CN113255956A - Urban atmospheric pollution prediction method - Google Patents
Urban atmospheric pollution prediction method Download PDFInfo
- Publication number
- CN113255956A CN113255956A CN202110352317.1A CN202110352317A CN113255956A CN 113255956 A CN113255956 A CN 113255956A CN 202110352317 A CN202110352317 A CN 202110352317A CN 113255956 A CN113255956 A CN 113255956A
- Authority
- CN
- China
- Prior art keywords
- pollution
- concentration
- diffusion
- data
- atmospheric
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000009792 diffusion process Methods 0.000 claims abstract description 46
- 239000011159 matrix material Substances 0.000 claims abstract description 20
- 238000010790 dilution Methods 0.000 claims abstract description 15
- 239000012895 dilution Substances 0.000 claims abstract description 15
- 239000000779 smoke Substances 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 4
- 238000001556 precipitation Methods 0.000 claims 1
- 239000013618 particulate matter Substances 0.000 description 12
- 238000012544 monitoring process Methods 0.000 description 8
- 230000007613 environmental effect Effects 0.000 description 3
- 238000011109 contamination Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000003344 environmental pollutant Substances 0.000 description 2
- 231100000719 pollutant Toxicity 0.000 description 2
- 239000008277 atmospheric particulate matter Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001010 compromised effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for predicting urban atmospheric pollution, which comprises the following steps: (A1) carrying out navigation in a city to obtain pollution sources and pollution concentration data; carrying out grid division on the navigation area; (A2) combining the pollution concentration data with grid division to obtain the grid data of the pollution concentration of the city; (A3) collecting geographic and meteorological data of the city to obtain a diffusion dilution matrixT(ii) a (A4) Substituting the pollution concentration gridding data and the diffusion dilution matrix into a narrow smoke cloud dilution matrix to obtain a pollution source intensity matrix with local characteristics, and further obtaining pollution source intensity dataQ(ii) a (A5) To obtain the diffusion concentration of the pollutionC(ii) a (A6) And superposing the predicted concentration after the source strong diffusion on any grid to obtain the gridded atmospheric pollution predicted concentration. The invention has the advantages of accurate prediction and the likeAnd (4) point.
Description
Technical Field
The invention relates to atmospheric pollution, in particular to a method for predicting urban atmospheric pollution.
Background
PM2.5Is one of the important pollutants in the atmosphere, and is suitable for the atmospheric environment and human bodyHealth is seriously compromised. Currently about PM2.5The monitoring technology of (2) is mature, and the PM is monitored at present2.5The pollution prediction technology is mainly based on an air quality model (WRF-Chem, CMAQ, CAMx and the like) or a characteristic data machine training method.
Conventional PM2.5The pollution prediction method needs a large amount of monitoring data and has a large investment. Local source list data are needed for prediction work based on the air quality mode, and many areas in China do not have complete source lists and are long in updating year limit, so that local characteristics and timeliness of prediction results are poor. But only consider big data machine training method, not consider PM from environmental point of view2.5Predicting PM in atmospheric pollution processes from emission sources to diffusion2.5There are limitations and defects in concentration.
In order to reduce the environmental monitoring cost, increase the environmental monitoring flexibility and solve the problem of pain points of lack of locality and lack of process property of the pollution prediction result, the patent provides a city atmospheric pollution prediction method, such as atmospheric PM2.5A pollution prediction method.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for predicting urban atmospheric pollution.
The purpose of the invention is realized by the following technical scheme:
the urban atmospheric pollution prediction method comprises the following steps:
(A1) carrying out navigation in a city to obtain pollution sources and pollution concentration data;
carrying out grid division on the navigation area;
(A2) combining the pollution concentration data with grid division to obtain the grid data of the pollution concentration of the city;
(A3) collecting geographic and meteorological data of the city to obtain a diffusion dilution matrix T;
(A4) substituting the pollution concentration gridding data and the diffusion dilution matrix into a narrow smoke cloud dilution matrix to obtain a pollution source intensity matrix with local characteristics, so as to obtain pollution source intensity data Q;
(A5) the concentration of the pollution diffusion C is obtained,
ux、uyand uzAre wind velocity vectors in x, y, z directions, respectively, H is the pollution source height, σx、σy、σzDiffusion parameters in the x, y and z directions respectively, and t is time;
(A6) and superposing the predicted concentration after the source strong diffusion on any grid to obtain the gridded atmospheric pollution predicted concentration.
