CN113420443B - Accurate stink simulation method coupled with peak-to-average factor - Google Patents
Accurate stink simulation method coupled with peak-to-average factor Download PDFInfo
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Abstract
The invention discloses an accurate stink simulation method for coupling peak-to-average factors, which comprises the following steps: peak mean factor experiment; monitoring points are arranged around the pollution source for on-site sampling. Monitoring the odor concentration for a long time by using an online electronic nose of a monitoring point; and (4) generating meteorological data, and extracting the hourly atmosphere stability information around the monitoring point in CALMET. And processing the obtained odor concentration data, and accurately simulating the peak mean factor coupling air quality model. The invention has the advantages that: and coupling the obtained peak value average factors under different atmospheric stability levels with an air quality model to obtain an instantaneous stink simulation value of the air quality model. The achievement can be used for predicting the odor occurrence condition of a new project, and has important reference significance for making odor management decisions, odor pollution environment influence evaluation guide rules, odor pollution quality standards and the like.
Description
Technical Field
The invention relates to the technical field of malodor pollution diffusion prediction, in particular to a precise malodor simulation method coupled with a peak-to-average factor.
Background
Malodor (odor) has become a hot problem of domestic and foreign research as a disturbing pollution. The number of complaints of national malodorous pollutants is second only to noise. In order to solve the environmental problem which is concerned by the public, how to evaluate the influence of the malodor on the population has become an important requirement in the field of atmospheric environment. The eighty-th method for preventing and treating the atmospheric pollution of the people's republic of China proposes that' the enterprises and public institutions and other production operators generate malodorous gas in production and operation activities, and should scientifically select sites and set reasonable protection distances. However, the research on the odor pollution in China is late, and no guidance for evaluating the odor environmental influence is established so far.
When the model is applied to prediction of odor pollution diffusion, a method combining olfaction test and air quality models (AERMOD, CALPUFF, AUSTAL and the like) is generally adopted internationally. The odor pollution has specificity based on human olfactory sensation as a judgment standard. The olfactory evaluation time is determined by the olfactory stimulation and the respiratory capacity of a human, generally takes seconds as a unit, and reflects the transient characteristic of the odor pollution. The influence of an air quality model on a certain malodor emission source is evaluated, and because the particularity of malodor pollution is not considered, the output result of the model is usually the substance concentration of an hour-average value or a day-average value, so that the instantaneous malodor concentration cannot be evaluated, the public perception of malodor is greatly underestimated, and the difference between the prediction result and the actual situation is large.
The research on the odor pollution in China is started late, and no odor environment influence evaluation guide rule is established so far. At present, two methods are generally used for evaluating the influence of the odor pollution on the environment, managing the environment and deciding. A method for managing the odor of a gas sample by collecting the gas sample at the factory boundary and a sensitive point of an enterprise, measuring the odor concentration of the sample in a laboratory by a professional sniffer and comparing the odor concentration with the limit value of the odor pollution emission standard. However, the method has high time and personnel cost, complex operation in the experimental process, large operation error, and incapability of monitoring for a long time, and cannot meet the increasing requirement of odor management in China. The other is to use the air diffusion models (ADMS, AERMOD and CALPUFF) of the regulations to carry out the estimation of the odor pollution to the factory boundaries and sensitive points of the enterprises, but the output results of the air diffusion models are the substance concentration and the odor concentration of 1h mean value generally. Due to the characteristic of paroxysmal emission of the odor pollution, the concentration of the odor substance and the instantaneous maximum value (such as 1s, 5s and 3min) of the odor concentration need to be simulated, and the influence degree of the odor pollution on the surrounding environment can be reduced by predicting the mean value of the odor substance and the odor concentration, so that the demand of odor pollution management cannot be met by directly applying the rule diffusion models.
