Health effect analysis method for ship benzene series emission under specific time and space

Abstract

The invention discloses a health effect analysis method for ship benzene series emission under specific time and space. The method comprises the following specific implementation steps: step 1, obtaining an emission factor of the ship benzene series in a specific space-time, calculating an emission flux of the ship benzene series in a given space-time range, and outputting a calculation result in a grid form with a specific resolution; 2, determining the contribution of the ship benzene series emission to the pollutant concentration of coastal and inland areas through an environment simulation model; and 3, establishing a pseudo-Poisson distribution generalized addition model of the ship benzene series emission concentration and the crowd disease burden and death rate, and analyzing the change condition of the health indexes corresponding to the change of the ship benzene series concentration per unit. According to the method, by calculating the emission factors of the ship benzene series, the blank of an emission list of shipping-related benzene series in a traffic source is filled, a direct relation between the emission concentration of the ship benzene series and the disease burden and the death rate of people is established, and scientific support is provided for further evaluating the health effect of ship benzene series pollutant emission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship pollutant emission, in particular to a health effect analysis method for ship benzene series emission under specific space-time. BACKGROUND

[0002] Benzene series (BTEX) is a kind of volatile aromatic organic compounds, including benzene, toluene, ethylbenzene, xylene (ortho / meta / para isomers), styrene and cumene. As an important component of volatile organic compounds (VOCs) in the atmosphere, BTEX has high photochemical reactivity, participates in the formation of photooxidants and secondary aerosols, and aggravates air pollution. Human body is mainly exposed to BTEX through inhalation, which accumulates in blood-rich organs and produces toxic effects on multiple systems such as respiratory, cardiovascular, nervous and immune systems. For example, long-term exposure to benzene can cause systemic inflammation and increase the risk of neurodegenerative diseases such as dementia and depression.

[0003] Global sea trade accounts for more than 80% of the world's total trade, and ship fuel consumption is mainly heavy fuel oil (79%), which produces a large amount of BTEX pollutants during combustion. However, existing emission inventory models (such as MEIC) lack global high spatiotemporal resolution ship benzene series emission data, which leads to inaccurate assessment of its contribution to air pollution and health effects. Shipping-related benzene series emissions have been long neglected, and there is a significant gap in traffic source emission inventory.

[0004] Current research has not established a direct quantitative correlation between ship benzene series emission concentration and population disease burden. Traditional health risk assessment relies mainly on fixed monitoring site data, which is difficult to capture the dynamic process of pollutant diffusion and regional heterogeneity. Therefore, it is urgent to develop a method that integrates emission flux calculation, atmospheric transport simulation and health effect modeling to provide scientific basis for shipping pollution control. SUMMARY

[0005] The embodiment of the present application provides a health effect analysis method for ship benzene series emission under specific space-time. The purpose is to establish a direct link between ship benzene series concentration and population disease burden and mortality rate, and the core is to clarify the high spatiotemporal resolution of global ship benzene series emission flux, so as to realize the analysis of population health effects related to shipping benzene series.

[0006] To solve the above technical problems, the technical scheme provided by the present application is: The present application provides a health effect analysis method for ship benzene series emission under specific space-time, comprising the following steps: Step 1, obtain the emission factor of ship benzene series under specific space-time, calculate the emission flux of ship benzene series in a given spatiotemporal range, and output the calculation results in the form of a specific resolution grid: (1) (2) (3) (4) wherein E i represents the total emission flux of BTEX in ship within given area i, EME i , EB i , EAE i represent the BTEX emission flux of main engine, boiler, and auxiliary engine respectively; W, BP, AEP represent the power of main engine, boiler, and auxiliary engine respectively; EF, BEF, AEEF represent the emission factor of main engine, boiler, and auxiliary engine respectively; LF and LLAF represent the load factor of main engine and low load adjustment factor of main engine respectively; T represents the length of given time interval; Step 2, determining the contribution of ship BTEX emission to pollutant concentration in coastal and inland areas through environmental simulation model; Step 3, establishing a quasi-Poisson distribution generalized additive model of ship BTEX emission concentration and population disease burden and mortality rate, to analyze the change of health index corresponding to the change of unit ship BTEX concentration: (5) wherein E(Y t,ij ) represents the expected value of the number of deaths or incidence rate Y of disease type t in place i on date j; β0 represents the constant term (intercept) of the model; bs(C ij ) represents the basis spline function for processing data, capturing the nonlinear relationship between the number of deaths or incidence rate Y and BTEX concentration C; ns(X1, df), ns(X2, df) represent the natural spline function for processing meteorological parameters X1, X2, wherein df represents the degree of freedom; Year j represents the year classification variable of date j, used to control long-term trend, DOW j represents the week classification variable of date j, used to control short-term trend.

