CN110941930A - Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city - Google Patents
Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city Download PDFInfo
- Publication number
- CN110941930A CN110941930A CN201911050496.2A CN201911050496A CN110941930A CN 110941930 A CN110941930 A CN 110941930A CN 201911050496 A CN201911050496 A CN 201911050496A CN 110941930 A CN110941930 A CN 110941930A
- Authority
- CN
- China
- Prior art keywords
- mongolian
- city
- diffusion
- unhealthy
- uulan
- 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 15
- 238000009792 diffusion process Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 12
- 238000004088 simulation Methods 0.000 claims abstract description 6
- 230000007613 environmental effect Effects 0.000 claims abstract description 5
- 238000010586 diagram Methods 0.000 claims abstract description 4
- 238000005259 measurement Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims 1
- 239000013618 particulate matter Substances 0.000 abstract description 5
- 239000003344 environmental pollutant Substances 0.000 abstract description 2
- 231100000719 pollutant Toxicity 0.000 abstract description 2
- 241000732617 Uvaria Species 0.000 abstract 2
- 238000003915 air pollution Methods 0.000 description 6
- 239000003245 coal Substances 0.000 description 3
- 239000000356 contaminant Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000010411 cooking Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 208000023504 respiratory system disease Diseases 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G01N15/075—
Abstract
The invention relates to a PM2.5 diffusion simulation diagram of Mongolian Uvaria city, which is characterized in that 2018 year-one-year data of 8 CAM monitoring stations are processed by an inverse distance weighting method (IDW), the PM2.5 diffusion simulation diagram of the Mongolian Uvaria city is drawn by taking months and seasons as units, and the PM2.5 level is classified according to human health related Air Quality Index (AQI) of Environmental Protection Agency (EPA). The method has the advantages that the PM2.5 pollution diffusion condition of an unmeasured area is accurately simulated and predicted, the chimney in the Mongolian yurt area is proved to be a main PM2.5 pollutant source, researchers and clients can clearly and definitely know the fine particulate matter diffusion condition of Mongolian Uulan Butot, and the method is also a basic information basis for carrying out related measures for reducing the fine particulate matter.
Description
Technical Field
The invention belongs to environmental science and air pollution research, accurately simulates and predicts the PM2.5 pollution diffusion condition of an unmeasured area, proves that a chimney in a Mongolian yurt area is a main PM2.5 pollutant source, and can enable researchers and clients to clearly and clearly know the fine particulate matter diffusion condition of Mongolian Uulan baset. In addition, it is the basic information basis for the implementation of relevant measures for reducing fine particulate matter.
Background
At present, air pollution in Wulanbai city is the first problem to be solved urgently. Wherein the fine particulate pollution accounts for 80% of the total air pollution, and is mainly from fire coal in Mongolian yurt areas. Has exceeded the world health organization air quality guidelines and the Mongolian fine particulate pollution standards. Moreover, it is becoming a major factor in respiratory diseases. Therefore, it is necessary to measure and simulate the diffusion of PM2.5 in the city of wulanduto. Based on the PM2.5 diffusion simulation diagram, the main particulate matter generation source and the healthy exposure condition can be determined, and the basis for implementing air pollution treatment measures is provided.
In Wulandutong, the locations of Continuous Air Monitoring (CAM) sites are sparse and there is a lack of facility and satellite data for high resolution measurement of the spread of atmospheric pollution areas in Wulanduton. Therefore, there is a need for relevant simulation and prediction of air pollution in unmeasured areas.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a novel simulation method for PM2.5 diffusion conditions of Mongolian Ukrabbe to overcome the defect of PM2.5 diffusion information data of Mongolian Ukrabbe.
The method applies an Inverse Distance Weighting (IDW) method to predict atmospheric pollution conditions. IDW is a deterministic method of spatial interpolation. Unknown position "zpThe value at "is calculated as a weighted average of the monitoring station measurements. The method assumes "zpThe value is affected more by the close-range measurement than by the far-range measurement. In other words, closer positions get higher weights, which decrease with distance. The contaminant measurement at "n" around the unknown position is known as i 1.… … n, value "z" at unknown positionp"calculate as:
Zppredicted value
Z1Measured value
d1 pTo the p power of the distance
∑n i＝1Sum of nearest points
To plot PM2.5 pollution, annual data from 2018 months 1 to 12 months 31 days of 8 CAM monitoring stations were measured using an ENVEA automated nephelometer. The 8 monitoring stations belong to the Meteorological and environmental monitoring Bureau of Wulanbarton and two government agencies for reducing air pollution. In these sites, NO is measured once per hourx,SO2CO, PM10, PM2.5 and O3A contaminant. The invention selects the measurement data of PM2.5 and processes the data by taking months and seasons as units.
