -1 This suggests that high temperatures pose a health hazard to people.

[0133] Regression coefficients of the interaction between environmental factors and temperature levels: β NAI-TM =1.08×10 -3 This shows that different environmental factors interact differently with temperature.

[0134] Based on the regression coefficients of each environmental factor, and the natural spline functions of temperature and relative humidity, the probability P of adverse outcomes is calculated, which is the comprehensive environmental health index of the target area.

[0135] Specifically, in step S300, the process of inputting the assessment value corresponding to each influencing factor into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index includes:

[0136] S340: Input the evaluation value corresponding to each influencing factor into the preset multi-factor logistic regression model for calculation to obtain the probability of adverse outcomes occurring in the area under test.

[0137] The exposure assessment level value corresponding to each influencing factor in the area to be tested is input into the constructed multifactor logistic regression model for calculation, and the probability of adverse outcomes occurring in the area to be tested is calculated, which serves as the data basis for constructing a comprehensive environmental health index.

[0138] S350: The comprehensive environmental health index of the test area is obtained by matching the probability of adverse outcomes in the test area with the preset comprehensive environmental health index level range.

[0139] The probability of adverse outcomes in the area to be tested is calculated and then substituted into a preset comprehensive environmental health level range for matching. The comprehensive environmental health index of the area to be tested is determined based on the comprehensive environmental health index corresponding to the level range that meets the criteria.

[0140] Specifically, the preset comprehensive environmental health index level range is obtained by substituting the historical assessment value set into the multi-factor logistic model to calculate the historical adverse outcome probability dataset of the area to be tested, and dividing it according to the probability distribution to obtain the comprehensive environmental health level range.

[0141] Specifically, in step S400, the environmental health risk assessment results of the area to be tested are determined based on the comprehensive environmental health index of the area to be tested, including:

[0142] S410: Based on the comprehensive environmental health index of the area to be tested and the preset comprehensive environmental health index level range, determine the environmental quality health risk level of the area to be tested and the corresponding travel and protection recommendations.

[0143] The comprehensive environmental health index of the target area is calculated based on the model to determine the probability of adverse outcomes in the area. It is then matched with a preset level range to determine the environmental quality health risk level of the area. Furthermore, based on the comprehensive environmental health index (CEHI) and the health travel recommendations for different groups, the corresponding travel and protection recommendations for the area are determined.

[0144] Optionally, based on the distribution of the probability of adverse outcomes occurring in the target area, the risk level can be divided into six levels: low risk, medium risk, relatively high risk, high risk, very high risk, and serious health hazard.

[0145] Optionally, the Comprehensive Environmental Health Index (CEHI) grading intervals can be as shown in Table 2:

[0146] Table 2

[0147]

[0148] Optionally, the Comprehensive Environmental Health Index (CEHI) recommendations for healthy travel for different groups can be shown in Table 3:

[0149] Table 3

[0150]

[0151]

[0152]

[0153] S420: Determine the environmental health risk assessment results for the area to be tested based on the environmental quality health risk level of the area to be tested and the corresponding travel and protection recommendations.

[0154] Based on the environmental quality health risk level of the target area and the corresponding travel and protection recommendations, determine the environmental health risk assessment results of the area to be tested, and explain the health risk assessment results and response measures to the public in the target area through broadcasts, large screens, news, and other means.

[0155] In another embodiment, regarding the ability to predict health risks, the Comprehensive Environmental Health Index (CEHI) obtained by the method of the present invention is compared with the Air Quality Index (AQI):

[0156] Using the same study area and time frame, air pollutant monitoring data from various monitoring stations in the study area were collected for historical periods. The individual coefficient (IAQI) for each pollutant was calculated using the following formula. p :

[0157]

[0158] Among them, C p For pollutant concentration, BP Hi and BP Lo They are respectively with C p High and low values ​​of similar pollutant concentration limits, I Hi and I Lo They represent BP respectively Hi and BP Lo The corresponding air quality index.