The peak-to-mean factor can convert the mean concentration into the instantaneous concentration, the conversion factor is a peak-to-mean factor (peak-to-mean factor), and the conversion factor is used for converting the mean concentration (such as 1h and 0.5h mean values) into the short-time peak concentration (such as 1s and 5s peak values), so that the requirement of monitoring the instantaneous maximum value of the malodor can be met, and the accuracy of malodor pollution simulation is improved.
The atmospheric diffusion models recommended in the environmental impact evaluation technology guidance atmospheric environment (HJ2.2-2018) of China are AERMOD, CALPUFF and ADMS, and can simulate the concentration of substances emitted by a single or multiple pollution sources and the hour-average concentration distribution of odor concentration at most based on meteorological characteristics and geographic characteristics of an evaluation range. However, for China, the three models are not combined with a relevant peak-to-average factor module to perform stench pollution simulation evaluation, and a stench pollution instantaneous maximum value result cannot be output. Therefore, the atmosphere diffusion model recommended by the national regulations cannot be directly applied to malodor pollution diffusion simulation and impact evaluation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an accurate stink simulation method coupled with a peak-to-average factor.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
an accurate stink simulation method coupled with a peak-to-average factor comprises the following steps:
s1: peak mean factor experiment;
s11: selecting a refuse landfill as a surface source, and a chemical plant as a point source; monitoring points are arranged around the pollution source for on-site sampling.
S12: the odor concentration is monitored for a long time by using the online electronic nose of a monitoring point, an odor concentration result is obtained when the interval time of the electronic nose is 1s, and the online electronic nose is used for continuously monitoring for one year.
S2: processing experimental data of peak value mean factor;
s21: processing meteorological data;
calculating and generating meteorological data in a meteorological numerical mode WRF mode; atmospheric stability data for each hour of the sample monitoring period was further obtained using the CALMET model.
The meteorological data includes: standard basic data and initial meteorological data;
wherein the standard basic data comprises: data terrain height, land utilization, land-water body mark and vegetation composition;
and calculating meteorological data by using a WRF model, wherein the grid interval is 2 km.
And importing meteorological data calculated by the WRF model into a CALMET model, wherein the CALMET model sets the meteorological data grid number 10(SN) × 10(WE), the grid interval is 1km, the range is 10km × 10km, and the number of vertical layers is 30. And finally, extracting the small atmosphere stability information around the monitoring point in CALMET.
S22: the odor concentration data obtained in S1 is processed as follows:
s221: the data were sorted by hour into several groups. Each set of data gave the mean, maximum of 98%. Wherein the maximum value of 98% is to eliminate the influence of concentration abnormal value monitored by the electronic nose.
S222: using Python, the ratio of the peak concentration to the average concentration for each set of data was calculated. The formula is as follows:
F＝C98/Cmean (1)
wherein F is the maximum value of the peak value mean value of each group of data of 98 percent, C98The 98 th maximum odor concentration value, C, for each set of datameanThe average odor concentration value for each set of data.
S223: classifying each group of odor concentration data according to the atmospheric stability grade based on the atmospheric stability data of each hour calculated by S21, wherein the atmospheric stability grade classification method utilizes a PasoQuel classification method. The atmospheric stability is divided into A, B, C, D, E, F six stability levels from unstable to stable.
Based on the data processing, peak-to-average factor values for converting the 1 h-average concentration into the 1s instantaneous peak concentration under different atmospheric stability are obtained.
And (3) calculating to obtain a coefficient mu by using a formula (2) based on the experimental peak concentration, the average concentration, the accumulation time of the peak concentration of 1s and the hour average concentration, and further calculating peak average factor F values corresponding to the 1min and 3min instantaneous time.
Wherein, cpRepresents the peak concentration, cmDenotes mean concentration, tmRepresents the cumulative time of the peak concentration, tpRepresents the cumulative time of the mean concentration, and μ is a coefficient.
S3: a peak-to-average factor coupled air quality model precision simulation method;
and (3) carrying out odor pollution diffusion simulation by using small and medium-scale rule models AERMOD, ADMS and CALPUFF, wherein the evaluation factor is odor concentration.