Compared with the prior art, the invention has the beneficial effects that:
1. compared with the traditional fixed station monitoring, the sailing mobile monitoring has the advantages of flexible operation, wide monitoring range, high spatial resolution and the like;
2. the demand of data volume is small, and the data usability is strong based on the one-time sailing acquisition source under the condition that the urban atmospheric emission source is stable;
3. the Gaussian diffusion model with the introduced time parameters considers the superposition effect of the smoke clusters, and the optimized Gaussian model has higher accuracy in multi-source diffusion simulation;
4. compared with a large-scale prediction mode, the method combining the source intensity inversion and the multi-source diffusion model is more suitable for the pollutant prediction in a small-scale range;
5. and the subsequent pollution prediction is carried out based on the actually measured data, and the method has the advantages of strong effectiveness, definite local pollution characteristics and the like.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a flow chart of a method for urban atmospheric pollution prediction according to an embodiment of the invention;
fig. 2 is a schematic diagram of a prediction result of the urban atmospheric pollution prediction method according to the embodiment of the invention.
Detailed Description
Fig. 1-2 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and reproduce the invention. Some conventional aspects have been simplified or omitted for the purpose of teaching the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will be within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the following alternative embodiments, but is only limited by the claims and their equivalents.
Example 1:
fig. 1 shows a flow chart of an urban atmospheric pollution prediction method according to an embodiment of the present invention, and as shown in fig. 1, the urban atmospheric pollution prediction method includes the following steps:
(A1) carrying out navigation in a city to obtain pollution sources and pollution concentration data;
carrying out grid division on the navigation area;
(A2) combining the pollution concentration data with grid division to obtain the grid data of the pollution concentration of the city;
(A3) collecting geographic and meteorological data of the city to obtain a diffusion dilution matrix T;
(A4) substituting the pollution concentration gridding data and the diffusion dilution matrix into a narrow smoke cloud dilution matrix to obtain a pollution source intensity matrix with local characteristics, so as to obtain pollution source intensity data Q;
(A5) the concentration of the pollution diffusion C is obtained,
ux、uyand uzAre wind velocity vectors in x, y, z directions, respectively, H is the pollution source height, σx、σy、σzDiffusion parameters in the x, y and z directions respectively, and t is time;
(A6) and superposing the predicted concentration after the source strong diffusion on any grid to obtain the gridded atmospheric pollution predicted concentration.
In order to obtain a prediction closer to the actual situation, further, the urban atmospheric pollution prediction method further comprises the following steps:
(A7) kriging interpolation was performed on the predicted concentrations using Arcgis.
To obtain more complete data, further, the geographic and meteorological data include topographical features, temperature, humidity, wind speed, wind direction, air pressure, rain, pollution source altitude, and atmospheric particulate matter dry and wet settling.
In order to accurately obtain the diffusion coefficient, further, the diffusion parameter is obtained by:
according to the atmospheric diffusion capability grade of the city, diffusion coefficients sigma in the directions of x, y and z are respectively obtained on a PassQuel diffusion curvex、σy、σz。
In order to accurately obtain the atmospheric diffusivity grade, further, the atmospheric diffusivity grade is obtained according to meteorological data, wherein the meteorological data comprises the cloud cover and the wind speed of the city.
Example 2:
urban PM (particulate matter) prediction method for urban atmospheric pollution according to embodiment 1 of the invention2.5Application example in pollution prediction.
In this application example, the urban atmosphere PM2.5A contamination prediction method comprising the steps of:
(A1) carrying out navigation in a city to obtain pollution sources and pollution concentration data;
for PM2.5Carrying out early-stage layout analysis on a heavily polluted city, defining functional districts of the city, and judging typical PM2.5A source of emissions. Develop multi-azimuth PM of a certain city2.5The navigation work is carried out, and the navigation areas comprise highways, factories, residential districts, parks, schools, hospitals, scenic spots, business centers and the like;
obtaining PM of a certain city by means of navigation monitoring2.5High spatial resolution density data, data scoreThe resolution is second level, and basically covers a main area of a certain city;
