A simulation method for predicting inorganic element deposition flux in atmospheric wet deposition

Abstract

This invention belongs to the field of ecological protection technology and relates to a simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition. This invention can be based on PM2.5... 2.5 Concentration effectively predicts the wet deposition flux of any TE (transient precipitate), with meteorological factors and pollution sources largely negligible. The wet deposition flux of metal elements involved in this invention shows significant correlations with the predicted values ​​of Ca, K, Na, Mg, Al, Fe, Mn, Ba, As, Ni, Sr, V, Cu, Pb, and Zn, with correlation coefficients ranging from p < 0.001. This indicates that the prediction model of this invention has the best predictive ability for each element. The correlation coefficients between the measured and predicted values ​​of Cr, Co, and Cd are 0.001.

Description

Technical Field

[0001] This invention belongs to the field of ecological protection technology and relates to a simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition. Background Technology

[0002] Aerosols are relatively stable suspension systems formed by the uniform dispersion of solid or liquid particulate matter in a gas. Numerous studies have found that the chemical composition of atmospheric particulate matter includes inorganic components, metallic elements, organic components, elemental carbon, etc., with water-soluble ions and metallic elements being important components. Water-soluble ions, being hydrophilic, can promote the formation of cloud condensation nuclei, thus significantly impacting climate and visibility. Metallic elements in atmospheric particulate matter are non-degradable and bioaccumulate, posing a significant potential threat to the environment and human health; for example, lead and cadmium are chemically toxic to humans. When atmospheric particulate matter enters the human body through respiration, the metals within can cause various bodily dysfunctions, leading to stunted growth and even triggering various cancers and heart diseases. Therefore, aerosols have a crucial impact on climate change, the atmospheric environment, and human health. With my country placing increasing emphasis on air pollution, research on atmospheric aerosols has gradually become a hot topic in scientific research.

[0003] Water-soluble ions have complex sources, including soil dust, construction dust, sea fog emissions, biomass combustion, fossil fuel combustion, and vehicle exhaust. Existing research indicates that Na+ ions in atmospheric particulate matter... + NH4 + Ca 2+ Mg 2+ Cl - SO4 2- and NO3 - It has a high content. Among them, NH4... + SO4 2- and NO3 - Mainly from SO2 and NO directly emitted into the atmosphere X Particles formed by the chemical reaction with NH3 gas are therefore also called secondary ions. Due to their unique physical and chemical properties, water-soluble ions have a significant impact on physical and chemical processes in the atmosphere. For example, SO42- 2- and NO3 -The formation of cloud condensation nodules increases the number of cloud droplets. Increased cloud cover not only cools the surface but also increases precipitation, affecting surface humidity and vegetation, thus altering surface albedo and further influencing climate. Metals in atmospheric particulate matter are non-degradable and bioaccumulate, posing a significant potential threat to the environment and human health. For example, lead and cadmium are chemically toxic to humans; when particulate matter enters the body through respiration, these metals can cause various functional disorders, leading to stunted growth and even cancer and heart disease. Airborne metals deposited on plants, entering soil or water, can also cause significant damage to the environment and human health through transfer and accumulation in the food chain. Specifically, soil microorganisms can alter the physical and chemical properties of the soil, affecting plant growth and crop productivity, leading to decreased crop yields. Wet deposition involves the deposition of metal-containing particles with precipitation or snow; in this process, metals dissolve in water droplets or ice crystals or bind to and adsorb onto the particle surface. This may promote the mobility and solubility of atmosphericly deposited heavy metals.

[0004] The study of inorganic elements in aerosols helps to reveal the sources and composition of aerosols and is an important means of studying atmospheric aerosols. Currently, most studies on elements in atmospheric wet deposition focus on deposition flux, concentration, and pollutant sources. Furthermore, the wet deposition of water-soluble ions and metal elements in the atmosphere is influenced by various factors such as rainfall, relative humidity, wind speed and direction, air temperature, and air pressure. Information on water-soluble ions and metal elements in atmospheric wet deposition and their removal rate parameters is scarce. Therefore, establishing models to predict the wet deposition flux of inorganic elements in atmospheric particulate matter and evaluate their long-term accumulation levels is particularly important for controlling pollution from water-soluble ions and metal elements. Previous studies on precipitation models of water-soluble ions and TEs have mainly focused on NH4. + NO3 - SO4 2- The study focused on highly toxic elements such as Cu, As, Zn, Ni, and Cr. Lagrange composite trajectory models, atmospheric multi-pollutant exchange models (FRAME), and ACTMs (atmospheric chemical transport models) were employed. However, these models have numerous input parameters, complex spatial grid divisions in the study area, and many practical constraints, resulting in weak generalization ability and unsatisfactory prediction results. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing a simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition, thereby providing a scientific basis for the control of inorganic element pollution and the protection of ecosystems.