Drawings
FIG. 1 is a PM2.5 distribution plot (. mu.g/m)3)；
FIG. 2 is a graph of year-round measurement data for various continuous air monitoring stations.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Diurnal PM2.5 data is processed in units of months and seasons, and PM2.5 levels are classified into the following categories according to the Environmental Protection Agency (EPA) human health related Air Quality Index (AQI): 0-12 mu g/m3Is good, 12.1-35.4 mu g/m3Is 'middle' 35.5-55.4 mu g/m3Is unhealthy for sensitive people, and has a concentration of 55.5-150.4 μ g/m3Is unhealthy, 150.5-250.4 μ g/m3Is "very unhealthy" and is greater than 250.5 μ g/m3Is "dangerous".
In fig. 1, the winter Mongolian climate is very cold (one of the coldest headquarters in the world), so homes in Mongolian regions use coal fuel (and a small amount of wood) for heating and cooking. PM2.5 emissions sometimes reach 900. mu.g/m3Particularly in the areas "Bayankhoshuu", "Zunsalaa" and "Tolgoit", in black and brown. The average PM2.5 value in winter is about 250 mu g/m3。
In spring, the Wulan Barton climate is windy and warm, and the average emission of PM2.5 is reduced to 55 mu g/m3. In early spring (3 months), the weather is cold, and the families in the Mongolian yurt area still use coal fuel.
The summer season of Ulandubator is hot and dryAnd (4) seasons. PM2.5 emissions are relatively low. But still a small amount of PM2.5 emissions of about 35. mu.g/m3. The PM2.5 is emitted as fugitive dust from unpaved roads and construction activities. As 90% of the yurt areas still have unpaved roads. Therefore, PM2.5 is distributed evenly throughout the urban area and is referred to as "medium yellow". Some areas are fresh and air, indicated by "white" on the map.
The autumn climate of Wulanbaoto is cooler and warmer than the winter. Therefore, PM2.5 is distributed evenly throughout the urban area. At this time, the average concentrations of PM2.5 at the beginning and end of the season were 40. mu.g/m3And 100. mu.g/m3。
8 monitoring stations were selected, each located at a position as shown in Table 1. The monitoring stations respectively monitor the PM2.5 conditions of the Mongolian yurt area, the apartment area and the industrial area.
Table 1 CAM monitoring station selected
In fig. 2, the PM2.5 measurements for each day for 8 monitoring stations are graphically represented. As can be seen from the figure, it is consistent with the national standard (50. mu.g/m)3Indicated by "red line") the PM2.5 emissions monitored at each station exceed national standards, in particular 10 months to 4 months (cold season of the year), and sometimes even more than 800 μ g/m3Or 16 times over the national standard.
Claims (3)
1. A method for simulating PM2.5 diffusion of Mongolian Uulanbarton city is characterized by comprising the following steps:
(1) using Inverse Distance Weighting (IDW), establishing "z" at the unknown locationp"calculation formula of value;
(2) measuring data of a certain whole year of 8 CAM monitoring sites by using an ENVEA automatic turbidity meter;
(3) drawing a PM2.5 diffusion simulation diagram of Mongolian Ukraubato city by taking months and seasons as units according to the collected data;
(4) the PM2.5 levels were classified according to the Environmental Protection Agency (EPA) human health related Air Quality Index (AQI).
2. The method of claim 1, wherein the Inverse Distance Weighting (IDW) method is based on the assumption of "zpThe value is affected more by the close-range measurement than by the far-range measurement.
3. The method of claim 1, wherein the PM2.5 levels are classified according to human health-related Air Quality Index (AQI), wherein the PM2.5 levels are 0-12 μ g/m3Is good, 12.1-35.4 mu g/m3Is 'middle' 35.5-55.4 mu g/m3Is unhealthy for sensitive people, and has a concentration of 55.5-150.4 μ g/m3Is unhealthy, 150.5-250.4 μ g/m3Is "very unhealthy" and is greater than 250.5 μ g/m3Is "dangerous".
