CN116864131B - Pollutant health risk assessment method and system - Google Patents
Pollutant health risk assessment method and system Download PDFInfo
- Publication number
- CN116864131B CN116864131B CN202310893426.3A CN202310893426A CN116864131B CN 116864131 B CN116864131 B CN 116864131B CN 202310893426 A CN202310893426 A CN 202310893426A CN 116864131 B CN116864131 B CN 116864131B
- Authority
- CN
- China
- Prior art keywords
- value
- risk
- area
- concentration
- pollution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 22
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 22
- 238000012502 risk assessment Methods 0.000 title claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 105
- 238000007405 data analysis Methods 0.000 claims abstract description 41
- 238000011156 evaluation Methods 0.000 claims abstract description 30
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 118
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 73
- 229910052717 sulfur Inorganic materials 0.000 claims description 73
- 239000011593 sulfur Substances 0.000 claims description 73
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 59
- 229910052799 carbon Inorganic materials 0.000 claims description 59
- 229910052757 nitrogen Inorganic materials 0.000 claims description 59
- 239000002245 particle Substances 0.000 claims description 38
- 238000004458 analytical method Methods 0.000 claims description 30
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 17
- 229910002090 carbon oxide Inorganic materials 0.000 claims description 14
- 150000001875 compounds Chemical class 0.000 claims description 14
- -1 nitrogen-containing compound Chemical class 0.000 claims description 14
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 6
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical class [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 6
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 6
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 6
- 239000000356 contaminant Substances 0.000 claims description 6
- AKEJUJNQAAGONA-UHFFFAOYSA-N sulfur trioxide Chemical compound O=S(=O)=O AKEJUJNQAAGONA-UHFFFAOYSA-N 0.000 claims description 6
- 238000011109 contamination Methods 0.000 claims description 4
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 claims description 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 3
- 229910021529 ammonia Inorganic materials 0.000 claims description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 3
- 239000001569 carbon dioxide Substances 0.000 claims description 3
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 3
- 239000003897 fog Substances 0.000 claims description 3
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 3
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 claims description 3
- 239000013618 particulate matter Substances 0.000 claims description 3
- 239000000779 smoke Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 238000005067 remediation Methods 0.000 abstract description 3
- 239000007789 gas Substances 0.000 description 7
- 239000000463 material Substances 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Medical Informatics (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the field of medical care informatics, in particular to a pollutant health risk assessment method and system, which are used for solving the problems that the existing gas quality monitoring system cannot monitor atmospheric pollutants in a monitoring area from multiple aspects, cannot judge the treatment effect of the monitoring area with high pollution degree and cannot effectively monitor the atmospheric pollution condition of the monitoring area; the system comprises a region monitoring module, a health evaluation platform, a data analysis module, a risk monitoring module and a risk alarm module; the method can monitor the monitoring area in real time, judge the remediation effect of the monitoring area with high pollution degree, display the remediation effect in a data mode, effectively monitor the atmospheric pollution condition of the monitoring area, evaluate the risk of the physical health of people according to the pollutant condition, ensure the good quality of living environment of people and ensure the physical health of people.
Description
Technical Field
The invention relates to the field of medical care informatics, in particular to a pollutant health risk assessment method and system.
Background
With the acceleration of industrialization progress, the problem of gas pollution is increasingly serious, and the gas pollution becomes one of important factors affecting the health of people. Therefore, the development of the gas pollutant health risk assessment method and system has important practical significance for scientifically assessing the influence of gas pollution on human health and formulating effective pollution control measures.
Patent application number CN201910694617.0 discloses a gas quality monitoring system comprising: the invention discloses a high-density gridding layout low-cost and multi-parameter integrated compact miniature gas quality monitoring scheme, which can fully cover in an area to realize high space-time resolution atmospheric pollution monitoring, and simultaneously realizes pollution source tracking, early warning and forecasting and the like by combining with application of informationized big data, thereby providing more timely and effective decision support for environmental pollution prevention and control, and still has the following defects: the atmospheric pollutants in the monitoring area cannot be monitored from multiple aspects, the remediation effect of the monitoring area with high pollution degree cannot be judged, and the atmospheric pollution condition of the monitoring area cannot be effectively monitored.