[0159] The air quality index (AQI) of each monitoring station is determined based on the maximum value of the individual coefficients, and the average air quality index (AQI) of the area is calculated based on the values ​​of each monitoring station.

[0160] The Spearman correlation coefficients between the Comprehensive Environmental Health Index (CEHI) and the Air Quality Index (AQI) and adverse health outcomes were calculated to be 0.046 and 0.023, respectively; indicating that the association between the Comprehensive Environmental Health Index (CEHI) and morbidity outcomes is stronger.

[0161] The Comprehensive Environmental Health Index (CEHI) and the Air Quality Index (AQI) were respectively incorporated into a conditional logistic regression model using natural spline functions to observe their relationship with adverse health outcomes (reflected by the odds ratio). Figure 2 As shown, Figure 2 Figure ① in the figure shows the linear relationship between the Comprehensive Environmental Health Index (CEHI) and adverse health outcomes; Figure 2Figure ② in the figure shows the linear relationship between the Air Quality Index (AQI) and adverse health outcomes; with the degrees of freedom set to 3, both the Comprehensive Environmental Health Index (CEHI) and the Air Quality Index (AQI) show an approximately linear relationship with adverse health outcomes.

[0162] Since both the Comprehensive Environmental Health Index (CEHI) and the Air Quality Index (AQI) show approximately linear relationships with adverse health outcomes, their linear terms were incorporated into a conditional logistic regression model. The relationship between the AQI and adverse health outcomes was compared to evaluate the effectiveness of the CEHI. Figure 3 As shown, for every interquartile range increase in the Comprehensive Environmental Health Index (CEHI), the odds ratio (OR) is 1.13 (95% CI 1.12-1.14), which is better at capturing the risk of disease development than the Air Quality Index (AQI) (OR: 1.07, 95% CI 1.06-1.08).

[0163] In another embodiment, using the same study area and time range, data were collected from two different locations in the study area during historical periods, and the Comprehensive Environmental Health Index (CEHI) for the same day was calculated for each location, and their individual differences were compared.

[0164] Select a specific date (year, month, day) and construct a Comprehensive Environmental Health Index (CEHI) for location A and location B in a city.

[0165] Based on the latitude and longitude of location A, the exposure levels of various environmental factors are assessed as follows:

[0166] PM 2.5 32.89 μg / m 3 NO2: 45.34 μg / m 3 O3: 45.81 μg / m 3 ;

[0167] NAI exposure level at location A: NAI: 2469 cells / cm³ 3 ;

[0168] Exposure levels of meteorological factors at location A: Temperature: 14.85℃; Relative humidity: 66.98%.

[0169] Based on the above exposure levels, the probability P of including adverse outcomes is 0.2316439, which falls within the range of 0.23 to 0.25. The environmental air quality health risk classification for location A is medium risk.

[0170] Based on the latitude and longitude of location B, the exposure levels of various environmental factors are assessed as follows:

[0171] PM 2.530.68 μg / m 3 NO2: 39.00 μg / m 3 O3: 51.34 μg / m 3 ;

[0172] NAI exposure level at location B: NAI: 3390 cells / cm³ 3 ;

[0173] Exposure levels of meteorological factors at location B: Temperature: 14.46℃; Relative humidity: 65.52%;

[0174] Based on the above exposure levels, the probability P of the adverse outcome is 0.223683, which falls within the range of 0 to 0.23. The ambient air quality health risk classification for location B is low risk.

[0175] Based on the Comprehensive Environmental Health Index (CEHI) of locations A and B, it can be found that even on the same day, the CEHI of different locations in a city is different. Therefore, the method of the present invention can assess environmental health risks based on user location and provide users with targeted travel and health protection suggestions.