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911050496.2A CN110941930A (en) | 2019-10-31 | 2019-10-31 | Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911050496.2A CN110941930A (en) | 2019-10-31 | 2019-10-31 | Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110941930A true CN110941930A (en) | 2020-03-31 |
Family
ID=69906188
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911050496.2A Pending CN110941930A (en) | 2019-10-31 | 2019-10-31 | Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110941930A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104834821A (en) * | 2015-05-13 | 2015-08-12 | 中国环境科学研究院 | Method for determining population exposure area according to environmental health risk assessment on river pollution specific pollutants and application of method |
| US20160091474A1 (en) * | 2014-09-29 | 2016-03-31 | Tanguy Griffon | Method and a System for Determining at Least One Forecasted Air Quality Health Effect Caused in a Determined Geographical Area by at Least One Air Pollutant |
| CN106446401A (en) * | 2016-09-22 | 2017-02-22 | 天津大学 | PM2.5 visualized dynamic diffusion simulation system based on GIS |
| CN106844626A (en) * | 2017-01-20 | 2017-06-13 | 武汉大学 | Using microblogging keyword and the method and system of positional information simulated air quality |
| US20190325334A1 (en) * | 2018-04-23 | 2019-10-24 | National Chung-Shan Institute Of Science And Technology | Method for predicting air quality with aid of machine learning models |
-
2019
- 2019-10-31 CN CN201911050496.2A patent/CN110941930A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160091474A1 (en) * | 2014-09-29 | 2016-03-31 | Tanguy Griffon | Method and a System for Determining at Least One Forecasted Air Quality Health Effect Caused in a Determined Geographical Area by at Least One Air Pollutant |
| CN104834821A (en) * | 2015-05-13 | 2015-08-12 | 中国环境科学研究院 | Method for determining population exposure area according to environmental health risk assessment on river pollution specific pollutants and application of method |
| CN106446401A (en) * | 2016-09-22 | 2017-02-22 | 天津大学 | PM2.5 visualized dynamic diffusion simulation system based on GIS |
| CN106844626A (en) * | 2017-01-20 | 2017-06-13 | 武汉大学 | Using microblogging keyword and the method and system of positional information simulated air quality |
| US20190325334A1 (en) * | 2018-04-23 | 2019-10-24 | National Chung-Shan Institute Of Science And Technology | Method for predicting air quality with aid of machine learning models |
Non-Patent Citations (5)
| Title |
|---|
| 刘妍月等: "长沙市大气中PM2.5浓度分布的空间插值方法比较", 环境监测管理与技, vol. 28, no. 2, pages 14 - 18 * |
| 卢月明等: "局部加权线性回归模型的PM2.5空间插值方法", 测绘科学, vol. 43, no. 11, pages 79 - 84 * |
| 柳笑盈: "基于GIS的空气质量指数空间插值方法研究", 中国优秀硕士学位论文全文数据库（工程科技Ⅰ辑）, pages 027 - 678 * |
| 蒋超 等: "2013—2016年珠三角地区PM2.5分布时空演变", 生态环境学报, vol. 27, no. 9, pages 1698 - 1705 * |
| 许亚辉等: "基于反距离加权空间插值方法的南京老城区 PM2.5的空间分布研究", 中国科技信息, no. 16, pages 48 - 49 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kuik et al. | Air quality modelling in the Berlin–Brandenburg region using WRF-Chem v3. 7.1: sensitivity to resolution of model grid and input data | |
| Shen et al. | Chemical characteristics of fine particles (PM1) from Xi'an, China | |
| Huang et al. | Land use patterns and SO2 and NO2 pollution in Ulaanbaatar, Mongolia | |
| Kordowski et al. | Carbon dioxide fluxes over an urban park area | |
| Masiol et al. | Hourly land-use regression models based on low-cost PM monitor data | |
| Mayer | Air pollution in cities | |
| Kassomenos et al. | Identification of sources and processes affecting particulate pollution in Thessaloniki, Greece | |
| Noth et al. | A spatial-temporal regression model to predict daily outdoor residential PAH concentrations in an epidemiologic study in Fresno, CA | |
| Jahn et al. | Particulate matter pollution in the megacities of the Pearl River Delta, China–A systematic literature review and health risk assessment | |
| Yilmaz et al. | Assessing the effects of different urban landscapes and built environment patterns on thermal comfort and air pollution in Erzurum city, Turkey | |
| Beig et al. | India’s Maiden air quality forecasting framework for megacities of divergent environments: The SAFAR-project | |
| Aguilera et al. | Land use regression models for crustal and traffic-related PM2. 5 constituents in four areas of the SAPALDIA study | |
| Peng et al. | The characteristics and local-regional contributions of atmospheric black carbon over urban and suburban locations in Shanghai, China | |
| Krasnov et al. | The spatio-temporal distribution of particulate matter during natural dust episodes at an urban scale | |
| Yang et al. | Do greenhouse gas emissions drive extreme weather conditions at the city level in China? Evidence from spatial effects analysis | |
| Liang et al. | Estimating climate change-related impacts on outdoor air pollution infiltration | |
| Shen et al. | A health risk-based threshold method to evaluate urban atmospheric environment carrying capacity in Beijing-Tianjin-Hebei (BTH) region | |
| Haszpra et al. | Estimation of greenhouse gas emission factors based on observed covariance of CO 2, CH 4, N 2 O and CO mole fractions | |
| McConnell et al. | Predicting residential ozone deficits from nearby traffic | |
| Luhar et al. | Modelling PM10 concentrations and carrying capacity associated with woodheater emissions in Launceston, Tasmania | |
| CN110941930A (en) | Method for simulating PM2.5 diffusion condition of Mongolian Uulan Batot city | |
| Sonomdagva et al. | Characteristics of PM10 and PM2. 5 in the Ambient Air of Ulaanbaatar, Mongolia | |
| Ferro et al. | Residential indoor and outdoor PM measured using low-cost monitors during the heating season in Monroe County, NY | |
| Tabatabaie et al. | Assessment of contribution of SO 2, CO, and NO 2 in different urban land use in Bushehr region, Iran | |
| Shaparaki et al. | A hybrid deterministic-statistical model integrating economic, meteorological and environmental variables to air pollution |
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 |