carrying out grid division on the navigation area: dividing the grid of the internal area by taking the route of the outermost periphery of the navigation as a boundary, wherein the divided grid is 1km multiplied by 1km according to the size of the boundary;
(A2) combining the pollution concentration data with grid division to obtain the grid data of the pollution concentration of the city, which specifically comprises the following steps:
high resolution PM2.5Combining the concentration data with the grids, and performing data dimension reduction treatment to enable each grid point to correspond to one PM2.5Concentration data, namely obtaining PM of a certain city2.5Concentration gridding data;
(A3) collecting geographic and meteorological data of the city, including terrain characteristics, temperature, humidity, wind speed, wind direction, air pressure, rainfall, pollution source altitude, PM2.5Obtaining a diffusion dilution matrix T by using data such as dry-wet sedimentation and the like, wherein the specific obtaining mode is the prior art in the field;
(A4) substituting the pollution concentration gridding data and the diffusion dilution matrix into a narrow smoke cloud dilution matrix (SSIM) mode to obtain a pollution source strong matrix with local characteristics, so as to obtain pollution source strong data Q;
(A5) the concentration of the pollution diffusion C is obtained,
ux、uyand uzAre wind velocity vectors in x, y, z directions, respectively, H is the pollution source height, σx、σy、σzDiffusion parameters in the x, y and z directions respectively, and t is time;
diffusion parameter σx、σy、σzThe obtaining method is as follows:
according to meteorological factors such as cloud cover, wind speed and sunshine, the atmospheric diffusion capacity is divided into six grades (table 1) of strong instability (A), instability (B), weak instability (C), neutrality (D), less instability (E) and stability (F), and the atmospheric diffusion capacity is obtained according to monitoringDetermining the atmospheric diffusion capacity grade according to meteorological data, substituting the atmospheric diffusion capacity grade into a Passerier (P-G) diffusion curve, and giving out a diffusion coefficient sigma in the directions of x, y and z by the P-G curvex、σy、σz；
TABLE 1
(A6) After t time, all the PM strongly discharged from the source2.5Through the atmospheric diffusion process, interactive diffusion is generated in the urban area grid, the predicted concentration after strong diffusion of all sources at the same grid point is superposed, and the gridded PM is obtained2.5Predicting concentration data;
(A7) taking into account the spatial correlation of the gridded data, Arcgis is adopted for PM2.5Kriging interpolation is carried out on gridding predicted concentration data to ensure that PM of a certain market2.5The concentration prediction result is closer to the actual situation, and the PM which is about to appear in the region can be visually reflected in space2.5Contamination, as shown in fig. 2.
Claims (6)
1. The urban atmospheric pollution prediction method comprises the following steps:
(A1) carrying out navigation in a city to obtain pollution sources and pollution concentration data;
carrying out grid division on the navigation area;
(A2) combining the pollution concentration data with grid division to obtain the grid data of the pollution concentration of the city;
(A3) collecting geographic and meteorological data of the city to obtain a diffusion dilution matrix T;
(A4) substituting the pollution concentration gridding data and the diffusion dilution matrix into a narrow smoke cloud dilution matrix to obtain a pollution source intensity matrix with local characteristics, so as to obtain pollution source intensity data Q;
(A5) the concentration of the pollution diffusion C is obtained,
ux、uyand uzAre wind velocity vectors in x, y, z directions, respectively, H is the pollution source height, σx、σy、σzDiffusion parameters in the x, y and z directions respectively, and t is time;
(A6) and superposing the predicted concentration after the source strong diffusion on any grid to obtain the gridded atmospheric pollution predicted concentration.
2. The urban atmospheric pollution prediction method according to claim 1, further comprising the steps of:
(A7) kriging interpolation was performed on the predicted concentrations using Arcgis.
3. The method of predicting urban atmospheric pollution according to claim 1, wherein said geographic and meteorological data include topographic features, temperature, humidity, wind speed, wind direction, air pressure, rain, pollution source altitude, and dry and wet precipitation of atmospheric particulates.
4. The urban atmospheric pollution prediction method according to claim 1, characterized in that the diffusion parameters are obtained by:
according to the atmospheric diffusion capability grade of the city, diffusion coefficients sigma in the directions of x, y and z are respectively obtained on a PassQuel diffusion curvex、σy、σz。
5. The method of predicting atmospheric pollution in cities of claim 4, wherein said level of atmospheric diffusivity is obtained from meteorological data including cloud cover and wind speed of said cities.
6. The method of predicting urban atmospheric pollution according to claim 1, wherein said pollution concentration data is atmospheric particulate concentration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110352317.1A CN113255956A (en) | 2021-03-31 | 2021-03-31 | Urban atmospheric pollution prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110352317.1A CN113255956A (en) | 2021-03-31 | 2021-03-31 | Urban atmospheric pollution prediction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113255956A true CN113255956A (en) | 2021-08-13 |
Family
ID=77181288
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110352317.1A Pending CN113255956A (en) | 2021-03-31 | 2021-03-31 | Urban atmospheric pollution prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113255956A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545565A (en) * | 2022-11-24 | 2022-12-30 | 江苏省生态环境大数据有限公司 | Method and system for managing and controlling total amount of pollution discharged from park based on atmospheric environment quality |
| CN117370772A (en) * | 2023-12-08 | 2024-01-09 | 北京英视睿达科技股份有限公司 | PM2.5 diffusion analysis method and system based on urban street topography classification |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008229A (en) * | 2014-04-30 | 2014-08-27 | 北京大学 | Method for building street pollutant concentration distribution model |
| CN107436343A (en) * | 2017-07-31 | 2017-12-05 | 南京南瑞集团公司 | It is a kind of to simulate the method for calculating sensitizing range pollutant concentration |
-
2021
- 2021-03-31 CN CN202110352317.1A patent/CN113255956A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008229A (en) * | 2014-04-30 | 2014-08-27 | 北京大学 | Method for building street pollutant concentration distribution model |
| CN107436343A (en) * | 2017-07-31 | 2017-12-05 | 南京南瑞集团公司 | It is a kind of to simulate the method for calculating sensitizing range pollutant concentration |
Non-Patent Citations (4)
| Title |
|---|
| 张久凤等: "粒子群优化算法在源强反算问题中的应用研究", 《中国安全科学学报》 * |
| 朱蓉等: "第2代大气污染物排放源强反演模式SSIM2及其在城市规划大气环境影响评价中的应用", 《气象科技》 * |
| 王戎骁等: "基于源项估计的污染气体扩散数据驱动仿真", 《系统仿真学报》 * |
| 范引琪等: "城市面源SO2允许排放总量的网格分配方法", 《气象科技》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545565A (en) * | 2022-11-24 | 2022-12-30 | 江苏省生态环境大数据有限公司 | Method and system for managing and controlling total amount of pollution discharged from park based on atmospheric environment quality |
| CN117370772A (en) * | 2023-12-08 | 2024-01-09 | 北京英视睿达科技股份有限公司 | PM2.5 diffusion analysis method and system based on urban street topography classification |
| CN117370772B (en) * | 2023-12-08 | 2024-04-16 | 北京英视睿达科技股份有限公司 | PM2.5 diffusion analysis method and system based on urban street topography classification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112418609B (en) | Surface-grid-point-based accurate tracing method for secondary atmospheric pollution | |
| Wehner et al. | Relationships between submicrometer particulate air pollution and air mass history in Beijing, China, 2004–2006 | |
| CN113255956A (en) | Urban atmospheric pollution prediction method | |
| CN104865353A (en) | Atmospheric pollution data acquisition method for industrial park based on unmanned aerial vehicle | |
| CN113624921B (en) | Multi-mode comprehensive pollution tracing method | |
| CN107491566B (en) | Method for quantitatively researching PM2.5 purification effect of urban forest | |
| CN111353634B (en) | Sulfur dioxide emission source optimization method based on mode initial field assimilation | |
| Feng et al. | Investigation on air pollution control strategy in Hangzhou for post-G20/pre-Asian-games period (2018–2020) | |
| CN110658307A (en) | Method for evaluating influence of pollution source on environmental air quality | |
| Ying et al. | Modeling air quality during the California Regional PM10/PM2. 5 Air Quality Study (CRPAQS) using the UCD/CIT source-oriented air quality model–Part I. Base case model results | |
| CN116070839A (en) | Multi-source data road micro-environment air quality contribution tracing method | |
| CN115420854B (en) | Atmospheric pollutant tracing method based on forward and backward model combination | |
| CN107967242A (en) | The analysis method and system that a kind of Forecast of Urban Traffic Flow influences air quality | |
| Li et al. | Vertical distributions of boundary-layer ozone and fine aerosol particles during the emission control period of the G20 summit in Shanghai, China | |
| Zhang et al. | Spatiotemporal estimation of the PM2. 5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China | |
| CN115964869A (en) | High-space-time resolution atmospheric pollution diffusion migration simulation method | |
| CN107767081B (en) | Method and device for evaluating atmospheric environmental impact of coal-fired power plant | |
| CN114692355B (en) | Motor vehicle pollution diffusion simulation method and system by coupling WRF (write driver software) and OpenFOAM (open-world automatic learning machine) models | |
| Ndletyana et al. | Spatial Distribution of PM 10 and NO 2 in Ambient Air Quality in Cape Town CBD, South Africa. | |
| Guldmann et al. | Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach | |
| Akbary et al. | Simulation and analysis of polluted days in Tehran | |
| Phadnis et al. | Evaluation of long-range transport models for acidic deposition in East Asia | |
| Wang et al. | Analysis of aqi change characteristics and correlation with PM2. 5 And PM10 in beijing-tianjin-hebei region | |
| CN113988540A (en) | Atmospheric pollution trend analysis method based on GIS technology | |
| Bowne | A simulation model for air pollution over Connecticut |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210813 |