[0176] Implementing the embodiments of the present invention has the following beneficial effects:

[0177] (1) Compared with previous environmental risk indices, such as the Air Quality Index (AQI) and the Air Quality Health Index (AQHI), this system considers the health impact of air pollutants and the interaction between pollutants and temperature. It also considers the potential health impacts of noise, pollen, NAI, temperature and relative humidity. By locating the user's address through the monitoring system, it can issue personalized comprehensive environmental health index (CEHI) and related suggestions to the user. This system is more comprehensive in terms of environmental health risk assessment and has important public health significance and practical value in preventing adverse environmental health risks.

[0178] (2) This invention assesses exposure levels based on latitude and longitude information to obtain assessment values, which can accurately assess the exposure levels of various influencing factors of an individual, thereby improving the accuracy of environmental health risk assessment. Furthermore, the assessment results are input into a multi-factor logistic regression model for calculation to obtain a comprehensive environmental health index, which can simultaneously consider the interaction between multiple factors and consider the impact of each influencing factor on environmental health risk assessment from a more comprehensive perspective, effectively improving the comprehensiveness and accuracy of environmental health risk assessment.

[0179] (3) This invention converts the comprehensive health risks of exposure to relevant factors such as air pollutants, noise, pollen, NAI, temperature, and relative humidity into an integer index, which is concise and easy to communicate with the public on daily risks.

[0180] (4) The present invention can also build a monitoring platform based on the Comprehensive Environmental Health Index (CEHI), which can calculate the Comprehensive Environmental Health Index (CEHI) of a specific location based on the user's location, and provide the user with personalized protection and travel suggestions.

[0181] like Figure 4 As shown, this embodiment of the invention also provides another method for environmental health risk assessment based on a comprehensive environmental health index, including:

[0182] Step 1: Data Collection

[0183] Step 1.1: Obtain records of adverse health outcomes, mainly by obtaining emergency room records from hospitals in the target area in recent years. Based on this, and according to the International Classification of Diseases, select emergency room records of respiratory and circulatory system diseases closely related to air pollutants.

[0184] Step 1.2: Collect the patient's permanent address information from the emergency room records, and convert the detailed structured address information into latitude and longitude coordinates using the geocoding / reverse geocoding service of the map software.

[0185] Step 1.3: Collect raster data of temperature and dew point temperature in the target area; obtain the latitude and longitude and monitoring data of various air pollutants, pollen, noise and NAI monitoring stations in the target area.

[0186] Step 2: Exposure assessment based on individual latitude and longitude

[0187] Step 2.1: Based on the latitude and longitude of the patient's permanent address, use the "extract" function of the "raster" package in R language to perform bilinear interpolation exposure assessment on the temperature and dew point temperature of the day; based on the obtained temperature and dew point temperature, use the "RH" function of the "humidity" package in R language to calculate the relative humidity, and obtain a more accurate temperature and relative humidity exposure information.

[0188] Step 2.2: Based on the latitude and longitude of the patient's permanent address, use the "idw" function of the "gstat" package in R language to perform an inverse distance-weighted exposure assessment of the concentration of air pollutants and negative oxygen ions (NAI) on that day.

[0189] Step 3: Building the Model

[0190] Step 3.1: Using a case-crossover design, assess the exposure-response relationship between air pollutants, temperature, relative humidity, noise, pollen, NAI and adverse health outcomes in a target area over a certain time period.

[0191] Step 3.2: Case-crossover design refers to a research method that selects a certain type of case and investigates the individual exposure status and degree at the time of the event, before the event, and after the event to determine whether there is a correlation between the exposure risk factor and the event and the degree of the correlation. The self-control method balances the influence of many individual factors on the outcome and is suitable for studies that produce exposure effects in the short term, such as cardiovascular and cerebrovascular events. The matching method saves sample size. Specifically, for each emergency room case, the same day of the week in the same month was set as the control date for the onset date, and exposure assessments for air pollutants, temperature, relative humidity, noise, pollen, and NAI were performed for each control date.

[0192] Step 3.3: Construction of the single-factor model.

