KR20240033466A - System and method for analyzing atmospheric environments of urban area - Google Patents

Abstract

The present invention relates to an urban air environment analysis system and an urban air environment analysis method using the same. The urban air environment analysis system according to the present invention includes a geographic information receiver that receives building information and road information for at least one area of the city; A weather information receiver that receives weather information and fine dust information for each region, and a weather information receiver that analyzes the characteristics of each weather factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information. An analysis unit, a meteorological correlation analysis unit that analyzes the correlation between meteorological factors and fine dust concentration for each region based on the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit, the reception A city that calculates the urban structure parameters for a certain area for each region based on the building information and road information, and analyzes the correlation between the calculated urban structure parameters of each region and the fine dust concentration in the region. Fine dust impact that calculates the influencing factors that affect the fine dust concentration in the area for each region based on the analysis information analyzed by the structural correlation analysis unit, the meteorological correlation analysis unit, and the urban structure correlation analysis unit. Includes analysis section.

Description

Urban air environment analysis system and urban air environment analysis method using the same {SYSTEM AND METHOD FOR ANALYZING ATMOSPHERIC ENVIRONMENTS OF URBAN AREA}

The present invention relates to an urban air environment analysis system and an urban air environment analysis method using the same. More specifically, the present invention relates to an urban air environment analysis system and a method for analyzing the urban air environment using the same. More specifically, the present invention relates to an urban air environment analysis system that can analyze the air environment characteristics of an urban area based on geographical information, meteorological information, and fine dust concentration information of the urban area. It relates to an existing urban air environment analysis system and a method of analyzing the city air environment using it.

Fine particulate matter is one of the air pollutants and refers to dust that is so small that it is invisible to the human eye. Fine dust can be divided into PM 10, which is 10㎛ or less in diameter, and PM2.5, which is 2.5㎛ or less. The main cause of fine dust is emissions from automobiles and power plants, and if fine dust enters the lungs of humans or animals during the breathing process, it can cause decreased lung function, lung disease, and heart disease. In particular, ultrafine dust (PM2.5) is problematic in that it can directly penetrate blood vessels through the alveoli.

The causes of dust are divided into natural and artificial causes, but most of them are artificial. Most artificial sources are caused by fuel combustion, and emissions from boilers, automobiles, and power generation facilities are the main sources. In addition, dust scattered from construction sites, roads, etc. also accounts for a large amount.

The main source of fine dust (PM2.5) is secondary pollutants generated by the atmospheric reaction of primary pollutants emitted from automobiles and thermal power plants, etc., and is mainly composed of sulfates, nitrates, and organic carbon.

In particular, in winter, when the use of heating fuel increases both domestically and internationally, pollutant emissions increase and the occurrence of high concentration phenomena increases. Pollutants imported not only domestically but also from abroad affect the domestic air. According to research results, it is estimated that approximately 30-50% of air pollutants are imported from overseas.

In urban areas, many studies have shown that indoor air quality in buildings is affected by pollutants emitted from the roads. Pollutants emitted from vehicles on the road can have a significant impact on the indoor fine dust concentration in buildings around the road. In actual urban areas, roads and buildings are arranged in various and complex shapes. This structure within the city is a major factor in air flow and the spread of pollutants. The importance of monitoring urban air quality is emerging through studies analyzing the impact of fine dust on urban populations.

Korean patent application: 10-2021-0002461 (2021.01.08) Korean patent application 10-2020-0131762 (2020.10.13)

The present invention provides an urban atmospheric environment analysis system that can analyze atmospheric environmental characteristics of urban areas based on geographic information, meteorological information, and fine dust concentration information of urban areas, and an urban atmospheric environment analysis method using the same.

The present invention provides an urban air environment analysis system that can provide a basis for determining whether inflow from outside and local pollution contribute to the concentration of fine dust in urban areas and an urban air environment analysis method using the same. do.

The present invention provides an urban air environment analysis system that can analyze the impact of the geographical structure of an urban area on the fine dust concentration in the area and a method for analyzing the urban air environment using the same.

The present invention provides an urban air environment analysis system that provides fine dust analysis results that can be used as a basis for urban design and environment-related policy decisions in the target area, and an urban air environment analysis method using the same.

The urban air environment analysis system according to the present invention includes a geographic information receiver that receives building information and road information for at least one area of the city, a weather information receiver that receives weather information and fine dust information for each area, and the receiver. A meteorological analysis unit that analyzes the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the weather information and fine dust information provided, and the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit. A meteorological correlation analysis unit that analyzes the correlation between meteorological factors and fine dust concentration for each region based on the above, and an urban structure for a certain area for each region based on the received building information and road information In the urban structure correlation analysis unit, which calculates the parameters and analyzes the correlation between the calculated urban structure parameters of each region and the fine dust concentration in the area, and the weather correlation analysis unit and the urban structure correlation analysis unit It includes a fine dust impact analysis unit that calculates influencing factors that affect the fine dust concentration in each region for each region based on the analyzed analysis information.

In one embodiment, the urban air environment analysis system may further include a similarity analysis unit that analyzes temporal and spatial similarities between fine dust concentrations in each region based on the received fine dust information.

In one embodiment, the similarity analysis unit calculates the Pearson correlation coefficient (R) between the fine dust concentrations in each region, and determines whether the trend of change in fine dust concentration between comparison regions matches based on time. can be analyzed.

In one embodiment, the similarity analysis unit may calculate the coefficient of divergence (COD) between the fine dust concentrations in each region and analyze whether the fine dust concentrations in the comparison regions are spatially consistent.

In one embodiment, the geographic information receiver receives building information for each region from a Geographic Information System and receives road information for each region from an Environmental Geographic Information Service. can do.

In one embodiment, the at least one area may correspond to an air quality measurement area where an air quality measurement device is installed in the city.