[0007] Preferably, the BTEX in step 1 comprises: benzene, toluene, ethylbenzene, xylene, styrene and cumene, wherein xylene is further divided into o-xylene, m-xylene and p-xylene.

[0008] Preferably, the method for obtaining the emission factor in step 1 comprises: first obtaining the BTEX concentration in ship exhaust through sampling and gas chromatography analysis, and calculating the actual emission mass of BTEX in combination with extraction volume and adsorbent load; based on the exhaust volume during sampling, sampling flow, engine power and sampling time length, the BTEX emission amount corresponding to unit energy consumption under standard working condition is converted, wherein the unit of unit energy consumption is kWh.

[0009] Preferably, the method for obtaining the benzene sampling concentration in step 1 comprises: taking an appropriate amount of sample and adding 5 mL of methanol, ultrasonic extraction for 30 minutes, then centrifugation at 4℃ 1200 rpm for 10 minutes, taking 1 mL of supernatant in a brown gas chromatography sample bottle, and using GC-MS to determine the concentration of benzene in the sample.

[0010] Preferably, in step 1, based on the Ship Technical Specifications Database (STSD) and Automatic Identification System (AIS) data, the emission flux of benzene from ships is calculated using the emission factor of benzene from ships, Wherein, the STSD data includes IMO number, MMSI number, ship name, ship type, total tonnage, net tonnage, ship size, year of construction, main engine power, auxiliary engine power, boiler parameters, fuel type, main engine loading factor, main engine low load adjustment factor; AIS data includes call sign, MMSI number, ship name, ship latitude and longitude information, heading, real-time speed, ship type, navigation state, time information, cargo state, draft.

[0011] Preferably, in step 1, the spatial resolution of the emission flux of benzene from ships includes 0.1°◊0.1°, 0.01°◊0.01°, 1 km◊1 km, and the time resolution includes hourly, daily, monthly, and yearly. Preferably, in the environmental simulation model of step 2, the WRF-CMAQ model, GEOS-Chem model or multi-escape model are included. Preferably, in step 3, the method for obtaining the population disease burden and mortality data is to establish a population cohort, collect individual health, lifestyle, and environmental exposure data through tracking studies, and the data includes basic demographic information, physiological and biochemical indicators, medical records, genetic information, and personal behavior habits.

[0012] Preferably, in step 3, the population disease burden and mortality data includes the incidence rate, number of deaths, and mortality rate of different disease types.

[0013] Preferably, in step 3, the disease types can be divided into respiratory system diseases, circulatory system diseases, urogenital system diseases, nervous system diseases, and digestive system diseases according to organ systems.

[0014] Preferably, in step 3, the gam() function is used to construct a generalized additive model for each regional data set, which is used to model the non-linear relationship between shipping-related benzene concentration and disease mortality.

[0015] Preferably, gam() adopts a Poisson distribution and uses log as a link function to accommodate the characteristics of continuous data and response variables. In the model, shipping-related benzene series concentrations are processed by a smoothing function bs(), and meteorological parameters are processed by a natural spline function ns() to capture the non-linear relationship between them and the number of disease deaths.

[0016] According to a second aspect of the present application, the present application claims an electronic device comprising: one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the method of any one of the preceding.

[0017] According to a third aspect of the present application, the present application claims a computer-readable storage medium having stored thereon a computer program, the computer program being loaded by a processor to perform the steps in the method of any one of the preceding.