[0193] Based on the concept of case-case crossover design, a one-way conditional logistic regression model was constructed using the "clogit" function of the "survival" package in the R language to select appropriate air pollutants (PM2.5). 2.5 PM 10 (NO2, SO2, CO, O3), noise, pollen, and NAI; the model formula is:

[0194] logit(P(case=1 in stratum i|x j ,tm,rh))=α stratum i +β j ×x j +ns(tm,4)+ns(rh,4) (4)

[0195] Where, stratum represents stratification based on patient ID, consisting of the patient's onset time (case=1) and its control time (case=0), P represents the conditional probability of a case occurring in the i-th stratum given the values ​​of factors, temperature, and relative humidity, α represents a constant or intercept for the i-th stratum, and x... j Indicates pollutants (SO2, NO2, PM) 10 O3, CO and PM 2.5 ), noise, pollen or NAI concentration, β j x represents j The regression coefficients of pollutants, ns() represents the smoothing parameter based on the natural smooth spline, and 4 indicates that the degrees of freedom are 4.

[0196] Step 3.4: Screening key factors.

[0197] The response coefficients between air pollutants and NAI were obtained, and the impact of air pollutants and NAI on health outcomes was determined based on the OR value. Since the units of NAI, noise, and pollen are different from those of pollutants, the IQR (interquartile range) was used as the standardized unit to calculate the OR value, which can better explain and compare the impact of different pollutant exposures on health outcomes.

[0198] Step 3.5: Construction of the multi-factor model.

[0199] Based on the odds ratio (OR) value of a single-factor exposure-response model and the actual environmental conditions of the study area, suitable environmental influencing factors, temperature, and relative humidity were selected. A multi-factor logistic regression model was constructed using the "glm" function of the "stats" package in R, considering the combined effects and interactions of multiple environmental factors. The model formula is as follows:

[0200] logit(P) = ∑ j β j ×c j +∑ j γ j ×(c j :TM)+β k ×TM+ns(rh,4)+α (5)

[0201] Where β j Regression coefficients representing environmental factors (air pollutants, noise, pollen, or NAI) indicate the impact of that factor on health outcomes; γ j is the regression coefficient of the interaction between pollutant j and temperature level TM, which measures the health impact of their combined effect; the remaining parameters are consistent with those referred to in the single-factor model.

[0202] Step 4: Construct a comprehensive health index

[0203] The probability P of emergency room risk was calculated using a multifactor logistic regression model, and the comprehensive environmental health index (CEHI) was finally constructed.

[0204] Step 4.1: Using the "predict" function of the "stat" package in R, based on the exposure levels of emergency cases and self-controls to environmental factors, calculate the probability P and its distribution range using the exposure-response relationship coefficient of the multivariate logistic regression model and the natural spline function.

[0205] Step 4.2: Based on the distribution range of probability P, set a cutoff value and divide it into six air health levels from 1 to 6 points, namely low risk, medium risk, relatively high risk, high risk, very high risk, and serious health hazard.

[0206] Step 4.3: Provide corresponding travel and protection recommendations for different groups of people in each air health level.

[0207] Step 5: Establish an environmental health risk assessment system

[0208] like Figure 5 As shown, based on the environmental health risk assessment method described in steps 1 to 4, a corresponding environmental health risk assessment system based on a comprehensive environmental health index is established, including:

[0209] Data acquisition module: Collects latitude and longitude data and monitoring concentration data of air pollutant and NAI concentration monitoring stations in the target area, as well as raster data of temperature and relative humidity, including historical data and real-time monitoring data; collects emergency information on respiratory and circulatory systems of various hospitals in the target area.

[0210] Data processing module: Obtain the user's specific address, convert it into latitude and longitude coordinates based on the user's location using Amap geocoding / reverse geocoding, and use bilinear interpolation to assess the temperature and relative humidity of the individual's location based on the latitude and longitude coordinates; use inverse distance weighting to assess the concentration of air pollutants, noise, pollen, and NAI at the individual's location.

[0211] Algorithm execution module: Calculates the probability P of adverse outcomes based on the exposure level of the target area over a historical period and emergency information.