In one embodiment, the characteristics of each meteorological factor include at least one of the main wind direction, frequency of wind direction, frequency of wind speed magnitude, average wind speed, wind speed fluctuation, and average temperature calculated on a daily, monthly, and annual basis for the corresponding area, The concentration characteristics of the fine dust may include at least one of the average fine dust (PM10) concentration and the average ultrafine dust (PM2.5) concentration calculated on a daily, monthly, and annual basis for the corresponding area.

In one embodiment, the weather correlation analysis unit calculates the correlation coefficient between wind speed and fine dust (fine dust and ultrafine dust) concentration, and calculates the correlation coefficient between temperature and fine dust (fine dust and ultrafine dust) concentration. It can be calculated.

In one embodiment, the urban structure parameters include road area ratio and building volume ratio, and the urban structure correlation analysis unit calculates the road area ratio occupied by the road in the horizontal x km × y km area centered on a point in the area. And, the building volume ratio occupied by the building can be calculated in the horizontal x km x y km area and the vertical z m height space.

In one embodiment, the urban structure correlation analysis unit calculates the correlation coefficient between the road area ratio and the average fine dust (PM10) concentration and the correlation coefficient between the road area ratio and the average ultrafine dust (PM2.5) concentration in each region, The correlation coefficient between the building volume ratio and the average fine dust (PM10) concentration and the correlation coefficient between the building volume ratio and the average ultrafine dust (PM2.5) concentration for each region can be calculated.

In one embodiment, the fine dust impact analysis unit is based on weather factors having a positive or negative correlation based on the correlation coefficient calculated by the weather correlation analysis unit and the correlation coefficient calculated by the urban structure correlation analysis unit. By using urban structure parameters that have a positive or negative correlation, influencing factors that affect the fine dust concentration in the area can be calculated.

The urban air environment analysis method according to the present invention includes the steps of a geographic information receiving unit receiving building information and road information for at least one area of the city, and a weather information receiving unit receiving weather information and fine dust information for each area. Step, a weather analysis unit analyzing the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information, a weather correlation analysis unit analyzing the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information, Analyzing the correlation between meteorological factors and fine dust concentration for each region based on the characteristics of each meteorological factor and the concentration characteristics of fine dust, the urban structure correlation analysis unit based on the received building information and road information A step of calculating urban structure parameters for a certain area for each region, analyzing the correlation between the calculated urban structure parameters of each region and the fine dust concentration in the region, and the fine dust impact analysis unit performs the weather It includes the step of calculating influencing factors that affect the fine dust concentration in each region for each region based on the analysis information analyzed in the correlation analysis unit and the urban structure correlation analysis unit.

In one embodiment, the urban atmospheric environment analysis method may further include a step of a similarity analysis unit analyzing temporal and spatial similarities between fine dust concentrations in each region based on the received fine dust information.

In one embodiment, the urban air environment analysis method searches for an area where the similar area estimation unit has a factor value that is the same as or within a preset range of the influence factor affecting the fine dust concentration of the analyzed area within the corresponding urban area. Additional steps may be included.

As described above, the urban air environment analysis system according to the present invention and the urban air environment analysis method using the same can analyze the air environment characteristics of urban areas based on geographical information, meteorological information, and fine dust concentration information of urban areas. there is.

The urban air environment analysis system according to the present invention and the urban air environment analysis method using the same can provide a basis for determining whether inflow from outside contributes to the concentration of fine dust in urban areas and whether local pollution occurs. .

The urban air environment analysis system according to the present invention and the urban air environment analysis method using the same can analyze the impact of the geographical structure of an urban area on the concentration of fine dust in the area.

The urban air environment analysis system according to the present invention and the urban air environment analysis method using the same can provide fine dust analysis results that can be used as a basis for urban design and environment-related policy decisions in the target area.

1 is a diagram illustrating an urban air environment analysis system according to an embodiment of the present invention.
Figure 2 is a diagram showing the configuration of the urban air environment analysis system of Figure 1
Figure 3 is a diagram showing a satellite image of the analysis target area and an example of distribution according to sub-division land cover classification of the area.
Figure 4 is a diagram showing an example of a wind rose graph calculated based on the wind measured at a ground weather measurement station in the analysis target area according to an embodiment of the present invention.
Figure 5 is a graph showing the monthly and yearly distribution of wind speed and temperature measured at a ground weather measurement station in the analysis target area according to an embodiment of the present invention.
Figure 6 is a graph showing the concentration of fine dust and ultrafine dust measured at ground air quality measurement stations in the analysis target area according to an embodiment of the present invention as a distribution graph for each measurement station.
Figure 7 is a graph showing the average fine dust concentration and average ultrafine dust concentration measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention by month and year.
Figure 8 is a graph showing the correlation and spatial variability distribution between fine dust concentration and ultrafine dust concentration measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention.
Figure 9 is a graph showing the correlation between wind speed, temperature, fine dust concentration, and ultrafine dust concentration measured at a ground measurement station in the analysis target area according to an embodiment of the present invention.
Figure 10 is a graph showing the seasonal concentration of fine dust measured at a ground measurement station in the analysis target area according to an embodiment of the present invention as a pollution rose graph.
Figure 11 is a graph showing the seasonal concentration of ultrafine dust measured at a ground measurement station in the analysis target area according to an embodiment of the present invention as a pollution rose graph.
Figure 12 is a graphical representation of the correlation between fine dust concentration, ultrafine dust concentration, and urban structure parameters measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention.
Figure 13 is a flow chart illustrating a method for analyzing urban air environment according to an embodiment of the present invention.

Hereinafter, specific details for implementing the urban air environment analysis system and the urban air environment analysis method using the same according to the present invention will be described.

1 is a diagram illustrating an urban air environment analysis system according to an embodiment of the present invention.

Referring to FIG. 1, the urban air environment analysis system 130 may be connected to a geographic information server 110, a weather information server 120, etc. through a network.

The geographic information server 110 may include a geographic information system (GIS) and an environmental geographic information service (EGIS). The geographic information server 110 includes a server that collects, stores, manipulates, and analyzes geographic information.