Disclosure of Invention
In order to overcome the technical problems described above, the present invention aims to provide a method and a system for estimating health risk of pollutants: the method comprises the steps of obtaining a sulfur concentration value, a nitrogen concentration value, a carbon concentration value and a particle concentration value in a monitoring area through an area monitoring module, obtaining pollution parameters including the sulfur deviation value, the nitrogen deviation value, the carbon deviation value and the particle deviation value through a health evaluation platform according to the sulfur concentration value, the nitrogen concentration value, the carbon concentration value and the particle concentration value, obtaining the pollution values through a data analysis module according to the pollution parameters, classifying the monitoring area into a pre-risk area and a safety area according to the pollution values, obtaining tracking parameters of the pre-risk area through a risk monitoring module, wherein the tracking parameters comprise an average value and a surface value, obtaining the tracking values through the data analysis module according to the tracking parameters, classifying the pre-risk area into a non-qualified risk area and a qualified risk area according to the tracking values, and sorting and displaying the pre-risk area, the safety area, the non-qualified risk area and the qualified risk area through a risk alarm module, so that the problem that the existing gas quality monitoring system cannot monitor atmospheric pollutants of the monitoring area from multiple aspects, cannot evaluate the effect of the monitoring area with high pollution level, and cannot monitor the atmospheric pollution condition of the monitoring area effectively.
The aim of the invention can be achieved by the following technical scheme:
A contaminant health risk assessment system comprising:
The area monitoring module is used for acquiring a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particle concentration value KN in the monitoring area and sending the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particle concentration value KN to the health evaluation platform;
The health evaluation platform is used for obtaining pollution parameters according to the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particle concentration value KN and sending the pollution parameters to the data analysis module; wherein the pollution parameters comprise sulfur bias value LP, nitrogen bias value DP, carbon bias value TP and particle bias value KP;
The data analysis module is used for obtaining a pollution value WR according to the pollution parameters, classifying the monitoring area into a pre-risk area and a safety area according to the pollution value WR, and sending the pre-risk area and the safety area to the risk alarm module; the system is also used for obtaining a tracking value GZ according to the tracking parameters, classifying the pre-risk area into an unqualified risk area and a qualified risk area according to the tracking value GZ, and sending the unqualified risk area and the qualified risk area to the risk alarm module;
The risk monitoring module is used for acquiring tracking parameters of the pre-risk area and sending the tracking parameters to the data analysis module; wherein the tracking parameters comprise uniform values JF and face values MF;
And the risk alarm module is used for sorting and displaying the pre-risk area, the safety area, the unqualified risk area and the qualified risk area.
As a further scheme of the invention: the specific process of the health assessment platform obtaining pollution parameters is as follows:
Sequentially inputting the standard concentration of the sulfur-containing compound, the standard concentration of the nitrogen-containing compound, the standard concentration of the carbon oxide and the standard concentration of the particulate matters in the atmosphere, and sequentially marking the sulfur standard value LB, the nitrogen standard value DB, the carbon standard value TB and the particulate standard value KB;
Substituting the sulfur concentration value LN and the sulfur standard value LB into the formula Obtaining a sulfur offset value LP;
Substituting the nitrogen concentration value DN and the nitrogen standard value DB into a formula Obtaining a nitrogen offset value DP;
Substituting the carbon concentration value TN and the carbon standard value TB into the formula Obtaining a carbon offset value TP;
Substituting the granularity concentration value KN and the granularity index value KB into a formula Obtaining a particle deviation value KP;
And transmitting the sulfur offset LP, the nitrogen offset DP, the carbon offset TP and the particle offset KP to a data analysis module.
As a further scheme of the invention: the specific process of the data analysis module for obtaining the pollution value WR is as follows:
substituting sulfur bias value LP, nitrogen bias value DP, carbon bias value TP and particle bias value KP into the formula Obtaining a pollution value WR, wherein delta is a preset error factor, delta is taken to be 1.105, w1, w2, w3 and w4 are respectively preset weight coefficients of a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP, w4 is more than w1 and more than w2 and more than w3 is more than 1.358, w1=2.04, w2=1.85, w3=1.64 and w4=2.35;
Comparing the contamination value WR with a preset contamination threshold WRy:
If the pollution value WR is larger than the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a pre-risk area, sending the pre-risk area to a risk alarm module, generating a risk monitoring instruction at the same time, and sending the risk monitoring instruction to the risk monitoring instruction;
If the pollution value WR is less than or equal to the pollution threshold WRy, the monitoring area corresponding to the pollution value WR is marked as a safety area, and the safety area is sent to the risk alarm module.
As a further scheme of the invention: the specific process of acquiring the tracking parameters by the risk monitoring module is as follows:
Dividing the preset risk tracking time into a plurality of acquisition moments according to a preset acquisition time period;
After receiving the risk monitoring instruction, acquiring a plurality of pollution values WR of the pre-risk area according to the acquisition time, and marking the pollution values WR as analysis values i, i=1, … … and n in sequence, wherein n is a natural number;
Acquiring the average value of all the analysis values i, and marking the average value as an average value JF;
Establishing a plane rectangular coordinate system by taking an analysis value i as an ordinate and taking an acquisition time as an abscissa, connecting all coordinate points by line segments to form an analysis wave line, connecting two end points of the analysis wave line to an X axis by a vertical line, acquiring the area of a graph formed between the analysis wave line and the X axis, and marking the area as a face value MF;
and sending the average value JF and the face value MF to a data analysis module.