[0212] Index Prediction Module: Assess the level of exposure to environmental factors based on the user's location, calculate the probability P of adverse outcomes, and return the corresponding level to the user in the Comprehensive Environmental Health Index (CEHI) level range of 1-6, explaining the health risks and providing the user with targeted travel and protection suggestions.

[0213] This invention also provides another environmental health risk assessment system based on a comprehensive environmental health index, including:

[0214] The first module is used to acquire the latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors. The influencing factors include temperature, relative humidity, and several environmental factors. The types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured.

[0215] The second module is used to perform exposure assessment on the monitoring data of each influencing factor based on latitude and longitude information, and obtain the assessment value corresponding to each influencing factor;

[0216] The third module is used to input the assessment value corresponding to each influencing factor into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes the interaction term between environmental factors and temperature.

[0217] The fourth module is used to analyze the comprehensive environmental health index and determine the environmental health risk assessment results of the area to be tested.

[0218] It is evident that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0219] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the methods described above.

[0220] This invention also provides an environmental health risk assessment device based on a comprehensive environmental health index, comprising:

[0221] At least one processor;

[0222] At least one memory for storing at least one program;

[0223] When the at least one program is executed by the at least one processor, the at least one processor implements the steps of the environmental health risk assessment method based on the comprehensive environmental health index as described in the above method embodiments.

[0224] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. The 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, the memory may optionally include remote memory located remotely 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.

[0225] It is evident that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented in the present device embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0226] Furthermore, embodiments of this application also disclose a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, causing the computer device to perform the methods described above.

[0227] This invention also provides a computer-readable storage medium storing a processor-executable program that, when executed by a processor, implements the above-described method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0228] It is understood 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.

[0229] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An environmental health risk assessment method based on a comprehensive environmental health index, characterized in that, include: The latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors, are obtained. These influencing factors include temperature, relative humidity, and several environmental factors. The types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured. Exposure assessment is performed on the monitoring data of each of the influencing factors based on the latitude and longitude information to obtain the assessment value corresponding to each of the influencing factors; The evaluation value corresponding to each of the influencing factors is input into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes an interaction term related to environmental factors and temperature. Based on the comprehensive environmental health index, the environmental health risk assessment results of the area to be tested are determined. The exposure assessment of the monitoring data of each influencing factor based on the latitude and longitude information of the area to be measured, to obtain the assessment value corresponding to each influencing factor, includes: Based on the latitude and longitude information and bilinear interpolation, a first exposure assessment is performed on the monitoring data of the temperature factor to obtain the assessment value corresponding to the temperature factor; Based on the latitude and longitude information and bilinear interpolation, a second exposure assessment is performed on the monitoring data of the relative humidity factor to obtain the assessment value corresponding to the relative humidity factor; Based on the latitude and longitude information and the inverse distance weighting method, a third exposure assessment is performed on the monitoring data of each of the environmental factors to obtain the assessment value corresponding to each of the environmental factors.

2. The method according to claim 1, characterized in that, The types of air pollutants to be measured are identified in the following ways: Acquire historical data of adverse outcomes in the area to be tested during the same period and historical assessment value sets for each of the influencing factors; the historical assessment value sets of the environmental factors include historical assessment value sets for several air pollutants; Based on the case-crossover design method, a one-way conditional logistic regression model for each of the air pollutants was constructed using the historical data of the adverse outcomes, the historical assessment value set of each of the air pollutants, and the historical assessment value set of temperature and relative humidity factors. The regression coefficient between each of the air pollutants and the adverse outcomes was then determined. An analysis is performed based on the regression coefficients between each of the aforementioned air pollutants and the adverse outcomes, and the types of the air pollutants to be measured are determined from several of the aforementioned air pollutants based on the analysis results.

