CN110907319A - Attribution analysis method for near-surface fine particulate matters - Google Patents
Attribution analysis method for near-surface fine particulate matters Download PDFInfo
- Publication number
- CN110907319A CN110907319A CN201911082932.4A CN201911082932A CN110907319A CN 110907319 A CN110907319 A CN 110907319A CN 201911082932 A CN201911082932 A CN 201911082932A CN 110907319 A CN110907319 A CN 110907319A
- Authority
- CN
- China
- Prior art keywords
- contribution
- mass concentration
- influence
- change rate
- influence factor
- 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.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 27
- 239000013618 particulate matter Substances 0.000 claims abstract description 50
- 238000000034 method Methods 0.000 claims abstract description 15
- 230000008859 change Effects 0.000 claims description 35
- 239000000443 aerosol Substances 0.000 claims description 26
- 230000003287 optical effect Effects 0.000 claims description 12
- 239000002245 particle Substances 0.000 claims description 10
- 238000010521 absorption reaction Methods 0.000 claims description 9
- 230000008033 biological extinction Effects 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 9
- 238000009795 derivation Methods 0.000 claims description 5
- 230000007246 mechanism Effects 0.000 claims description 5
- 239000003102 growth factor Substances 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 230000035945 sensitivity Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 abstract description 17
- 230000005540 biological transmission Effects 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 6
- 230000007613 environmental effect Effects 0.000 abstract description 5
- 238000012544 monitoring process Methods 0.000 abstract description 5
- 239000010419 fine particle Substances 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 238000012937 correction Methods 0.000 description 4
- 238000002156 mixing Methods 0.000 description 4
- 230000007423 decrease Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 239000003344 environmental pollutant Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 231100000719 pollutant Toxicity 0.000 description 2
- 239000002243 precursor Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000000926 atmospheric chemistry Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000011164 primary particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000012950 reanalysis Methods 0.000 description 1
- 230000000241 respiratory effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000011343 solid material Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000013179 statistical model Methods 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—
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses an attribution analysis method of near-surface fine particulate matters, which can be used for directly observing PM (particulate matter)2.5The contribution of mass concentration is subjected to attribution analysis, compared with a chemical transmission model, the method abandons the uncertainty of an emission source, enhances the proportion of observation in attribution, and has the characteristics of simple calculation, high efficiency and more suitability for near-real-time satellite monitoring service. By utilizing the human and meteorological dominant contributions calculated by the influence factor variability and the evolution quantity, the human and meteorological contribution to the PM is obtained2.5The dominant contribution ratio of the evolution provides important support for environmental monitoring and treatment.
Description
Technical Field
The invention relates to the field of satellite remote sensing, in particular to an attribution analysis method for near-ground fine particles.
Background
Suspended Particulate Matter (PM) in the atmosphere can not only scatter and absorb solar radiation, which affects the energy balance of the ground atmosphere, but also can cause reduced horizontal visibility near the ground, which poses serious threats to public transportation safety (highways, airports, etc.). The increase in atmospheric particulates can also affect cloud albedo and life causes, indirectly causing climate change. Meanwhile, the environmental impact of atmospheric particulates is also not negligible. The particles with particle size less than 10 μm can be inhaled by human body to generate respiratory tractLess than 2.5 μm Particulate Matter (PM) to cause harm2.5) Can enter the alveoli causing a variety of diseases. Currently, the rapid development in china causes an excessive amount of artificial particulate matter to be retained in the atmosphere, which can cause public health problems in a short period of time. However, a near-ground atmospheric pollutant observation network arranged by government agencies (U.S. EPA, china environmental monitoring central station) is only limited to site observation, and is difficult to capture regional distribution and changes of PM.
In recent years, much research on gas and particulate matters is carried out, and the research mainly comprises the following steps:
(1) a ratio method, namely a method for directly correlating atmospheric particulates with the optical thickness of the aerosol obtained by remote sensing observation;
(2) statistical method, which is to use AOD and PM2.5For PM, a set weight value of the statistical correlation of2.5And performing weighted statistical calculation.