Sampling survey point source and surface source stink source emission node.
The calculation method of the point source and surface source malodor emission rate is shown in the formulas (1) and (2).
Q1＝C·V (1)
In the formula Q1Point source malodor emission rate, ou/s; c is odor concentration ou/m3(ii) a V is the smoke outlet air quantity m3/s；Q2Is the non-point source odor emission rate, ou/s; l is the blowing rate of the purge gas, m/s; s1Is the surface area of the wind tunnel, m2(ii) a S is the total area of the facility, m2。
Inputting meteorological data according to the requirement of an evaluation period;
and selecting a small value of the odor concentration when the model is output, obtaining the odor concentration value of each grid point every hour all the year around, and synchronously outputting the corresponding atmosphere stability grade. And (3) utilizing python to enable the obtained peak-to-average value factors under different atmospheric stability conditions to correspond to the atmospheric stability grade and odor concentration data obtained by the model one by one, and further multiplying and coupling the peak-to-average value and the hour odor concentration to obtain an odor concentration instantaneous simulation value.
Preferably, before the electronic nose monitors the odor concentration in the first step, the electronic nose is domesticated by collecting and monitoring odor gas samples around an enterprise to perform artificial olfaction experiments and sensor measurement, so that a guarantee is provided for accurate monitoring of the odor concentration.
Compared with the prior art, the invention has the advantages that:
the peak value average value factors under different atmospheric stability levels obtained by the method are coupled with the air quality model to obtain the instantaneous stink simulation value of the air quality model. The achievement can be used for predicting the malodor generation condition of a newly-built project, and has important reference significance for making malodor management decisions, malodor pollution environment influence evaluation guide rules, malodor pollution quality standards and the like.
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FIG. 1 is a technical route chart of an accurate malodor simulation method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings by way of examples.
As shown in fig. 1, a precise malodor simulation method coupled with a peak-to-average factor includes:
first, peak mean factor experiment
(1) A certain refuse landfill (surface source) and a chemical plant (point source, 2) are selected as typical pollution source setting experimental researches. Monitoring points are arranged around the pollution source for on-site sampling.
(2) The odor concentration is monitored by using the online electronic nose for a long time, the odor concentration result is obtained by the electronic nose at the interval of 1s, and the online electronic nose is continuously monitored for 2020 years.
(3) Before the electronic nose monitors the odor concentration, the artificial olfaction experiment and the sensor measurement are carried out on the odor sample around the enterprise to carry out the electronic nose domestication, so that the accurate monitoring of the odor concentration is guaranteed.
Second, peak mean factor experiment data processing
(1) Meteorological data processing
And acquiring meteorological data such as atmospheric stability and the like of each hour during sampling monitoring by using a WRF-CALMET mode.
Calculating and generating meteorological data of a monitoring field by adopting a meteorological numerical model (WRF); wherein the standard basic data such as data terrain height, land utilization, land-water body marks, vegetation composition and the like are all derived from a USGS database in the United states; the initial meteorological data was obtained using the re-analytical FNL database (http:// rda. ucar. edu) of NCEP/NCAR at the national environmental forecasting center. The WRF model calculates meteorological data as 2020 annual time-by-time data, and the grid spacing is 2km (one set of meteorological data in every 2km range).
And importing meteorological data calculated by the WRF model into a CALMET model, wherein the CALMET model sets the meteorological data grid number 10(SN) × 10(WE), the grid interval is 1km, the range is 10km × 10km, and the number of vertical layers is 30. And finally, extracting the small atmosphere stability information around the monitoring point in CALMET.
(2) Peak to mean factor
Processing odor concentration data obtained by the experiment, and comprising the following steps:
the first step is as follows: the data were sorted by hour into several groups. Each set of data gave the mean, maximum of 98%. Wherein the maximum value of 98% is to eliminate the influence of concentration abnormal value monitored by the electronic nose.