[0018] Compared with the prior art, the present application has the following beneficial technical effects: The present application fills in the blank of the emission inventory of shipping-related benzene series in the traffic source by calculating the emission factor of the benzene series of the ship, and establishes a direct link between the emission concentration of the benzene series of the ship and the disease burden and mortality of the population. The method can be flexibly set according to the demand, and provides scientific support for further evaluating the health effects of the emission of the benzene series of the ship. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Figure 1 The workflow diagram of the method for analyzing the health effects of the emission of the benzene series of the ship under a specific space-time provided by the embodiments of the present application.

[0021] Figure 2 The benzene emission factor diagram of different ship types provided by the embodiments of the present application.

[0022] Figure 3 The global annual variation diagram of the benzene emission flux of the ship from 2019 to 2022 provided by the embodiments of the present application.

[0023] Figure 4The structure module diagram of the electronic equipment corresponding to the health effect analysis method for ship benzene series emission under specific space-time provided by the embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0025] The present application provides a health effect analysis method for ship benzene series emission under specific space-time. By calculating the emission factor of ship benzene series, the emission inventory blank of shipping related benzene series in the traffic source is filled, thereby establishing the direct connection between the ship benzene series emission concentration and the disease burden and mortality rate of the population, which is described in detail below. The present application includes three parts: obtaining the emission factor of ship benzene series under specific space-time, calculating the emission flux of ship benzene series in a given space-time range, and outputting the calculation results in the form of a grid with a specific resolution; determining the contribution of ship benzene series emission to the pollutant concentration in coastal and inland areas through an environmental simulation model; establishing a generalized additive model of Poisson distribution for ship benzene series emission concentration and disease burden and mortality rate of the population, and analyzing the changes of health indicators corresponding to the changes of unit ship benzene series concentration.

[0026] Step 1, obtaining the emission factor of ship benzene series under specific space-time, calculating the emission flux of ship benzene series in a given space-time range, and outputting the calculation results in the form of a grid with a specific resolution, (1) (2) (3) (4) In the formula, E i represents the total emission flux of ship benzene series in a given area i, EME i , EB i , EAE i respectively represent the benzene series emission fluxes of main engine, boiler and auxiliary engine; W, BP and AEP respectively represent the power of main engine, boiler and auxiliary engine; EF, BEF and AEEF respectively represent the emission factors of main engine, boiler and auxiliary engine; LF and LLAF respectively represent the loading factor of main engine and the low load adjustment factor of main engine; T is the length of the given time interval.

[0027] Benzene series include: benzene, toluene, ethylbenzene, xylene, styrene and cumene. Among them, xylene is further divided into o-xylene, m-xylene and p-xylene.

[0028] A sampling head consisting of a membrane filter holder and an adsorbent sleeve was used to sample both gaseous and particulate benzene series from ship pollutants. The membrane filter holder includes a membrane holder, a quartz fiber filter, and a stainless steel mesh. The adsorbent sleeve is constructed from a polytetrafluoroethylene outer tube and a glass sampling tube within. The bottom of the glass sampling tube is a glass sieve plate. The inner tube consists of two layers of 1 cm thick polyurethane foam, with a 5 cm high layer of XAD-2 macroporous resin (styrene-divinylbenzene polymer) in the middle. A controlled flow of flue gas was passed through the sampling head, where benzene series in the particulate and gaseous phases were collected by the quartz fiber filter and the XAD-2 macroporous resin, respectively.

[0029] The method for obtaining the BTEX emission factor of ships is as follows: first, the concentration of BTEX in the ship's exhaust is obtained through sampling and gas chromatography analysis, and the actual emission mass of BTEX is calculated based on the extraction volume and adsorbent loading; based on the exhaust volume, sampling flow, engine power and sampling time during the sampling period, the BTEX emission corresponding to the unit energy consumption (in kilowatt-hours) under standard operating conditions is uniformly converted. (6) Where c is the sampling concentration of BTEX on board ships (μg / L); V e is the extraction volume (mL); M s is the total mass of the adsorbent (g); m0 is the mass used for sample measurement (g); Q is the exhaust volume (kg / h); ρ is the air density at 300 ° C (kg / m 3 F is the sampling flow rate (L / min); P is the power (kW); and T is the sampling duration (h). See Figure 2 for benzene emission factors for different ship types.