For example, the geographic information server 110 includes a geographic information database, and the geographic information database is graphic data in the form of a map or drawing indicating the location and shape of various geographical phenomena such as administrative boundaries, roads, and shapes of buildings. Data), includes descriptive or attribute data in the form of numbers and letters that describe spatial characteristics such as population, building area, and place names.

The urban air environment analysis system 130 may connect to the geographic information server 110 and receive geographic information for a specific area.

The weather information server 120 includes a server that collects, analyzes, stores, and distributes weather information. The weather information server 120 may store weather information and air quality information received from a weather station where a weather observation device is installed and an air quality measurement station where an air quality measurement device is installed. For example, the weather information server 120 may provide weather information measured by the Automated Synoptic Observing System (ASOS), the Air Quality Monitoring System (AQMS), or the Road Air Quality Monitoring System. , RAQMS) can receive and store air quality information measured. In one embodiment, weather information may include temperature, wind speed, wind direction, relative humidity, precipitation, etc. Air quality information may include fine dust concentration, ultrafine dust concentration, ozone concentration, and other gas concentrations (SO2, NOx, CO, CO2, O3, THC, etc.).

The urban air environment analysis system 130 can connect to the weather information server 120 and receive weather information for a specific area.

The urban atmospheric environment analysis system 130 analyzes the characteristics of each weather factor and the concentration characteristics of fine dust for each region based on the received weather information, and determines the correlation between the weather factors and fine dust concentration. Each can be analyzed. In addition, the urban air environment analysis system 130 can analyze the correlation between the urban structure parameters of each region and the fine dust concentration of the region based on the received weather information and geographic information. Based on the analysis results, the urban air environment analysis system 130 can calculate influencing factors that affect the fine dust concentration in the area.

The urban air environment analysis system 130 may display the analysis results on the screen under user control.

Hereinafter, the process of analyzing the atmospheric environment characteristics of an urban area in the urban atmospheric environment analysis system 130 based on geographic information, weather information, and fine dust concentration information of the urban area will be described.

FIG. 2 is a diagram showing the configuration of the urban air environment analysis system of FIG. 1.

Referring to FIG. 2, the urban air environment analysis system 130 includes a network unit 202, a memory 204, a weather information receiver 206, a geographic information receiver 208, a fine dust correlation analysis unit 210, and It includes a fine dust impact analysis unit 220, and the fine dust correlation analysis unit 210 includes a weather analysis unit 212, a similarity analysis unit 214, a weather correlation analysis unit 216, and an urban structure correlation analysis unit. Includes part 218.

The network unit 202 is connected to an external network and can be connected to the geographic information server 110 or the weather information server 120 through an external network. The memory 204 may store data used in the operation of the urban air environment analysis system 130, data used to analyze air environment characteristics, or analysis result data.

The weather information receiver 206 receives weather information (e.g., temperature, wind speed, wind direction, relative humidity, precipitation, etc.) and fine dust (e.g., fine dust) for at least one area of the city from the weather information server 120. Receive information on dust (PM10) concentration and ultrafine dust (PM2.5) concentration. In one embodiment, the weather information receiver 206 receives at least one analysis target area (for example, an area of a city) from the user, and sends the weather information and fine-grained information for the analysis target area to the weather information server. You can request it at (120).

The geographic information receiving unit 208 requests building information and road information for the at least one analysis target area from the geographic information server 110, and provides building information for each area (e.g., building area, building elevation, building location, etc.) and road information (e.g., road area, road location, etc.). The weather information receiving unit 206 and the geographic information receiving unit 208 may each receive information about the same analysis target area. In one embodiment, the geographic information receiver 208 may receive building information from a spatial information system (GIS) and road information from an environmental spatial information service (EGIS).

In one embodiment, the analysis target area may correspond to a measurement area where a weather observation device or air quality measurement device is installed in the city. For example, the area to be analyzed may correspond to the Synoptic Meteorological Observatory (ASOS), Urban Air Quality Monitoring Service (AQMS), or Roadside Air Quality Monitoring Station (RAQMS) area.

The fine dust correlation analysis unit 210 analyzes the correlation between meteorological factors and urban structure and fine dust concentration.

The weather analysis unit 212 analyzes the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information.

In one embodiment, the characteristics of each meteorological factor include at least one of the main wind direction, frequency of wind direction, frequency of wind speed magnitude, average wind speed, wind speed fluctuation, and average temperature calculated on a daily, monthly, and annual basis for the area, and The dust concentration characteristics may include at least one of the average fine dust (PM10) concentration and the average ultrafine dust (PM2.5) concentration calculated on a daily, monthly, and annual basis for the relevant area.

Below, for convenience of explanation, the hourly average wind speed, wind direction, and temperature measured at Suwon ASOS (ASOS119) and eight Suwon urban air quality measurement stations during a preset period (e.g., the last 10 years (2011~2020)) are provided. and characteristics of each meteorological factor, correlation between meteorological factors and fine dust concentration in the area, and urban structure parameters based on hourly fine dust (PM10) and ultrafine dust (PM2.5) concentrations measured at roadside air monitoring stations. We will explain the process of analyzing the correlation between and the fine dust concentration in the area.

Figure 3 is a diagram showing a satellite image of the analysis target area and an example of distribution according to sub-division land cover classification of the area.

Referring to Figure 3, (a) in Figure 3 shows the locations of the ground weather measurement station (ASOS, red dot) and the ground air quality measurement station (AQMS and RAQMS, yellow dot) located within Suwon-si, Gyeonggi-do, indicated by the red area. It is a drawing. The meteorological information used in the analysis was wind speed, wind direction, and temperature from Suwon ASOS (ASOS 119), and the air quality measurement data was fine dust concentration (PM10) and ultrafine dust concentration measured at eight air quality measurement stations located in Suwon City. (PM2.5). Figure 3(b) is a diagram showing detailed land cover information around Suwon-si, Gyeonggi-do. In one embodiment, urban structure parameters for a certain area around a measurement station can be calculated using building elevation information from a geographic information system (GIS) and detailed land cover information from an environmental spatial information service (EGIS).