[0006] The objective of this invention can be achieved through the following technical solution: a simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition, the method comprising the following steps:

[0007] S1. Provide the assumptions and control for the effects of other variables;

[0008] S2. Collect aerosol particulate matter data based on the target area conditions, and then establish apparent clearance rate (ASR) and inorganic element and aerosol particulate matter mass values ​​(C) based on the target area conditions. Q ) database.

[0009] S3. Obtain atmospheric aerosol concentration and rainfall data for the target area within a preset time period;

[0010] S4. Input the obtained atmospheric aerosol concentration and rainfall data into the wet deposition prediction equation to obtain the deposition flux.

[0011] In the above simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition, the assumptions in step S1 include: inorganic elements in precipitation are uniformly distributed in the air; raindrops are free of impurities before falling and effectively sweep over suspended particulate matter in their path during descent; the distribution of trace elements in atmospheric particulate matter is random and uniform, and inorganic matter in the atmosphere is attached to suspended particles.

[0012] In the simulation method described above for predicting the deposition flux of inorganic elements in atmospheric wet deposition, the apparent scavenging rate (ASR) is calculated using the following formula:

[0013] Where C TE The concentration of TE in each rainfall event; C Q This is the ratio of TE to the mass of particulate matter. For PM 2.5 concentration.

[0014] In the simulation method described above for predicting the deposition flux of inorganic elements in atmospheric wet deposition, the wet deposition prediction equation is: Where α is the regression coefficient used to correct for the variation of TE distribution with particle size; C Q This is the ratio of TE to the mass of particulate matter. For PM 2.5 Concentration; U R The average speed of the raindrops.

[0015] In the simulation method described above for predicting the deposition flux of inorganic elements in atmospheric wet deposition, the wet deposition prediction equation is:

[0016] Where c, u, and ε are the parameters of the multiple linear regression, representing PM, U, and U, respectively.R The nonlinear correlation with model error; α is the regression coefficient used to correct for changes in TE distribution with granularity; C Q This is the ratio of TE to the mass of particulate matter. For PM 2.5 Concentration; U R The average speed of the raindrops.

[0017] In the above simulation method for predicting inorganic element deposition flux in atmospheric wet deposition, C Q The following calculation formula must be satisfied:

[0018] M TE =V a ·C PM ·C Q M TE TE is the measured value in the precipitation sample; Va is the volume of air washed away during the rainfall process; C PM This refers to PM concentration.

[0019] In the above-mentioned simulation method for predicting inorganic element deposition flux in atmospheric wet deposition, M TE The following calculation formula must be satisfied:

[0020] Where N TE C represents the wet settling flux. TE V represents the concentration of TE in each rainfall event. R For the volume of the rain sample, U R Let be the average velocity of the raindrops, A be the cross-sectional area of ​​the wet sedimentation collector, TE be the trace element, and t be the rainfall time.

[0021] Preferably, the trace elements include at least one of Ca, K, Na, Mg, Al, Fe, Mn, Ba, As, Ni, Sr, V, Cu, Pb, and Zn.

[0022] This invention introduces the scavenging ratio (SR), defined as the ratio of the volume of air (Va) swept away by rainfall to the amount of rainfall (Vr). Under the assumptions of current theory, it is also equal to the ratio of the TE concentration in rainwater to the TE concentration in the air, as shown in the following equation.

[0023]

[0024] However, a significant limitation is that some variables used to calculate this parameter are difficult to obtain or measure. This is because the aerodynamic diameter is <2.5 (PM). 2.5 Airborne particles (PM) are more likely to have health effects, and current air quality standards typically focus on this finer detail. 2.5Data became easier to obtain than total suspended particulate matter (TSP). For PM 2.5 Simultaneous measurement with TSP showed a strong linear correlation between fine particulate matter and total suspended particulate matter. Once the variation of TE content in particulate matter of different sizes is ignored, we can further define the apparent removal rate as the ratio of TE concentration in precipitation to TE concentration in air.

[0025]

[0026] In the formula: PM 2.5 Concentration (µg / m³); C Q It is TE and PM 2.5 The mass fraction. Therefore, wet Precipitates per TE, N TE (μg·m 2 ·h -1 The flux can be estimated based on the amount of TE captured by airborne particles, PM concentration, rainfall rate, and removal rate.

