KR101382507B1 - Air quality forecast and management system - Google Patents

Abstract

An air quality forecasting and managing system is disclosed. The present invention relates to a system for forecasting and managing air quality through a modeling technique. The present invention is able to forecast air quality synthetically by comprising: a forecast meteorological model module which forecasts future weather information by using an MM5/UM 72 hours forecast meteorological model which is a meteorological model provided by a meteorological administration; an air quality forecast management module which locally displays air quality by performing an aerosol analysis and a yellow dust modeling and calculating the concentration of each air pollutant; and a display module which displays a watch or warning area based on an air quality index and an image which are generated in the air quality forecast management module. [Reference numerals] (210) Emission data unit; (211) Meteorological model; (212) Satellite data unit; (220) Chemical transport model; (230) Aerosol dynamic model; (240) Natural yellow dust model; (260) Dust concentration calculating unit; (261) Gas concentration calculating unit; (262) Aerosol concentration calculating unit; (270) Regional air quality model

Description

Air quality prediction and management system {AIR QUALITY FORECAST AND MANAGEMENT SYSTEM}

The present invention is an apparatus for predicting and managing air quality, and more specifically, by analyzing comprehensively the air quality including yellow dust and displaying it in time series, changes in air quality to be generated in the future during environmental impact assessment and national policy establishment. This study relates to an air quality prediction and management system that can provide important data to predict the project's feasibility and establish air quality management policies.

As is well known, the management of the air environment to proactively and efficiently respond to rapid environmental changes such as global warming has become a very important topic all over the world, and in urban areas and industrial areas that emit large amounts of air pollutants locally. Management of the air environment has emerged as a necessary condition for human life in a comfortable atmosphere.

Therefore, in recent years, air stations have been installed in urban areas and industrial areas that generate a large amount of air pollutants, and air alarms are used to monitor the level of pollution (concentration) of air pollutants observed through these air stations, and to warn billboards and broadcasting media. The system is widely used.

Most developed countries have been actively developing air monitoring network information management and forecasting modeling techniques related to air quality forecasting and warning systems for a long time and are currently operating by national or local governments. However, in Korea, a large amount of money is invested to manage the measurement network to collect real-time air quality data and emission data.However, there is not enough system to forecast and comprehensively manage air quality by using it. As a result, the efficiency of environmental administration is inferior.

Using the huge budget, the Ministry of Environment's air quality measurement network data, emission data, and meteorological data from the Korea Meteorological Administration to monitor the air pollution model and air diffusion modeling by displaying real-time air pollution levels from direct air quality monitoring and monitoring. It is necessary to take into account the pollutant factors such as pollutant emissions, weather and topographical conditions in the target area, and to establish a comprehensive air quality forecasting and warning system that can predict the degree of pollution in China and North Korea.

Such a conventional air quality management system is shown in FIG.

1 is a technology disclosed in Korean Patent Application Publication No. 2011-0128511 (name: 3-D real-time air management system using the web). In FIG. 1, user A (001) is a homepage 002 configured on the web through the Internet. In the case of accessing the membership, the web server administrator approves the membership registration, and accordingly, the management screen for each user ID (003) to input the user's workplace and the point source specification (Fig. 7) and the surface source specification. You will have permission to connect. A user 001 is stored in the user-specific DB of the integrated modeling module 009 and used as input data for performing the atmospheric diffusion model 012.

In addition to the A user 001, the B users 004 and C users 006 subscribed in the same manner, when they subscribe to the homepage using their respective IDs and passwords, manage screens 005 and 007 tailored to their respective IDs. When the input data is stored in the DB and the atmospheric diffusion modeling module 012 is driven, the modeling results are derived using the respective input data.

In the modeling step, when the meteorological data measured from the meteorological observation equipment 008 is stored by the meteorological data collection module 010, the three-dimensional meteorological data is generated by the meteorological modeling module 011 using the data.

In the atmospheric diffusion modeling module 012, the atmospheric diffusion modeling is performed using the three-dimensional weather data calculated by the weather modeling module 011 and the source data inputted by each user (001, 004, 006) through the homepage. As a result, the equal density curve for each user's area is derived as an image in the modeling image generating module 013. In addition, the modeling results store the contribution modeling result of each source for each point of interest in the point of interest contribution concentration storage module 014 to search or display the modeling image and the point of interest contribution concentration results through the homepage at any time. It is configured to have backup function for modeling result generated in this process.

However, this type of air management system is to identify the contribution of air pollutants emitted to point or cotton pollutant sites to the surrounding areas, and analyzes the models that affect air quality by area. Not enough to predict quality.

Korea Air Quality Forecasting System is introduced as a technology for making such a comprehensive forecast.

