Spatial and Temporal Distribution Characteristics of Dust Concentration Based on Satellite in Mining Area

Spatial and Temporal Distribution Characteristics of Dust Concentration Based on Satellite in Mining Area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and Temporal Distribution Characteristics of Dust Concentration Based on Satellite in Mining Area xukai dong, zhigao Liu, Erhui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4190469/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract In order to study the spatial and temporal migration characteristics of dust diffusion and the impact of mining dust on urban environment in Pingshuo mining area, the distribution law of PM2.5 and PM10 concentration in Pingshuo mining area was analyzed by satellite remote sensing monitoring technology. The results show that the correlation coefficients between the monthly average concentrations of PM 2.5 and PM 10 in Shuozhou City obtained by satellite inversion and the monitoring data of national control stations are 0.88 and 0.63, respectively, indicating a high reliability of satellite inversion data. The spatial distribution of dust concentration in Pingshuo mining area shows a low level in summer and autumn, and a high level in winter and spring, with significant dust accumulation phenomenon in winter. The air quality situation in Pingshuo mining area is best from June to September, with an increase in particle concentration in April and May, obvious pollution phenomena in January and March, and poor air quality conditions. The correlation analysis of dust concentration between urban areas and mining areas reveals a significant spatial discontinuity at the boundary between urban areas and mining areas, showing lower levels at the boundary while higher levels are observed within both urban areas as well as mining areas. This indicates that the mining area is not fundamentally responsible for the increase in dust concentration in urban areas, as changes in dust concentration within the mining area have no significant impact on urban areas. Pollution characteristics Remote sensing inversion Dust diffusion Mining area environment Environmental pollution Environmental protection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction The negative environmental effects caused by mining make the monitoring and protection of ecological environment in mining area become the research focus and hot spot, and the monitoring and prevention technology of dust pollution become the key problem of green mining in open-pit coal mine (Liu et al., 2020; Yu et al., 2023;Hu et al., 2020 ; Hu 2021 ). Many scholars have done a lot of research on dust initiation, diffusion and dust suppression measures and monitoring technology under the influence of open-pit coal mining. Xia et al. ( 2022 ) set up enhanced Coal dust Index (ECDI) to extract coal dust pollution information from mining area with Wucaiwan open pit coal mine as the research area, and developed a new mining area pollution monitoring method to provide technical support for environmental control and mine planning. Gao ( 2015 ) and Yang ( 2016 ) respectively studied the application of Internet of Things technology in open-pit production safety monitoring, including the prototype realization of monitoring system, system reliability design, and effectiveness design of system remote information transmission, etc., providing a reference for the design of dust digital monitoring system in open-pit mines. Ning (2015) analyzed solid dust pollutants in open-pit mining areas and identified three key factors: pollution source, transmission medium and pollution audience. Tang et al . (2017, 2018a, 2018b) used computational fluid dynamics and other methods to study the effects of pit geometry characteristics and temperature inversion on dust accumulation in open pit. Tian et al. ( 2021 ) believe that there is a positive correlation between dust concentration and vehicle speed, and the dust concentration caused by transport vehicles running in the upwind environment is higher than that in the downwind environment. Cong et al . (2001) used numerical simulation to calculate the concentration distribution of coal dust particles under the annual average wind speed and maximum wind speed in the mining area. Witt et al. ( 2002 ) used the computer dynamic fluid model to predict the motion trajectory of dust generated by the transmission device. Based on the experimental data, the particle size distribution prediction model of dust with different wind speed was established. Eba et al . (2020) and Khazins et al . (2020) developed a method based on multi-layer artificial neural network and fuzzy learning to predict the law of vertical and horizontal distribution of dust emission during open-pit blasting. Hosseini et al . (2021) combined dimensional analysis with multi-layer perceptron and radial basis function neural network and applied it to the prediction of explosive fallout, effectively improving the prediction effect of neural network. Xiong et al. ( 2017 ) proposed to monitor urban dust pollution sources by combining medium resolution satellite data with high resolution satellite data. Ge et al. ( 2017 ) analyzed the seasonal variation characteristics of PM 2.5 concentration by using enhanced regression tree simulation, and the results showed that the pollution was serious and lasted for a long time in winter, while the pollution was light in summer. At present, many researchers have not fully studied the impact range of dust in open-pit mines, the impact degree of dust in mining areas and the contribution degree to urban dust pollution, and the above problems have become the key problems to be solved urgently. At the same time, the cost of ground-based observation is also quite expensive. The atmospheric particle monitoring technology using satellite remote sensing has the technical advantages of large-scale, continuous monitoring, objective and accurate, high time continuity, high space continuity, etc., which can better monitor and evaluate the dust concentration and spatio-temporal changes in open-pit mines. Quantify the contribution degree of the mining area to the surrounding cities' dust, improve the understanding of the characteristics of dust sources and sink and the diffusion process, predict the future distribution law of dust concentration in Pingshuo mining area, and increase the reliability of prediction (Yan et al., 2022 ; Chen et al., 2021 ). Therefore, the spatial and temporal distribution characteristics of dust pollution in open-pit mining areas still need to be further studied. Taking Pingshuo Mining area of Shuozhou City as the research area, based on long-term on-site monitoring and practical work, the spatio-temporal distribution characteristics of PM 2.5 and PM 10 in Pingshuo mining area were detected and identified by using the combination of ground monitoring and satellite monitoring system, and the law and influence range of dust diffusion caused by mining in Pingshuo mining area were determined, with a view to providing reference for dust monitoring and control in mining area. Study area and data source 2.1 Study area profile Pingshuo mining area belongs to warm temperate semi-arid climate zone with dry climate. The four seasons are distinct, the temperature difference between day and night is large, and the spring and winter wind is large, and the wind and sand climate is serious. The annual average precipitation is 428 mm, mostly concentrated in July, August and September, accounting for 75% of the annual precipitation. The dominant wind direction in the region is northwest wind. Pingshuo mining area includes three large open-pit mines, Anjialing, Antaibao and Dongopen-pit, which are 25Km away from the city center. While mining areas provide indispensable and important coal resources, dust pollution may cause serious impact on urban environmental protection. Therefore, it is necessary to monitor the temporal and spatial variation characteristics of dust concentration in mining area, determine the dust diffusion law and influence range caused by mining in Pingshuo mining area, and provide reference for urban dust control. The location distribution of open pit in Pingshuo Mining area is shown in Fig. 1 。 