As a further scheme of the invention: the specific process of the data analysis module obtaining the tracking value GZ is as follows:
Substituting the average value JF and the face value MF into a formula Obtaining a tracking value GZ, wherein e and pi are mathematical constants, f1 and f2 are preset proportionality coefficients of a uniform value JF and a surface value MF respectively, f1+f2=1, 0 < f2 < f1 < 1, f1=0.57 is taken, and f2=0.43;
comparing the tracking value GZ with a preset tracking threshold GZy:
if the tracking value GZ is larger than the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as an unqualified risk area, and sending the unqualified risk area to a risk alarm module;
If the tracking value GZ is less than or equal to the tracking threshold GZy, marking the pre-risk area corresponding to the tracking value GZ as a qualified risk area, and sending the qualified risk area to the risk alarm module.
As a further scheme of the invention: a method of contaminant health risk assessment comprising the steps of:
Step S1: the regional monitoring module divides a region needing pollutant health risk assessment into a plurality of monitoring regions;
Step S2: the area monitoring module acquires the concentration of the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters in the monitoring area, marks the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters as a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particulate concentration value KN in sequence, and sends the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particulate concentration value KN to the health evaluation platform; wherein the sulfur-containing compound comprises sulfur dioxide, sulfur trioxide, and hydrogen sulfide; the nitrogen-containing compound comprises nitric oxide, nitrogen dioxide and ammonia; the carbon oxides include carbon monoxide and carbon dioxide; the particulate matter comprises dust, smoke and fog;
Step S3: a user sequentially inputs the standard concentration of the sulfur-containing compound, the standard concentration of the nitrogen-containing compound, the standard concentration of the carbon oxide and the standard concentration of the particulate matters in the atmospheric environment in a health evaluation platform, and sequentially marks the standard concentrations as a sulfur standard value LB, a nitrogen standard value DB, a carbon standard value TB and a particulate standard value KB;
step S4: the health evaluation platform substitutes the sulfur concentration value LN and the sulfur standard value LB into the formula Obtaining a sulfur offset value LP;
step S5: the health evaluation platform substitutes the nitrogen concentration value DN and the nitrogen standard value DB into the formula Obtaining a nitrogen offset value DP;
step S6: the health evaluation platform substitutes the carbon concentration value TN and the carbon standard value TB into the formula Obtaining a carbon offset value TP;
step S7: the health evaluation platform substitutes the concentration KN and the standard KB into the formula Obtaining a particle deviation value KP;
Step S8: the health evaluation platform sends a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP to the data analysis module;
step S9: the data analysis module substitutes the sulfur offset value LP, the nitrogen offset value DP, the carbon offset value TP and the particle offset value KP into the formula Obtaining a pollution value WR, wherein delta is a preset error factor, delta is taken to be 1.105, w1, w2, w3 and w4 are respectively preset weight coefficients of a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP, w4 is more than w1 and more than w2 and more than w3 is more than 1.358, w1=2.04, w2=1.85, w3=1.64 and w4=2.35;
Step S10: the data analysis module compares the pollution value WR with a preset pollution threshold WRy:
If the pollution value WR is larger than the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a pre-risk area, sending the pre-risk area to a risk alarm module, generating a risk monitoring instruction at the same time, and sending the risk monitoring instruction to the risk monitoring instruction;
if the pollution value WR is less than or equal to the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a safety area, and sending the safety area to a risk alarm module;
step S11: the risk monitoring module divides the preset risk tracking time into a plurality of acquisition moments according to a preset acquisition time period;
Step S12: the risk monitoring module acquires a plurality of pollution values WR of the pre-risk area according to the acquisition time after receiving the risk monitoring instruction, and marks the pollution values WR as analysis values i, i=1, … …, n and n are natural numbers in sequence;
Step S13: the risk monitoring module obtains the average value of all the analysis values i and marks the average value as an average value JF;
Step S14: the risk monitoring module establishes a plane rectangular coordinate system by taking an analysis value i as an ordinate and taking an acquisition time as an abscissa, connects all coordinate points with line segments to form an analysis wave line, connects two end points of the analysis wave line with an X axis with a vertical line, acquires the area of a graph formed between the analysis wave line and the X axis, and marks the area as a face score MF;
step S15: the risk monitoring module sends the average value JF and the face value MF to the data analysis module;
Step S16: the data analysis module substitutes the average value JF and the face value MF into the formula Obtaining a tracking value GZ, wherein e and pi are mathematical constants, f1 and f2 are preset proportionality coefficients of a uniform value JF and a surface value MF respectively, f1+f2=1, 0 < f2 < f1 < 1, f1=0.57 is taken, and f2=0.43;
step S17: the data analysis module compares the tracking value GZ with a preset tracking threshold GZy:
if the tracking value GZ is larger than the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as an unqualified risk area, and sending the unqualified risk area to a risk alarm module;
if the tracking value GZ is less than or equal to the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as a qualified risk area, and sending the qualified risk area to a risk alarm module;
step S18: after receiving the pre-risk area and the safety area, the risk alarm module sorts the pre-risk area according to the sequence from the big pollution value WR to the small pollution value WR, and sorts the safety area according to the sequence from the small pollution value WR to the big pollution value WR;
Step S19: after receiving the unqualified risk areas and the qualified risk areas, the risk alarm module sorts the unqualified risk areas according to the sequence from the big tracking value GZ to the small tracking value GZ, and sorts the qualified risk areas according to the sequence from the small tracking value GZ to the big tracking value GZ.