The air quality forecasting system of the Korea Air Quality Forecasting System is supported by the Korea Institute of Environmental Science and Technology (KIEST) for the next-generation research fund (project title: Development of the commercialization package for the air quality warning system, research period: 2004.12-2007.11 He is led by Prof. Yoon Seo Seo's research team, Department of Environmental Engineering, Anyang University, and the Institute of the Environment at the University of North Carolina at Chapel Hill (UNC), International Joint Research, and Enney It is an air quality numerical forecasting system developed by Tek's participating company.

The Korea Air Quality Forecasting System receives data from the Regional Data Assimilation and Prediction System (RDAPS) of the Korea Meteorological Administration and recalculates the three-dimensional wind fields in East Asia and the Korean Peninsula using MM5 (Mesoscale Model 5). Based on the data from the East Asia (Ace-Asia) and domestic emissions (Ministry of Environment CAPSS) data, the modeling emission data classified by time are generated using the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE).

Using the 3D wind field and species classification data calculated above, the CMAQ (Community Multiscale Air Quality) model, which is a chemical transportation model, was used to determine the East Asian region (27km resolution), the Korean Peninsula region (9km resolution), and the metropolitan area (3km resolution). The modeling system predicts the air quality concentrations including fine dust and ozone for 48 hours.

However, such a Korean air quality forecasting system has a problem to be used as a comprehensive air quality prediction and management system.

In addition to the above-mentioned problems of regional air quality forecasts, the recent rapid economic growth in Asia, including China, has resulted in an increase in the release of artificial pollutants in the region, and the occurrence of naturally occurring yellow dust in northern China and Mongolia. have.

In addition, in Southeast Asia, BC and VOCs caused by vegetation incineration are very high, and yellow dust from northern China and Mongolia is mixed with pollutants emitted from industrial zones in China during long distances, causing complex chemical reactions. Problems are emerging.

In addition, effective air quality management and policies require, among other things, an accurate understanding of the current state of pollution and a quantitative interpretation of the factors that affect the degree of pollution. Many real-time environmental measurement techniques related to the collection of weather, air quality and emissions data have been developed and are in use. However, there is a lack of comprehensive technology on air quality forecasting and warning that can integrate and utilize high quality data collected in real time.

Such naturally occurring yellow dust and artificially generated aerosols affect climate change by changing radiative forcing, and thus, it is necessary to develop a comprehensive air quality prediction and management system that can consider both yellow dust, artificial aerosols, and air pollutants.

Accordingly, an object of the present invention to solve this problem is to provide an air quality prediction and management system that can predict future air quality changes.

It is another object of the present invention to provide a comprehensive air quality prediction and management system that can consider both yellow dust, artificial aerosols and air pollutants.

In another aspect, the present invention is to provide an air quality prediction and management system that can consider the occurrence of yellow dust in Asia, including China.

In addition, another object of the present invention is to provide an air quality prediction and management system capable of forecasting in real time by performing air diffusion modeling using weather data and emission data measured in real time.

Another object of the present invention is to provide an air quality prediction and management system capable of quantifying air quality or displaying an air quality advisory or alarm area.

The air quality prediction and management system through the modeling technique of the present invention to achieve this purpose is to use the MM5 / UM 72 hour predictor listing, a meteorological field model provided by the Korea Meteorological Administration, to obtain the corresponding historical and current weather information. Performs modeling using predictor listing module for predicting future weather information, modeling data of the forecaster listing module, air pollutant emission data, chemical transport model and satellite data. Air quality prediction management module for calculating the concentration of each air pollutant to image the air quality locally and simultaneously calculating the air quality index, and the image and air quality index generated by the air quality prediction management module. Configured to include a display module for displaying the warning or alarm area based on the .

The air quality prediction management module receives the modeling data of the predictor listing module, an air pollutant emission data section, a chemical transport model and satellite data, and an aerosol dynamics model for modeling aerosols, and data from the predictor listing module and satellite data section. The natural dust model for performing the yellow dust modeling, the aerosol dynamics model and the natural dust model as modeling data of the gas concentration, the aerosol concentration, and the concentration calculation unit for calculating the dust concentration, and the concentration calculation unit The concentration of each air pollutant can be configured to include the regional air quality model that calculates the air quality index at the same time as image data to display the air quality locally.

In addition, the emission data section collects pollutants such as NOx, CO, NMVOC, PM ₁₀ , PM _2.5 , BC, and OC to perform emission modeling, and the emission modeling uses grid-specific emission inputs.

The chemical transport model described above is classified into photochemical model, gas phase chemistry, and liquid chemistry model using the CMAQ model, and the aerosol dynamics model includes dust, BC, OC, and SIA in the emission data sheet. Receive data on Dust-SIA-BC-OC, sea-salt, nucleation, condensation and evaporation, coagulation, wet and dry deposition from the chemical transport model Receive data of wet and dry deposition, hygroscopic growth, and use SCAPE2 to calculate secondary contaminants.