Both methods need to rely on a large amount of ground observation data (non-satellite remote sensing data) for support, and the satellite data cannot be independently used to obtain the concentration of the near-ground fine particulate matters.
(3) A coupling method, which is to obtain AOD and PM by using a chemical transmission model2.5The ratio of the time to the time is transmitted to AOD obtained by satellite observation, and then the near-ground PM is estimated2.5. The method depends on the simulation result of the chemical transmission model, however, the chemical transmission model needs to input the ground emission source information, the meteorological field information and the lower ground information, which causes the result to have large deviation compared with the actual result (the AOD and PM acquired by the mode are specially designated here)2.5Ratio of (d).
However, none of the above studies have been directed to causing near-surface PM2.5The reason for the change of mass concentration is discussed, because the statistical model does not have a physical basis, and the atmospheric chemical mode influence factors are too much to be clarified.
Existing ground PM2.5The change of mass concentration is mostly simulated by using a model to achieve the purpose of analyzing the cause. The chemical transmission model is used as a main method for model simulation, and the analysis of the atmospheric fine particulate matters is mainly realized by switching on and off an artificial emission source. Chemical transport modelA lot of information needs to be input externally, including the emission source and the meteorological field. The emission source refers to primary particles and precursors thereof which are artificially emitted, and the uncertainty of the emission source in China is large at present, so that the analysis result of the artificial contribution is influenced. Fig. 1 shows the concentration of particulate matter obtained by chemical transport model simulation, which is greatly different from the observed value of a station. This is sufficient to explain that chemical transport models cannot accurately capture particulate matter concentrations due to the effects of the emissions source.
The meteorological field refers to three-dimensional meteorological grid information from a global model and comprises various meteorological elements such as temperature, pressure intensity, humidity, wind speed and wind direction. Because of the many prediction parameters, there are more than 200 meteorological parameters which can only represent the environmental bearing capacity, and the main variables include: the system comprises a water-cooling system, a water. Meanwhile, the meteorological contribution is difficult to accurately represent due to mutual correlation among the meteorological contribution.
When the analysis is performed by a chemical transmission model, when the human influence is discussed, the human influence is obtained by subtracting the current latest emission source (generally using an east Asian emission source) as a comparison value from the emission source input mode before the industrial revolution as an initial value. When the meteorological influence is discussed, the emission source is not changed, the diffusion influence of the particulate matters is driven only by the change generated by the meteorological field input from the outside, and the difference estimation meteorological influence of different years is calculated. However, this approach relies heavily on near-surface emissions source information. The near-surface emission source is closely related to economic development, and the time variation thereof is significant. At present, east asian emission sources are updated only to 2010 and obviously cannot meet the research requirements. In addition, atmospheric chemistry research based on ground sampling observation found that PM in areas of major cities (e.g., beijing)2.5The secondary aerosol contributes more and human factors play an important role, but the chemical inversion mechanism in the chemical mode is complex, and the chemical inversion mechanism is difficult to realize at presentPM in these regions2.5The continuous increase in mass concentration is associated with emissions inventory changes and it is difficult to account for short term explosive increases.
In addition, the chemical transmission model has huge calculation amount and consumes a lot of time and labor. With a typical regional model, using 64 CPUs, a 3-fold grid nesting of Asian European continent regions (81 km-27km-9km, inner grid region-city only), simulating for 2 hours takes 37 minutes to be realistic. From this, it is assumed that it takes 4 years to simulate 15-year data.