The invention has the beneficial effects that:
According to the pollutant health risk assessment method and system, a region monitoring module is used for acquiring a sulfur concentration value, a nitrogen concentration value, a carbon concentration value and a particle concentration value in a monitoring region, a health assessment platform is used for acquiring pollution parameters according to the sulfur concentration value, the nitrogen concentration value, the carbon concentration value and the particle concentration value, the pollution parameters comprise a sulfur deviation value, a nitrogen deviation value, a carbon deviation value and a particle deviation value, a data analysis module is used for acquiring the pollution values according to the pollution parameters, classifying the monitoring region into a pre-risk region and a safety region according to the pollution values, a risk monitoring module is used for acquiring tracking parameters of the pre-risk region, the tracking parameters comprise an average value and a surface value, a data analysis module is used for acquiring the tracking values according to the tracking parameters, classifying the pre-risk region into a non-qualified risk region and a qualified risk region according to the tracking values, and classifying and sequencing and displaying the pre-risk region, the safety region, the non-qualified risk region and the qualified risk region through a risk alarm module; according to the pollutant health risk assessment method, firstly, atmospheric pollutants in all monitoring areas are monitored, then, pollution parameters are obtained, the pollution values obtained by the pollution parameters are used for measuring the comprehensive pollution conditions of various atmospheric pollutants, the pollution levels of the atmospheric pollutants are higher as the pollution values are larger, adverse effects on the health of people in the monitoring areas are easier to cause, therefore, pre-risk areas are screened out and continuously monitored, the atmospheric pollutants in the pre-risk areas are subjected to treatment, and then, the tracking values of the pre-risk areas are monitored, the tracking values are used for measuring the treatment effect conditions of the pre-risk areas, the greater the tracking values are, the worse the treatment effect of the pre-risk areas is, the longer the pre-risk areas are in a pollution state, and the influence on the health of the people for a long time is easy to lead to the body health of the people to be drastically reduced; the pollutant health risk assessment method not only can monitor the monitoring area in real time, but also can judge the treatment effect of the monitoring area with high pollution degree, so that the treatment effect is displayed in a data mode, the atmospheric pollution condition of the monitoring area can be effectively supervised, the risk assessment can be carried out on the physical health of people according to the pollutant condition, the living environment quality of people is guaranteed to be good, and the physical health of people is guaranteed.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a contaminant health risk assessment system of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1, the embodiment is a pollutant health risk assessment system, which includes the following modules: the system comprises an area monitoring module, a health evaluation platform, a data analysis module, a risk monitoring module and a risk alarm module;
The area monitoring module is used for acquiring a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particle concentration value KN in a monitoring area and sending the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particle concentration value KN to the health evaluation platform;
The health evaluation platform is used for obtaining pollution parameters according to a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particle concentration value KN and sending the pollution parameters to the data analysis module; wherein the pollution parameters comprise sulfur bias value LP, nitrogen bias value DP, carbon bias value TP and particle bias value KP;
The data analysis module is used for obtaining a pollution value WR according to pollution parameters, classifying a monitoring area into a pre-risk area and a safety area according to the pollution value WR, and sending the pre-risk area and the safety area to the risk alarm module; the system is also used for obtaining a tracking value GZ according to the tracking parameters, classifying the pre-risk area into an unqualified risk area and a qualified risk area according to the tracking value GZ, and sending the unqualified risk area and the qualified risk area to the risk alarm module;
the risk monitoring module is used for acquiring tracking parameters of the pre-risk area and sending the tracking parameters to the data analysis module; wherein the tracking parameters comprise uniform values JF and face values MF;
The risk alarm module is used for sorting and displaying the pre-risk area, the safety area, the unqualified risk area and the qualified risk area.
Example 2:
Referring to fig. 1, the embodiment is a method for evaluating the health risk of a contaminant, which includes the following steps:
Step S1: the regional monitoring module divides a region needing pollutant health risk assessment into a plurality of monitoring regions;
Step S2: the area monitoring module acquires the concentration of the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters in the monitoring area, marks the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters as a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particulate concentration value KN in sequence, and sends the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particulate concentration value KN to the health evaluation platform; wherein the sulfur-containing compound comprises sulfur dioxide, sulfur trioxide, and hydrogen sulfide; the nitrogen-containing compound comprises nitric oxide, nitrogen dioxide and ammonia; the carbon oxides include carbon monoxide and carbon dioxide; the particulate matter comprises dust, smoke and fog;
Step S3: a user sequentially inputs the standard concentration of the sulfur-containing compound, the standard concentration of the nitrogen-containing compound, the standard concentration of the carbon oxide and the standard concentration of the particulate matters in the atmospheric environment in a health evaluation platform, and sequentially marks the standard concentrations as a sulfur standard value LB, a nitrogen standard value DB, a carbon standard value TB and a particulate standard value KB;
step S4: the health evaluation platform substitutes the sulfur concentration value LN and the sulfur standard value LB into the formula Obtaining a sulfur offset value LP;
step S5: the health evaluation platform substitutes the nitrogen concentration value DN and the nitrogen standard value DB into the formula Obtaining a nitrogen offset value DP;
step S6: the health evaluation platform substitutes the carbon concentration value TN and the carbon standard value TB into the formula Obtaining a carbon offset value TP;
step S7: the health evaluation platform substitutes the concentration KN and the standard KB into the formula Obtaining a particle deviation value KP;
Step S8: the health evaluation platform sends a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP to the data analysis module;
step S9: the data analysis module substitutes the sulfur offset value LP, the nitrogen offset value DP, the carbon offset value TP and the particle offset value KP into the formula Obtaining a pollution value WR, wherein delta is a preset error factor, delta is taken to be 1.105, w1, w2, w3 and w4 are respectively preset weight coefficients of a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP, w4 is more than w1 and more than w2 and more than w3 is more than 1.358, w1=2.04, w2=1.85, w3=1.64 and w4=2.35;
Step S10: the data analysis module compares the pollution value WR with a preset pollution threshold WRy:
If the pollution value WR is larger than the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a pre-risk area, sending the pre-risk area to a risk alarm module, generating a risk monitoring instruction at the same time, and sending the risk monitoring instruction to the risk monitoring instruction;
if the pollution value WR is less than or equal to the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a safety area, and sending the safety area to a risk alarm module;
step S11: the risk monitoring module divides the preset risk tracking time into a plurality of acquisition moments according to a preset acquisition time period;
Step S12: the risk monitoring module acquires a plurality of pollution values WR of the pre-risk area according to the acquisition time after receiving the risk monitoring instruction, and marks the pollution values WR as analysis values i, i=1, … …, n and n are natural numbers in sequence;
Step S13: the risk monitoring module obtains the average value of all the analysis values i and marks the average value as an average value JF;
Step S14: the risk monitoring module establishes a plane rectangular coordinate system by taking an analysis value i as an ordinate and taking an acquisition time as an abscissa, connects all coordinate points with line segments to form an analysis wave line, connects two end points of the analysis wave line with an X axis with a vertical line, acquires the area of a graph formed between the analysis wave line and the X axis, and marks the area as a face score MF;
step S15: the risk monitoring module sends the average value JF and the face value MF to the data analysis module;
Step S16: the data analysis module substitutes the average value JF and the face value MF into the formula Obtaining a tracking value GZ, wherein e and pi are mathematical constants, f1 and f2 are preset proportionality coefficients of a uniform value JF and a surface value MF respectively, f1+f2=1, 0 < f2 < f1 < 1, f1=0.57 is taken, and f2=0.43;
step S17: the data analysis module compares the tracking value GZ with a preset tracking threshold GZy:
if the tracking value GZ is larger than the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as an unqualified risk area, and sending the unqualified risk area to a risk alarm module;
if the tracking value GZ is less than or equal to the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as a qualified risk area, and sending the qualified risk area to a risk alarm module;
step S18: after receiving the pre-risk area and the safety area, the risk alarm module sorts the pre-risk area according to the sequence from the big pollution value WR to the small pollution value WR, and sorts the safety area according to the sequence from the small pollution value WR to the big pollution value WR;
Step S19: after receiving the unqualified risk areas and the qualified risk areas, the risk alarm module sorts the unqualified risk areas according to the sequence from the big tracking value GZ to the small tracking value GZ, and sorts the qualified risk areas according to the sequence from the small tracking value GZ to the big tracking value GZ.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (2)