In addition, the natural yellow dust model is to predict the amount of yellow dust generation using the Asian Dust Aerosol Model 2 (ADAM2) short-term prediction model for the four seasons, the concentration calculation unit calculates the gas concentration calculation unit and aerosol concentration to calculate the gas concentration constituted by calculating the dust concentration to calculate the aerosol concentration calculating section and the dust density, and the dust concentration of the dust concentration calculation unit PM _10, PM _{2 .5} with TSP (Total Particles suspended) to data based on the output from the natural DSS model To calculate.

The regional air quality model is based on the gas concentration, the aerosol concentration, and the dust concentration analyzed by the concentration calculation unit. The air quality is model 1 in the whole Asian region, model 2 in the East Asian region, model 3 in the Korean Peninsula region, and urban scale. The model 4 is then output to the 3D concentration distribution for each pollutant, the time series distribution for each pollutant at a specific point, the amount of deposition of each pollutant, and the vertical integration concentration for each pollutant.

Therefore, according to the air quality prediction and management system of the present invention, it is possible to comprehensively predict the future air quality change, so that it is important to establish the feasibility and air management policy of the project during environmental impact assessment and national policy establishment. There is an effect that can be provided.

In addition, according to the air quality prediction and management system of the present invention, it is possible to predict the air quality by processing the air quality modeling by processing meteorological observation data and air emissions data, it is possible to evaluate the results.

In addition, according to the air quality prediction and management system of the present invention, it is possible to predict the phenomenon affecting climate change because it can predict the air quality comprehensively to consider both yellow dust, artificial aerosol and air pollutants.

In addition, according to the air quality prediction and management system of the present invention, by using the air diffusion modeling technology to quantitatively identify the causal relationship between the source and the receptor has the effect of real-time monitoring and forecasting the air environment.

In addition, according to the air quality prediction and management system of the present invention, since the air diffusion modeling is performed by using the weather data and the emission data measured in real time, the impact of the source to the surroundings in real time, and by using the real-time forecast results By establishing an action plan such as emission reduction, it is possible to prevent the surrounding air environment in advance.

In addition, according to the air quality prediction and management system of the present invention, since the air quality is numerically displayed or the air quality advisory or alarm area is displayed in time series, the air quality can be easily predicted.

1 is a view of a conventional air quality management system,
2 is a main configuration of the air quality prediction and management system of the present invention,
3 is a detailed configuration diagram of the air quality prediction and management module of the present invention;
4 is a flow chart for explaining the air quality prediction and management method of the present invention;
5 is a diagram illustrating the distribution of ozone among the model results of the present invention;
6 is a view showing the dust concentration (PM ₁₀ ) of the model result of the present invention,
And,
FIG. 7 is a time-series diagram of a regional air quality model.

It is to be understood that the words or words used in the present specification and claims are not to be construed in a conventional or dictionary sense and that the inventor can properly define the concept of a term in order to describe its invention in the best possible way And should be construed in light of the meanings and concepts consistent with the technical idea of the present invention.

Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, "" module, "and" device "Lt; / RTI >

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

2 is a main configuration of the air quality prediction and management system of the present invention, as shown, the air quality forecast and management system (AIRFAMS: Air Quality Forecast And Management System) of the present invention is largely predicted meteorological field module 110 ), The air quality prediction and management module 200, the air quality index and the display module 140.

Each of these configurations can be combined in various ways depending on the purpose of the air management system and the desired results.

The air quality prediction and management system (AIRFAMS) of the present invention predicts the air quality in the air quality prediction management module 200 using the predictor listing module 110 provided by the Korea Meteorological Administration and displays it as the air quality index 130. At the same time, the display module 140 is configured to display the predicted air quality.

The predictor listing module 110 uses MM5 / UM 72 hours predictor listing, which is a weatherfield model provided by the Korea Meteorological Administration.

These predictor listings are intended to analyze local weather characteristics and to use them as input data for air quality modeling, and analyze wind field characteristics using MM5 for forecasts, WRF models, and CALMET models for obtaining detailed weather data for local regions. Although, the present invention will be described as using the MM5 / UM 72 hours predictor listing.

UM (Unified Model) is an integrated numerical model of the Korea Meteorological Agency. It is a model that can be executed in the same model from Global UM to Regional UM and even climate model. MM5 (Mesoscale Model 5) is a regional climate model. The Meteorological Prediction System of the Meteorological Administration, a Mesoscale Model developed by Anthes and Warner at the Pennsylvania State University in the early 70s, was used as a three-dimensional atmospheric dynamics model. Applicable to the micro-weather phenomenon.

Since its inception, it is a model that can be simulated through physical processes with the addition of multi-grid structure, non-statics, and four-dimensional data assimilation, along with improvements in physical processes such as radiation, convection, turbulence, radiation drag, and surface.

The air quality prediction management module 200 analyzes the data by modeling the data input from the predictor listing 110 and predicts the air quality and displays it as the air quality index 130 and at the same time predicts the display module 140. It is configured to indicate the air quality.

To this end, the air quality prediction management module 200 is configured to receive emission data, chemical transport models, meteorological field models, and satellite data to model regional air quality models.

Referring to the detailed configuration diagram of the air quality prediction and management module of the present invention of FIG. 3, the air quality prediction management module 200 includes an emission data unit 210, a chemical transport model 220, a weather field model 211, The satellite data 212 is input and modeled in the aerosol dynamics model 230 and the natural yellow dust model 240, and the modeled gas, aerosol, and yellow dust concentrations are determined by the air quality index (AQI) in the regional air quality model 270. Three-dimensional concentration distribution of each pollutant, time series distribution of each pollutant at a specific point, deposition amount of each pollutant, and vertical integration concentration of each pollutant are analyzed to generate an image.

In detail, the emission data unit 210 is configured to perform modeling with data of NOx, CO, NMVOC, PM ₁₀ , PM _2.5 , BC, OC, which are emitted pollutants.

Emission modeling is a process for generating emission data necessary for air quality diffusion modeling considering chemical reactions. Grid generated from the air policy support system developed by National Institute of Environmental Science and Technology to use SMOKE, which is currently open to EPA, in Korea. Emissions input data are used.

Among the data collected from these emission data sources 210, Korea's environmental standard is defined as NO _{2, which} is one of several kinds of nitrogen oxides, but the raw unit for calculating pollutant emissions is defined as NOx. In order to compare quality predictions with environmental agencies, the predicted NOx value should be converted to NO2.

In particular, since nitrogen oxides are emitted at various rates in various forms, they are severely changed by chemical reactions in the atmosphere. EIA most common Gaussian diffusion model, a non-reactive material to only prediction are designed to allow 100% conversion that assumption, and the total conversion method and acting volume of the NOx value prediction to the NO ₂ predicted to NO ₂ for which at The mass ratio method (PVMRM), the RIVAD / ARM3 chemical formula in CALPUFF, and the ozone limit method (OLM) should be used for conversion.

The chemical transport model 220 uses the CMAQ model (Community Multi-scale Air Quality Model) for air quality modeling, which was developed by the US Environmental Protection Agency (US EPA) to compensate for the shortcomings of the existing air pollution model. , Photochemical model, gas phase chemistry, and liquid chemistry model.

CMAQ can be modeled in various sizes from local scale to local scale modeling due to the variety of modeling areas, and can consider various pollutants at the same time. Aerosols, which are of great interest to the back, also have the feature to be calculated at the same time.

The weather field model 211 uses the MM5 / UM 72 hour predictor listing, which is a weather field model provided by the Korea Meteorological Administration, which performs modeling using corresponding past weather information and current weather information to predict future weather information.

These predictor listings are intended to analyze local weather characteristics and to use them as input data for air quality modeling, and analyze wind field characteristics using MM5 for forecasts, WRF models, and CALMET models for obtaining detailed weather data for local regions. Do it.

The satellite data unit 212 is a means widely used in environmental change monitoring research in a wide range of regions, and can detect various air pollutants and can quantitatively evaluate regional environment changes.

Aerosols in the atmosphere are environmental pollutants that can cause human respiratory diseases and directly or indirectly affect the global radiation balance, leading to a global climate change cooling effect.

In addition, physical and chemical changes in the atmosphere and long- and long-distance movements affect each other, which may lead to disputes between countries due to the relationship between air pollution sources and affected areas. Therefore, spatio-temporal observation and monitoring of air aerosols is essential for continuous air environment monitoring and air quality change prediction.

The aerosol dynamics model 230 generally shows that the dust particles remain dry near the source, but the phase changes by reacting with a gas such as SO ₂ or NO x during long distance transport (Zhang et al., 1994; Parungo et al. , 1995; Phadnis and Carmichael, 2000; Song and Carmichael, 2001; Jordan et al., 2003). In addition, the pseudo-first-order reaction is used to calculate the removal of gaseous matter from the surface of particles (Phadnis and Carmichael, 2000), and the interaction between gas and aerosols is carried out using the method of Jeong and Park (2008).

That is, the aerosol dynamics model 230 in the present invention was developed by Chang and Park (2004), and the gas phase reaction was performed by SOx and NH in the CIT (California Institute of Technology) chemical process (Russell et al., 1988). Liquid phase reactions, including ₃ , were performed using the Regional Acid Deposition Model, Walcek and Taylor, 1986; Chang et al., 1987, and include the dissolution of water-soluble contaminants and the liquid phase oxidation of SO2 ( Chang and Park, 2004).