Therefore, there is an urgent need to analyze each precursor and directly related factors through long-term, area-covering observation data.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an artificial and meteorological attribution analysis method for estimating near-ground fine particles by satellite remote sensing, which directly utilizes observation information to solve PM (particulate matter)2.5And (4) attribution analysis of human and meteorological factors of mass concentration contribution.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for attribution analysis of near-surface fine particulate matter, comprising the steps of:
1) establishing PM2.5Remote Sensing (PMRS) model for satellite observation based near-ground PM2.5The mass concentration is estimated, and the model formula is as follows:
wherein: AOD is aerosol optical thickness; FMF is the ratio of fine mode aerosol to total optical thickness; VEf is a volume extinction ratio parameter with FMF as an argument, which is obtained by empirical fitting; PBLH is the planet boundary layer height; RH is the relative humidity of the environment; rhof,dryIs the fine particulate effective density; f. of0Characterizing fine particulate matter moisture absorption growth factors with RH as an independent variable;
2) physical mechanism-based defined PM2.5Remote Sensing (PMRS) models, each derived modelInfluence factor on PM2.5The mass concentration contribution is specifically carried out as follows:
first, taking logarithm of model formula:
then further derivation and arrangement are carried out to obtain the formula as follows:
obtaining each influence factor pair PM2.5Contribution of mass concentration;
3) performing attribution analysis:
first, the relative change rate of each influence factor is calculated from a theoretical angle:
wherein: RV represents relative change rate, x represents influence factor in the model, sensitivity of change of the influence factor by one unit of change rate is compared, insensitive factors are filtered out and can be included in residual error;
after finding the key variable through the relative change rate, estimating the PM pair by each influence factor2.5The time-space change rate of the mass concentration evolution is analyzed for main influence factors and regional differences, the base value and the evolution trend of the contribution of the main influence factors and the regional differences are compared, and the artificial or meteorological dominant contribution can be expressed as follows according to the variability and the physical significance of the change rate and the contribution of the artificial dominant factor and the meteorological dominant factor:
the observation information obtained by satellite remote sensing is utilized to carry out the near-ground PM on the polluted region of China2.5High qualityAnd calculating the evolution contribution of the degree to obtain the influence of human and gas phase factors on the near-ground particles.
Further, in the step 2), each influence factor is applied to the PM through a Jacobian matrix2.5The influence of mass concentration is resolved.
Further, in the step 3), the density parameter pair PM is obtained through the relative change rate of each influence factor2.5The effect of mass concentration is relatively insignificant.
The attribution analysis method of the near-surface fine particulate matters can be used for directly observing PM (particulate matter)2.5The contribution of mass concentration is subjected to attribution analysis, compared with a chemical transmission model, the method abandons the uncertainty of an emission source, enhances the proportion of observation in attribution, and has the characteristics of simple calculation, high efficiency and more suitability for near-real-time satellite monitoring service.
Drawings
FIG. 1 is a comparison of chemical transport mode simulated particulate matter concentration versus site observations;
FIG. 2 is a schematic diagram of near-surface fine particle mass concentration and error for aerosols at different blend layer heights;
FIG. 3 is a schematic representation of the change in the relative humidity of the aerosol due to the increase in the moisture absorption of the aerosol;
FIG. 4 shows the variation of AOD, FMF, PBLH and the relative rate of change of density within a reasonable range of parameters;
FIG. 5 shows the PM near the ground surface of a Chinese polluted area for 15 years2.5The evolution of mass concentration is artificially and meteorological in contribution;
fig. 6 is a logic flow diagram of the method of the present invention for attribution analysis of near-surface fine particulate matter.
Detailed Description
In the description of the present embodiment, the terms "upper", "lower", "front", "rear", "left", "right", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as specifically indicating or implying relative importance.
In order to more clearly illustrate the technical solution of the present invention, the following description is made in the form of specific embodiments.