Figure 4 is a diagram showing an example of a wind rose graph calculated based on the wind measured at a ground meteorological station in the analysis target area according to an embodiment of the present invention, and Figure 5 is an analysis target according to an embodiment of the present invention. This is a graphical representation of the monthly and yearly distribution of wind speed and temperature measured at ground meteorological stations in the region.

Referring to Figure 4, you can see the wind direction characteristics analyzed based on the wind direction measured at Suwon ASOS from January 1, 2011 to December 31, 2020. For example, the main winds at the ASOS branch in Suwon were west and northwest, and the frequencies of the two wind directions were analyzed to be 15.6% and 14.2%, respectively. The frequency of each section according to the size of the wind speed is analyzed to be mostly 1.5~3.3 ms ^-1 (44.6%) and 0.4~1.5 ms ^-1 (40.0%) based on the viewport anemometer section.

Referring to Figure 5, you can see the monthly and yearly fluctuation characteristics of Suwon ASOS wind speed and temperature from January 1, 2011 to December 31, 2020.

Referring to Figure 5 (a), as a result of monthly analysis of Suwon ASOS wind speed from 2011 to 2020, the average wind speed tended to be high in spring, and the monthly average wind speed was 1.9ms ^-1 (October) ~ It is analyzed to be distributed between 2.4 ms ^-1 (April).

As a result of analyzing the Suwon ASOS temperature by month in the same manner as the wind speed, the distribution of the average temperature clearly showed a tendency to be high in summer and low in winter, with the average monthly temperature ranging from -1.7°C (January) to 27.0°C (8 It is analyzed to have a range between months.

Referring to (b) in Figure 5, the size of variation within the temperature data classified by month is small in summer and relatively large in spring and fall, which means that the large daily temperature difference in spring and fall is reflected in the range of temperature variation for each month. It can be analyzed as

Referring to Figure 5 (c) and Figure 5 (d), as a result of classifying wind speed and temperature by year, the average wind speed and average temperature were 2.03 ms ^-1 (2013) to 2.18 ms ^-1 (2020), respectively. ), it is analyzed to fluctuate between 12.9°C (2011) and 14.5°C (2016), and there is no clear annual trend.

Figure 6 is a diagram showing the concentration of fine dust and ultrafine dust measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention as a distribution graph for each measurement station, and Figure 7 is a graph showing the concentration of fine dust and ultrafine dust measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention. This is a graph showing the average fine dust concentration and average ultrafine dust concentration measured at ground air quality measurement stations in the analysis target area by month and year.

Figure 6 shows seven urban air quality monitoring stations located within Suwon City from January 1, 2011 to December 31, 2020 [Sinpung-dong AQMS (ID: 131111), Ingye-dong AQMS (131112), Gwanggyo-dong AQMS (131113), Yeongtong-dong. Fine dust (PM10) concentration and ultrafine dust (PM2. 5) Shows a box plot of concentration.

During the analysis period, the average fine dust (PM10) and ultrafine ^dust (PM2.5) concentrations at the urban air quality monitoring station ranged from 40.3 μgm ^-3 (Homaesil-dong AQMS) to 55.3 μgm ^-3 (Dongsuwon RAQMS) and 21.9 μgm ^{-3, respectively.} (Homaesil-dong AQMS) ~ 25.6 μgm ^-3 (Dongsuwon RAQMS). The average fine dust concentration was found to be lowest at the Homaesil-dong AQMS branch and highest at the Dongsuwon RAQMS branch.

The standard deviation ranges of fine dust (PM10) and ultrafine dust (PM2.5) concentrations are 25.4 μgm ^-3 (Homaesil-dong AQMS) ~ 35.7 μgm ^{- 3} (Dongsuwon RAQMS) and 15.5 μgm ^-3 (Homaesil-dong AQMS) ~ 20.5, respectively. Analyzed by μgm ^-3 (hyperchromatic AQMS). It is analyzed that the variation in fine dust concentration is small in measurement areas with low average fine dust concentration, and the variation is large in measurement areas with high average fine dust concentration.

Figure 7 shows the monthly and annual average fine dust (PM10) and ultrafine dust (PM2.5) concentrations of the urban air quality monitoring station located in downtown Suwon. Measurement of ultrafine dust (PM2.5) concentrations began after 2015.

It is analyzed that both fine dust (PM10) and ultrafine dust (PM2.5) tend to have low average concentrations from July to September and high average concentrations in winter. The point with the highest average concentration of fine dust (PM10) and ultrafine dust (PM2.5) during the analysis period was Dongsuwon RAQMS (RAQMS 131116). The monthly average concentration of fine dust (PM10) and ultrafine dust (PM2.5) was The ranges are analyzed as 34.8 μgm ^-3 (September) to 75.8 μgm ^-3 (March) and 14.0 μgm ^-3 (September) to 41.0 μgm ^-3 (March), respectively.

The monthly average fine dust (PM10) and ultrafine dust (PM2.5) concentrations at Homaesil-dong AQMS (AQMS131118), the point with the lowest average concentration, range from 23.9μgm ^-3 (September) to 49.7μgm ^-3 (March), respectively. It is analyzed as 13.2μgm ^-3 (September) to 30.5μgm ^-3 (January). The overall average annual concentration of fine dust was analyzed to show a tendency to decrease over time, and the average concentration in 2020 was analyzed to be the lowest at most measurement points.

Referring again to FIG. 2, the similarity analysis unit 214 analyzes fine dust (fine dust (PM10) and ultrafine dust (PM2.5)) in each region based on the fine dust information received through the weather information receiver 206. ) Analyze the temporal and spatial similarities between concentrations.

In one embodiment, the similarity analysis unit 214 calculates the Pearson correlation coefficient (R) between the fine dust concentrations in each analysis target area to determine the trend of change in fine dust concentration between comparison regions based on time. You can analyze whether they match. For example, the similarity analysis unit 214 can calculate the Pearson correlation coefficient between the fine dust (PM10) concentration of the Homaesil-dong AQMS and the fine dust (PM10) concentration of the Dongsuwon RAQMS based on time, and the second of the Homaesil-dong AQMS. The Pearson correlation coefficient between fine dust (PM2.5) concentration and ultrafine dust (PM2.5) concentration of Dongsuwon RAQMS can be calculated. The more consistent the temporal fine dust concentration change trends between comparison regions are, the closer the Pearson correlation coefficient is to 1. The similarity analysis unit 214 can calculate that the closer the Pearson correlation coefficient between comparison areas is to 1, the closer the fine dust concentration change trend is. The similarity analysis unit 214 may calculate the consistency of the fine dust concentration change trend as a percentage, with 0 as 0% and 1 as 100% based on the calculated Pearson correlation coefficient value.

In one embodiment, the similarity analysis unit 214 calculates the coefficient of divergence (COD) between the fine dust concentrations in each analysis target area and analyzes whether the fine dust concentrations in the comparison area are spatially consistent. can do. The more the fine dust concentrations between comparison areas are spatially consistent, the closer the divergence coefficient is to 0, and the more different the fine dust concentrations are, the closer the divergence coefficient is to 1.

The similarity analysis unit 214 can calculate that the fine dust concentration spatially matches as the divergence coefficient between comparison areas approaches 0. The similarity analysis unit 214 may calculate the consistency of the fine dust concentration change trend as a percentage, with 0 as 100% and 1 as 0%, based on the calculated divergence coefficient value.

Figure 8 is a graph showing the correlation and spatial variability distribution between fine dust concentration and ultrafine dust concentration measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention.

Referring to Figure 8, the Pearson correlation coefficient ranges of fine dust (PM10) concentration and ultrafine dust (PM2.5) concentration during the analysis period were analyzed to be 0.79 to 0.96 and 0.80 to 0.94, respectively. In addition, the ranges of emission coefficients for fine dust (PM10) and ultrafine dust (PM2.5) concentrations were analyzed to be 0 to 0.16 and 0 to 0.08, respectively.

The average correlation coefficients of fine dust (PM10) and ultrafine dust (PM2.5) at eight atmospheric measurement points (atmospheric measurement stations) are 0.91 and 0.87, respectively, which is higher than that of ultrafine dust (PM2.5). It can be seen that the temporal variability between points is more similar. However, in the case of ultrafine dust (PM2.5), the start date of concentration measurement is later than that of fine dust (PM10), and the start date of measurement at each point is also different. In addition, the number of data on ultrafine dust (PM2.5) concentration used in the analysis is smaller than that of fine dust (PM10), so the correlation coefficient is analyzed to be small. When compared over the same period, the correlation coefficient of ultrafine dust (PM2.5) is analyzed to be higher.

In the case of the divergence coefficient, which reflects spatial consistency, it is analyzed that the spatial consistency of ultrafine dust (PM2.5) concentration is higher than that of fine dust (PM10). The average divergence coefficients of fine dust (PM10) and ultrafine dust (PM2.5) concentrations at eight atmospheric measurement points (atmospheric measurement stations) are analyzed to be 0.04 and 0.02, respectively.

Referring again to FIG. 2, the meteorological correlation analysis unit 216 determines the meteorological factors and fine dust concentration for each analysis target area based on the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit 212. Analyze the correlation between each.

In one embodiment, the weather correlation analysis unit 216 calculates the correlation coefficient between wind speed and fine dust (fine dust (PM10) and ultrafine dust (PM2.5)) concentration, and calculates the correlation coefficient between the temperature and fine dust (fine dust) The correlation coefficient between (PM10) and ultrafine dust (PM2.5)) concentrations can be calculated. For example, the weather correlation analysis unit 216 calculates the Pearson correlation coefficient between wind speed and fine dust (fine dust (PM10) and ultrafine dust (PM2.5)) concentration, and calculates the Pearson correlation coefficient between temperature and fine dust (fine dust (PM2.5)). The Pearson correlation coefficient between (PM10) and ultrafine dust (PM2.5)) concentrations can be calculated. Depending on the implementation example, the weather correlation analysis unit 216 may calculate Spearman Rank, Kendall Tau, point-biserial correlation coefficient, Phi, etc. It may be possible. The weather correlation analysis unit 216 can analyze the correlation between weather factors and fine dust concentration based on the calculated correlation coefficient.

Figure 9 is a graph showing the correlation between wind speed, temperature, fine dust concentration, and ultrafine dust concentration measured at a ground measurement station in the analysis target area according to an embodiment of the present invention.

Figure 9 (a) shows the time average wind speed of Suwon ASOS (ASOS 119) from 2011 to 2020 and the time average concentration of fine dust (PM10) at 8 atmospheric measurement points (atmospheric measurement stations, classified by identification ID) in Suwon City, seconds. The correlation coefficient between the time average concentration of fine dust (PM2.5) is shown, and (b) in Figure 9 shows the time average temperature, the time average concentration of fine dust (PM10) at eight atmospheric measurement points (atmospheric measurement stations) in Suwon, and ultrafine dust. (PM2.5) Indicates the correlation coefficient between time average concentrations.

Overall, it is analyzed that there is a weak negative correlation (e.g., negative correlation coefficient) between measured meteorological factors (wind speed, temperature) and fine dust concentration, and fine dust concentration has a negative correlation with temperature rather than wind speed. It is analyzed that appears somewhat strongly. It is analyzed that the concentration of ultrafine dust (PM2.5) has a stronger negative correlation with wind speed and temperature than fine dust (PM10). The reason why temperature has a relatively higher correlation with fine dust concentration than wind speed is that, as shown in the monthly analysis, fine dust concentration tends to be relatively low in summer when temperature is high, and fine dust concentration tends to be high in winter when temperature is low. It is analyzed that this is because it is relatively clear.

When analyzing the correlation between measured meteorological factors and fine dust concentration by season, it is found that wind speed and fine dust concentration have a negative correlation in all seasons. In the case of temperature, it is analyzed that a correlation coefficient between -0.06 and 0.16 appears in spring and summer, a negative correlation is observed at measuring stations in fall (correlation coefficient range: -0.29 to -0.11), and a positive correlation occurs in winter. It is analyzed that a correlation appears (correlation coefficient range: 0.13~0.31)

Figure 10 is a graph showing the seasonal concentration of fine dust measured at a ground measurement station in the analysis target area according to an embodiment of the present invention, and Figure 11 is a graph showing the concentration of seasonal fine dust measured at a ground measurement station in the analysis target area according to an embodiment of the present invention. This is a diagram showing the concentration of ultrafine dust by season measured in a pollution rose graph.

Figures 10 and 11 show wind direction data measured at Suwon ASOS (ASOS 119) and fine dust (PM10) concentration and ultrafine dust (PM2) measured at Gosaek-dong AQMS (AQMS 131117), which is closest to Suwon ASOS (linear distance 750 m). .5) Shows the seasonal pollution rose map prepared using concentration. In Figure 10, (a) is spring (March to May), (b) is summer (June to August), (c) is fall (September to November), and (d) is winter (December). It shows the pollution rose map prepared using fine dust (PM10) concentration from February to February. In Figure 11, (a) is spring (March to May), (b) is summer (June to August), (c) is fall (September to November), and (d) is winter (December). It shows the pollution level map prepared using the concentration of ultrafine dust (PM2.5) from February to February).

It is analyzed that the proportion of west-northwest winds and west winds were high in all seasons, with a clear increase in the frequency of west winds in spring, east and south winds in summer, and west-northwest winds in winter. It can be seen that the frequency of high fine dust concentrations was high in the spring and winter, while the frequency of relatively low fine dust concentrations increased in the summer and fall.

Referring again to FIG. 2, the urban structure correlation analysis unit 218 calculates urban structure parameters for a certain area for each analysis target area based on the received building information and road information, and calculates the urban structure parameters for each area calculated. Analyze the correlation between urban structural parameters and fine dust concentration in the area.

In one embodiment, urban structure parameters may be predefined and stored by the user. In one embodiment, the urban structure correlation analysis unit 218 calculates the building volume ratio occupied by the building in the horizontal x (m) × y (m) area and the vertical z (m) area, and the horizontal x (m) × The road area ratio occupied by roads in the y(m) area can be calculated as an urban structure parameter. Here, x, y, z are integers and can be set by the user.

Below, for convenience of explanation, the building volume ratio and road area ratio for an area of 2,000 (m) × 2,000 (m) horizontally and 150 m vertically centered on the eight air measurement points (atmospheric measurement stations) in Suwon City are presented as an urban structure. The explanation will be based on the assumption that the calculation is made with parameters.

The urban structure correlation analysis unit 218 calculates the correlation coefficient between the road area ratio and the average fine dust (PM10) concentration and the correlation coefficient between the road area ratio and the average ultrafine dust (PM2.5) concentration in each region, respectively. Calculate the correlation coefficient between the building volume ratio and the average fine dust (PM10) concentration and the correlation coefficient between the building volume ratio and the average ultrafine dust (PM2.5) concentration. In one embodiment, the urban structure correlation analysis unit 218 may calculate the Pearson correlation coefficient between the urban structure parameters and the fine dust concentration. Depending on the implementation example, the urban structure correlation analysis unit 218 calculates Spearman Rank, Kendall Tau, point-biserial correlation coefficient, Phi, etc. You may. The urban structure correlation analysis unit 218 can analyze the correlation between the urban structure parameters and the fine dust concentration in the area based on the calculated correlation coefficient.

Figure 12 is a graph showing the correlation between fine dust concentration, ultrafine dust concentration, and urban structure parameters measured at a ground air quality measurement station in the analysis target area according to an embodiment of the present invention.

Figure 12 shows the average concentration of fine dust (PM10) and ultrafine dust (PM2.5) measured at eight urban air quality measurement stations in Suwon during the analysis period, and the road area ratio in a horizontal 2 km × 2 km area centered on the air quality measurement station, It shows a scatter plot of the correlation coefficient between the building volume ratio occupied by the building in the horizontal area and the space at a vertical height of 150 m. Figure 12 (a) is a scatter plot of the correlation between the average concentration of fine dust (PM10) and the road area ratio, (b) of Figure 12 is a scatter plot of the correlation between the average concentration of fine dust (PM10) and the building volume ratio, and (c) of Figure 12 is a scatterplot of the correlation between the average concentration of ultrafine dust (PM2.5) and the road area ratio, and Figure 12 (d) shows a scatterplot of the correlation between the average concentration of ultrafine dust (PM2.5) and the building volume ratio.

The correlation coefficients between the average concentration of fine dust (PM10) and ultrafine dust (PM2.5) by measurement station and the road area ratio (building volume ratio) are analyzed to be 0.34 (0.41) and 0.48 (0.61), respectively. Overall, the concentration of ultrafine dust (PM2.5) was analyzed to have a higher correlation with urban structural parameters than the concentration of fine dust (PM10), and the correlation with the building volume ratio was analyzed to be somewhat higher than that with the road area ratio.

The average fine dust concentration of the Dongsuwon RAQMS (ⓐ in Figure 12) installed on the roadside was the highest, and the Homaesil-dong AQMS (ⓑ in Figure 12) showed a lower fine dust concentration compared to other locations.

As a result of performing the analysis excluding the data from these two points, the correlation coefficients between the average concentration of fine dust (PM10) and ultrafine dust (PM2.5) and the road area ratio (building volume ratio) were -0.15 (-0.48), respectively. ) and 0.74 (0.85). Overall, the concentration of ultrafine dust (PM2.5) was found to have a higher correlation with urban structural parameters than the concentration of fine dust (PM10), and the correlation with building volume ratio was found to be somewhat higher than that of road area ratio.

When the analysis target area is limited to Suwon City, the concentration of fine dust (PM10) appears to be almost unaffected by the road area ratio, and the concentration of fine dust (PM10) is not affected by road emissions or reduction of ground wind speed by buildings. It is analyzed that this is because it is influenced by the concentration of fine dust (PM10) flowing in from outside Suwon City.

On the other hand, the concentration of ultrafine dust (PM2.5) appears to be higher as the road area is larger (larger road area ratio) and the proportion occupied by buildings is higher (larger building volume ratio). The wider the road, the more ultrafine dust (PM2.5) is emitted directly from mobile pollutants. The higher the proportion of buildings, the lower the ground wind speed, which can increase the concentration of ultrafine dust (PM2.5). It is analyzed that the concentration of dust (PM2.5) is affected by the structural characteristics of the area.

Referring again to FIG. 2, the fine dust impact analysis unit 220 determines the area for each analysis target area based on the analysis information analyzed by the weather correlation analysis unit 216 and the urban structure correlation analysis unit 218. Calculate the influencing factors that affect the concentration of fine dust. In one embodiment, the fine dust impact analysis unit 220 includes a weather factor and urban structure correlation analysis unit 218 having a positive or negative correlation coefficient based on the correlation coefficient calculated by the weather correlation analysis unit 216. Based on the correlation coefficient calculated in , the influencing factors that affect the concentration of fine dust (PM2.5, PM10) in the area can be calculated using urban structure parameters with positive or negative correlation coefficients.

For example, if wind speed and temperature have a negative correlation coefficient for a specific area and the road area ratio has a positive correlation coefficient, the fine dust impact analysis unit 220 determines the fine dust (PM2.5, PM10) in the area. ) The factors that worsen the concentration can be calculated by road area, and the factors that reduce the concentration of fine dust (PM2.5, PM10) can be calculated by temperature and wind speed. In one embodiment, the fine dust impact analysis unit 220 may calculate the degree of influence of the influencing factors that affect the concentration of fine dust (PM2.5, PM10) based on the size of the correlation coefficient. For example, the fine dust impact analysis unit 220 sets the degree of influence as “high” when the size of the correlation coefficient of the influencing factor is greater than or equal to the first value, and sets the degree of influence as “medium” when it is between the first value and the second value. If it is below the second value, the degree of influence can be calculated as “low”.

The display unit (not shown) can display a map of the urban area on the screen, and display the influencing factors that affect the concentration of fine dust in the analysis target area and the degree of influence of the influencing factors on the screen. For example, the display unit (not shown) may overlay the influencing factors that affect fine dust concentration and the degree of influence of the influencing factors on the corresponding area of the map.

In one embodiment, the urban air environment analysis system 130 may further include a similar area estimation unit (not shown). The similar area estimation unit (not shown) is the same as the value of the influencing factor that affects the concentration of fine dust (PM2.5, PM10) in the analysis target area within the relevant urban area or is within a preset range (first setting range). You can search for regions with values. The similar area estimation unit (not shown) may display the analysis target area and areas searched as having the same or preset values of influence factors on the display unit (not shown).

For example, if the similar area estimation unit (not shown) assumes that the temperature and wind speed within Suwon City are the same, the urban structure parameter of the second analysis target area (e.g., Ingye-dong AQMS (131112)) within Suwon City ( For example, another area (a horizontal area of 2 km × 2 km and a space with a height of 150 m vertically between the horizontal area and the corresponding horizontal area) with the same (or within a preset error range) urban structure parameters (road area ratio and building volume ratio) The search may be performed, and the second analysis target area and the searched area may be linked and displayed on a display unit (not shown). That is, the similar area estimation unit (not shown) may search for one or more or all areas within the city with similar fine dust concentration trends as the second analysis target area and display them on the display unit (not shown). In another embodiment, the similar area estimation unit determines the first setting range from the urban structure parameter value of the analysis target area to the temporal similarity or spatial similarity (Pearson correlation coefficient or divergence) with the fine dust concentration of the analysis target area in the similarity analysis unit 214. Coefficient) may be set to the value of the urban structure parameters (e.g., road area ratio and building volume ratio) of the area within the preset error range.

Figure 13 is a flowchart explaining a method for analyzing urban air environment according to an embodiment of the present invention.

Referring to FIG. 13, the geographic information receiving unit 208 receives building information and road information for at least one area (analysis target area) of the city (step S1310), and the weather information receiving unit 206 receives information on buildings in each area. Receive weather information and fine dust information (step S1320). In one embodiment, the urban air environment analysis system may receive input from the user as an analysis target area for at least one area. The urban air environment analysis system can input the analysis target area from a map, input latitude/longitude values, provide air quality measurement areas where air quality measurement devices are installed, and input at least one area among the measurement areas. You may receive it.

The weather analysis unit 212 analyzes the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information (step S1330). The meteorological correlation analysis unit 216 analyzes the correlation between meteorological factors and fine dust concentration for each region based on the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit 212 ( Step S1340).

The urban structure correlation analysis unit 216 calculates urban structure parameters for a certain area for each region based on the received building information and road information, and calculates the urban structure parameters for each region and the fine details of the region. Analyze the correlation between dust concentrations (step S1350).

The fine dust impact analysis unit 220 determines the impact of fine dust concentration in each region on the basis of the analysis information analyzed by the weather correlation analysis unit 216 and the urban structure correlation analysis unit 218. Factors are calculated (step S1360).

The similarity analysis unit 214 analyzes the temporal and spatial similarities between fine dust concentrations in each region based on the received fine dust information. The similar area estimation unit (not shown) can search for areas with factor values that are the same as or within a preset range of the influence factor values that affect the fine dust concentration of the analyzed area within the relevant urban area.

The analysis method and operation of the weather analysis unit 212, the similarity analysis unit 214, the weather correlation analysis unit 216, the urban structure correlation analysis unit 218, and the fine dust impact analysis unit 220 are shown in Figures 2 to 2. As described in Figure 12.

Although the present invention has been described as an embodiment of the present invention, the technical idea of the present invention is not limited to the above embodiments, and can be implemented with various urban air environment analysis systems and urban air environment analysis methods using the same without departing from the technical idea of the present invention. You can.

202: network unit 204: memory
206: Weather information receiver 208: Geographic information receiver
210: Fine dust correlation analysis unit 212: Weather analysis unit
214: Similarity analysis unit 216: Weather correlation analysis unit
218: Urban structure correlation analysis unit 220: Fine dust impact analysis unit

Claims (14)

A geographic information receiver that receives building information and road information for at least one area of the city;
A weather information receiving unit that receives weather information and fine dust information for each region;
A weather analysis unit that analyzes the characteristics of each meteorological factor and the concentration characteristics of fine dust for each region based on the received weather information and fine dust information;
A meteorological correlation analysis unit that analyzes the correlation between meteorological factors and fine dust concentration for each region based on the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit;
Based on the received building information and road information, urban structure parameters for a certain area are calculated for each region, and the correlation between the calculated urban structure parameters for each region and the fine dust concentration in the region is analyzed. Urban Structure Correlation Analysis Department; and
City atmosphere including a fine dust impact analysis unit that calculates influencing factors affecting the fine dust concentration in each region for each region based on the analysis information analyzed by the weather correlation analysis unit and the urban structure correlation analysis unit. Environmental Analysis System.
According to paragraph 1,
An urban air environment analysis system further comprising a similarity analysis unit that analyzes temporal and spatial similarities between fine dust concentrations in each region based on the received fine dust information.
The method of claim 2, wherein the similarity analysis unit
An urban air environment analysis system that calculates the Pearson correlation coefficient (R) between the fine dust concentrations in each region and analyzes whether the trend of fine dust concentration changes between compared regions over time is consistent.
The method of claim 2, wherein the similarity analysis unit
An urban air environment analysis system that calculates the coefficient of divergence (COD) between the fine dust concentrations in each region and analyzes whether the fine dust concentrations in the comparison regions are spatially consistent.
The method of claim 1, wherein the geographic information receiver
An urban air environment analysis system that receives building information for each region from the Geographic Information System and road information for each region from the Environmental Geographic Information Service.
According to paragraph 1,
A city air environment analysis system in which the at least one area corresponds to an air quality measurement area where an air quality measurement device is installed in the city.
According to paragraph 1,
The characteristics of each meteorological factor include at least one of the main wind direction, frequency of wind direction, frequency of wind speed magnitude, average wind speed, wind speed fluctuation, and average temperature calculated on a daily, monthly, and annual basis for the region,
The concentration characteristics of the fine dust include at least one of the average fine dust (PM10) concentration and the average ultrafine dust (PM2.5) concentration calculated on a daily, monthly, and annual basis for the area. An urban air environment analysis system.
The method of claim 1, wherein the weather correlation analysis unit
An urban air environment analysis system that calculates the correlation coefficient between wind speed and fine dust (fine dust and ultrafine dust) concentration, and the correlation coefficient between temperature and fine dust (fine dust and ultrafine dust) concentration.
According to paragraph 1,
The urban structure parameters include road area ratio and building volume ratio,
The urban structure correlation analysis unit calculates the road area ratio occupied by the road in the horizontal x km Urban air environment analysis system that calculates volume ratio.
(Here, n is a natural number (setting value), z is a natural number (setting value))
The method of claim 9, wherein the urban structure correlation analysis unit
Calculate the correlation coefficient between road area ratio and average fine dust (PM10) concentration and the correlation coefficient between road area ratio and average ultrafine dust (PM2.5) concentration for each region, respectively.
An urban air environment analysis system that calculates the correlation coefficient between the building volume ratio and the average fine dust (PM10) concentration in each region, and the correlation coefficient between the building volume ratio and the average ultrafine dust (PM2.5) concentration.
The method of claim 1, wherein the fine dust impact analysis unit
Weather factors having a positive or negative correlation based on the correlation coefficient calculated by the weather correlation analysis unit and urban structures having a positive or negative correlation based on the correlation coefficient calculated by the urban structure correlation analysis unit An urban air environment analysis system that uses parameters to calculate influencing factors that affect fine dust concentration in the area.
A geographical information receiving unit receiving building information and road information for at least one area of the city;
A weather information receiving unit receiving weather information and fine dust information for each region;
A weather analysis unit analyzing characteristics of each weather factor and concentration characteristics of fine dust for each region based on the received weather information and fine dust information;
A meteorological correlation analysis unit analyzing the correlation between meteorological factors and fine dust concentration for each region based on the characteristics of each meteorological factor and the concentration characteristics of fine dust received from the weather analysis unit;
The urban structure correlation analysis unit calculates urban structure parameters for a certain area for each region based on the received building information and road information, and calculates the urban structure parameters for each region and the fine dust concentration in the region. Analyzing the correlation between; and
A fine dust impact analysis unit calculates influencing factors affecting the fine dust concentration of the area for each region based on the analysis information analyzed by the weather correlation analysis unit and the urban structure correlation analysis unit. Urban air environment analysis method.
According to clause 12,
An urban air environment analysis method further comprising the step of a similarity analysis unit analyzing temporal similarity and spatial similarity between fine dust concentrations in each region based on the received fine dust information.
According to clause 12,
An urban air environment analysis method further comprising the step of a similar area estimation unit searching for an area within the urban area having a factor value that is the same as or in a preset range as the value of an influence factor affecting the fine dust concentration of the analyzed area. .