Aerosols are calculated by dividing particles with a diameter of 0.02-74 mm into 12 intervals, and using dust, black carbon (BC), organic carbon (OC), secondary inorganic aerosol (SIA), and dust-SIA-BC-OC Six types of aerosols are considered, sea-salt, nucleation, condensation / evaporation, coagulation, wet and dry deposition, and hygroscopic growth. (hygroscopic growth) process. SCAPE2 (Simulating Composition of Atmospheric Particles at Equilibrium 2) was used to calculate the concentration of secondary contaminants (Kim et al., 1993a, b; Kim and Seinfeld, 1995) and included a heterogeneous reaction between gas phase and aerosol. (Pandis et al., 1993).

Dry deposition uses an inferential method that includes particle dropping rates (Wesely et al., 1985; Wesely, 1989; Seinfeld and Pandis, 1998; Park, 1998), and wet deposition uses the scavenging coefficient (Seinfeld and Pandis, 1998; Slinn and Slinn, 1980). Hygroscopic growth was calculated using the ZSR relationship (Zdanovskii, 1948; Stokes and Robinson, 1966; Chang and Park, 2004).

Accordingly, the aerosol dynamics model 230 receives data on dust, BC, OC, SIA, Dust-SIA-BC-OC, and sea-salt from the emission data base 210, and transports chemicals. Receive data from nucleation, condensation and evaporation, coagulation, wet and dry deposition, and hygroscopic growth from model 220, using SCAPE2 Calculate secondary pollutants.

In this case, the dry deposition using the inferential method, including the heterogeneous reaction between gaseous pollutants and aerosols, the scavenging coefficient for wet deposition, and the ZSR relation for hygroscopic growth.

The natural dust model 240 uses the Asian Dust Aerosol Model 2 (ADAM2), which is a short-term prediction model for the four seasons of yellow dust.

The yellow sand prediction model, ADAM2, was developed by Park and In (2003) and In and Park (2002), and was improved by Park and Lee (2004). Use ADAM2 improved by (2010). The source of yellow dust in Asia was divided into sand, ferns, loess, and mixed wasteland to determine the yellow dust occurrence area by statistical method, and obtain the weather conditions of yellow dust occurrence by each soil (Park and In, 2003), and the amount of yellow dust is critical. It is proportional to the quadratic of the frictional speed, and the amount of yellow sands is adjusted in consideration of the reduction factors caused by vegetation (Park and Lee, 1994). Improved yellow dust reduction using the vegetation index of spot / vegetation (NDVI) (Park et al., 2010a).

To this end, the natural yellow dust model 240 receives relevant data from the meteorological field model 211 and the satellite data unit 212 to analyze the yellow dust occurrence conditions.

The concentration calculation unit 260 operates to calculate a gas concentration, an aerosol concentration, and a dust concentration using data analyzed by the aerosol dynamic model 230.

To this end, the concentration calculation unit 260 includes a gas concentration calculation unit 261 for calculating a gas concentration, an aerosol concentration calculation unit 262 for calculating an aerosol concentration, and a dust concentration calculation unit 263 for calculating a dust concentration. do.

The gas concentration calculation unit 261 calculates concentrations of SO ₂ , NO _x , NH ₃ , and O ₃ based on the analysis data calculated from the aerosol dynamics model 230, and the aerosol concentration calculation unit 262 uses BC and OC. The concentrations of aerosols in the form of, PM ₁₀ , PM _2.5 , SO ₄ , No ₃ , and NH ₄ in ionic form are calculated.

Here PM ₁₀ (Particulate Matter Less than 10㎛) means aerosol of less than 10㎛ size.

Further, to produce a dust concentration of the dust concentration calculation section 263 is a natural DSS model PM _10, PM _{2 .5} with TSP (Total Particles suspended) to data based on the calculated from (240).

Here, TSP (Total suspended Particles) is referred to as total suspended dust, total suspended particulate matter or total particulate matter, and generally refers to all suspended dust of 50 μm or less. If the particle size is 10㎛ or more, the health of the human body is less affected, and the appearance is often affected.

The regional air quality model 270 is configured to output air quality in four model regions and resolutions according to the gas concentration, the aerosol concentration, and the dust concentration analyzed by the concentration calculation unit 260, and the three-dimensional concentration for each pollutant. The distribution, time series distribution of each pollutant at a specific point, deposition amount of each pollutant, and vertical integration concentration of each pollutant are analyzed to generate an image.

Four models of this air quality are shown in Table 1.

model name Area and resolution Model 1 All Asian Area, 27km × 27km Model 2 East Asia Zone, 9km × 9km Model 3 Korean Peninsula Area, 3km × 3km Model 4 Urban area such as Gyeongin area (user selectable), 1km × 1km

In other words, model 1 is 27km × 27km in size, and model 2 is 9km × 9km in East Asia, model 3 is 3km × 3km in size, and model 4 is 1km × 1km in size. Represent city areas (user selectable), such as region.

Area analysis of such a model is performed first by M1 model (Asian region) of 27Km x 27Km grid, and receives the result as input data, M2 model (East Asian region) of 9Km x 9Km grid is received, and then the result is 3Km. The M3 model (Korean Peninsula region) of the x 3Km grid is performed, and then the M4 model (urban scale) of the 1Km x 1Km grid is received.

As described above, the regional air quality model 270 is configured to display not only concentrations of gaseous pollutants, aerosols, and yellow sands, but also deposition amounts and vertical integration concentrations in various formats such as horizontal distribution and time series. .

That is, in order to enable the applicant to share the data through the worldwide network through the WEBAIR (global air quality display and management system) website as well as the AIRFAMS website.

For this purpose, the model results centered on three-dimensional air pollutants, aerosol concentration contours, point-by-point air pollutant concentrations, time series of aerosol concentrations, air quality index (AQI) and advisory or alarm zones, and point (M4) centered on user requirements. Display it through PC, notebook, mobile phone, PDA, Tablet, etc.

These results are illustrated in Figures 5-7.

5 is a diagram illustrating a three-dimensional concentration distribution of ozone among the model results of the present invention, FIG. 6 is a three-dimensional distribution diagram of the dust concentration (PM ₁₀ ) among the model results of the present invention, and FIG. The air quality model is shown in time series.

The Air Quality Index (AQI) presents several types of air quality, an example of which is shown in Table 2.

AQI Meaning Health effects 0 to 50 Air pollution-related disease group will not affect the level Good 51-100 Levels that can cause minor effects in chronic exposure to the patient group Moderate 101-150 Levels that can cause adverse effects on patients and sensitive groups Unhealthy for Sensible groups 151-200 Causes harmful effects on patients and sensitive groups (children, the elderly, etc.), the level at which the general public may experience health discomfort Unhealthy 201-300 Serious effects on acute exposure to patients and sensitive groups, mild to normal levels Very unhealthy 301 ~ 500 The level at which emergency measures may occur in patients and sensitive groups or may cause harmful effects to the general population. Hazardous

Referring to Table 2, if the AQI is 0 to 50, the health index is “good”, which means that the effect is not induced even in the air pollution related disease group, and in the case of 51 to 100, the “normal” chronic exposure to the patient group. In the case of 101-150, it means the level that can cause harmful effects on patients and sensitive groups.

In addition, in the case of 151 to 200, "bad" causes harmful effects on patients and sensitive groups (children, the elderly, etc.), and means that the general public may experience health discomfort, and in the case of 201 to 300, "very bad" "Acute exposure to patients and sensitive groups may cause serious effects, levels of weakness to the general population may be induced, and in the case of 301 to 500," danger "may result in first aid to patients and sensitive groups, or Means a level at which harmful effects can be caused.

The type of such air quality is illustrated as an example, and may be classified and displayed differently according to a model.

The display module 140 generates an image by analyzing a three-dimensional concentration distribution for each pollutant, a time series distribution for each pollutant at a specific point, a deposition amount of each pollutant, and a vertical integration concentration for each pollutant. Divide by (Advisory) and Warning (Warning) and divide the model area into four areas.

As described above, the air quality prediction and management system of the present invention is similar to the concept of predicting and displaying air quality in comparison with the existing air quality forecasting system in Korea, but the actual model area, model execution history, and data presentation are shown in Table 3 below. There are differences as shown in Table 4.

Table 3 compares the model area of the present invention in Table 2 with the Korea Air Quality Forecasting System (KAQ), and Table 4 compares the differences in model performance and data presentation.

AIRFAMS KAQ M1
(27kmx27km) Lattice 320 x 250
Including all yellow dust sources about 1/2 of x-axis, about 2/3 of y-axis
Not including some of the yellow dust sources M2
(9kmx9km) Lattice 283 x 253
Including areas of pollution in eastern China Korean Peninsula only M3
(3kmx3km) Lattice 214 x 223
Korean Peninsula Area Including metropolitan area M4
(1kmx1km) Including metropolitan areas none

Comparing the M1 model in Table 3, the present invention includes all the yellow dust sources and the Korean Air Quality Forecasting System (KAQ) is composed of regions that do not include some of the sources of the yellow dust.

Since the present invention predicts and manages comprehensive air quality using the yellow dust prediction model, it is the principle that all yellow dust sources around the Korean peninsula are included.