[0030] The method for obtaining the sampling concentration of benzene series in ships is as follows: take an appropriate amount of sample, add 5 mL of methanol, ultrasonically extract for 30 minutes, then centrifuge at 4°C and 1200 rpm for 10 minutes, take 1 mL of the supernatant into a brown gas chromatography injection bottle, and use GC-MS to determine the concentration of benzene series in the sample.

[0031] Based on the Ship Technical Specifications Database (STSD) and Automatic Identification System (AIS) data, the ship benzene series emission flux is calculated using the ship benzene series emission factor, wherein the STSD data includes IMO number, MMSI number, ship name, ship type, total tonnage, net tonnage, ship size, year of construction, main engine power, auxiliary engine power, boiler parameters, fuel type, main engine loading factor, and main engine low load adjustment factor; and the AIS data includes call sign, MMSI number, ship name, ship latitude and longitude information, heading, real-time speed, ship type, navigation state, time information, cargo state, and draft.

[0032] The spatial resolution of the ship benzene series emission flux includes 0.1°◊0.1°, 0.01°◊0.01°, and 1 km◊1 km, and the temporal resolution includes hourly, daily, monthly, and yearly. Taking the spatial resolution of 0.1°◊0.1° as an example, the ship benzene series emission flux data is filled into 6,480,000 global grids using ArcGIS, and each grid is assigned a unique ObjectID. The annual variation of benzene emission flux from 2019 to 2022 is shown in FIG. 3.

[0033] Step 2: Determine the contribution of ship benzene series emissions to pollutant concentrations in coastal and inland areas through environmental simulation models.

[0034] The environmental simulation model includes WRF-CMAQ, GEOS-Chem, or a multi-escape model, wherein the WRF-CMAQ can be simplified as follows: (7) In the formula, C i represents the concentration of ship benzene series in the atmosphere at location i; represents the change of ship benzene series concentration over time; u represents the wind speed vector; ·(uC i ) represents the advection effect; K represents the turbulent diffusion coefficient; ·(К C i ) represents the diffusion effect; S is the source term, which represents the emission of ship benzene series, i.e., the increase of benzene series mass in unit volume of air per unit time due to ship emission sources; L is the loss term, which represents the decrease of ship benzene series emission due to processes such as deposition; and R is the chemical reaction term, which represents chemical conversion.

[0035] The parameters for calculating the advection in the WRF-CMAQ model include wind speed, wind direction, altitude, and terrain surface data, the parameters for calculating the diffusion include temperature, humidity, turbulent diffusion coefficient, atmospheric stability, wind speed, boundary layer height, and terrain surface data, the parameters for calculating the loss term include dry deposition rate parameter and wet deposition rate parameter, and the parameters for calculating the chemical reaction term include temperature, reaction rate constant, and solar radiation intensity.

[0036] The GEOS-Chem model can be simplified as follows: (8) In the formula, C i represents the concentration of ship benzene series in the atmosphere at location i; represents the change of the concentration of ship benzene series with time; u represents the wind speed vector; · (uC i ) represents the advection transport term, which is driven by the global wind field; K represents the turbulent diffusion coefficient; · (К C i ) represents the turbulent diffusion term, which represents the local mixing process; E i is the ship and other artificial or natural emission term; D i is the deposition loss, which represents the reduction of ship benzene series emissions due to processes such as dry deposition, wet deposition, etc.; P i is the chemical generation of benzene series in the atmosphere; C i represents the chemical reaction consumption of benzene series itself.

[0037] The multi-fugacity model can be simplified as follows: (9) In the formula, Mi represents the mass of benzene series in medium i; fi represents the fugacity of benzene series in the medium; Dij is the transfer rate coefficient of medium i to medium j; Ei is the emission input source term, which represents the mass flow of benzene series from ship emissions into the air, water body or other medium; Ri represents the loss caused by chemical reaction or biodegradation; Di represents the loss caused by processes such as deposition, adsorption, volatilization or runoff, etc.

[0038] Step 3, establish a quasi-Poisson distribution generalized additive model of ship benzene series emission concentration and population disease burden and mortality rate, analyze the changes of health indicators corresponding to the changes of unit ship benzene series concentration, (5) In the formula, E(Y t,ij ) represents the expected value of the number of deaths or incidence rate Y of disease type t at location i on date j; β0 represents the constant term (intercept) of the model; bs(C ij) represents the basis spline function that processes the data, capturing the non-linear relationship between the number of deaths or incidence Y and the concentration of BTEX C; ns(X1, df), ns(X2, df) represent the natural spline function that processes the meteorological parameters X1, X2, where df represents the degrees of freedom; Year j represents the year categorical variable of date j, used to control long-term trends, DOW j represents the week categorical variable of date j, used to control short-term trends.

[0039] The method for obtaining population disease burden and mortality data is to establish a population cohort, collect individual health, lifestyle, and environmental exposure data through tracking studies, including basic demographic information, physiological and biochemical indicators, medical records, genetic information, and personal behavior habits.

[0040] The population disease burden and mortality data include the incidence, number of deaths, and mortality of different disease types.

[0041] The disease types can be divided into respiratory system diseases, circulatory system diseases, urogenital system diseases, nervous system diseases, and digestive system diseases according to organ systems.

[0042] The gam() function is used to construct a generalized additive model for each regional data set, which is used to model the non-linear relationship between shipping-related BTEX concentration and disease mortality. The gam() function adopts the Poisson distribution and uses the log as the link function to adapt to the characteristics of continuous data and response variables. In the model, the shipping-related BTEX concentration is processed by the smoothing function bs(), and the meteorological parameters are processed by the natural spline function ns() to capture the non-linear relationship between them and the number of disease deaths.

[0043] In another embodiment of the present application, referring to FIG. 4, the present application also provides an electronic device, in particular: The electronic device can include a processor with one or more processing cores, a memory with one or more computer readable storage media, a power supply, and an input unit, etc. Among them: The processor is the control center of the electronic device, connects each part of the entire electronic device by using various interfaces and lines, executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory and calling data stored in the memory, thereby overall monitoring the electronic device. Optionally, the processor can include one or more processing cores; the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, and preferably, the processor can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor.

[0044] The memory can be used to store software programs and modules, and the processor executes various functions and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory can also include a memory controller to provide access for the processor to the memory.

[0045] The electronic device also includes a power supply for supplying power to each component, and preferably, the power supply can be logically connected to the processor through a power management system, thereby realizing functions such as management of charging, discharging, and power consumption management through the power management system. The power supply can also include one or more direct current or alternating current power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power state indicator, and any other components.

[0046] The electronic device can also include an input unit, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0047] Although not shown, the electronic device can further include a display unit and the like, which will not be described herein. In particular, in the present embodiment, the processor in the electronic device will load the executable file corresponding to the process of one or more application programs into the memory according to the following instructions, and run the application program stored in the memory by the processor, thereby realizing various functions.

[0048] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by related hardware controlled by the instructions, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0049] In some embodiments of the present application, the present application also provides a computer readable storage medium, which can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the order processing method provided by the embodiments of the present application.

[0050] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing the health effects of benzene series emissions from ships at a specific time and space, characterized in that: The steps include: Step 1: Obtain the emission factor of BTEX from ships in a specific time and space, calculate the emission flux of BTEX from ships within a given time and space range, and output the calculation results in the form of a grid with a specific resolution: （1） （2） （3） （4） Where E i represents the total emission flux of BTEX from ships in a given area i, EME i , EB i 、EAE i Represent the BTEX emission flux of main engine, boiler and auxiliary engine respectively; W, BP, and AEP are the main engine, boiler, and auxiliary engine power, respectively; EF, BEF, and AEEF are the main engine, boiler, and auxiliary engine emission factors, respectively; LF and LLAF are the main engine loading factor and the main engine low load adjustment factor, respectively; T is the length of the given time interval; Step 2: Determine the contribution of ship BTEX emissions to pollutant concentrations in coastal and inland areas through an environmental simulation model; Step 3: Establish a quasi-Poisson distribution generalized additive model for the relationship between ship BTEX emission concentrations and the disease burden and mortality rate of the population, and analyze the changes in health indicators corresponding to changes in BTEX concentration per unit ship: （5） Where E (Y t,ij ) represents the expected value of the number of deaths or incidence rate Y of disease type t at location i on date j; β0 represents the constant term (intercept) of the model; bs(C ij ) represents the basic spline function of the processed data, capturing the nonlinear relationship between the number of deaths or incidence rate Y and the concentration of benzene series C; ns(X1,df), ns(X2,df) represent the natural spline function of the processed meteorological parameters X1, X2, where df represents the degree of freedom; Year j A categorical variable representing the year of date j, used to control long-term trends, DOW j Categorical variable representing the day of the week of date j, used to control short-term trends.

2. The method according to claim 1, characterized in that The benzene series in step 1 include: benzene, toluene, ethylbenzene, xylene, styrene and isopropylbenzene, wherein xylene is further divided into o-xylene, m-xylene and p-xylene.

3. The method according to claim 1, characterized in that The method for obtaining the emission factor described in step 1 includes: first, obtaining the concentration of benzene series in the ship's exhaust gas through sampling and gas chromatography analysis, and calculating the actual emission mass of benzene series in combination with the extraction volume and adsorbent loading; based on the exhaust volume, sampling flow, engine power and sampling time during the sampling period, converting it to the benzene series emission corresponding to unit energy consumption under standard operating conditions, where the unit of unit energy consumption is kWh.

4. The method according to claim 1, wherein The method for obtaining the sampling concentration of benzene series in step 1 includes: taking an appropriate amount of sample, adding 5 mL of methanol, ultrasonic extraction for 30 minutes, then centrifuging at 4°C and 1200 rpm for 10 minutes, taking 1 mL of the supernatant into a brown gas chromatography injection bottle, and using GC-MS to determine the concentration of benzene series in the sample.

5. The method according to claim 1, wherein In step 1, the emission flux of BTEX from ships is calculated using the emission factors of BTEX from ships based on the Ship Technical Specifications Database (STSD) and Automatic Identification System (AIS) data. Among them, STSD data includes IMO number, MMSI number, ship name, ship type, gross tonnage, net tonnage, ship size, year of construction, main engine power, auxiliary engine power, boiler parameters, fuel type, main engine loading factor, and main engine low load adjustment factor; AIS data includes call sign, MMSI number, ship name, ship latitude and longitude information, heading, real-time speed, ship type, navigation status, time information, cargo status, and draft depth.

6. The method according to claim 1, wherein In step 1, the spatial resolutions of ship BTEX emission fluxes include 0.1°◊0.1°, 0.01°◊0.01°, and 1 km◊1 km, and the temporal resolutions include hourly, daily, monthly, and yearly.

7. The method according to claim 1, characterized in that The environmental simulation models in step 2 include: WRF-CMAQ model, GEOS-Chem model or multi-fugacity model.

8. The method according to claim 1, characterized in that In step 3, the method for obtaining population disease burden and mortality data is to establish a population cohort and collect individual health, lifestyle, and environmental exposure data through follow-up studies. The data include basic demographic information, physiological and biochemical indicators, medical records, genetic information, and personal behavioral habits.

9. The method according to claim 1, characterized in that In step 3, the population disease burden and mortality data include the incidence, number of deaths, and mortality rates of different disease types.

10. The method according to claim 1, characterized in that In step 3, disease types can be divided by organ system into respiratory system diseases, circulatory system diseases, genitourinary system diseases, nervous system diseases, and digestive system diseases.

11. The method according to claim 1, wherein In step 3, a generalized additive model was constructed for each regional dataset using the gam() function to model the nonlinear relationship between shipping-related BTEX concentrations and disease deaths.

12. The method according to claim 11, wherein gam() adopts a Poisson distribution and uses the log as the link function to accommodate continuous data and the characteristics of the response variable. In the model, shipping-related BTEX concentrations are treated using the smoothing function bs(), and meteorological parameters are treated using the natural spline function ns() to capture the nonlinear relationship between them and the number of deaths from the disease.

13. An electronic device, characterized in that: The electronic device includes: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method according to any one of claims 1 to 12.

14. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the method according to any one of claims 1 to 12.

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