Examples
The method for attribution analysis of the near-surface fine particulate matters aims to research PM2.5The change of mass concentration is subjected to artificial and gas phase factor attribution analysis, the basis is established in a remote sensing estimation method with a physical basis, and the remote sensing estimation method is PM2.5The remote sensing (PMRS) model has the following concrete formula:
wherein: AOD is aerosol optical thickness; FMF is the ratio of fine mode aerosol to total optical thickness; VEf is a volume extinction ratio parameter with FMF as an argument, which is obtained by empirical fitting; PBLH is the planet boundary layer height; RH is the relative humidity of the environment; rhof,dryIs the fine particulate effective density; f. of0Characterizing fine particulate matter moisture absorption growth factors with RH as an independent variable;
based on a PMRS model with clear physical mechanism, the invention aims to develop PM based on remote sensing observation2.5The mass concentration evolution is attributed to the analytical method. PM (particulate matter)2.5The evolution of mass concentration is influenced by key parameters in a PMRS model, and the AOD represents the extinction sum of aerosol in an atmosphere column, so that the concentration of particulate matters can be explained to a certain extent. The fine particle ratio (FMF) expresses the fraction of fine particles in the total particles, and the product of AOD and FMF is indicative of the degree of attenuation of visible light by fine particles suspended in the atmosphere. And then the extinction of the particles can be converted into the physical volume of the particles by using the bridge parameter of the volume extinction ratio. Further, the PMRS model comprises vertical correction and moisture absorptionThe two parts are respectively solved by a boundary layer height-vertical model and a relative humidity-moisture absorption model. First, for the vertical model, a common study assumes a vertical distribution of near-surface extinction with height that decreases exponentially with height, so the total aerosol optical thickness and near-surface aerosol optical thickness can be used as kext= AOD/H estimation. When only the height of the mixed layer is changed, the optical thickness of the whole aerosol layer is not changed, and the PM is estimated by directly utilizing the AOD2.5The results obtained were also unchanged, but the near-surface particulate concentration varied due to the different heights of the mixing layers. We varied the height of the mixed layer (as shown in fig. 2) with the case where the height of the mixed layer was 1km as a reference value. As the aerosol mixing layer increases from 1km, the error increases, and as the mixing layer height decreases, the error also increases. The relative error increases by 50% for each increase or decrease in the height of the blending layer by a true value of 1/2.
When the relation between the particulate matter scattering and the mass concentration thereof is theoretically calculated, the influence of the relative humidity of the environment on the scattering caused by the moisture absorption growth of the particulate matter is very important. Both scattering and density change after the particles absorb moisture. The increase in aerosol volume causes the aqueous portion of the particulate matter to be mistakenly considered as a high density solid material, thus resulting in an overestimation of the particulate matter mass concentration. Water and dry aerosol substances are not distinguished in the remote sensing inverted particle spectrum distribution, and PM is observed by a station2.5The mass concentration is the dry aerosol concentration. Therefore, in the estimation process, correction of the hygroscopicity of the aerosol is necessary. Calculating the volume of water as the aerosol material density would otherwise cause large errors. The moisture absorption growth correction factor of the optical thickness of the aerosol can be calculated by using the moisture absorption correction factor:
and calculating the extinction change condition of the dry aerosol at different relative humidity environments by using the moisture absorption growth factor. Assuming that the density and volume spectral distribution of the aerosol are not changed, when the relative humidity of the environment is changed from 10% to 40%, the change of the scattering coefficient caused by the hygroscopic growth of the aerosol is not obvious, and when the relative humidity of the environment is changed to exceed 40%, the scattering growth is obviously enhanced (as shown in fig. 3). The mass concentration of fine particulate matter obtained by aerosol optical thickness calculations alone increases falsely with increasing extinction. When the ambient relative humidity is 80%, the relative error increases to 350%.
Thus, the model factors are paired to PM2.5The contribution of mass concentration can be explained by the product of its rate of change and the amount of change.
First, taking logarithm of model formula:
then further derivation and arrangement are carried out to obtain the formula as follows:
the derivation formula represents the influence factors to the PM2.5Mass concentration contribution, which is a mathematical solution with physical significance. The purpose of taking the logarithm is to decompose the variables that were originally in the form of products in order to find their independent effects. Derivation is carried out to obtain the variable, and the variable is subjected to the PM under the condition that the variable is changed by 1 unit length2.5The contribution of each variable can be clearly seen in this way.
The partial derivative before each plus sign is an expression of a gradient, and the partial derivatives can be used as a vector, called a Jacobian matrix, and the matrix is used for analyzing the influence of each variable on PM, and then subsequent attribution analysis is carried out.