In addition, when looking at the M2 model, the present invention is intended to include the region of pollution in eastern China, but the Korea Air Quality Forecasting System (KAQ) includes only the Korean Peninsula, so that it is not possible to comprehensively predict the air quality.

division AIRFAMS KAQ Forecast weather UM Office 72 hours
Predictive weather field use RDAPS weather station provided by the Korea Meteorological Administration Yellow Sand Prediction Model Use of Asian Dust Aerosol Model version 2 (ADAM2) Module
=> Proven model in Asian dust in various papers Use CMAQ-supplied modules
Express results -In addition to the concentrations of gaseous pollutants, aerosols and yellow dusts by component, the amount of deposition and vertical integration are displayed in various formats such as horizontal distribution and time series.
-Express through global network through WEBAIR (global air quality display and management system) website as well as AIRFAMS homepage -PM ₁₀ , ozone (O ₃ ), NO ₂ , temperature field




User defined area setting function Custom Area Centering feature in M4
has exist none Air Quality Index /
Advisory Alarm Area Display Function Provide air quality index
Provide warning / alarm area none

Referring to Table 4, the predicted meteorological field of the present invention uses the UM72 hour predicted meteorological field provided by the Korea Meteorological Administration, but the Korea Air Quality Forecasting System (KAQ) uses the RDAPS meteorology provided by the Korean Meteorological Administration, and the present invention uses the yellow dust prediction model. In various papers, the Asian Dust Aerosol Model version 2 (ADAM2) module, which is used by the Korea Meteorological Agency, which has been proven in Asian dust, is used, but the Korea Air Quality Forecasting System (KAQ) uses the CMAQ provision module.

In addition, when comparing the result display screen, the present invention expresses not only concentrations of gaseous pollutants, aerosols, yellow dusts, etc., but also deposition amounts and vertical integration concentrations in various formats such as horizontal distribution and time series, and the AIRFAMS homepage. Although it is configured to be displayed through a worldwide network through WEBAIR (Global Air Quality Display and Management System) website, Korea Air Quality Forecasting System (KAQ) simply provides PM ₁₀ , ozone (O ₃ ), NO ₂ , and temperature field. It is configured to.

In the user defined area setting function, the present invention has a user defined area center setting function in the M4 model, but there is no such function in the Korea Air Quality Forecasting System (KAQ). While providing an air quality index and a warning / alarm area, the Korea Air Quality Forecasting System (KAQ) does not have this function.

Hereinafter, the air quality prediction and management method of this invention is demonstrated using the structure mentioned above.

4 is a flowchart illustrating an air quality prediction and management method according to the present invention. As shown in FIG. 4, the air quality prediction and management method according to the present invention is a predicted meteorological field in a region M4 (city area) desired by a selected user. A model is performed (S310).

The prediction weather field module 110 uses MM5 / UM 72 hour predictor listing, which is a weather field model provided by the Korea Meteorological Administration.

These predictor listings are intended to analyze local weather characteristics and to use them as input data for air quality modeling, and analyze wind field characteristics using MM5 for forecasts, WRF models, and CALMET models for obtaining detailed weather data for local regions. can do

Therefore, in step S320, detailed weather data and emission data of the region are input using the MM5 / WRF model to perform weather field modeling.

At this time, when there is the latest available emission data, the updated latest emission data is input.

Subsequently, in step S330, the chemical transport model 220 is analyzed using an atmospheric chemical model such as CMAQ and CB5 to input data into the aerosol dynamics model 230.

 In order to perform step S330, the Asian emissions of INTEX-B (2006) and the Korean Peninsula emissions of CAPSS 2007 are analyzed and input to the chemical transport model 220.

When the analysis of the chemical transport model 220 is completed in step S330, the related data is transferred to the aerosol dynamics model 230 to analyze the dust particles (S340).

The aerosol dynamics model 230 shows that the gas phase reaction includes SOx and NH ₃ in the California Institute of Technology (CIT) chemical process (Russell et al., 1988), and the liquid phase reaction includes the Regional Acid Deposition Model, Walcek. and Taylor, 1986; Chang et al., 1987), to analyze the dissolution of water-soluble contaminants and the liquid phase oxidation of SO2.

In addition, the meteorological field model performed in step S320 is analyzed for yellow dust in step S350.

To this end, the natural dust model 240 receives relevant data from the weather field model 211 and the satellite data unit 212 and performs modeling using the yellow dust prediction model of ADAM2.

The natural dust model 240 uses the Asian Dust Aerosol Model 2 (ADAM2), which is a short-term prediction model for the four seasons of yellow dust.

The ADAM2 modeling method, which is a yellow dust prediction model, uses the statistical method to determine the yellow dust occurrence area and to determine the yellow dust occurrence condition by using the sand, fern, loess, and mixed wasteland in Asia. , 2003), the amount of yellow dust is proportional to the fourth power of the critical friction rate, and the amount of yellow dust is adjusted in consideration of the reduction factors caused by vegetation (Park and Lee, 1994). Improved yellow dust reduction using the vegetation index of spot / vegetation (NDVI) (Park et al., 2010a).