3. The method according to claim 2, characterized in that, The single-factor conditional logistic regression model for each of the aforementioned air pollutants is constructed in the following manner: Based on the historical data of adverse outcomes, the historical assessment values ​​of the air pollutants, and the historical assessment values ​​of temperature and relative humidity, a matching group dataset is constructed; the matching groups in the matching group dataset indicate the matching relationship between the occurrence of adverse outcomes and the air pollutants, temperature, and relative humidity factors in a certain period. Based on the first preset degree of freedom and the historical evaluation value set of the temperature factor, a first natural spline function is constructed; Based on the second preset degree of freedom and the historical evaluation value set of the relative humidity factor, a second natural spline function is constructed; Based on the matching group dataset, the first natural spline function, and the second natural spline function, a one-way conditional logistic regression model corresponding to the air pollutant is constructed.

4. The method according to claim 3, characterized in that, The regression coefficients between the air pollutants and the adverse outcomes were determined in the following manner: Based on the maximum likelihood method and the matching group dataset, the conditional likelihood function of the single-factor conditional logistic regression model corresponding to the environmental factors is constructed. The conditional likelihood function is numerically optimized to obtain the regression coefficients, the odds ratios of the regression coefficients, and the confidence intervals, so as to determine the regression coefficients between the environmental factors and the adverse outcomes.

5. The method according to claim 4, characterized in that, The multifactor logistic regression model is constructed in the following manner: Obtain historical data on adverse outcomes in the region under test during the same period and a set of historical evaluation values ​​for each of the influencing factors; Based on the case-crossover design method, and using the historical data of adverse outcomes, the historical assessment value set of each environmental factor, and the historical assessment value set of temperature and relative humidity factors, a univariate conditional logistic regression model corresponding to each environmental factor is constructed, and the regression coefficient between each environmental factor and the adverse outcome is determined. Based on the single-factor conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome, the multi-factor logistic regression model is constructed.

6. The method according to claim 5, characterized in that, The multi-factor logistic regression model is constructed based on the single-factor conditional logistic regression model corresponding to each environmental factor and the regression coefficient between each environmental factor and the adverse outcome, including: Based on the matching group dataset corresponding to each environmental factor, determine the temperature curve in the single-factor conditional logistic regression model corresponding to each environmental factor; The temperature curves in the single-factor conditional logistic regression model corresponding to each environmental factor are analyzed to determine the high temperature threshold, and temperature level variables and interaction terms between environmental factors and temperature levels are set according to the high temperature threshold. Based on the second natural spline function, the single-factor conditional logistic regression model corresponding to each environmental factor, the regression coefficient between each environmental factor and the adverse outcome, the temperature level variable, and the interaction term between environmental factors and temperature level, the multi-factor logistic regression model is constructed.

7. The method according to claim 1, characterized in that, The step involves inputting the evaluation value corresponding to each of the influencing factors into a preset multi-factor logistic regression model for calculation to obtain a comprehensive environmental health index, including: The evaluation value corresponding to each of the aforementioned influencing factors is input into a preset multi-factor logistic regression model for calculation, thereby calculating the probability of adverse outcomes occurring in the region under test. The comprehensive environmental health index of the test area is obtained by matching the probability of adverse outcomes occurring in the test area with a preset comprehensive environmental health index level range.

8. An environmental health risk assessment system based on a comprehensive environmental health index, characterized in that, include: The first module is used to acquire the latitude and longitude information of the area to be measured, as well as monitoring data of several influencing factors. The influencing factors include temperature, relative humidity, and several environmental factors. The types of environmental factors include noise, pollen, negative oxygen ions, and several types of air pollutants to be measured. The second module is used to perform an exposure assessment on the monitoring data of each of the influencing factors based on the latitude and longitude information, and to obtain an assessment value corresponding to each of the influencing factors; The third module is used to input the evaluation value corresponding to each of the influencing factors into a preset multi-factor logistic regression model for calculation to obtain the comprehensive environmental health index; the multi-factor logistic regression model includes an interaction term related to environmental factors and temperature. The fourth module is used to analyze the comprehensive environmental health index and determine the environmental health risk assessment results of the area to be tested.

9. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the method as described in any one of claims 1 to 7.

Images