Attribution analysis first calculates the relative rate of change of each influencing factor from a theoretical point of view:
wherein: RV represents relative change rate, x represents influence factor in the model, sensitivity of change of the influence factor by one unit of change rate is compared, insensitive factors are filtered out and can be included in residual error;
the variation of the relative variability of different variables in a reasonable value range is shown in FIG. 4, and it can be seen that other variables besides the density variable have significant variation, so that the density is applied to PM2.5The effect of mass concentration is relatively insignificant. However, the relative density exhibits a small change, so it is combined with higher order small quantities as a residual due to analysis.
Further, each influence factor pair PM is estimated2.5And analyzing the time-space change rate of the mass concentration evolution, and analyzing main influence factors and regional differences. The base values and the evolution trends of their contributions are compared, and the contributions are attributed to the artificial dominant and the meteorological dominant according to the variability and the physical significance. The anthropogenic or weather-dominated contribution can be expressed as:
near-ground PM of Chinese polluted area by using satellite remote sensing product and reanalysis data2.5The evolution contribution of (2) is calculated to obtain the influence of human and gas phase factors on the near-ground particles. In this example, the calculation and analysis from 2001 to 2015 for 15 years are carried out, and the human and gas phase factors to PM are obtained2.5The effect of mass concentration, see fig. 5. As can be seen from the figure, the contribution of human activities to the fine particulate matter near the surface has played a major role, escalating from 2001 to 2007. In 2008, due to the continuous starting of treatment projects such as blue sky action, the contribution of artificial activities to the concentration of fine particulate matters is remarkably reduced. The opposite is true for meteorological contribution, which has risen slowly in small amplitude since 2004 until 2015, which is a measure of the near-ground PM2.5The relieving effect of (b) gradually disappears. Maintaining this trend of change, the contribution to the near-surface fine particulate matter caused by future changes in meteorological conditions is likely to tend to be straight, thereby increasing near-surface pollutant accumulation. Hair brushA logic flow diagram of the attribution analysis method in the present disclosure is seen in fig. 6.
It is important to point out that the invention obtains the human and meteorological contribution to PM through the human and meteorological dominant contribution calculated by utilizing the influence factor variability and the evolution quantity2.5The dominant contribution ratio of the evolution provides important support for environmental monitoring and treatment.
Finally, it is to be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not intended to be limiting. It will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention, and these changes and modifications are to be considered as within the scope of the invention.
Claims (3)
1. A method for attribution analysis of near-surface fine particulate matter, characterized by comprising the steps of:
1) establishing PM2.5Remote Sensing (PMRS) model for satellite observation based near-ground PM2.5The mass concentration is estimated, and the model formula is as follows:
wherein: AOD is aerosol optical thickness; FMF is the ratio of fine mode aerosol to total optical thickness; VEf is a volume extinction ratio parameter with FMF as an argument, which is obtained by empirical fitting; PBLH is the planet boundary layer height; RH is the relative humidity of the environment; rhof,dryIs the fine particulate effective density; f. of0Characterizing fine particulate matter moisture absorption growth factors with RH as an independent variable;
2) physical mechanism-based defined PM2.5Remote Sensing (PMRS) model, deriving model influence factor pairs for PM2.5The mass concentration contribution is specifically carried out as follows:
first, taking logarithm of model formula:
then further derivation and arrangement are carried out to obtain the formula as follows:
obtaining each influence factor pair PM2.5Contribution of mass concentration;
3) performing attribution analysis:
first, the relative change rate of each influence factor is calculated from a theoretical angle:
wherein: RV represents relative change rate, x represents influence factor in the model, sensitivity of change of the influence factor by one unit of change rate is compared, insensitive factors are filtered out and can be included in residual error;
after finding the key variable through the relative change rate, estimating the PM pair by each influence factor2.5The time-space change rate of the mass concentration evolution is analyzed for main influence factors and regional differences, the base value and the evolution trend of the contribution of the main influence factors and the regional differences are compared, and the artificial or meteorological dominant contribution can be expressed as follows according to the variability and the physical significance of the change rate and the contribution of the artificial dominant factor and the meteorological dominant factor:
utilize satellite remote sensing observation information to the near-ground PM of the Chinese pollution area2.5And calculating the evolution contribution of the mass concentration to obtain the influence of human and gas phase factors on the near-ground particles.