The second step is that: using Python, the ratio of the peak concentration to the average concentration for each set of data was calculated. The formula is as follows:
F＝C98/Cmean (1)
wherein F is the maximum value of the peak value mean value of each group of data of 98 percent, C98The 98 th maximum odor concentration value, C, for each set of datameanThe average odor concentration value for each set of data.
The third step: classifying each group of odor concentration data according to the atmospheric stability grade based on the calculated atmospheric stability data of each hour, wherein the atmospheric stability grade classification method utilizes a PasoQuel classification method. The atmospheric stability from unstable to stable may be divided A, B, C, D, E, F into six stability levels.
Based on the data processing, peak-to-average factor values for converting the 1 h-average concentration into the 1s instantaneous peak concentration under different atmospheric stability are obtained. Further, based on the experimental peak concentration, the mean concentration, the cumulative time of the peak concentration 1s, and the hour mean concentration, the coefficient μmay be calculated by using the formula (2), and the peak mean factor F corresponding to the instantaneous time of 1min, 3min, and the like may be further calculated.
Wherein, cpRepresents the peak concentration, cmDenotes mean concentration, tmRepresents the cumulative time of the peak concentration, tpRepresents the cumulative time of the mean concentration, and μ is a coefficient.
(3) Peak-to-average factor coupling air quality model accurate simulation method
The odor pollution belongs to local gas diffusion and often occurs in a small area range, so the odor pollution diffusion simulation is carried out by using small and medium-scale regulation models AERMOD, ADMS and CALPUFF recommended by environmental impact evaluation technology guidance atmospheric environment (HJ 2.2-2018).
And sampling and surveying odor source emission nodes such as point sources and surface sources. Aiming at the surface source, a wind tunnel method is adopted for sampling, and the wind tunnel sampling method is widely used for determining the strength of the odor pollution unorganized surface source. The purpose of this sampling approach is to present the actual condition of malodorous contamination using a reduced model.
The calculation method of the point source and surface source malodor emission rate is shown in the formulas (1) and (2).
Q1＝C·V (1)
In the formula Q1Point source malodor emission rate, ou/s; c is odor concentration ou/m3(ii) a V is the smoke outlet air quantity m3/s；Q2Is the non-point source odor emission rate, ou/s; l is the blowing rate of the purge gas, m/s; s1Is the surface area of the wind tunnel, m2(ii) a S is the total area of the facility, m2。
Weather data are input according to the requirement of an evaluation period, and if the influence of weather conditions on the environment is evaluated for several days or a certain season, the weather data in the period can be input. The topographic data and the land use type data can be obtained on a WebGIS website, the land use type data generally adopts 1km precision, and the topographic data generally adopts 90m precision. The requirements of meteorological data, building data, evaluation range, grid division and the like can be found in appendix B of environmental impact evaluation technical guide atmospheric environment (HJ 2.2-2018).
And selecting a small value of the odor concentration when the model is output, obtaining the odor concentration value of each grid point every hour all the year around, and synchronously outputting the corresponding atmosphere stability grade. On the basis, the peak mean value factors under different atmospheric stability conditions obtained by the method are in one-to-one correspondence with atmospheric stability grade and odor concentration data obtained by a model by utilizing python, and the peak mean value is multiplied by the hour odor concentration to be coupled to obtain an odor concentration instantaneous simulation value.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make numerous other specific variations and combinations based on the teachings of the present disclosure without departing from the spirit or scope of the present invention.
Claims (2)
1. An accurate stink simulation method coupled with a peak-to-average factor is characterized by comprising the following steps:
s1: peak mean factor experiment;
s11: selecting a refuse landfill as a surface source, and a chemical plant as a point source; monitoring points are arranged around the pollution source for on-site sampling;
s12: the odor concentration is monitored for a long time by using an online electronic nose of a monitoring point, an odor concentration result is obtained when the interval time of the electronic nose is 1s, and the online electronic nose is used for continuously monitoring for one year;
s2: processing experimental data of peak value mean factor;
s21: processing meteorological data;
calculating and generating meteorological data in a meteorological numerical mode WRF mode; further obtaining atmospheric stability data of each hour during sampling monitoring by using a CALMET model;
the meteorological data includes: standard basic data and initial meteorological data;
wherein the standard basic data comprises: data of terrain height, land utilization, land-water body mark and vegetation composition;
calculating meteorological data by using a WRF model, wherein the grid interval is 2 km;
introducing meteorological data calculated by the WRF model into a CALMET model, wherein the CALMET model sets the meteorological data grid number of 10, SN x 10, WE, grid interval of 1km, range of 10km x 10km and vertical layer number of 30 layers; finally, extracting the hourly atmosphere stability information around the monitoring point in CALMET;
s22: the odor concentration data obtained in S1 is processed as follows:
s221: classifying the data into a plurality of groups according to each hour; each group of data obtains an average value and a maximum value of 98 percent; wherein the maximum value of 98% is to eliminate the influence of concentration abnormal value monitored by the electronic nose;
s222: calculating the ratio of the peak concentration to the average concentration of each group of data by using Python; the formula is as follows:
F＝C98/Cmean (1)
wherein F is the maximum value of the peak value mean value of each group of data of 98 percent, C98The 98 th maximum odor concentration value, C, for each set of datameanThe average odor concentration value of each group of data;
s223: classifying each group of odor concentration data according to the atmospheric stability grade based on the atmospheric stability data of each hour calculated by S21, wherein the atmospheric stability grade classification method utilizes a PasoQuel classification method; dividing the atmospheric stability from unstable to stable into A, B, C, D, E, F six stability levels;
based on the data processing, peak value average factor values of 1h average concentration converted into 1s instantaneous peak value concentration under different atmospheric stability are obtained;
calculating to obtain a coefficient mu by using a formula (2) based on the experimental peak concentration, the average concentration, the accumulation time of the peak concentration of 1s and the hour average concentration, and further calculating peak average factor F values corresponding to the 1min and 3min instantaneous time;
wherein, cpRepresents the peak concentration, cmDenotes mean concentration, tmRepresents the cumulative time of the peak concentration, tpRepresents the cumulative time of the mean concentration, μ is a coefficient;
s3: peak-to-average factor coupling air quality model accurate simulation method
Carrying out odor pollution diffusion simulation by using small and medium-scale rule models AERMOD, ADMS and CALPUFF, wherein the evaluation factor is odor concentration;
sampling and surveying a point source and a surface source stink source emission node;
the calculation method of the point source and surface source malodor emission rate is shown in formulas (1) and (2);
Q1＝C·V (1)
in the formula Q1Point source malodor emission rate, ou/s; c is odor concentration ou/m3(ii) a V is the smoke outlet air quantity m3/s；Q2Is the non-point source odor emission rate, ou/s; l is the blowing rate of the purge gas, m/s; s1Is the surface area of the wind tunnel, m2(ii) a S is the total area of the facility, m2；
Inputting meteorological data according to the requirement of an evaluation period;
selecting a small value of the odor concentration during model output to obtain the odor concentration value of each grid point every hour all the year around, and synchronously outputting a corresponding atmosphere stability grade; and (3) utilizing python to enable the obtained peak-to-average value factors under different atmospheric stability conditions to correspond to the atmospheric stability grade and odor concentration data obtained by the model one by one, and further multiplying and coupling the peak-to-average value and the hour odor concentration to obtain an odor concentration instantaneous simulation value.
2. The accurate malodor simulation method coupled with peak-to-average power factor as claimed in claim 1, wherein: before the electronic nose monitors the odor concentration in the first step, the electronic nose domestication is carried out by collecting odor gas samples around a monitoring enterprise to carry out artificial olfaction experiments and sensor measurement, so that guarantee is provided for accurate monitoring of the odor concentration.
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