[0027]

[0028] Here, α is the regression coefficient, used to correct for changes in TE distribution with granularity.

[0029] From a practical point of view, the logarithmic transformation is applicable to this equation.

[0030]

[0031] Where c, u, and ε are the parameters of the multiple linear regression, representing PM, U, and U, respectively. R The nonlinear correlation with model error. Since the raindrop removal efficiency is sufficiently high, it's easy to conclude that ASR has a mass fraction several orders of magnitude higher than TE in PM and α. Comparing the last three terms on the right-hand side of the equation, logC... Q The value and change of logα are negligible. In other words, the change in ASR determines the accuracy of TE wet settling flux prediction.

[0032]

[0033] Perform multiple linear regression between and .

[0034] Compared with the prior art, the present invention has the following beneficial effects: the present invention can be based on PM 2.5The concentration can effectively predict the wet deposition flux of any TE, and meteorological factors and pollution sources can be ignored to a certain extent. The wet deposition flux of the metal elements involved in the present invention has a significant correlation with the predicted values of Ca, K, Na, Mg, Al, Fe, Mn, Ba, As, Ni, Sr, V, Cu, Pb, and Zn. The correlation coefficients of each element range from p < 0.001, indicating that the prediction model of the present invention has the best prediction ability for each element. The correlation coefficients between the measured values and the predicted values of Cr, Co, and Cd are 0.001 < p < 0.01, which can better predict these elements, and the predicted values have good credibility. Description of the Drawings

[0035] Figure 1 It is the scavenging model of rainfall on atmospheric particulate matter in Example 1.

[0036] Figure 2 For PM 2.5 The proportion of trace metal elements and the apparent scavenging rate of metal elements calculated based on the model.

[0037] Figure 3 It is a comparison chart of the measured wet deposition flux of TEs and the predicted wet deposition flux of the box model.

[0038] Figure 4 It is the correlation between the measured value and the predicted value of the wet deposition flux of metal elements. Detailed Embodiments

[0039] The following are specific embodiments of the present invention, which further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0040] Example 1:

[0041] In this example, the method of the present invention is further explained by taking the metal elements in PM 2.5 in aerosol particulate matter as an example.

[0042] S1. Give the assumption conditions and control the influence of other variables; the inorganic elements in precipitation are evenly distributed in the air; there is no impurity before the raindrop falls and it effectively sweeps the suspended particulate matter on its path during the falling process; the distribution of trace elements in atmospheric particulate matter has randomness and uniformity, and the inorganic substances in the atmosphere adhere to the suspended particles.

[0043] S2. Collect aerosol particulate matter data according to the situation of the target area, and then establish a database of the apparent scavenging rate (ASR) and the mass value of inorganic elements and aerosol particulate matter (C Q ) belonging to the target area according to the situation of the target area;

[0044] The apparent scavenging rate (ASR) is calculated by the following formula:

[0045] Where C TE The concentration of TE in each rainfall event; C Q This is the ratio of TE to the mass of particulate matter. For PM 2.5 concentration.

[0046] S3. Obtain atmospheric aerosol concentration and rainfall data for the target area within a preset time period;

[0047] S4. Input the obtained atmospheric aerosol concentration and rainfall data into the wet deposition prediction equation to obtain the deposition flux. The wet deposition prediction equation is:

[0048]

[0049] The method for graphically displaying aerosol metal element data in this embodiment is as follows:

[0050] The present invention provides a model for the removal of atmospheric particulate matter by rainfall, as follows: Figure 1 As shown, the ASR and C used in the model Q The value is based on the equation The calculations were performed. After establishing a box model of the wet settling process of TEs, from... Figure 2 It can be seen that PM was observed 2.5 Concentration of TEs in C Q The various patterns of apparent clearance rate (ASR) are generally constant and normally distributed. TE C Q , ASR and U R This can be seen in Eq. In logN TE logC Q logASR and logU R Perform multiple linear regression between them. When comparing the last three terms on the right side of the equation, logC Q The values ​​and changes of logα are negligible. In other words, the variation of ASR determines the accuracy of TEs wet settling flux prediction.

[0051] Figure 3 A comparison plot of measured wet deposition fluxes and box model predicted wet deposition fluxes for TEs. Rainfall and PM2.5 were used. 2.5 Box model prediction performance of concentration and constant ASR. (The following appears to be a separate, unrelated sentence: Constituent PM) 2.5 The predicted values ​​for higher crustal elements (sodium, magnesium, calcium, barium, iron, aluminum, potassium, and manganese) did not match the measured results. Other elements showed better predictions. This highlights the inherent advantage of this developed box model, as it can be based on PM2.5 concentrations. 2.5The concentration can effectively predict the wet deposition flux of any TE. Therefore, meteorological factors and pollution sources can be ignored to a certain extent in this model. The results show that the wet deposition flux of TEs is only related to the PM 2.5 concentration and rainfall.

[0052] Figure 4 shows the correlation between the measured values and the model predicted values of the wet deposition flux during the study period (2016 - 2019). The wet deposition fluxes of the metal elements involved in this study show significant correlations with the predicted values of Ca, K, Na, Mg, Al, Fe, Mn, Ba, As, Ni, Sr, V, Cu, Pb, and Zn. The correlation coefficients of each element range from p < 0.001, preliminarily indicating that the prediction model of this study has the best prediction ability for each element. The correlation coefficients between the measured values and the predicted values of Cr, Co, and Cd are 0.001 < p < 0.01, and this invention can predict these elements well, and the predicted values have good credibility. The model has a poor prediction for the Li element (p < 0.05), indicating that there is no correlation or significant relationship between the measured values and the predicted values, Figure 3 and Figure 4 shows the prediction effect of the model of this invention.

[0053] For the parts where the mid - values of the technical scope claimed by this invention are not exhausted in the embodiments herein, and for the new technical solutions formed by the equivalent replacement of single or multiple technical features in the technical solutions of the embodiments, they are also within the scope claimed by this invention; at the same time, in all the listed or unlisted embodiments of this invention's solutions, the various parameters in the same embodiment only represent an example of its technical solution (that is, a feasible solution), and there is no strict coordination and limitation relationship between the various parameters. Among them, the various parameters can be mutually replaced when not violating the axioms and the requirements of this invention, except as otherwise specially stated.

[0054] The technical means disclosed in this invention's solutions are not limited to the technical means disclosed above, but also include the technical solutions composed of any combination of the above technical features. The above is the specific implementation manner of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as within the protection scope of this invention.

Claims

1. A simulation method of predicting the deposition flux of inorganic elements in atmospheric wet deposition, characterized by, The method comprises the following steps: S1, giving a hypothetical condition, controlling the influence of other variables; S2, collecting aerosol particle data according to the target area situation, and then establishing a database of apparent removal rate and inorganic elements and aerosol particle mass value belonging to the target area according to the target area situation; S3, obtaining atmospheric aerosol concentration and rainfall data within a preset time of the target area; S4, inputting the obtained atmospheric aerosol concentration and rainfall data into a wet deposition prediction equation to obtain a deposition flux; The wet deposition prediction equation is: ; where c, u, and e are multilinear regression parameters representing PM, U R and the nonlinear correlation of model error; α a is a regression coefficient used to correct the variation of the TE distribution with particle size; C Q TE is the ratio of the TE to the mass of the particulate matter; C PM2.5 For PM 2.5 Concentration; U R Average velocity of raindrops; TE is a trace element; wherein N TE is the wet deposition flux, and ASR is the apparent clearance rate. The hypothetical conditions in step S1 include: the inorganic elements in the precipitation are uniformly distributed in the air; the raindrops are free of impurities before falling and effectively sweep the suspended particulate matter on their path during the falling process; the distribution of trace elements in atmospheric particulate matter has randomness and uniformity, and the inorganic matter in the atmosphere is attached to the suspended particles.

2. The simulation method for predicting the deposition flux of inorganic elements in atmospheric wet deposition according to claim 1, characterized in that, The apparent clearance is calculated by the following formula: wherein C TE Cteis the concentration of TE in each rainfall; C Q Cteis the concentration of TE in each rainfall; C PM2.5 PM for PM 2.5 Concentration; TE for trace elements.

3. The simulation method of predicting the deposition flux of inorganic elements in atmospheric wet deposition according to claim 2, characterized in that, C Q satisfies the following calculation formula: wherein M TE is the measured amount of TE in the precipitation sample; V a is the volume of air flushed by the rainfall event; C PM is the PM concentration; TE is trace element.

4. The simulation method of predicting the deposition flux of inorganic elements in atmospheric wet deposition according to claim 3, characterized in that, M TE satisfies the following calculation formula: ; wherein N TE is the wet deposition flux, C TE is the concentration of TE in each rainfall, V R is the rain sample volume, U R is the average speed of raindrops, A is the cross-sectional area of the wet deposition collector, TE is a trace element, t is the rainfall time.

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