1. A contaminant health risk assessment system, comprising:
The area monitoring module is used for acquiring a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particle concentration value KN in the monitoring area and sending the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particle concentration value KN to the health evaluation platform;
The health evaluation platform is used for obtaining pollution parameters according to the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particle concentration value KN and sending the pollution parameters to the data analysis module; wherein the pollution parameters comprise sulfur bias value LP, nitrogen bias value DP, carbon bias value TP and particle bias value KP; the specific process of the health assessment platform obtaining pollution parameters is as follows:
Sequentially inputting the standard concentration of the sulfur-containing compound, the standard concentration of the nitrogen-containing compound, the standard concentration of the carbon oxide and the standard concentration of the particulate matters in the atmosphere, and sequentially marking the sulfur standard value LB, the nitrogen standard value DB, the carbon standard value TB and the particulate standard value KB;
Substituting the sulfur concentration value LN and the sulfur standard value LB into the formula Obtaining a sulfur offset value LP;
Substituting the nitrogen concentration value DN and the nitrogen standard value DB into a formula Obtaining a nitrogen offset value DP;
Substituting the carbon concentration value TN and the carbon standard value TB into the formula Obtaining a carbon offset value TP;
Substituting the granularity concentration value KN and the granularity index value KB into a formula To a particle bias value KP;
transmitting the sulfur offset LP, the nitrogen offset DP, the carbon offset TP and the particle offset KP to a data analysis module;
The data analysis module is used for obtaining a pollution value WR according to the pollution parameters, classifying the monitoring area into a pre-risk area and a safety area according to the pollution value WR, and sending the pre-risk area and the safety area to the risk alarm module; the system is also used for obtaining a tracking value GZ according to the tracking parameters, classifying the pre-risk area into an unqualified risk area and a qualified risk area according to the tracking value GZ, and sending the unqualified risk area and the qualified risk area to the risk alarm module;
the specific process of the data analysis module for obtaining the pollution value is as follows:
substituting sulfur bias value LP, nitrogen bias value DP, carbon bias value TP and particle bias value KP into the formula Obtaining a pollution value WR, wherein delta is a preset error factor, delta is taken to be 1.105, w1, w2, w3 and w4 are respectively preset weight coefficients of a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP, w4 is more than w1 and more than w2 and more than w3 is more than 1.358, w1=2.04, w2=1.85, w3=1.64 and w4=2.35;
Comparing the contamination value WR with a preset contamination threshold WRy:
If the pollution value WR is larger than the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a pre-risk area, sending the pre-risk area to a risk alarm module, generating a risk monitoring instruction at the same time, and sending the risk monitoring instruction to the risk monitoring module;
if the pollution value WR is less than or equal to the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a safety area, and sending the safety area to a risk alarm module;
the specific process of the data analysis module obtaining the tracking value is as follows:
Substituting the average value JF and the face value MF into a formula Obtaining a tracking value GZ, wherein e and pi are mathematical constants, f1 and f2 are preset proportionality coefficients of a uniform value JF and a surface value MF respectively, f1+f2=1, 0 < f2 < f1 < 1, f1=0.57 is taken, and f2=0.43;
comparing the tracking value GZ with a preset tracking threshold GZy:
if the tracking value GZ is larger than the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as an unqualified risk area, and sending the unqualified risk area to a risk alarm module;
if the tracking value GZ is less than or equal to the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as a qualified risk area, and sending the qualified risk area to a risk alarm module;
The risk monitoring module is used for acquiring tracking parameters of the pre-risk area and sending the tracking parameters to the data analysis module; wherein the tracking parameters comprise uniform values JF and face values MF;
The specific process of acquiring the tracking parameters by the risk monitoring module is as follows:
Dividing the preset risk tracking time into a plurality of acquisition moments according to a preset acquisition time period;
After receiving the risk monitoring instruction, acquiring a plurality of pollution values WR of the pre-risk area according to the acquisition time, and marking the pollution values WR as analysis values i, i=1, … … and n in sequence, wherein n is a natural number;
Acquiring the average value of all the analysis values i, and marking the average value as an average value JF;
Establishing a plane rectangular coordinate system by taking an analysis value i as an ordinate and taking an acquisition time as an abscissa, connecting all coordinate points by line segments to form an analysis wave line, connecting two end points of the analysis wave line to an X axis by a vertical line, acquiring the area of a graph formed between the analysis wave line and the X axis, and marking the area as a face value MF;
transmitting the average value JF and the face value MF to a data analysis module;
And the risk alarm module is used for sorting and displaying the pre-risk area, the safety area, the unqualified risk area and the qualified risk area.
2. A method for contaminant health risk assessment comprising the steps of:
Step S1: the regional monitoring module divides a region needing pollutant health risk assessment into a plurality of monitoring regions;
Step S2: the area monitoring module acquires the concentration of the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters in the monitoring area, marks the sulfur-containing compound, the concentration of the nitrogen-containing compound, the concentration of the carbon oxide and the concentration of the particulate matters as a sulfur concentration value LN, a nitrogen concentration value DN, a carbon concentration value TN and a particulate concentration value KN in sequence, and sends the sulfur concentration value LN, the nitrogen concentration value DN, the carbon concentration value TN and the particulate concentration value KN to the health evaluation platform; wherein the sulfur-containing compound comprises sulfur dioxide, sulfur trioxide, and hydrogen sulfide; the nitrogen-containing compound comprises nitric oxide, nitrogen dioxide and ammonia; the carbon oxides include carbon monoxide and carbon dioxide; the particulate matter comprises dust, smoke and fog;
Step S3: a user sequentially inputs the standard concentration of the sulfur-containing compound, the standard concentration of the nitrogen-containing compound, the standard concentration of the carbon oxide and the standard concentration of the particulate matters in the atmospheric environment in a health evaluation platform, and sequentially marks the standard concentrations as a sulfur standard value LB, a nitrogen standard value DB, a carbon standard value TB and a particulate standard value KB;
step S4: the health evaluation platform substitutes the sulfur concentration value LN and the sulfur standard value LB into the formula Obtaining a sulfur offset value LP;
step S5: the health evaluation platform substitutes the nitrogen concentration value DN and the nitrogen standard value DB into the formula Obtaining a nitrogen offset value DP;
step S6: the health evaluation platform substitutes the carbon concentration value TN and the carbon standard value TB into the formula Obtaining a carbon offset value TP;
step S7: the health evaluation platform substitutes the concentration KN and the standard KB into the formula Obtaining a particle deviation value KP;
Step S8: the health evaluation platform sends a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP to the data analysis module;
step S9: the data analysis module substitutes the sulfur offset value LP, the nitrogen offset value DP, the carbon offset value TP and the particle offset value KP into the formula Obtaining a pollution value WR, wherein delta is a preset error factor, delta is taken to be 1.105, w1, w2, w3 and w4 are respectively preset weight coefficients of a sulfur bias value LP, a nitrogen bias value DP, a carbon bias value TP and a particle bias value KP, w4 is more than w1 and more than w2 and more than w3 is more than 1.358, w1=2.04, w2=1.85, w3=1.64 and w4=2.35;
Step S10: the data analysis module compares the pollution value WR with a preset pollution threshold WRy:
If the pollution value WR is larger than the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a pre-risk area, sending the pre-risk area to a risk alarm module, generating a risk monitoring instruction at the same time, and sending the risk monitoring instruction to the risk monitoring module;
if the pollution value WR is less than or equal to the pollution threshold WRy, marking a monitoring area corresponding to the pollution value WR as a safety area, and sending the safety area to a risk alarm module;
step S11: the risk monitoring module divides the preset risk tracking time into a plurality of acquisition moments according to a preset acquisition time period;
Step S12: the risk monitoring module acquires a plurality of pollution values WR of the pre-risk area according to the acquisition time after receiving the risk monitoring instruction, and marks the pollution values WR as analysis values i, i=1, … …, n and n are natural numbers in sequence;
Step S13: the risk monitoring module obtains the average value of all the analysis values i and marks the average value as an average value JF;
Step S14: the risk monitoring module establishes a plane rectangular coordinate system by taking an analysis value i as an ordinate and taking an acquisition time as an abscissa, connects all coordinate points with line segments to form an analysis wave line, connects two end points of the analysis wave line with an X axis with a vertical line, acquires the area of a graph formed between the analysis wave line and the X axis, and marks the area as a face score MF;
step S15: the risk monitoring module sends the average value JF and the face value MF to the data analysis module;
Step S16: the data analysis module substitutes the average value JF and the face value MF into the formula Obtaining a tracking value GZ, wherein e and pi are mathematical constants, f1 and f2 are preset proportionality coefficients of a uniform value JF and a surface value MF respectively, f1+f2=1, 0 < f2 < f1 < 1, f1=0.57 is taken, and f2=0.43;
step S17: the data analysis module compares the tracking value GZ with a preset tracking threshold GZy:
if the tracking value GZ is larger than the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as an unqualified risk area, and sending the unqualified risk area to a risk alarm module;
if the tracking value GZ is less than or equal to the tracking threshold GZy, marking a pre-risk area corresponding to the tracking value GZ as a qualified risk area, and sending the qualified risk area to a risk alarm module;
step S18: after receiving the pre-risk area and the safety area, the risk alarm module sorts the pre-risk area according to the sequence from the big pollution value WR to the small pollution value WR, and sorts the safety area according to the sequence from the small pollution value WR to the big pollution value WR;