1.2 Data source and preprocessing The satellite data in this paper comes from the HiMAWARi-8 meteorological satellite, which is one of the Himawari series satellites designed and manufactured by the Japan Aerospace Exploration Agency. The HiMAWARi-8 satellite is positioned at 140° E, and the observation frequency is generally once every 10 minutes. Its observation range can reach 120°×120° (80°E-20°W, 60°N-60°S), covering East Asia, the Western Pacific, Australia and other large areas, most of China can achieve good observation. Himawari-8 satellite is a stationary satellite that can obtain color images. The new sensor Advanced Himawari Imager (AHI) on HimaWARi-8 satellite has a rich band setting, including 16 bands in visible light, near infrared and thermal infrared. The spatial resolution of the AHI sensor can reach 0.5 km to 1 km in the visible band and 1 km to 2 km in the infrared band. Table 1 Product information Remote sensing instrument Observed frequency Observation range Spatial resolution Himawari-8 10min/次 120°×120° 0.5 ~ 1km The monitoring data collected by the national control station includes longitude, latitude, PM 2.5 concentration and other attributes, and the data is collected every 1 hour. The data collected by Meteorological station monitoring data include longitude, latitude, air pressure, two-minute average wind direction, two-minute average wind speed, temperature, relative humidity, horizontal visibility and other properties, with a time resolution of 1 h. The monitoring data of the ground monitoring station in the mining area mainly include longitude, latitude, temperature, humidity, wind speed, atmospheric pressure, PM 2.5 , PM 10 , TSP (Total Suspended Particulates), wind direction and other attributes, with a time resolution of 30 min. Visibility meter monitoring data mainly includes longitude, latitude, visibility and other attributes, collection frequency is once an hour. Inversion method and verification 2.1 Principle and algorithm of Aerosol Optical Depth (AOD) inversion The methods of retrieving (AOD) from remote sensing satellites are mainly divided into Dense Dark Vegetation (DDV) algorithm and Deep Blue (DB) algorithm. The former uses the ratio of the red and blue band data of the sensor and the surface reflectivity database of the Medium Resolution Imaging Spectrometer (MODIS) to invert the AOD. The latter uses the apparent reflectance of the blue wave range of the sensor, calculates the spectral database of the mixed pixel, uses the surface reflectance database, establishes the lookup table by the 6S model, and interpolates the AOD based on inverse distance weighting and ill-conceived equations. The DB algorithm is used to invert the AOD in Shuozhou. Assuming that the surface is Lambertian and the atmospheric level is uniform, the apparent reflectance \({\rho _{TOA}}\) (GENG et al. 2021 ) received by the AHI sensor can be expressed as: $${\rho _{TOA}}\left( {\eta {\text{s}},\eta v,\alpha } \right)={\rho _0}\left( {{\eta _s},{\eta _v},\alpha } \right)+\frac{{T\left( {{\eta _s}} \right)T\left( {{\eta _v}} \right){\rho _s}\left( {{\eta _s},{\eta _v},\alpha } \right)}}{{1 - {\rho _s}\left( {{\eta _s},{\eta _v},\alpha } \right)S}}$$ 1 \(\eta {\text{s}}\) is the cosine of solar zenith angle; \(\eta v\) is the cosine of Sensor zenith Angle; \(\alpha\) is the relative azimuth (the absolute difference between the observed azimuth and the sun's azimuth); \({\rho _0}\) is the equivalent reflectance of atmospheric range radiation; is the atmospheric transmittance༛ \({\rho _s}\) is the surface reflectance of the Lambertian body༛ is the hemispherical reflectance of the lower atmospheric boundary. AHI sensor is used for the inversion of AOD. Firstly, satellite remote sensing data should be pre-processed (pre-processing steps include: Radiometric calibration, apparent reflectance calculation, observation Angle data calculation, projection conversion, resample, cloud removal and other operations), so as to remove the influence of surface reflectance and form a spatially and temporally consistent dataset for training and verification of the constructed PM 2.5 and PM 10 concentration estimation model. The input parameters of the model are: AOD obtained by inversion, air temperature (A temp), surface temperature (S temp), wind speed (wind S), wind direction (wind D), relative humidity (RH), pressure (SP), visibility (VIS). The output of the model is the estimated concentration of PM 2.5 and PM10. Finally, the missing values and invalid data caused by rainfall were removed from the data, and the data were normalized to obtain the final PM2.5 and PM10 concentration data. The technical roadmap of PM 2.5 and PM 10 based on satellite inversion is shown in Fig. 2 . 2.2 Satellite inversion data and verification Figure 3 compares the monthly average concentrations PM 2.5 and PM 10 of Shuozhou City obtained from the national control station and satellite inversion. As can be seen from Fig. 3, the satellite inversion results of PM 2.5 concentration are in good agreement with the observed values of the national control stations, and the correlation coefficient (R 2 ) is 0.8858. The PM 2.5 concentration monitored by the national control stations is systematically high, but the change law of PM 2.5 concentration is consistent as a whole. In contrast, the consistency of PM 10 concentration obtained by the national control station and the satellite inversion is slightly lower, and its correlation coefficient (R 2 ) is 0.6338. The PM 10 concentration value obtained by the satellite inversion in March, April and May is higher than that monitored by the national control station, but the change rule of concentration is basically the same. The fluctuation range of PM 10 concentration obtained by satellite retrieval is higher than that obtained by station observation, which may be due to the fact that the concentration obtained by satellite retrieval is also affected by the change of the concentration of particles in the middle and upper troposphere and other factors, so its range is larger than that obtained by ground-based observation. The characteristics of dust concentration diffusion in mining area and its correlation with urban pollution 3.1 Dust accumulation characteristics and spatial distribution of concentration in Pingshuo mining area The satellite inversion data of PM 2.5 and PM 10 concentration in winter in January were selected to obtain the distribution of dust particles in the mining area and Shuozhou City, as shown in Fig. 4 , and the three-dimensional distribution characteristics of dust in Pingshuo mining area as shown in Fig. 5. In the figure, the horizontal distance represents the east-west direction, and the vertical distance represents the north-south direction. Shuocheng District is located in the south of Pingshuo Mining area. It can be seen from Fig. 4 (a) that there is a discontinuity between the dust concentration generated in the mining area and the concentration in the urban area, and the dust particles mainly diffuse to the southeast, but not to the urban area. Moreover, it can be seen from Fig. 4 (b) that PM 10 is mainly concentrated in the northeast of the mining area in winter, but there is a discontinuity between the concentration and Pingshuo mining area. At the same time, it can be seen from Fig. 5 that dust in open-pit mining areas has obvious concentration characteristics in winter, but the diffusion range is limited to the vicinity of the open-pit mine, the dust concentration around the mining area is low, and the dust distribution in winter presents the characteristics of "strong concentration and weak dispersion". In addition, the open-pit mine may also have a dust suction effect in winter, resulting in a decrease in the dust concentration around the mine and an increase in the concentration above the mine. In order to describe the spatial distribution characteristics of dust in Pingshuo mining area more clearly and accurately, the inversion data of PM2.5 concentration were selected, and the spatial distribution of PM2.5 concentration in Pingshuo mining area and urban area was obtained. The geographical ranges of Antaibao, Anjialing and Dong open pit mines were identified. According to the climatological definition (in the northern hemisphere, March to May is spring, June to August is summer, September to November is autumn, and December to February is winter), the PM2.5 concentration distribution maps of Shuozhou and Pingshuo mining areas were divided into four seasons, as shown in Fig. 6. Compared with other seasons, PM2.5 is concentrated in the mining area in winter, with the highest concentration reaching 44µg/m3. This is because the temperature of the atmosphere near the ground in the northern winter is lower than that of the upper atmosphere, forming a stable inversion structure, and the air can not convection up and down, and it is difficult to disperse after the dust accumulation, and it is easy to occur serious dust pollution events. In spring, dust concentration in the mining area is still concentrated, and the dust concentration near the mining area is higher, but the concentration degree is lower than that in winter, and the maximum concentration in the mining area is 39µg/m3. In summer and autumn, the concentration of PM2.5 in the mining area decreased significantly compared with that in winter and spring. In addition, the geographical range of the mining area and part of the urban area is identified. Dust generated in the mining area spreads along the downwind direction, and the PM2.5 diffusion range is about 3Km in winter. Dust generated in the mining area cannot spread to the urban area, and there is discontinuity between the mining area and the urban area. In spring, PM2.5 only shows high and low distribution near the mining area, which is greatly affected by customs, and the PM2.5 concentration near the urban area is low, and the dust in the mining area does not directly affect the urban area. With higher temperature and increased precipitation in summer and autumn, PM2.5 concentration decreases overall, and there is no significant difference between the PM2.5 concentration in mining area and urban area. Similarly, the distribution diagram of PM10 concentration is shown in Fig. 7, and its distribution rule is roughly consistent with that of PM2.5 concentration. However, the concentration of PM10 in spring is higher than that in winter, with a maximum value of 174.4µg/m3. This is due to the high concentration of PM10 particles affected by strong sandstorms in spring due to windy and sandy weather. When the concentration decreased in summer and autumn, the diffusion range of PM10 was about 3.5Km, and the spatial diffusion pattern showed a seasonal distribution. The research results can reflect that the dust produced in Pingshuo mining area has little or no influence on the urban area, and the contribution of dust in mining area to urban environmental pollution is small. In summary, according to the geographical location identification and spatial distribution analysis results of Pingshuo mining area and urban area, the following conclusions can be drawn: The spatial distribution of PM2.5 in the whole Shuozhou City and Pingshuo Mining area is low in summer and autumn, and high in winter and spring. In the spatial distribution map, there is an obvious spatial discontinuity at the junction between Pingshuo Mining area and urban area, which is low at the junction and high in urban area and mining area. Moreover, dust diffusion degree varies with time. It shows that there is no direct correlation between Pingshuo mining area and the local increase of dust concentration in the urban area. The local increase of dust in the mining area does not affect the urban area, and the diffusion of dust particles to the surrounding area is small. 3.2 Characteristics of dust migration in Pingshuo mining area The inversion data of PM2.5 concentration for 9 consecutive months were selected, and the selected research area was divided into four areas outside the mining area, the mining area, the urban boundary area of the mining area and the urban area according to their relative geographical location. The dust diffusion and distribution rules of different months were sorted out according to the four seasons of winter, spring, summer and autumn, as shown in Fig. 8. In January, dust accumulation occurred in the mining area, and the PM2.5 concentration decreased first and then increased in the boundary area, but the overall concentration decreased, and the PM2.5 concentration gradually increased in the urban area. The results show that the dust produced in the mining area cannot be diffused to the urban area, and the impact on the urban environment is small, which is consistent with the previous analysis. The PM2.5 concentration in February showed a decreasing trend, but the distribution pattern was basically the same as that in January. According to the obtained results, the corresponding research area can be divided into four stages: Off-mine area, the Gathering area, the Descending area and the Elevation area. In spring (March to May), the concentration of PM2.5 in the mining area is still high in other areas, and the concentration degree and concentration value are gradually reduced, and the distribution law of each area is basically consistent with that of winter. The PM2.5 concentration in summer (June-August) gradually decreases from north to south, which can be divided into four stages: Off-mine impact area, the Stationary area, the Descending area and the stationary area. The PM2.5 concentration in autumn (September) is basically unchanged, roughly around 15ug/m3. The distribution law of PM10 is basically the same as that of PM2.5 concentration, but in May, the concentration of PM10 suddenly produces agglomeration phenomenon, and the concentration value gradually increases. The reason is that the concentration of PM10 suddenly increases due to the dust generated in spring, resulting in agglomeration phenomenon, and the overall PM10 concentration in spring is greater than that in winter. The PM10 concentration in autumn (September) is basically unchanged at around 41ug/m3. The results show that the dust diffusion phenomenon of Pingshuo mining area is seasonal distribution, and the accumulation phenomenon is easy to occur near the mining area in spring and winter temperature inversion, and the winter accumulation phenomenon is the most serious. In summer and autumn, the diffusion of dust generated in the mining area is mainly affected by wind speed and temperature factors, and the dust generated in the mining area is difficult to disperse to the urban area. In summer and autumn, the dust outside the mining area will be absorbed by the mining area after it spreads to the mining area, and the dust concentration will gradually decrease. The concentration of PM2.5 and PM10 in each season is basically lower than the national secondary air quality standard (75ug /m3 and 150ug /m3), and only in May, the PM10 concentration is higher than the secondary standard, and the overall pollution degree of the mining area is small. Based on the conclusion of the study, the mining area is not the main source of pollution in urban areas, and in the study of dust pollution control in open pit mines, the seasonal factors of dust diffusion and special factors such as strong dust weather should be fully considered, so as to propose corresponding control measures, which cannot be generalized. 3.3 Analysis of pollution situation in Pingshuo mining area Statistics on the air quality of PM2.5 and PM10 in Pingshuo Mining area and the proportion of days with different air quality grades were conducted. As shown in Fig. 10, the air quality was the best from June to September, and the percentage of days in the "excellent" level were 100%, 96%, 82% and 100%, respectively. The PM2.5 level decreased from January to May. "Light pollution", "moderate pollution" and "heavy pollution" phenomena are produced, especially in January and March the pollution situation is the most serious, the air quality is poor, the pollution percentage is 19% and 21% respectively, of which March even appeared "serious pollution". The statistical results of PM10 air quality in the mining area are roughly the same as that of PM2.5, and the air quality is above good from June to September, but the frequency of severe pollution increases from January to May, especially in spring, when the frequency of severe pollution is higher. The reason for this phenomenon is that there is less precipitation in spring and the surface is extremely dry and loose. When the wind blows past, it will draw a large amount of dust into the air, causing atmospheric pollution. To sum up, the months with the best air quality in Pingshuo Mining area are mainly from June to September, the particulate matter concentration in April and May has increased, and the pollution phenomenon is obvious in January and March, and the air quality is poor. 3.4 Correlation analysis of dust concentration between urban area and mining area In order to verify whether dust generated by production and mining in mining areas has an impact on urban areas, PM2.5 and PM10 concentration distribution rules of three regions in different months were selected respectively, as shown in Fig. 11. From winter to autumn, the concentration of PM2.5 and PM10 showed a decreasing trend. From January to May, the concentration distribution of PM2.5 from the mining area to the urban area showed a trend of decreasing first and then increasing, indicating that the dust generated in the mining area could not spread to the urban area, and there was no obvious correlation with the urban area. From June to September, the concentration distribution of PM2.5 from the mining area to the urban area showed a gradually decreasing trend, but the overall concentration value did not change much, indicating that the concentration in summer and autumn was basically affected by the weather, and the contribution of dust production in the mining area was very low. Compared with PM2.5, the variation of PM10 concentration in the three regions is not obvious with the month. The PM10 concentration in the three regions shows no correlation from January to March, and the PM10 concentration in the three regions shows a sudden increase in May, and the concentration value ordering in the three regions shows fluctuations, and there is a fluctuation zone. From June to September, the PM10 concentration values in different regions were basically the same and were divided into consistent areas. The results show that mining area is not the root cause of the increase of dust concentration in urban area. The dust concentration at the junction of mining area and Shuocheng District is basically the same in summer and autumn, but in winter and spring, the dust concentration at the junction of mining area and Shuocheng District is lower than that of Shuocheng District and mining area, and the change of dust concentration in mining area has no obvious influence on the urban area. Conclusions Based on satellite monitoring and inversion technology, the spatial and temporal distribution characteristics of dust concentration in Pingshuo open-pit mining area are studied, and the main conclusions are as follows: (1) The concentration of PM2.5 and PM10 retrieved by satellite is in good agreement with the observed values of the national control station, and the correlation coefficient (R2) is 0.8858 and 0.6338, respectively. (2) The dust concentration in Pingshuo mining area is relatively high in winter, and there is an agglomeration phenomenon, which disappears in summer and autumn. It shows that the distribution of dust in mining area is affected by seasonal factors such as climate and temperature. (3) The air quality in Pingshuo Mining area is the best from June to September, and the particulate matter concentration in April and May has increased, while the pollution phenomenon is obvious in January and March, and the air quality is poor. (4) The correlation analysis between the dust concentration in the urban area and the mining area shows that the mining area is not the root cause of the increase of the dust concentration in the urban area, and the change of the dust concentration in the mining area has no obvious impact on the urban area. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author contributions Xukai Dong: Conceptualization, Investigation, Methodology, Software, Data curation, Writing – original draft and Writing – review & editing. Erhui Zhang: Writing – review & editing, Visualization, Supervision. Zhigao Liu: Visualization, Investigation. Data Availability Statement All data are incorporated in to the article. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Ethical approval The work meets all ethical issues. Consent to participate The author has nothing relevant to declare. Consent for publication All authors have a consensus regarding the publication of the work. References Chen XF, Zheng FJ, Guo D, Wang LL, Zhao LM, Li JG, Li L, Zhang YH, Zhang KN, Xi M, Li KT. Chen XF, Zheng FJ, Guo D, (2021) Review of machine learning methods for aerosol quantitative remote sensing. National Remote Sensing Bulletin. 25(11):2220-2233. Cong XC, Zhang GY, Zhan SF (2007) Numerical calculation of mine powder diffusion movement. 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Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 07 Jun, 2024 Reviewers agreed at journal 23 Apr, 2024 Reviewers invited by journal 23 Apr, 2024 Editor assigned by journal 03 Apr, 2024 First submitted to journal 01 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4190469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294574855,"identity":"8f790d29-3088-4189-ad8a-01deb55c4cc0","order_by":0,"name":"xukai dong","email":"","orcid":"","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":false,"prefix":"","firstName":"xukai","middleName":"","lastName":"dong","suffix":""},{"id":294574856,"identity":"206562ae-b661-4052-b2a3-ed3487c958fb","order_by":1,"name":"zhigao Liu","email":"","orcid":"","institution":"BEIJING EACON TECHNOLOGY CO.,LTD","correspondingAuthor":false,"prefix":"","firstName":"zhigao","middleName":"","lastName":"Liu","suffix":""},{"id":294574857,"identity":"52f65bb9-2899-4e9b-92f3-4620cf9028e3","order_by":2,"name":"Erhui Zhang","email":"data:image/png;base64,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","orcid":"","institution":"China University of Mining and Technology - Beijing","correspondingAuthor":true,"prefix":"","firstName":"Erhui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-03-30 03:57:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4190469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4190469/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-34367-7","type":"published","date":"2024-07-18T16:13:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55334779,"identity":"0d0d7280-1480-45a3-996f-15bb730b3b40","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1582586,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and distribution map of open pit mine in Pingshuo mining area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/f89395b9daf7f11791f14378.png"},{"id":55334776,"identity":"b2dfb2e8-6b1f-4ebe-ba4a-5ff4a995d53a","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48878,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical roadmap of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e retrieval based on remote sensing satellite\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/c995b92325f2c6be64b773b7.png"},{"id":55334777,"identity":"ced52fb6-a1dd-437c-b85b-42c8228b2c65","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358596,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/6e568d8a79b19d4c4b5ac5a4.png"},{"id":55334866,"identity":"c4a7aedf-bb91-4d37-ae68-891c5c697fd5","added_by":"auto","created_at":"2024-04-25 21:55:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2116664,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/6983192fe4e2b78be9321ddc.png"},{"id":55334781,"identity":"f1db350f-54cf-4ea9-951b-21d7660805cd","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":696934,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/63ae2057f7bdf58fefb9ebd1.png"},{"id":55334783,"identity":"bbc15f96-9bd5-4830-a8ee-8411a0c52008","added_by":"auto","created_at":"2024-04-25 21:47:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1254535,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/5277cce1dc1b0b4dd7c4c27e.png"},{"id":55334785,"identity":"573c5dec-bbb6-45b9-8eaf-773553cd7778","added_by":"auto","created_at":"2024-04-25 21:47:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1056340,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/1013ec824e4c336251e76fed.png"},{"id":55334867,"identity":"342c0532-a41c-429b-943a-55aa97cf95a8","added_by":"auto","created_at":"2024-04-25 21:55:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":325859,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/ca058259e37cfcd4cab76fcf.png"},{"id":55334778,"identity":"ab9ca101-d4e2-43f6-b08d-333f6b0cd1a3","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":328435,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/49885ee2cb1464d0bae4bf61.png"},{"id":55334782,"identity":"07a7ab22-c9a2-4bfd-8be2-86bcb85d26ae","added_by":"auto","created_at":"2024-04-25 21:47:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":402672,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/dc1f1bff384abc79a1e6cae5.png"},{"id":55334786,"identity":"93f5825a-4ae8-4380-8a2c-678f02278dc8","added_by":"auto","created_at":"2024-04-25 21:47:36","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":260762,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/039fc93105dda9aaa087cca1.png"},{"id":61596937,"identity":"b09e1065-1e49-449a-9042-711818464b70","added_by":"auto","created_at":"2024-08-01 17:30:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11728466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4190469/v1/e72cd480-ec9c-4c0a-ac37-1525b3083edd.pdf"}],"financialInterests":"","formattedTitle":"Spatial and Temporal Distribution Characteristics of Dust Concentration Based on Satellite in Mining Area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe negative environmental effects caused by mining make the monitoring and protection of ecological environment in mining area become the research focus and hot spot, and the monitoring and prevention technology of dust pollution become the key problem of green mining in open-pit coal mine (Liu et al., 2020; Yu et al., 2023;Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many scholars have done a lot of research on dust initiation, diffusion and dust suppression measures and monitoring technology under the influence of open-pit coal mining.\u003c/p\u003e \u003cp\u003eXia et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) set up enhanced Coal dust Index (ECDI) to extract coal dust pollution information from mining area with Wucaiwan open pit coal mine as the research area, and developed a new mining area pollution monitoring method to provide technical support for environmental control and mine planning. Gao (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Yang (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) respectively studied the application of Internet of Things technology in open-pit production safety monitoring, including the prototype realization of monitoring system, system reliability design, and effectiveness design of system remote information transmission, etc., providing a reference for the design of dust digital monitoring system in open-pit mines. Ning (2015) analyzed solid dust pollutants in open-pit mining areas and identified three key factors: pollution source, transmission medium and pollution audience. Tang \u003cem\u003eet al\u003c/em\u003e. (2017, 2018a, 2018b) used computational fluid dynamics and other methods to study the effects of pit geometry characteristics and temperature inversion on dust accumulation in open pit. Tian et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) believe that there is a positive correlation between dust concentration and vehicle speed, and the dust concentration caused by transport vehicles running in the upwind environment is higher than that in the downwind environment. Cong \u003cem\u003eet al\u003c/em\u003e. (2001) used numerical simulation to calculate the concentration distribution of coal dust particles under the annual average wind speed and maximum wind speed in the mining area. Witt et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) used the computer dynamic fluid model to predict the motion trajectory of dust generated by the transmission device. Based on the experimental data, the particle size distribution prediction model of dust with different wind speed was established. Eba \u003cem\u003eet al\u003c/em\u003e. (2020) and Khazins \u003cem\u003eet al\u003c/em\u003e. (2020) developed a method based on multi-layer artificial neural network and fuzzy learning to predict the law of vertical and horizontal distribution of dust emission during open-pit blasting. Hosseini \u003cem\u003eet al\u003c/em\u003e. (2021) combined dimensional analysis with multi-layer perceptron and radial basis function neural network and applied it to the prediction of explosive fallout, effectively improving the prediction effect of neural network. Xiong et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposed to monitor urban dust pollution sources by combining medium resolution satellite data with high resolution satellite data. Ge et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) analyzed the seasonal variation characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e concentration by using enhanced regression tree simulation, and the results showed that the pollution was serious and lasted for a long time in winter, while the pollution was light in summer.\u003c/p\u003e \u003cp\u003eAt present, many researchers have not fully studied the impact range of dust in open-pit mines, the impact degree of dust in mining areas and the contribution degree to urban dust pollution, and the above problems have become the key problems to be solved urgently. At the same time, the cost of ground-based observation is also quite expensive. The atmospheric particle monitoring technology using satellite remote sensing has the technical advantages of large-scale, continuous monitoring, objective and accurate, high time continuity, high space continuity, etc., which can better monitor and evaluate the dust concentration and spatio-temporal changes in open-pit mines. Quantify the contribution degree of the mining area to the surrounding cities' dust, improve the understanding of the characteristics of dust sources and sink and the diffusion process, predict the future distribution law of dust concentration in Pingshuo mining area, and increase the reliability of prediction (Yan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the spatial and temporal distribution characteristics of dust pollution in open-pit mining areas still need to be further studied. Taking Pingshuo Mining area of Shuozhou City as the research area, based on long-term on-site monitoring and practical work, the spatio-temporal distribution characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in Pingshuo mining area were detected and identified by using the combination of ground monitoring and satellite monitoring system, and the law and influence range of dust diffusion caused by mining in Pingshuo mining area were determined, with a view to providing reference for dust monitoring and control in mining area.\u003c/p\u003e"},{"header":"Study area and data source","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area profile\u003c/h2\u003e \u003cp\u003ePingshuo mining area belongs to warm temperate semi-arid climate zone with dry climate. The four seasons are distinct, the temperature difference between day and night is large, and the spring and winter wind is large, and the wind and sand climate is serious. The annual average precipitation is 428 mm, mostly concentrated in July, August and September, accounting for 75% of the annual precipitation. The dominant wind direction in the region is northwest wind. Pingshuo mining area includes three large open-pit mines, Anjialing, Antaibao and Dongopen-pit, which are 25Km away from the city center. While mining areas provide indispensable and important coal resources, dust pollution may cause serious impact on urban environmental protection. Therefore, it is necessary to monitor the temporal and spatial variation characteristics of dust concentration in mining area, determine the dust diffusion law and influence range caused by mining in Pingshuo mining area, and provide reference for urban dust control. The location distribution of open pit in Pingshuo Mining area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e。\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Data source and preprocessing\u003c/h2\u003e \u003cp\u003eThe satellite data in this paper comes from the HiMAWARi-8 meteorological satellite, which is one of the Himawari series satellites designed and manufactured by the Japan Aerospace Exploration Agency. The HiMAWARi-8 satellite is positioned at 140\u0026deg; E, and the observation frequency is generally once every 10 minutes. Its observation range can reach 120\u0026deg;\u0026times;120\u0026deg; (80\u0026deg;E-20\u0026deg;W, 60\u0026deg;N-60\u0026deg;S), covering East Asia, the Western Pacific, Australia and other large areas, most of China can achieve good observation.\u003c/p\u003e \u003cp\u003eHimawari-8 satellite is a stationary satellite that can obtain color images. The new sensor Advanced Himawari Imager (AHI) on HimaWARi-8 satellite has a rich band setting, including 16 bands in visible light, near infrared and thermal infrared. The spatial resolution of the AHI sensor can reach 0.5 km to 1 km in the visible band and 1 km to 2 km in the infrared band.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProduct information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote sensing instrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservation range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHimawari-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10min/次\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u0026deg;\u0026times;120\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u0026thinsp;~\u0026thinsp;1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe monitoring data collected by the national control station includes longitude, latitude, PM\u003csub\u003e2.5\u003c/sub\u003e concentration and other attributes, and the data is collected every 1 hour. The data collected by Meteorological station monitoring data include longitude, latitude, air pressure, two-minute average wind direction, two-minute average wind speed, temperature, relative humidity, horizontal visibility and other properties, with a time resolution of 1 h. The monitoring data of the ground monitoring station in the mining area mainly include longitude, latitude, temperature, humidity, wind speed, atmospheric pressure, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, TSP (Total Suspended Particulates), wind direction and other attributes, with a time resolution of 30 min. Visibility meter monitoring data mainly includes longitude, latitude, visibility and other attributes, collection frequency is once an hour.\u003c/p\u003e \u003c/div\u003e"},{"header":"Inversion method and verification","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Principle and algorithm of Aerosol Optical Depth (AOD) inversion\u003c/h2\u003e \u003cp\u003eThe methods of retrieving (AOD) from remote sensing satellites are mainly divided into Dense Dark Vegetation (DDV) algorithm and Deep Blue (DB) algorithm. The former uses the ratio of the red and blue band data of the sensor and the surface reflectivity database of the Medium Resolution Imaging Spectrometer (MODIS) to invert the AOD. The latter uses the apparent reflectance of the blue wave range of the sensor, calculates the spectral database of the mixed pixel, uses the surface reflectance database, establishes the lookup table by the 6S model, and interpolates the AOD based on inverse distance weighting and ill-conceived equations. The DB algorithm is used to invert the AOD in Shuozhou.\u003c/p\u003e \u003cp\u003eAssuming that the surface is Lambertian and the atmospheric level is uniform, the apparent reflectance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _{TOA}}\\)\u003c/span\u003e\u003c/span\u003e (GENG et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) received by the AHI sensor can be expressed as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\rho _{TOA}}\\left( {\\eta {\\text{s}},\\eta v,\\alpha } \\right)={\\rho _0}\\left( {{\\eta _s},{\\eta _v},\\alpha } \\right)+\\frac{{T\\left( {{\\eta _s}} \\right)T\\left( {{\\eta _v}} \\right){\\rho _s}\\left( {{\\eta _s},{\\eta _v},\\alpha } \\right)}}{{1 - {\\rho _s}\\left( {{\\eta _s},{\\eta _v},\\alpha } \\right)S}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\eta {\\text{s}}\\)\u003c/span\u003e \u003c/span\u003eis the cosine of solar zenith angle; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta v\\)\u003c/span\u003e\u003c/span\u003e is the cosine of Sensor zenith Angle; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e is the relative azimuth (the absolute difference between the observed azimuth and the sun's azimuth); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _0}\\)\u003c/span\u003e\u003c/span\u003e is the equivalent reflectance of atmospheric range radiation;\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003eis the atmospheric transmittance༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _s}\\)\u003c/span\u003e\u003c/span\u003e is the surface reflectance of the Lambertian body༛\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e is the hemispherical reflectance of the lower atmospheric boundary.\u003c/p\u003e \u003cp\u003eAHI sensor is used for the inversion of AOD. Firstly, satellite remote sensing data should be pre-processed (pre-processing steps include: Radiometric calibration, apparent reflectance calculation, observation Angle data calculation, projection conversion, resample, cloud removal and other operations), so as to remove the influence of surface reflectance and form a spatially and temporally consistent dataset for training and verification of the constructed PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentration estimation model.\u003c/p\u003e \u003cp\u003eThe input parameters of the model are: AOD obtained by inversion, air temperature (A temp), surface temperature (S temp), wind speed (wind S), wind direction (wind D), relative humidity (RH), pressure (SP), visibility (VIS). The output of the model is the estimated concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and PM10. Finally, the missing values and invalid data caused by rainfall were removed from the data, and the data were normalized to obtain the final PM2.5 and PM10 concentration data. The technical roadmap of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e based on satellite inversion is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Satellite inversion data and verification\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;3 compares the monthly average concentrations PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e of Shuozhou City obtained from the national control station and satellite inversion. As can be seen from Fig.\u0026nbsp;3, the satellite inversion results of PM\u003csub\u003e2.5\u003c/sub\u003e concentration are in good agreement with the observed values of the national control stations, and the correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) is 0.8858. The PM\u003csub\u003e2.5\u003c/sub\u003e concentration monitored by the national control stations is systematically high, but the change law of PM\u003csub\u003e2.5\u003c/sub\u003e concentration is consistent as a whole.\u003c/p\u003e \u003cp\u003eIn contrast, the consistency of PM\u003csub\u003e10\u003c/sub\u003e concentration obtained by the national control station and the satellite inversion is slightly lower, and its correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) is 0.6338. The PM\u003csub\u003e10\u003c/sub\u003e concentration value obtained by the satellite inversion in March, April and May is higher than that monitored by the national control station, but the change rule of concentration is basically the same. The fluctuation range of PM\u003csub\u003e10\u003c/sub\u003e concentration obtained by satellite retrieval is higher than that obtained by station observation, which may be due to the fact that the concentration obtained by satellite retrieval is also affected by the change of the concentration of particles in the middle and upper troposphere and other factors, so its range is larger than that obtained by ground-based observation.\u003c/p\u003e "},{"header":"The characteristics of dust concentration diffusion in mining area and its correlation with urban pollution","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1 Dust accumulation characteristics and spatial distribution of concentration in Pingshuo mining area\u003c/h2\u003e\n \u003cp\u003eThe satellite inversion data of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentration in winter in January were selected to obtain the distribution of dust particles in the mining area and Shuozhou City, as shown in Fig. \u003cspan\u003e4\u003c/span\u003e, and the three-dimensional distribution characteristics of dust in Pingshuo mining area as shown in Fig.\u0026nbsp;5. In the figure, the horizontal distance represents the east-west direction, and the vertical distance represents the north-south direction. Shuocheng District is located in the south of Pingshuo Mining area.\u003c/p\u003e\n \u003cp\u003eIt can be seen from Fig. \u003cspan\u003e4\u003c/span\u003e(a) that there is a discontinuity between the dust concentration generated in the mining area and the concentration in the urban area, and the dust particles mainly diffuse to the southeast, but not to the urban area. Moreover, it can be seen from Fig. \u003cspan\u003e4\u003c/span\u003e (b) that PM\u003csub\u003e10\u003c/sub\u003e is mainly concentrated in the northeast of the mining area in winter, but there is a discontinuity between the concentration and Pingshuo mining area. At the same time, it can be seen from Fig. 5 that dust in open-pit mining areas has obvious concentration characteristics in winter, but the diffusion range is limited to the vicinity of the open-pit mine, the dust concentration around the mining area is low, and the dust distribution in winter presents the characteristics of \u0026quot;strong concentration and weak dispersion\u0026quot;. In addition, the open-pit mine may also have a dust suction effect in winter, resulting in a decrease in the dust concentration around the mine and an increase in the concentration above the mine.\u003c/p\u003e\n \u003cp\u003eIn order to describe the spatial distribution characteristics of dust in Pingshuo mining area more clearly and accurately, the inversion data of PM2.5 concentration were selected, and the spatial distribution of PM2.5 concentration in Pingshuo mining area and urban area was obtained. The geographical ranges of Antaibao, Anjialing and Dong open pit mines were identified. According to the climatological definition (in the northern hemisphere, March to May is spring, June to August is summer, September to November is autumn, and December to February is winter), the PM2.5 concentration distribution maps of Shuozhou and Pingshuo mining areas were divided into four seasons, as shown in Fig. 6.\u003c/p\u003e\n \u003cp\u003eCompared with other seasons, PM2.5 is concentrated in the mining area in winter, with the highest concentration reaching 44\u0026micro;g/m3. This is because the temperature of the atmosphere near the ground in the northern winter is lower than that of the upper atmosphere, forming a stable inversion structure, and the air can not convection up and down, and it is difficult to disperse after the dust accumulation, and it is easy to occur serious dust pollution events. In spring, dust concentration in the mining area is still concentrated, and the dust concentration near the mining area is higher, but the concentration degree is lower than that in winter, and the maximum concentration in the mining area is 39\u0026micro;g/m3. In summer and autumn, the concentration of PM2.5 in the mining area decreased significantly compared with that in winter and spring. In addition, the geographical range of the mining area and part of the urban area is identified. Dust generated in the mining area spreads along the downwind direction, and the PM2.5 diffusion range is about 3Km in winter. Dust generated in the mining area cannot spread to the urban area, and there is discontinuity between the mining area and the urban area. In spring, PM2.5 only shows high and low distribution near the mining area, which is greatly affected by customs, and the PM2.5 concentration near the urban area is low, and the dust in the mining area does not directly affect the urban area. With higher temperature and increased precipitation in summer and autumn, PM2.5 concentration decreases overall, and there is no significant difference between the PM2.5 concentration in mining area and urban area.\u003c/p\u003e\n \u003cp\u003eSimilarly, the distribution diagram of PM10 concentration is shown in Fig. 7, and its distribution rule is roughly consistent with that of PM2.5 concentration. However, the concentration of PM10 in spring is higher than that in winter, with a maximum value of 174.4\u0026micro;g/m3. This is due to the high concentration of PM10 particles affected by strong sandstorms in spring due to windy and sandy weather. When the concentration decreased in summer and autumn, the diffusion range of PM10 was about 3.5Km, and the spatial diffusion pattern showed a seasonal distribution. The research results can reflect that the dust produced in Pingshuo mining area has little or no influence on the urban area, and the contribution of dust in mining area to urban environmental pollution is small.\u003c/p\u003e\n \u003cp\u003eIn summary, according to the geographical location identification and spatial distribution analysis results of Pingshuo mining area and urban area, the following conclusions can be drawn: The spatial distribution of PM2.5 in the whole Shuozhou City and Pingshuo Mining area is low in summer and autumn, and high in winter and spring. In the spatial distribution map, there is an obvious spatial discontinuity at the junction between Pingshuo Mining area and urban area, which is low at the junction and high in urban area and mining area. Moreover, dust diffusion degree varies with time. It shows that there is no direct correlation between Pingshuo mining area and the local increase of dust concentration in the urban area. The local increase of dust in the mining area does not affect the urban area, and the diffusion of dust particles to the surrounding area is small.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 Characteristics of dust migration in Pingshuo mining area\u003c/h2\u003e\n \u003cp\u003eThe inversion data of PM2.5 concentration for 9 consecutive months were selected, and the selected research area was divided into four areas outside the mining area, the mining area, the urban boundary area of the mining area and the urban area according to their relative geographical location. The dust diffusion and distribution rules of different months were sorted out according to the four seasons of winter, spring, summer and autumn, as shown in Fig.\u0026nbsp;8.\u003c/p\u003e\n \u003cp\u003eIn January, dust accumulation occurred in the mining area, and the PM2.5 concentration decreased first and then increased in the boundary area, but the overall concentration decreased, and the PM2.5 concentration gradually increased in the urban area. The results show that the dust produced in the mining area cannot be diffused to the urban area, and the impact on the urban environment is small, which is consistent with the previous analysis. The PM2.5 concentration in February showed a decreasing trend, but the distribution pattern was basically the same as that in January. According to the obtained results, the corresponding research area can be divided into four stages: Off-mine area, the Gathering area, the Descending area and the Elevation area. In spring (March to May), the concentration of PM2.5 in the mining area is still high in other areas, and the concentration degree and concentration value are gradually reduced, and the distribution law of each area is basically consistent with that of winter. The PM2.5 concentration in summer (June-August) gradually decreases from north to south, which can be divided into four stages: Off-mine impact area, the Stationary area, the Descending area and the stationary area. The PM2.5 concentration in autumn (September) is basically unchanged, roughly around 15ug/m3.\u003c/p\u003e\n \u003cp\u003eThe distribution law of PM10 is basically the same as that of PM2.5 concentration, but in May, the concentration of PM10 suddenly produces agglomeration phenomenon, and the concentration value gradually increases. The reason is that the concentration of PM10 suddenly increases due to the dust generated in spring, resulting in agglomeration phenomenon, and the overall PM10 concentration in spring is greater than that in winter. The PM10 concentration in autumn (September) is basically unchanged at around 41ug/m3.\u003c/p\u003e\n \u003cp\u003eThe results show that the dust diffusion phenomenon of Pingshuo mining area is seasonal distribution, and the accumulation phenomenon is easy to occur near the mining area in spring and winter temperature inversion, and the winter accumulation phenomenon is the most serious. In summer and autumn, the diffusion of dust generated in the mining area is mainly affected by wind speed and temperature factors, and the dust generated in the mining area is difficult to disperse to the urban area. In summer and autumn, the dust outside the mining area will be absorbed by the mining area after it spreads to the mining area, and the dust concentration will gradually decrease. The concentration of PM2.5 and PM10 in each season is basically lower than the national secondary air quality standard (75ug /m3 and 150ug /m3), and only in May, the PM10 concentration is higher than the secondary standard, and the overall pollution degree of the mining area is small. Based on the conclusion of the study, the mining area is not the main source of pollution in urban areas, and in the study of dust pollution control in open pit mines, the seasonal factors of dust diffusion and special factors such as strong dust weather should be fully considered, so as to propose corresponding control measures, which cannot be generalized.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Analysis of pollution situation in Pingshuo mining area\u003c/h2\u003e\n \u003cp\u003eStatistics on the air quality of PM2.5 and PM10 in Pingshuo Mining area and the proportion of days with different air quality grades were conducted. As shown in Fig.\u0026nbsp;10, the air quality was the best from June to September, and the percentage of days in the \u0026quot;excellent\u0026quot; level were 100%, 96%, 82% and 100%, respectively. The PM2.5 level decreased from January to May. \u0026quot;Light pollution\u0026quot;, \u0026quot;moderate pollution\u0026quot; and \u0026quot;heavy pollution\u0026quot; phenomena are produced, especially in January and March the pollution situation is the most serious, the air quality is poor, the pollution percentage is 19% and 21% respectively, of which March even appeared \u0026quot;serious pollution\u0026quot;. The statistical results of PM10 air quality in the mining area are roughly the same as that of PM2.5, and the air quality is above good from June to September, but the frequency of severe pollution increases from January to May, especially in spring, when the frequency of severe pollution is higher. The reason for this phenomenon is that there is less precipitation in spring and the surface is extremely dry and loose. When the wind blows past, it will draw a large amount of dust into the air, causing atmospheric pollution.\u003c/p\u003e\n \u003cp\u003eTo sum up, the months with the best air quality in Pingshuo Mining area are mainly from June to September, the particulate matter concentration in April and May has increased, and the pollution phenomenon is obvious in January and March, and the air quality is poor.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.4 Correlation analysis of dust concentration between urban area and mining area\u003c/h2\u003e\n \u003cp\u003eIn order to verify whether dust generated by production and mining in mining areas has an impact on urban areas, PM2.5 and PM10 concentration distribution rules of three regions in different months were selected respectively, as shown in Fig.\u0026nbsp;11. From winter to autumn, the concentration of PM2.5 and PM10 showed a decreasing trend. From January to May, the concentration distribution of PM2.5 from the mining area to the urban area showed a trend of decreasing first and then increasing, indicating that the dust generated in the mining area could not spread to the urban area, and there was no obvious correlation with the urban area. From June to September, the concentration distribution of PM2.5 from the mining area to the urban area showed a gradually decreasing trend, but the overall concentration value did not change much, indicating that the concentration in summer and autumn was basically affected by the weather, and the contribution of dust production in the mining area was very low.\u003c/p\u003e\n \u003cp\u003eCompared with PM2.5, the variation of PM10 concentration in the three regions is not obvious with the month. The PM10 concentration in the three regions shows no correlation from January to March, and the PM10 concentration in the three regions shows a sudden increase in May, and the concentration value ordering in the three regions shows fluctuations, and there is a fluctuation zone. From June to September, the PM10 concentration values in different regions were basically the same and were divided into consistent areas. The results show that mining area is not the root cause of the increase of dust concentration in urban area. The dust concentration at the junction of mining area and Shuocheng District is basically the same in summer and autumn, but in winter and spring, the dust concentration at the junction of mining area and Shuocheng District is lower than that of Shuocheng District and mining area, and the change of dust concentration in mining area has no obvious influence on the urban area.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on satellite monitoring and inversion technology, the spatial and temporal distribution characteristics of dust concentration in Pingshuo open-pit mining area are studied, and the main conclusions are as follows:\u003c/p\u003e \u003cp\u003e(1) The concentration of PM2.5 and PM10 retrieved by satellite is in good agreement with the observed values of the national control station, and the correlation coefficient (R2) is 0.8858 and 0.6338, respectively.\u003c/p\u003e \u003cp\u003e(2) The dust concentration in Pingshuo mining area is relatively high in winter, and there is an agglomeration phenomenon, which disappears in summer and autumn. It shows that the distribution of dust in mining area is affected by seasonal factors such as climate and temperature.\u003c/p\u003e \u003cp\u003e(3) The air quality in Pingshuo Mining area is the best from June to September, and the particulate matter concentration in April and May has increased, while the pollution phenomenon is obvious in January and March, and the air quality is poor.\u003c/p\u003e \u003cp\u003e(4) The correlation analysis between the dust concentration in the urban area and the mining area shows that the mining area is not the root cause of the increase of the dust concentration in the urban area, and the change of the dust concentration in the mining area has no obvious impact on the urban area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXukai Dong: Conceptualization, Investigation, Methodology, Software, Data curation, Writing \u0026ndash; original draft and Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eErhui Zhang: Writing \u0026ndash; review \u0026amp; editing, Visualization, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhigao Liu: Visualization, Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are incorporated in to the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e The work meets all ethical issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eThe author has nothing relevant to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eAll authors have a consensus regarding the publication of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen XF, Zheng FJ, Guo D, Wang LL, Zhao LM, Li JG, Li L, Zhang YH, Zhang KN, Xi M, Li KT. 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Plos One.17(4).\u003c/li\u003e\n\u003cli\u003eXiong W, Xu Y, Li J, Nie Y, Lou Q (2017) Urban dust pollution sources monitorina based on medium and hiah resolution satellite imaaery in Tianiin. Remote Sensina Information. 32(3):45-49.\u003c/li\u003e\n\u003cli\u003eYan YT, Lu XM, Wang JJ, Chen MN, Zhou LG, Ma WC (2022) Remote estimation of PM2.5 based on GaoFen-4 satellite data in the Yangtze Rive Delta urban agglomeration. China Environmental Science. 42(03):1005-1012.\u003c/li\u003e\n\u003cli\u003eYang CH (2016) Digital open pit mine automated production monitoring system based on the internet of things. Computer Programming Skills \u0026amp; Maintenance. (22)25-26+35.\u003c/li\u003e\n\u003cli\u003eYu HX, Zahidi I (2023) Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: An overview. Science of the total environment,864.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pollution characteristics, Remote sensing inversion, Dust diffusion, Mining area environment, Environmental pollution, Environmental protection","lastPublishedDoi":"10.21203/rs.3.rs-4190469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4190469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to study the spatial and temporal migration characteristics of dust diffusion and the impact of mining dust on urban environment in Pingshuo mining area, the distribution law of PM2.5 and PM10 concentration in Pingshuo mining area was analyzed by satellite remote sensing monitoring technology. The results show that the correlation coefficients between the monthly average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in Shuozhou City obtained by satellite inversion and the monitoring data of national control stations are 0.88 and 0.63, respectively, indicating a high reliability of satellite inversion data. The spatial distribution of dust concentration in Pingshuo mining area shows a low level in summer and autumn, and a high level in winter and spring, with significant dust accumulation phenomenon in winter. The air quality situation in Pingshuo mining area is best from June to September, with an increase in particle concentration in April and May, obvious pollution phenomena in January and March, and poor air quality conditions. The correlation analysis of dust concentration between urban areas and mining areas reveals a significant spatial discontinuity at the boundary between urban areas and mining areas, showing lower levels at the boundary while higher levels are observed within both urban areas as well as mining areas. This indicates that the mining area is not fundamentally responsible for the increase in dust concentration in urban areas, as changes in dust concentration within the mining area have no significant impact on urban areas.\u003c/p\u003e","manuscriptTitle":"Spatial and Temporal Distribution Characteristics of Dust Concentration Based on Satellite in Mining Area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 21:47:30","doi":"10.21203/rs.3.rs-4190469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-06-08T02:48:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-24T02:33:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-23T13:39:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-03T04:53:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-04-01T21:10:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb7fa0e5-48d2-45dc-90be-87ba4d7bdabc","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:16:02+00:00","versionOfRecord":{"articleIdentity":"rs-4190469","link":"https://doi.org/10.1007/s11356-024-34367-7","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-07-18 16:13:08","publishedOnDateReadable":"July 18th, 2024"},"versionCreatedAt":"2024-04-25 21:47:30","video":"","vorDoi":"10.1007/s11356-024-34367-7","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34367-7","workflowStages":[]},"version":"v1","identity":"rs-4190469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4190469","identity":"rs-4190469","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}