When the yellow sand modeling is completed in step S350, the concentration is calculated in the concentration calculation unit 260 by using the aerosol dynamics modeling in step S340 and the yellow dust prediction modeling in step S350, and a model result is generated (S360).

In step S360, data on SO ₂ , NO _x , NH ₃ , O ₃ as gaseous pollutants, and dust aerosol, ionic form SO ₄ , NO _{3 ,} NH ₄ , BC, OC, PM ₁₀ , PM _{2 as} aerosols generating a model results such as _.5, sea-salt, and dust to and generates results for PM _10, PM _{2 .5} and TSP.

In operation S370, an image to be displayed is generated using the generated model result.

The image is generated by analyzing the three-dimensional concentration distribution of each pollutant, time series distribution of each pollutant at a specific point, deposition amount of each pollutant, and vertical integration concentration of each pollutant in the air quality model 270 by region. Create an image.

In addition, at step S370, an image to be displayed is generated and at the same time, an air quality index (AQI) can be calculated and presented.

The Air Quality Index (AQI) presents several types of air quality. An example of such an air quality index is shown in Table 2.

 The data on each pollutant, air quality index, and warning / alarm area generated in step S370 are displayed so that they can be displayed through the global network through WEBAIR (global air quality display and management system) homepage as well as the AIRFAMS homepage (S380). .

As described above, the air quality prediction and management system of the present invention uses the emission data, the chemical transport model, and the meteorological field model. However, the air quality is comprehensively estimated and managed using the yellow dust prediction model. Prediction management is now possible.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art.

110: Forecaster listing 130: Air quality index (AQI)
140: display module 200: air quality prediction and management model
210: emission data book 211: meteorological field model
212: satellite data unit 220: chemical transport model
230: Aerosol dynamics model 240: Natural yellow sand model
260: concentration calculation unit 261: gas concentration calculation unit
262: aerosol concentration calculation unit 263: dust concentration calculation unit
270 Regional Air Quality Models

Claims (10)

In the air quality prediction and management system through modeling techniques,
A predictor listing module for predicting future weather information by performing modeling using corresponding past weather information and current weather information using the MM5 / UM 72 hour predictor listing provided by the Korea Meteorological Administration;
Emission data base and chemical transportation, which collects modeling data of the predictor listing module and pollutants NOx, CO, NMVOC, PM ₁₀ , PM _2.5 , BC, and OC and models emissions using grid emission data. An aerosol dynamics model for modeling aerosols by receiving a model and satellite data, a natural yellow model for receiving yellow dust modeling using the Asian Dust Aerosol Model 2 (ADAM2) by receiving data from the predictor listing module and the satellite data unit, and Concentration calculation unit for calculating gas concentration, aerosol concentration, and dust concentration as modeling data of the aerosol dynamics model and the natural dust model, and the concentration of each air pollutant calculated by the concentration calculation unit to display the air quality locally. An air quality prediction management module including an air quality model for each region for imaging the data and calculating an air quality index; and
And a display module configured to display an advisory or alarm area based on the image generated by the air quality prediction management module and the air quality index.
The regional air quality model
According to the gas concentration, the aerosol concentration, and the dust concentration analyzed by the concentration calculation unit, the air quality is output to the model 1 of the whole Asian region, the model 2 of the East Asian region, the model 3 of the Korean peninsula region, and the model 4 region of the city scale. Air Quality Prediction and Management System characterized by generating images of three-dimensional concentration distribution for each pollutant, time series distribution for each pollutant at a specific point, deposition amount of each pollutant, and vertical integration concentration for each pollutant .
delete delete delete The method according to claim 1,
The chemical transport model
Air quality prediction and management system using CMAQ model to classify photochemical model, gas phase chemistry, and liquid chemistry model.
The method according to claim 1,
The aerosol dynamics model
Receive data on dust, BC, OC, SIA, Dust-SIA-BC-OC and sea-salt from the emission data section, nucleation, condensation and evaporation from the chemical transport model Air quality prediction and management system that receives data of condensation / evaporation, coagulation, wet and dry deposition, and hygroscopic growth, and calculates secondary pollutants using SCAPE2.
delete The method according to claim 1,
The concentration calculation unit
A gas concentration calculating part calculating a gas concentration, an aerosol concentration calculating part calculating an aerosol concentration, and a dust concentration calculating part calculating a dust concentration, wherein the dust concentration calculating part is a PM based on the data calculated from the natural yellow dust model. ₁₀ , Air quality prediction and management system for calculating dust concentrations of PM _2.5 and Total suspended Particles (TSP).

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