Step S19: after receiving the unqualified risk areas and the qualified risk areas, the risk alarm module sorts the unqualified risk areas according to the sequence from the big tracking value GZ to the small tracking value GZ, and sorts the qualified risk areas according to the sequence from the small tracking value GZ to the big tracking value GZ.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310893426.3A CN116864131B (en) | 2023-07-20 | 2023-07-20 | Pollutant health risk assessment method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310893426.3A CN116864131B (en) | 2023-07-20 | 2023-07-20 | Pollutant health risk assessment method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116864131A CN116864131A (en) | 2023-10-10 |
| CN116864131B true CN116864131B (en) | 2024-05-31 |
Family
ID=88221437
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310893426.3A Active CN116864131B (en) | 2023-07-20 | 2023-07-20 | Pollutant health risk assessment method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116864131B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113418841A (en) * | 2021-06-23 | 2021-09-21 | 四川省生态环境监测总站 | Completion method for air quality particulate matter concentration prediction data |
| CN113487098A (en) * | 2021-07-14 | 2021-10-08 | 清华苏州环境创新研究院 | Atmospheric pollution early warning information expression and display method |
| CN115274108A (en) * | 2022-07-28 | 2022-11-01 | 中国环境科学研究院 | Pollutant health risk assessment method and device |
| CN115409410A (en) * | 2022-09-21 | 2022-11-29 | 神华新街能源有限责任公司 | Method and device for evaluating pollution of underground water in mining area and electronic equipment |
| CN116050951A (en) * | 2023-04-03 | 2023-05-02 | 云南碧翔物联网科技有限公司 | Pollution monitoring method and system based on data analysis model |
| CN116151621A (en) * | 2023-02-24 | 2023-05-23 | 安徽理工大学 | Atmospheric pollution treatment risk detection system based on data analysis |
| WO2023115159A1 (en) * | 2021-12-23 | 2023-06-29 | Max Finselbach | System and method for remotely monitoring water quality |
-
2023
- 2023-07-20 CN CN202310893426.3A patent/CN116864131B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113418841A (en) * | 2021-06-23 | 2021-09-21 | 四川省生态环境监测总站 | Completion method for air quality particulate matter concentration prediction data |
| CN113487098A (en) * | 2021-07-14 | 2021-10-08 | 清华苏州环境创新研究院 | Atmospheric pollution early warning information expression and display method |
| WO2023115159A1 (en) * | 2021-12-23 | 2023-06-29 | Max Finselbach | System and method for remotely monitoring water quality |
| CN115274108A (en) * | 2022-07-28 | 2022-11-01 | 中国环境科学研究院 | Pollutant health risk assessment method and device |
| CN115409410A (en) * | 2022-09-21 | 2022-11-29 | 神华新街能源有限责任公司 | Method and device for evaluating pollution of underground water in mining area and electronic equipment |
| CN116151621A (en) * | 2023-02-24 | 2023-05-23 | 安徽理工大学 | Atmospheric pollution treatment risk detection system based on data analysis |
| CN116050951A (en) * | 2023-04-03 | 2023-05-02 | 云南碧翔物联网科技有限公司 | Pollution monitoring method and system based on data analysis model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116864131A (en) | 2023-10-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114674988B (en) | Air on-line monitoring system based on wireless network | |
| US6623975B1 (en) | Method and system for vehicle emission testing | |
| CN112229952B (en) | Method for monitoring toxic and harmful gases in chemical industrial park | |
| CN115290831B (en) | Air quality detection method | |
| CN116627079B (en) | Operation monitoring management system for laboratory ventilation equipment | |
| CN117193203A (en) | Alloy material processing production line monitoring system based on data analysis | |
| CN116864131B (en) | Pollutant health risk assessment method and system | |
| CN113267601B (en) | Industrial production environment remote real-time monitoring cloud platform based on machine vision and data analysis | |
| CN114970977A (en) | Abnormal data detection method and system for digital urban air quality monitoring data | |
| Alattar et al. | Evaluating particulate matter (PM2. 5 and PM10) impact on human health in Oman based on a hybrid artificial neural network and mathematical models | |
| CN109856321A (en) | The determination method of abnormal high level point | |
| Ismail et al. | Statistical modeling approaches for PM10 forecasting at industrial areas of Malaysia | |
| CN116644957A (en) | Urban air pollution early warning method based on meteorological and environmental monitoring data | |
| Herts et al. | Cloud service ThingSpeak for monitoring the surface layer of the atmosphere polluted by particulate matters | |
| CN111047160A (en) | Pollution cause analysis method and device, readable storage medium and electronic equipment | |
| Kumar et al. | Internet of Things (IoT) Enabled Air Quality Monitoring System for Conventional and UAV Application. | |
| CN113804595B (en) | Multi-parameter air quality monitoring system | |
| Alattar et al. | Neural and Mathematical Predicting Models for Particulate Matter Impact on Human Health in Oman | |
| CN112257354B (en) | Reverse positioning method for air pollution source under dynamic wind field condition | |
| KR101782934B1 (en) | Dilution correction factor and approximate modeling method for driving vehicle mass conversion method | |
| CN117054616B (en) | Remote middle station atmosphere pollution tracing system based on artificial intelligence | |
| Mane et al. | Basic instrumentation | |
| CN117540229B (en) | Atmospheric environment monitoring method based on clustering algorithm | |
| Istiqomah et al. | Implementation of Fuzzy Tsukamoto Algorithm on Smart Node Sensors for Air Quality Monitoring | |
| CN115950797B (en) | Pollutant tracing method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |