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    • 专利摘要:
    The present invention relates to a CWT-based quantitative analysis method for an external source of an atmospheric primary pollutant, including following steps: step Sl: acquiring backward trajectory data of a research area using an airflow trajectory HYSPLIT model; step 52: gridding the backward trajectory data, importing hourly observing values of an atmospheric pollutant in the research area, and calculating a concentration-weighted trajectory (CWT); further introducing a weight factor Wij to reduce a model uncertainty caused when the trajectory data is relatively small, thereby obtaining WCWT; and step S3: calculating a CWT percentage PCWT, and further quantitatively analyzing the potential source area percentage of the atmospheric primary pollutant. The present invention can better resolves the problem that prior art cannot quantitatively reflect the external source of the atmospheric primary pollutant to provide strong support for regional management and control of atmospheric pollution treatment in the research area.
    公开号:NL2027637A
    申请号:NL2027637
    申请日:2021-02-24
    公开日:2021-10-19
    发明作者:Zhang Hui；Gao Jixi；Pei Wenming；Gong Long；Qiao Yajun；Xu Yuanchang；Hu Mengtian；Ma Mengxiao；Qiu Kuanbiao；Qu Jining
    申请人:Nanjing Inst Of Environmental Sciences Mee；Shanghai Municipal Bureau Of Ecology And Env；
    IPC主号:
    专利说明:

    -1- CWT-BASED QUANTITATIVE ANALYSIS METHOD FOR EXTERNAL SOURCE OFATMOSPHERIC PRIMARY POLLUTANT
    TECHNICAL FIELD The present invention relates to the field of atmospheric pollutant detection, and in particular, to a CWT-based quantitative analysis method for an external source of atmospheric primary pollutant.
    BACKGROUND In recent years, the source of atmospheric particulate matter in our country has shown regional characteristics, and the regional transportation of atmospheric particulate matter has gradually become a focus of research. Since the secondary chemical reaction process of PM2s is more complicated, existing research focusses on the source of primary particulate matter of PM. Scholars at home and abroad often use two kinds of methods to study interregional transmission of a pollutant: (1) Air quality model simulation methods based on emission source inventory such as wrf- chem/wrf-CALPUFF/wrf-CMAQ etc.{Cheng ZHOU et al., 2019; Chen CUL, 2015; Jiacheng CHANG et al., 2017). These methods establish models and predict concentrations of atmospheric pollutants based on physical and chemical processes under specific meteorological fields, source emissions, and initial boundary conditions. Such methods have higher reliability and completeness but with complicated operations. Meanwhile, credibility of the calculation results depends on the emission source inventory, and not updating the inventory in time and the uncertainty of parameter settings will affect the accuracy rate{Pengcheng SONG et al, 2019). (2) In recent years, a method of combining Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model with potential source contribution function (PSCF) method and concentration-weighted trajectory (CWT) method is used to study path transportation and transportation sources of the atmospheric pollutant without depending on the emission source inventory due to its simple operation.
    -2- Such method has been broadly used by more and more scholars. At present, potential source area analysis based on the HYSPLIT model mainly identifies locations of the source areas and transmission paths through backward trajectory clustering, PSCF analysis and CWT analysis. The trajectory clustering analysis only reflects a direction of a source of an airflow trajectory, but cannot analyze a direction of a source of a pollution trajectory. The PSCF analysis can only qualitatively analyze a location of a potential source area of external transmission, but cannot reflect a pollution level of the pollution trajectory; and the CWT can analyze the pollution level of the pollution trajectory, but cannot reflect a percentage of a pollution concentration of each grid. In order to quantitatively analyze the source of air pollutant in a research area, a new more in-depth and quantitative method is highly desirable.
    SUMMARY The present invention aims to provide a CWT-based quantitative analysis method for an external source of atmospheric primary pollutant, and resolve a problem that the prior art cannot quantitatively reflect the external source of the atmospheric primary pollutant. In order to realize the above purpose, a technical solution provided by the present invention is: a CWT-based quantitative analysis method for an external source of atmospheric primary pollutant, including the following steps: step S1: acquiring backward trajectory data of a research area using an airflow trajectory HYSPLIT model; step S2: gridding the backward trajectory data, importing hourly observing values of atmospheric pollutant in the research area, and calculating a concentration- weighted trajectory (CWT); further introducing a weight factor Wj; to reduce a model uncertainty caused when the trajectory data is relatively small and obtain WCWT;
    -3- step S3: calculating a CWT percentage (PCWT), an area with a high percentage being a high pollution source area of the atmospheric primary pollutant. A meteorological data source in step S1 is reanalysis data in a Global Data Assimilation System (GDAS) (ftp://arlftp.arthg.noaa.gov/pub/archives/gdas1/) provided by NCEP/NCAR. The meteorological data in step S1 selects to use grid data with a resolution of
    0.5 degrees*0.5 degrees, HYSPLIT software is used to perform the backward trajectory calculation, and a coordinate of an atmospheric monitoring point in the research area is selected as a starting point of the backward trajectory for simulation calculation. Further, a simulation time in step S1 is set to 72h, and each day is divided into four time periods for simulation, which respectively are: 0 o'clock, 6 o'clock, 12 o'clock, 18 o'clock, a simulated height is between 100-300 meters, and the time is divided into spring from March to May, summer from June to August, autumn from September to November, and winter from December to February of next year. In step S2, a calculation formula of the average weighted concentration trajectory CWT is as follows: _ 1 M . Cij = SH 7 i= CT (1); wherein: Cj is an average weighted concentration on a grid ij; / is a trajectory; M is a total number of the trajectories; C; is a concentration value of the correspondingly monitored atmospheric pollutant when the trajectory / passes through the grid ij; Wj is a weight factor; Tij is a time that the trajectory / stays in the grid ij; and preferably, in step S2, a resolution of the grid is selected to be less than 0.5 degrees. Further, in order to reduce the uncertainty when the number nj of all trajectories that pass through the grid is relatively small, a weight function Wj is introduced, and a formula is as follows:
    -4-
    1.00 3avg < ny;
    0.70 avg <n; < 3avg Wi71 0.42 0.5avg < n;; < avg
    0.17 nij S 0.5avg wherein: nj is the number of all the trajectories that pass through the grid, and avg is an average number of the trajectories in each grid in the research area. WCWTi=WixCy; (2); wherein, WCWT;; is an average weighted concentration on the grid ij; Cj is an average weighted concentration on the grid ij; Wi is a weight factor; and m and n are total numbers of rows and columns of a CWT grid, respectively.
    In step S3, a formula of the PCWT is as follows: WCWT; Pe (3). ym Sn WOWTij (3) When a Pj value is relatively large, it means that a percentage of the grid ij in the WCWT value of the total grid concentration-weighted trajectories is large, and an area with a high percentage is a high pollution source area of the atmospheric primary pollutant.
    PCWT values of the concentration-weighted trajectories of different source areas are counted through administrative boundaries and relative source area directions, and an area having obvious influence on the research area is calculated out.
    In comparison with the prior art, the advantageous effects of the present invention are: the CWT-based quantitative analysis method for the external source of atmospheric primary pollutant of the present invention further proposes a PCWT method on the basis of analysis of a CWT model extended from a HYSPLIT backward model to quantitatively analyze the source of the atmospheric pollutant in the research area. This new quantitative analysis method for the external source of the atmospheric pollutant can better resolve a problem that prior art cannot quantitatively reflect the external source of the atmospheric primary pollutant, which
    -5- provides strong support for regional management control of atmospheric pollution treatment in the research area.
    BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flowchart of a CWT-based quantitative analysis method for an external source of atmospheric primary pollutant according to an embodiment of the present invention. FIG. 2 is a diagram of yearly backward trajectory of a HYSPLIT model in a research area. FIG. 3 is a diagram of yearly WCWT statistics in the research area. FIG. 4 is a diagram of PCWT statistics of source area directions relative to the research area. FIG. 5 is a diagram of PCWT statistics of the research area counted according to administrative division. FIG. 6 is a variation trend of a PCWT value of PM, 5s in potential source areas at home and abroad of the research area from 2015 to 2018. FIG. 7 is a variation trend of a PCWT value of PM>s in major foreign potential source areas of the research area from 2015 to 2018.
    DETAILED DESCRIPTION OF THE EMBODIMENTS The present invention is further described in conjunction with the following exemplary embodiments. A CWT-based quantitative analysis method for an external source of atmospheric primary pollutant, includes following steps: Step S1: acquiring backward trajectory data of a research area.
    -6- Specifically, reanalysis data of Global Data Assimilation System (GDAS) (ftp://arlftp.arlhg.n0aa.gov/pub/archives/gdas1/} provided by NCEP/NCAR is acquired.
    Grid data with a resolution of 0.5 degrees*0.5 degrees is selected and used, HYSPLIT software is used to perform backward trajectory calculation, and a coordinate of an atmospheric monitoring point in the research area is selected as a starting point of the backward trajectory for simulation calculation. A simulation time is set to 72h, and each day is divided into four time periods for simulation, which respectively are: 0 o'clock, 6 o'clock, 12 o'clock, 18 o'clock, a simulated height is between 100-300 meters.
    Step S2: gridding the backward trajectory data, importing daily pollutant data, and performing CWT analysis.
    Specifically, a Convert to TGS Files function of a TrajStat module of Meteoinfo is used to convert a GDAS file into a TGS format, and then a Convert to Shape File function is used to convert the TGS file into a Shp format. A Join Trajectory Layers function is used to merge the trajectories for subsequent analysis according to the time period (such as season or year) required by the research.
    The pollutant data at the monitored point is imported, and date and pollutant concentration are selected. A date format needs to be set to year, month, day and time, for example, 1:00 on January 1, 2015 is written as 2015010101; and a null value is recorded as 0. A calculation formula of the CWT is as follows: _ 1 M . Cij = SFT b= CG Ty (1); wherein: Cj is an average weighted concentration on the grid ij; / is a trajectory; M is a total number of the trajectories; Cy is a concentration value of the correspondingly monitored atmospheric pollutant when the trajectory / passes through the grid ij; Tig is a time that the trajectory / stays in the grid ij; and
    -7- a resolution of the grid is selected to be less than 0.5 degrees while performing the CWT analysis.
    Further a weight factor Wj is introduced to reduce a model uncertainty caused when the trajectory data is relatively small and obtain WCWTy; In order to reduce the model uncertainty caused when the nj is relatively small, it needs to be multiplied by a weight function Wij to obtain a WCWT value, and specifically, the weight is set as follows:
    1.00 3avg < ny;
    0.70 avg <n; < 3avg Wit 0.42 0.5avg < nij S avg
    0.17 nij < 0.5avg wherein: nj is the number of all the trajectories that pass through the grid, and avg is an average number of the trajectory end points in each grid in the research area: WCWTi=WjxG; Step S3: calculating a CWT percentage (PCWT), an area with a high percentage being a high pollution source area of the atmospheric primary pollutant.
    Specifically, the WCWT analysis results are opened in ArcGlS, a sum of the WCWT values of all the grids is counted to calculate out a percentage of the WCWT value of each grid. A calculation formula of the PCWT is as follows: p= WCWT;; SL NL wewTy wherein, WCWT, is an average weighted concentration on the grid ij; m and n are total numbers of rows and columns of a CWT grid, respectively.