2. The attribution analysis of claim 1The method is characterized in that in the step 2), each influence factor is applied to the PM through a Jacobian matrix2.5The influence of mass concentration is resolved.
3. The attribution analysis method according to claim 1, wherein in the step 3), the density parameter pair PM is obtained through the relative change rate of each influence factor2.5The effect of mass concentration is relatively insignificant.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911082932.4A CN110907319B (en) | 2019-11-07 | 2019-11-07 | Attribution analysis method for near-surface fine particulate matters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911082932.4A CN110907319B (en) | 2019-11-07 | 2019-11-07 | Attribution analysis method for near-surface fine particulate matters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110907319A true CN110907319A (en) | 2020-03-24 |
| CN110907319B CN110907319B (en) | 2021-02-09 |
Family
ID=69816629
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911082932.4A Active CN110907319B (en) | 2019-11-07 | 2019-11-07 | Attribution analysis method for near-surface fine particulate matters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110907319B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111912754A (en) * | 2020-07-23 | 2020-11-10 | 安徽省气象科学研究所 | Remote sensing inversion method for near-surface particulate matter concentration |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105528650A (en) * | 2015-12-02 | 2016-04-27 | 江苏省电力公司信息通信分公司 | Machine room temperature and humidity prediction method based on principle component analysis and BP neural network |
| JP2017086284A (en) * | 2015-11-06 | 2017-05-25 | 大和ハウス工業株式会社 | Sleep advice system |
| CN107491566A (en) * | 2016-06-12 | 2017-12-19 | 中国科学院城市环境研究所 | A kind of method of Quantitative study urban forests to PM2.5 catharsis |
| CN109408848A (en) * | 2018-08-24 | 2019-03-01 | 河海大学 | A kind of distributed attribution method considering Runoff Evolution temporal-spatial heterogeneity |
| CN109916788A (en) * | 2019-01-14 | 2019-06-21 | 南京大学 | A kind of differentiation different zones discharge variation and meteorological condition variation are to PM2.5The method that concentration influences |
| CN110261272A (en) * | 2019-07-05 | 2019-09-20 | 西南交通大学 | Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution |
-
2019
- 2019-11-07 CN CN201911082932.4A patent/CN110907319B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017086284A (en) * | 2015-11-06 | 2017-05-25 | 大和ハウス工業株式会社 | Sleep advice system |
| CN105528650A (en) * | 2015-12-02 | 2016-04-27 | 江苏省电力公司信息通信分公司 | Machine room temperature and humidity prediction method based on principle component analysis and BP neural network |
| CN107491566A (en) * | 2016-06-12 | 2017-12-19 | 中国科学院城市环境研究所 | A kind of method of Quantitative study urban forests to PM2.5 catharsis |
| CN109408848A (en) * | 2018-08-24 | 2019-03-01 | 河海大学 | A kind of distributed attribution method considering Runoff Evolution temporal-spatial heterogeneity |
| CN109916788A (en) * | 2019-01-14 | 2019-06-21 | 南京大学 | A kind of differentiation different zones discharge variation and meteorological condition variation are to PM2.5The method that concentration influences |
| CN110261272A (en) * | 2019-07-05 | 2019-09-20 | 西南交通大学 | Based on geographical detection with PCA to the Key Influential Factors screening technique of PM2.5 concentration distribution |
Non-Patent Citations (3)
| Title |
|---|
| 梁子谦: "《中国粮食综合生产能力与安全研究》", 30 June 2007, 中国财政经济出版社 * |
| 王斌会: "《计量经济学模型及B语言应用》", 31 May 2015, 暨南大学出版社 * |
| 赵爱梅: "星载传感器和地基观测网FMF数据融合及其在PM2.5遥感估算中的应用", 《中国优秀硕士学位论文全文数据库工程科技I辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111912754A (en) * | 2020-07-23 | 2020-11-10 | 安徽省气象科学研究所 | Remote sensing inversion method for near-surface particulate matter concentration |