    Below FIG. 2 to FIG. 7 illustrate the CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant, the research area takes Tieling City as an example, the atmospheric primary pollutant takes PMs as an example, PM: s data comes from four monitoring stations in Tieling City from 2015 to
    -8- 2018: Huigong Street west, Jinshajiang Road north, Water Park, east section of Yinzhou Road. Specifically, according to the parameter setting of the present invention, the CWT analysis results are shown in FIG. 3, The larger the CWT value, the greater the potential source area concentration, and the more serious influence on the research area. It can be seen that in the four years, the pollution in 2017 is the most serious. High-value areas at home are mainly concentrated in eastern inner Mongolia, central Liaoning Province, central and western Jilin Province, and cities around the Bohai Bay, and high-value areas abroad are mainly distributed in Russia, Mongolia, and North Korea. The potential source areas of PM2.5 in Tieling City mainly come from three directions: the source areas in the northwest have the widest distribution area and the farthest source, and the 72-hour trajectories can be traced to Mongolia and Russia. A transportation distance from the source areas in the southwest takes second place, including southern Liaoning Province and urban areas around Bohai Bay. A transportation distance from the source areas in the northeast is the shortest, which are mainly distributed around Tieling City, including Siping City, Liaoyuan City and so on. FIG. 4 is based on administrative boundaries, and the PCWT of the area is counted, so that pollution levels of the research area affected by different countries, provinces and cities are obtained. The four-year average PCWT of Siping City located in the northeast of Tieling City is 3.15%, which is the city having the highest percentage of influence on Tieling City from 2015 to 2018; Shenyang City located in the southwest of Tieling City takes second place, and the four-year average PCWT is
    3.07%. The four-year average PCWT of Tieling City is 3.03%; then the four-year average PCWTs of Yingkou City, Liangyang City, An'shan City and Panjin City located in the southwest of Tieling City are 2.97%, 2.95%, 2.93% and 2.83%, respectively; and the four-year average PCWTs of Songyuan City, Baicheng City, and Tongliao City where the Horgin Sand Land is located, in the Songnen Plain area in the central and western Jilin Province located in the northwest of Tieling City are 2.82%, 2.73%, and
    2.72%, respectively,
    -9- FIG. 5 counts the PCWTs in eight directions of the research area: east, south, west, north, northeast, northwest, southeast, and southwest.
    1. A contribution value of the source area in the northwest direction has a highest percentage among the three, and the source area in this direction includes Horgin Sand Land, Sandy Land in Northwest Liaoning, and Northern Agro-pastoral Transitional Zone. The four-year average percentage was 27.36%, kept rising from 2015 to 2017, reached the highest of 30.01% in 2017, and decreased to 23.92% in
    2018. 2. The source area in the northeast direction is mainly from the central and western Jilin Province, southern Heilongjiang Province and other surrounding industrial and residential areas. The four-year average percentage was 18.51%, increased slightly from 2015 to 2017, reached the highest of 19.39% in 2017, and decreased to 17.31% in 2018. 3. The source area in the southwest direction includes the urban agglomerations in central and eastern Liaoning, the cities around the Bohai Sea and the transportation cross the sea of the Beijing-Tianjin-Hebei region. The four- year average percentage was 15.73%, reached the highest of 17.70% in 2015, decreased continuously from 2016 to 2017, reached the lowest of 13.86% in 2017, and started to rise slightly to 14.53% since 2018. FIG. 6 is a variation trend of a PCWT value of PM; in potential source areas at home and abroad of the research area from 2015 to 2018. The source PCWT value of PMs in Tieling City has been reduced from 81.45% to 65.95% in the past four years. The PCWT value of the potential source area abroad has significantly increased within four years. The PCWT value increased from 18.55% to 34.05%, wherein the PCWT value in Russia has increased the most and increased from 5.54% to 11.32%. The PCWT value in Mongolian came next and increased from 3.03% to 6%, and the PCWT value in North Korea increased from 0.6% to 1.46%. This indicates that the contribution value of the potential source area at home to PM:2; in Tieling City has decreased year by year, while the contribution value of the potential source area abroad has increased year by year. FIG. 7 is a variation trend of a PCWT value of PM2 5 in major foreign potential source areas of the research area from 2015 to 2018. The potential source areas
    -10- abroad are mainly located in the cities of Ondorhaan, BARUUN-URT, and Choybalsan in eastern Mongolia, and Ulan-Ude, Chita, and Akinskoye in Zabaikalsky Krai Territory of Russia. The above areas are dominated by mining as well as farming and animal husbandry. The PM; pollution, produced by industrial dust of mining, straw burning of the farming and animal husbandry, and desertification due to overgrazing, is transported by the northwest wind to affect Tieling. The source areas in North Korea are mainly located in the Sinuiju Special Administrative Region and Pyongyang, which may be related to the large-scale urban development and industrial construction in these areas in recent years.
    What are described above are merely preferred embodiments of the present invention without limiting the present invention in any form. Any simple change, equivalent replacement and improvement made to the above embodiments by any person skilled in the art according to the technical essence of the present invention without departing from the scope of the technical solution of the present invention still belong to the scope for protection of the technical solution of the present invention.
    权利要求:
    Claims (8)
    [1]
    A CWT-based quantitative analysis method for an external source of atmospheric primary pollutant, comprising the following steps: step S1: obtaining backpath data of a survey area using an airflow path HY SPLIT model; step S2: rasterizing the backward trajectory data, importing hourly observation values of an atmospheric pollutant in the survey area, and calculating a concentration-weighted trajectory, CWT; Ci = 5 SM CT; (1): ij SM Ti Iz=1 vi +ijt A) where C i ; is an average weighted concentration on the grid ij; [ is a job; M is a total number of jobs; C is a concentration value of the correspondingly monitored atmospheric pollutant as the track 7 intersects the grid ij; T;;; a time is how long the track / remains in the grid ij; further introducing a weight factor W;; to reduce a model uncertainty caused if the orbit data is relatively small and obtain WCWT, and wherein a formula is as follows: WCWT;=WyxCy(2): where C;; is an average weighted concentration on the grid ij; Wi; is a weight factor; and m and » are respectively total numbers of rows and columns of a CWT grid; and step S3: calculating a percentage CWT ("CWT percentage", PCWT), wherein a high percentage region is a high pollution source region of the atmospheric primary pollutant, and wherein a formula is as follows: YER I wewTy ”
    [2]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that a
    S12 - meteorological data source, in step S1, is reanalysis data in a Global Data Assimilation System ("Global Data Assimilation System", GDAS) provided by NCEP/NCAR.
    [3]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary polluter according to claim 1, characterized in that meteorological data selects, in step S1, to use raster data with a resolution of 0.5 degrees * 0.5 degrees , wherein HYSPLIT software is used to perform the reverse trajectory calculation, and wherein a coordinate of an atmospheric monitor point in the survey area is selected as a starting point of the reverse trajectory for simulation calculation.
    [4]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that a simulation time, in step S1, is set to 72h, and wherein each day is divided into four simulation time periods, which are respectively: 0 hours, 6 hours, 12 hours, 6 hours, where a simulated altitude is between 100 - 300 meters, and where the time is divided into spring from March - May, summer from June - August, autumn from September — November, and winter from December — February of next year.
    [5]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that, in step S2, a resolution of the grid is selected to be less than 0.5 degrees.
    [6]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that in order to reduce the uncertainty if the number n;; of all paths crossing the grid is relatively small, a weight function W i ; is introduced, and where a formula is as follows:
    S13 -
    1.00 avg < ny; i avg < ny; < 3avg "1042 05avg <n; S avg
    0.17 ny; < 0.5avg where: n;; is the number of all the lanes intersecting the grid, and avg is an average number of the lanes in each grid in the search area.
    [7]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that if a P‚; value is relatively large 1s, it means that a percentage of the grid ij in the WCWT- value of the concentration-weighted orbits of the total grid is large, and has a clear influence on the study area.
    [8]
    The CWT-based quantitative analysis method for the external source of the atmospheric primary pollutant according to claim 1, characterized in that PCWT values of the concentration-weighted trajectories from different sources are counted by administrative boundaries and relative source area directions, and influence on the research area is calculated.
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    同族专利:
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    引用文献:
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    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    CN202010143804.2A|CN111458456A|2020-03-04|2020-03-04|CWT-based quantitative analysis method for external sources of primary atmospheric pollutants|
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