| CN111912754B (en) * | 2020-07-23 | 2023-03-28 | 安徽省气象科学研究所 | Remote sensing inversion method for near-surface particulate matter concentration |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
| CN113327136B (en) * | 2021-06-23 | 2023-06-02 | 中国平安财产保险股份有限公司 | Attribution analysis method, attribution analysis device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110907319B (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zaveri et al. | Particle‐resolved simulation of aerosol size, composition, mixing state, and the associated optical and cloud condensation nuclei activation properties in an evolving urban plume | |
| Wu et al. | Particle hygroscopicity and its link to chemical composition in the urban atmosphere of Beijing, China, during summertime | |
| Chen et al. | Observation of aerosol optical properties and particulate pollution at background station in the Pearl River Delta region | |
| Leng et al. | Insights into a historic severe haze event in Shanghai: synoptic situation, boundary layer and pollutants | |
| Cheng et al. | Mixing state of elemental carbon and non‐light‐absorbing aerosol components derived from in situ particle optical properties at Xinken in Pearl River Delta of China | |
| Massling et al. | Atmospheric black carbon and sulfate concentrations in Northeast Greenland | |
| Kondo et al. | Emissions of black carbon in East Asia estimated from observations at a remote site in the East China Sea | |
| Levi et al. | On the association between characteristics of the atmospheric boundary layer and air pollution concentrations | |
| Hu et al. | Trans-Pacific transport and evolution of aerosols: Evaluation of quasi-global WRF-Chem simulation with multiple observations | |
| Tombette et al. | PM 10 data assimilation over Europe with the optimal interpolation method | |
| Panchenko et al. | An empirical model of optical and radiative characteristics of the tropospheric aerosol over West Siberia in summer | |
| Ferrero et al. | Aerosol optical properties in the Arctic: The role of aerosol chemistry and dust composition in a closure experiment between Lidar and tethered balloon vertical profiles | |
| Yumimoto et al. | JRAero: the Japanese reanalysis for aerosol v1. 0 | |
| Kuang et al. | Stratosphere‐to‐troposphere transport revealed by ground‐based lidar and ozonesonde at a midlatitude site | |
| Devi et al. | Observation-based 3-D view of aerosol radiative properties over Indian Continental Tropical Convergence Zone: implications to regional climate | |
| De Foy et al. | Modelling constraints on the emission inventory and on vertical dispersion for CO and SO 2 in the Mexico City Metropolitan Area using Solar FTIR and zenith sky UV spectroscopy | |
| Guth et al. | First implementation of secondary inorganic aerosols in the MOCAGE version R2. 15.0 chemistry transport model | |
| Galperin et al. | The long-range transport of ammonia and ammonium in the Northern Hemisphere | |
| CN110907319B (en) | Attribution analysis method for near-surface fine particulate matters | |
| Castagna et al. | Multiscale assessment of the impact on air quality of an intense wildfire season in southern Italy | |
| Formenti et al. | STAAARTE‐MED 1998 summer airborne measurements over the Aegean Sea 1. Aerosol particles and trace gases | |
| Huang et al. | Impacts of transported background pollutants on summertime western US air quality: model evaluation, sensitivity analysis and data assimilation | |
| Kang et al. | Temporal variations of PM concentrations, and its association with AOD and meteorology observed in Nanjing during the autumn and winter seasons of 2014–2017 | |
| Guth et al. | Primary aerosol and secondary inorganic aerosol budget over the Mediterranean Basin during 2012 and 2013 | |
| Kim et al. | Chemical apportionment of shortwave direct aerosol radiative forcing at the Gosan super-site, Korea during ACE-Asia |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |