CN106599380B

CN106599380B - Method for predicting volatile organic pollutants in indoor air of wading public place

Info

Publication number: CN106599380B
Application number: CN201611033244.5A
Authority: CN
Inventors: 陆凯; 牛志广
Original assignee: Tianjin University
Current assignee: Tianjin University
Priority date: 2016-11-15
Filing date: 2016-11-15
Publication date: 2020-03-31
Anticipated expiration: 2036-11-15
Also published as: CN106599380A

Abstract

The invention discloses a method for predicting volatile organic pollutants in indoor air in wading public places, which comprises the following steps: determining a public place to be simulated, numbering the rooms in the public place in sequence, further determining the room where the pollution source is located, and determining the air circulation relationship among the rooms; respectively establishing a pollutant transport equation in each room with an indoor pollution source and a pollutant transport equation in each room without the indoor pollution source; the contaminant concentration function for each room is solved separately by mathematical software and repeated multiple times using the monte carlo method, the solution results being given in the form of confidence intervals. The method can simulate an unsteady environment system.

Description

Method for predicting volatile organic pollutants in indoor air of wading public place

Technical Field

The invention relates to a prediction method of indoor air volatile organic pollutants in public places, in particular to a prediction method of indoor air volatile organic pollutants in public places involved in water.

Background

Indoor water such as tap water and direct drinking water often contains various volatile organic pollutants (VOCs). Indoor water discharges air through water facilities such as a faucet, a shower nozzle, a bathtub, a swimming pool and the like, and VOCs in the indoor water volatilize into the air and become a pollution source for polluting the indoor environmental air in wading. Due to the diversity of indoor environments, the VOCs pollution in the indoor environment is simulated by using the mathematical model, so that heavy analysis and monitoring work is avoided, and the human health risk evaluation of the VOCs in the indoor environment is facilitated.

McKone introduced a predictive method (i.e., a three-compartment model) for modeling VOCs in an indoor environment in 1987 (see McKonet. human exposure to volatile organic compounds in house hold water: the index inhibition pathway [ J ]. Environmental Science & Technology, 1987, 21 (12): 1194-. The basic principle of the method is as follows: the household environment is divided into three chambers, and the concentration of VOCs is solved based on the mass conservation law and the mass transfer theory of indoor water facilities by means of PREMOD/MODAID software. The method has an important influence on indoor environment system simulation, but is difficult to apply to the indoor environment system in public places. In fact, the opening and use of pollution sources (taps, showerheads) are linked to the behaviour of people in the environment. The number of people in the home environment is small, and the behaviors of people are regular; in public places, the number of people is huge, and the randomness of the behaviors of people is strong. Therefore, the method for describing the behaviors of the people in the household environment in the three-chamber model cannot be popularized to public places.

Hsu proposed in 2009 a predictive method for modeling indoor swimming pool environmental VOCs (see Hsu HT, Chen MJ, Lin CH, et al. Chloroform in iron pool swing-pool air: monitering and adsorbing coupled with the effects of environmental conditions and occupancy [ J ]. Water Res, 2009, 43 (15): 3693-3704. monitoring and mathematical modeling of indoor swimming pool chloroform considering environmental conditions and occupancy behavior, Water research, 2009). The method can be applied to the swimming pool space of an indoor swimming pool (except accessory spaces such as a shower room and a dressing room of the swimming pool) and can be popularized to other indoor spaces with wide water surfaces (such as an indoor hot spring vacation area). The basic principle of the method is as follows: based on hydrodynamics and mass transfer principles, the influence of factors such as indoor gas flow and the action of customers using the swimming pool on VOCs is quantitatively described by using a mathematical relational expression, and simulation is realized by depending on certain software (not mentioned in the text). The main drawbacks of this method are: accessory space is not considered, and a large amount of VOCs are inhaled by customers in a shower room; the occupation behavior of the customers cannot be automatically simulated, and the number of the customers is an input parameter of the program; the unsteady state process of the indoor environment cannot be continuously simulated, and only a prediction result under a certain environmental condition can be given.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a prediction method of indoor air volatile organic pollutants in a wading public place, which can simulate an unsteady environment system.

A prediction method for volatile organic pollutants in indoor air of wading public places comprises the following steps:

step 101: determining a public place to be simulated, numbering each room in the public place in sequence, further determining the room where a pollution source is located, and determining the air circulation relationship among the rooms, wherein the pollution source is divided into a complete mixed flow and a piston flow;

step 102: respectively establishing pollutant transport equations in all rooms with indoor pollution sources and pollutant transport equations in all rooms without indoor pollution sources according to the mass conservation principle;

the transport equation of the pollutants in each room with a pollution source in the room is shown as the following formula 1:

the transport equation of the pollutants in each chamber without pollution sources in the chamber is shown as the following formula 2:

in the above formulas 1 and 2: v_iIs the room volume; t is time; q. q.s_{i_j}、q_{j_i}Respectively, the ventilation rate of entering a room with serial number j from a room with serial number i and the ventilation rate of entering a room with serial number i from a room with serial number j; j ranges from 1 to n +1, wherein n is the total number of rooms, n +1 represents outdoor space, and j is not equal to i; wherein S in the above formula 1_i(t) is a function of the rate of release of contaminants from the room having the source of contamination; i represents the serial number of any room with a pollution source, and j represents the serial number of the room with the serial number i in an air circulation relationship; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i; wherein i in the above formula 2 represents the serial number of any room without pollution source, and j represents the room serial number having air circulation relation with the room with serial number i; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i;

step 103: solving a pollutant release rate function in the pollution source room in the formula 1, which comprises the following specific steps:

(a) the establishment of a release model for a completely mixed flow pollution source is shown in formula 3:

the release model established for the plug flow pollution source is shown as the formula 4:

in formulas 3 and 4: (K)_OLA)_iThe total mass transfer coefficient of the liquid phase of the pollution source; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; g_i(t) is a time function of the number of customers associated with a source of contamination; h_iIs the henry constant; c_w,iThe water body concentration of the room pollutants with the serial number i; a. the_iMass transfer area as a source of contamination;

in equation 3, i represents the room number where the completely mixed stream pollution source under consideration is located; k is a radical of_L,refIs the liquid phase mass transfer coefficient of the reference mass; d_l,refIs the liquid phase diffusion coefficient of the reference substance; d_g,iAnd D_l,iGas phase and liquid phase diffusion coefficients of the contaminants released by the contamination source, respectively; epsilon_iAn energy dissipation rate for the water surface occupancy activity; rho_w,iIs the water density; mu.s_w,iIs the water viscosity; rho_a,iIs the air density; mu.s_a,iIs an air viscosity; p is mechanical work power of the water surface occupation behavior and a constant; v_w,iIs the volume of the pool; v. of_iThe wind speed on the water surface is a pollution source.

In formula 4: i represents the room serial number of the considered piston flow pollution source; q_L,iPiston flow pollution source flow G in room with serial number i_i(t) is a function of time as a function of the number of water utility openings;

(b) determining a time function of the number of customers related to a certain pollution source in the formula 3 and the formula 4 by using a queuing theory principle, wherein the specific steps are as follows:

the first step is as follows: respectively establishing a logic structure and data parameters;

the logic structure comprises:

abstracting the behavior of a customer in a public place, establishing a random service system, and determining which behaviors of the customer in which rooms are associated with pollution sources;

determining business rules of public places, paying attention to business hours and making defined relevant regulations on customer behaviors;

determining customer arrival flow characteristics, comprising: number of customers, randomness of customer arrival, independence of customer arrival, and volatility of customer arrival;

determining characteristics of a service system, comprising: the parallel mode and the number of the service mechanisms, the randomness of service time and the fluctuation of the service time;

determining queuing and service rules, including: the waiting mode of the customer before entering the service organization and the order in which the customer receives the service;

the data parameters comprise:

establishing a time function of the arrival time interval of the customer, which comprises the following specific processes: firstly, acquiring a plurality of client arrival time sequences of a research object; secondly, calculating a typical customer arrival time sequence; thirdly, converting the typical customer arrival time sequence into a customer arrival rate time function by using a curve fitting method; finally, the time function of the arrival rate of the customer is subjected to reciprocal calculation to obtain a time function of the arrival time interval of the customer;

determining the stay time of the customer;

and secondly, calculating a time function of the number of the customers related to the pollution source according to the logic structure and the data parameters in the first step, wherein the specific steps are as follows:

step 201: generating the arrival time of each customer and acquiring the maximum value of the number of the customers;

step 202: generating the stay time of each customer and calculating the time when each customer arrives and leaves a certain room;

step 203: calculating the number of customers in a certain room at each moment according to the times of the customers arriving at each room generated in the step 201 and the step 202;

(c) substituting the result obtained in the step (b) into formula 3 and formula 4;

step 104: formula 3 and formula 4 in step 103 are substituted into formula 1 according to the type of the pollution source, then the pollutant concentration function of each room is solved through mathematical software by using formula 1 and formula 2 respectively and the solution is repeated for a plurality of times by using a Monte Carlo method, and the solution result is given in the form of a confidence interval.

The invention has the beneficial effects that:

(1) an unsteady environment system can be simulated. The indoor public place environment system is obviously an unsteady system (namely, the pollutant concentration can change along with time), and the algorithm implementation of the prediction method can realize continuous automatic simulation under the conditions of given initial conditions, simulation time length and simulation step length.

(2) The public place as a whole was simulated. Public places often contain multiple spaces. For example, indoor swimming pool can be divided into rooms such as swimming pool space, shower room, dressing room and hall. As another example, hotels are typically divided into multiple rooms. The prediction method has good expansibility, and the algorithm is easy to adjust according to the specific structure of the simulation object when being realized.

(3) The behavior of the customer can be quantitatively simulated. The behavior of people in public places is divided into two categories, arrival and stay (with water utilities). The method utilizes the queuing system analysis theory, can quantitatively track the arrival and stay process of each customer when an algorithm is realized, so that the use condition of water facilities (pollution sources) is simulated, and the difficulty caused by the randomness of the behaviors of the customers is solved.

Drawings

FIG. 1 is a schematic view of a natatorium stochastic service system according to example 1;

FIG. 2 is a technical scheme of example 1;

FIG. 3 is a time function of the arrival rate of customers in example 1;

FIG. 4 is a graph showing the results of simulation and verification of the concentration of contaminants in the space of the swimming pool of the natatorium in example 1;

FIG. 5 is a graph showing the simulation results and the verification results of the concentration of contaminants in the shower cubicles of the male natatorium in example 1.

Detailed Description

The present invention will be described in further detail with reference to specific embodiments.

The invention discloses a method for predicting volatile organic pollutants in indoor air in wading public places, which comprises the following steps of:

step 101: determining a public place to be simulated, numbering the rooms in the public place in sequence, further determining the room where the pollution source is located, and determining the air circulation relationship among the rooms; the pollution source comprises complete mixed flow and plug flow, wherein the complete mixed flow comprises water facilities such as the water surface of a swimming pool, the water surface of a bathtub, the water surface of a hot spring pool and the like; the piston flow comprises water using facilities such as a faucet, a shower nozzle, a toilet and the like.

in the above formulas 1 and 2: v_iIs the room volume; t is time; q. q.s_{i_j}、q_{j_i}Respectively, the ventilation rate of entering a room with serial number j from a room with serial number i and the ventilation rate of entering a room with serial number i from a room with serial number j; j ranges from 1 to n +1, wherein n is the total number of rooms, n +1 represents outdoor space, and j is not equal to i; wherein S in the above formula 1_i(t) is a function of the rate of release of contaminants from the room having the source of contamination; i represents the serial number of any room with a pollution source, and j represents the serial number of the room with the serial number i in an air circulation relationship; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i; wherein i in the above formula 2 represents any one of the rooms without a source of contaminationA serial number j represents a room serial number having an air circulation relationship with the room with the serial number i; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i.

Step 103: solving a pollutant release rate function S in the pollution source room in the above formula 1_i(t), the concrete steps are as follows:

in the above equation, equation 3 is the final expression for the fully mixed flow pollutant source pollutant release. Formula 4 is a basic expression thereof. When the final expression formula 3 is established, formula 9 and formula 10 are substituted into formula 8, and formula 7 is substituted into formula 6; then substituting the formula 6 and the formula 8 into the formula 5; and finally substituting the formula 5 into the formula 4 and finishing to obtain the formula 3.

The release model is established for the plug flow pollution source as follows (formula 11):

in formulas 3 and 11: k_OL,iThe total mass transfer coefficient of the liquid phase of the pollution source; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; g_i(t) is a time function of the number of customers associated with a source of contamination; h_iIs the henry constant; c_w,iThe water body concentration of the room pollutants with the serial number i; is the henry constant; a. the_iMass transfer area as a source of contamination;

in equation 3, i represents the room number where the completely mixed stream pollution source under consideration is located; k is a radical of_L,iIs the liquid phase mass transfer coefficient; k is a radical of_G,iIs the gas phase mass transfer coefficient; k is a radical of_L,refIs the liquid phase mass transfer coefficient, constant, of a reference substance (oxygen is typically used); d_l,refIs the liquid phase diffusion coefficient, constant, of the reference substance; d_g,iAnd D_l,iGas phase and liquid phase diffusion coefficients of the contaminants released by the contamination source, respectively; epsilon_iAn energy dissipation rate for the water surface occupancy activity; rho_w,iIs the water density; mu.s_w,iIs the water viscosity; rho_a,iIs the air density; mu.s_a,iIs an air viscosity; sc (Sc)_iIs the schmitt number; re_iIs Reynolds number; p is mechanical work power of the water surface occupation behavior and a constant; v_w,iIs the volume of the pool; v. of_iThe wind speed on the water surface is a pollution source.

In formula 11: i represents the room serial number of the considered piston flow pollution source; q_L,iPiston flow pollution source flow in room with serial number i; g_i(t) is a function of the number of water utility openings as a function of the number of customers associated with the pollution source used in the method. Mass transfer parameter (K) of this type of pollution source_OLA)_iCan be calculated synchronously according to the method disclosed in the literature (CorsirL, Howard C. Volatilization Rates from Water to Indo)or Air，Phase II[R]Washington, DC: united States Environmental Protection Agency, Office of research and Development, 1998 the rate of evaporation of contaminants from water to indoor air (second phase report) [ R]Washington, d.c., the united states environmental protection agency, 1998).

C in formulae 3 and 11_w,iCan be determined by reference to the national standard methods for contaminant determination (see national environmental protection agency, Water and wastewater monitoring and analysis methods [ M ]]Beijing: chinese environmental science press 2002), and then the concentration distribution is fitted using a normal log distribution.

D in formulas 3 to 11_g,i、D_l,i、ρ_w,i、μ_w,i、ρ_a,i、μ_a,i、P、D_l,ref、k_L,refIsoparameters, which can be determined by reference to the handbook of estimates of physicochemical parameters (see Lyman WJ, RosenblattDH, ReehlWJ. handbook of chemical Property evaluation Methods: Environmental behavor of organic Compounds [ M]New York: McGraw-Hill Book Company, 1990. handbook of chemical Property estimation methods: environmental behavior of organic pollutants [ M]New york: miichel books, inc).

V in formulas 3 to 11_i、A_i、V_w,i、Q_L,i、q_{i_j}、q_{j_i}、v_iAnd the parameters can be actually measured on site.

(b) Determining time function G of number of customers related to certain pollution source in formula 3 and formula 11 by using queuing theory principle_i(t), the concrete steps are as follows:

the logic structure comprises:

business rules for the public place are determined. Emphasis is placed on business hours and related regulations that define customer behavior.

Determining customer arrival flow characteristics, comprising: number of customers, randomness of customer arrival, independence of customer arrival, and volatility of customer arrival.

Determining characteristics of a service system (i.e. treating a customer's stay in a public place as being served) comprising: the parallel mode and the number of the service mechanisms, the randomness of the service time and the fluctuation of the service time.

Determining queuing and service rules, including: the manner in which the customer waits before entering the service facility and the order in which the customer receives service.

The data parameters comprise:

a customer inter-arrival time function arrival (t) is established. The specific process is as follows: first, a plurality of (preferably 15 or more) customer arrival time series of the study subjects are acquired. The customer arrival time sequence refers to a discrete observation sequence of the number of customers arriving in a unit time in a certain working day business hour. The data is provided by a public place manager. Secondly, calculating a typical customer arrival time sequence, namely a method for averaging corresponding time points of a plurality of discrete observation sequences. Again, a typical customer arrival time series is converted to a customer arrival rate time function rate (t) using a curve fitting method. Finally, the function of the arrival rate and time of the customer is reciprocal to obtain a function of the time interval between the arrival of the customer, as shown in the following formula 12.

The customer dwell time is determined. The stay time of the customer refers to the time distribution spent by the customer in each room (or performing some action) of the public place, and is used for simulating the stay process of the customer. This parameter is typically determined by questionnaires or field measurements.

Secondly, calculating a time function G of the number of the customers related to the pollution source according to the logic structure and the data parameters in the first step_i(t): the method comprises the following specific steps:

step 201: the method for generating the arrival time of each customer and acquiring the maximum value of the number of the customers can be specifically realized by the following method:

(1) generating the 1 st customer arrival time t_1,0From the time of arrival of the customerInterval function Arrival (t) and initial business time t₀Determining;

(2) accumulating the number of customers by 1, wherein the number of customers k is 2;

(3) generating a kth customer arrival time t_k,0The time function of arrival time interval of the customers, Arrival (t) and the arrival time t of the k-1 st customer_k-1,0Determining;

(4) judging the arrival time t of the kth customer_k,0If not, entering step 202, if yes, entering step 5;

(5) accumulating the number k of the customers by 1, and repeating the step (3) and the step (4);

step 202: the method for generating the stay time of each customer and calculating the time when each customer arrives at and leaves a certain room can be specifically realized by the following method:

(6) recording the maximum value Max of the number of customers as k, setting the number of the customers as 0 to recount (k as 0), and setting the number of rooms as 1;

(7) circularly generating all stay time d of the arriving customer in the room i_k,i；

(8) The customer number k is 1;

(9) time t when 1 st customer leaves room i_1,iIs equal to the time when the 1 st customer leaves the room i-1 and the stay time d of the 1 st customer in the room i_1,i(i.e. t)_1,i＝t_1,i-1+d_1,i)；

(10) Accumulating the number of customers by 1, wherein the number of customers k is 2;

(11) the number of customers who have not left room i when the kth customer arrives is compared with the number of service desks in room i. If the number of customers who have not left the room i is small, the time t when the k-th customer leaves the room i_k,iEqual to the time when the kth customer leaves the room i-1 and the length of stay of the kth customer in the room i (i.e., t)_k,i＝t_k,i-1+d_k,i). If the number of the service desks in the room i is small, the kth customer needs to queue up, t_k,iEqual to the departure time of the previous customer in the queue and d_k,iAnd (4) summing.

(12) Accumulating the number of customers by 1, judging the size relationship between k and Max, if k is less than or equal to Max, repeating (11), and if k is greater than Max, entering the step (13);

(13) accumulating the number of rooms by 1, and repeating the steps (7) to (12) until all the rooms are calculated;

step 203: the number of customers in a certain room at each time is calculated according to the times of the customers arriving at each room generated in step 201 and step 202, which can be specifically realized by the following method:

(14) the number of rooms i equals 1, and the time t equals t₀The number k of customers is 1;

(15) judging t_k,iT is not less than t_k,i<t +1, if yes, it means that customer k is in room i at time t, G_i(t) add up to 1;

(16) accumulating the number of customers by 1, judging the size relationship between k and Max, if k is less than or equal to Max, repeating (15), and if k is greater than Max, entering the step (17);

(17) accumulating the time numbers by 1, judging whether t is in the business time range, if so, repeating the steps (15) to (16), and if not, entering the step (18);

(18) and (4) adding 1 to the number of rooms, and repeating the steps (15) to (17) until all the rooms are calculated.

(c) Substituting the result obtained in step (b) into formula 3 and formula 11;

step 104: substituting equations 3 and 11 in step 103 into equation 1 according to the type of the pollution source, and then respectively solving the pollutant concentration function C of each room by mathematical software (e.g. MATLAB) using equations 1 and 2_i(t) of (d). Due to the randomness of the customer behaviors and the concentration of the water pollutants, the Monte Carlo method is used for repeatedly solving for many times, and the solving result is given in the form of a confidence interval.

The present invention is described in detail below with reference to example 1.

Example 1 a certain indoor natatorium M in tianjin was used as the subject and the study period was spring. Two VOCs, chloroform (TCM) and monobromo dichloromethane (BDCM) were simulated.

Step 101: the natatorium M interior space is divided into 8 sections and numbered sequentially, including the pool space (room 1), office (room 2), employee rest room (room 3), male shower (room 4), male changing room (room 5), female shower (room 6), female changing room (room 7) and hall (room 8). The completely mixed flow pollution source is the water surface of the swimming pool and is positioned in the swimming pool space (1 in total). The plug flow sources of contamination were showerheads, located in male (36) and female (26) showers. The air circulation relationship of each indoor space is as follows: the swimming pool space is directly connected with the space except the male changing room and the female changing room; the office is connected with the swimming pool space; the employee rest room is connected with the swimming pool space; the male shower room is connected with the swimming pool space and the male changing room; the female shower room is connected with the swimming pool space and the female changing room; the male changing room is connected with a hall and a male shower room; the woman changing room is connected with the hall and the woman shower room; the hall is connected with the swimming pool space, the male changing room and the female changing room.

Step 102: the equations 1-2 can be expressed as the following ordinary differential equations (equations 13-20):

in the formula: the subscripts 1 to 9 denote physical quantities of the spaces such as pool space, office, employee rest room, male shower room, male changing room, female shower room, female changing room, hall, and outdoor, respectively.

(a) The establishment of a release model for the water surface of the swimming pool in the swimming pool space is shown in formula 21:

the release model established for a male shower enclosure showerhead is shown in equation 22:

establishing a release model for the showerhead of a female shower enclosure is shown in equation 23:

water body concentration C of TCM and BDCM_w,iDetermination (see national environmental protection agency. Water and wastewater monitoring analysis method [ M)]Beijing: chinese environmental science press, 2002) and a fitting distribution as shown in table 1, the first parameter of the lognormal distribution is the mean value, and the second parameter is the standard deviation.

TABLE 1 distribution of concentration of TCM and BDCM in body of M water in swimming pool (. mu.g/L)

In addition, to verify the accuracy of the model, the air concentrations of TCM and BDCM in the pool space and male shower were also measured using carbon adsorption-carbon disulfide desorption/gas chromatography (HJ645-2013) (fig. 4-5).

The molar masses Mw (g/mol) were determined from the literature: 119.378(TCM), 163.829(BDCM), 92.139 (toluene). Determination of the boiling point T from the literature_b(K) The method comprises the following steps 334.32(TCM), 363.15(BDCM), 383.78 (toluene).Determination of LeBas molar volume V 'from literature'_B(cm³Per mol): 92.3(TCM), 94.7(BDCM), 118.2 (toluene).

Determination of the water temperature T from actual measurements_w,i(K) The method comprises the following steps 301 (swimming pool water T)_w,1) 311 (shower water T)_w,4/6)。

According to the water temperature T_wDetermination of the water viscosity μ_w,i(uPa · s): 0.8327 (swimming pool water u)_w,1) 0.6821 (shower water μ)_w,4/6)。

According to the formula in the literature (see Lyman WJ, RosenblattDH, ReehlWJ. handbook of chemical Property evaluation Methods: Environmental beer of organic Compounds [ M)]New York: McGraw-Hill Book Company, 1990. handbook of chemical Property estimation methods: environmental behavior of organic pollutants [ M]New york: McHill book Co.) and the parameters Mw, T_b、V′_B、T_w,iCalculation of the gas phase diffusion coefficient D_g,i(cm²/s)：

TABLE 2 gas phase diffusion coefficient D_g,i(cm²/s)

According to the formula in the literature (see Lyman WJ, RosenblattDH, ReehlWJ. handbook of chemical Property evaluation Methods: Environmental beer of organic Compounds [ M)]New York: McGraw-Hill Book Company, 1990. handbook of chemical Property estimation methods: environmental behavior of organic pollutants [ M]New york: mike hill books corporation) and a parameter μ_w,i,V′_BCalculating the liquid phase diffusion coefficient D_l,i(cm²/s)：

TABLE 3 liquid phase diffusion coefficient D_l,i(cm²/s)

According to the software EPA EPI Suite^TMFormula (II) and parameter T_w,iCalculation of the Henry constant H_i(dimensionless):

TABLE 4 Henry constant H_i

According to the formula in the literature (Corsi R L, Howard C. Volatilization Rates from Waterto Indor Air, Phase II [ R ]]Washington, DC: united States environmental protection Agency, Office of Research and Development, 1998 the rate of evaporation of contaminants from water to indoor air (second phase report) [ R]Washington D.C., environmental protection agency of the United states, 1998) using the parameter D of the pollutant and the reference substance toluene_l,i、D_g,i、H_iSynchronously calculating the product (K) of total liquid-phase mass transfer coefficient and mass transfer area of single shower nozzle_OLA)_4/6(L/min)：13.10(TCM)、12.78(BDCM)。

Determining mechanical work power P (kg.m) of swimmer from literature²/s³)：60。

Determination of swimming pool surface area A from actual measurements₁(m²): 1265.05. determination of swimming pool water volume V by actual measurement_w,1(m³)：2403.58。

Determination of air density ρ from literature in pool space_a,1(kg/m³): 1.15. determination of swimming pool temperature T from actual measurement_a,1(K) The method comprises the following steps 302. According to the formula in the literature (CRane company. flow of fluids through values, fits, and technical Paper No.410[ R. ]]1988. krey corporation, usa. fluid flow of valves, fittings and pipes: technical report 410[ R ]]1988.) and parameter T_a,iCalculating the air viscosity mu_a,1(uPa·s)：1.88E-2。

Determination of swimming pool surface wind speed v by actual measurement₁(m/s)：0.2。

According to the formula in the literature (Hsu HT, ChenmJ, Lin CH, et al, Chloroform in indexing-polyol air: monitoring and modeling coupled with the effects of environmental conditions and environmental activities [ J HT]Water Research, 2009, 43 (15): 3693-3704. monitoring and mathematical simulation of chloroform in indoor swimming pools, considering environmental conditions and occupancy behavior of swimming pools, Water research，2009.Guo Z，Roache NF.Overall mass transfer coefficient for pollutantemissions from small water pools under simulated indoor environmentalconditions[J]Annals of Occuptical Hygiene Journal, 2003, 47 (4): 279-286. estimation of total mass transfer coefficient for small water surface contaminant release in simulated indoor environment, professional hydraulics, 2003) and parameter D_l,1、ρ_w,1、μ_w,1、P、V_w,1Calculating swimming pool liquid phase mass transfer coefficient k_L,1(m/s). According to the formula in the literature (Mackay D, Yeun ATK, Mass transfer synergistic reactions for promotion of organic solvents from water [ J].Environmental Science&Technology, 1983, 17 (4): 211-217. law of mass transfer rate of organic solvent evaporating from water, environmental science and technology, 1983) and parameter D_g,1、A₁、v₁、μ_a,1、ρ_a,1Calculating swimming pool liquid phase mass transfer coefficient k_G,1(m/s). The calculation result expression is substituted into equation 21.

Shower nozzle flow Q determined by actual measurement_L,4/6(L/min)：6.5。

Determination of the volume V of space from actual measurements_i(L)：

TABLE 5 volume in space V_i

Determination of the ventilation rate q from the measured values_{i_j}(L/min)：

TABLE 6 air exchange Rate q in the hours of operation_{i_j}(L/min)

TABLE 7 Ventilation Rate q during non-Business hours_{i_j}(L/min)

(b) Determining time function G of number of customers related to pollution sources in formulas 21-23 by using queuing theory principle₁(t)、G₄(t)、G₆(t)：

The first step is as follows: a logical structure is established. The whereabouts of a typical natatorium patron are generally: changing room, swimming pool, shower room, changing room, and rest in hall (figure 1). Therefore, the random service system is divided into 5 service organizations connected in series. The correlation of customer behavior with contaminants is: the customer can turn on or off the shower nozzle in the shower room, namely the number of pollution sources is influenced; the steady flow state of the pool water surface, i.e., the pollutant release rate, can be promoted or slowed by a customer swimming the pool. Note that solving for pool space and shower customer counts is a key to the calculation, so the process of customers leaving the shower for changing clothes, and then resting at the lobby, can be ignored in the simulation of the stochastic service system. The business hours of the natatorium are divided into two occasions, namely a morning exercise occasion 06: 00-8: 00 and a night occasion 10: 00-21: 00. Customers should stop entering the pool 60min before the end of business hours for each session and leave the pool 20min before. Characteristics of the natatorium M patron arrival stream: customers only arrive during the business hours of the natatorium; a single arrival of a customer; the time of arrival of the customers follows an exponential distribution; the arrival processes of the customers are independent; customer arrival is a non-stationary process. Characteristics of the natatorium M service system: the space of the dressing room and the swimming pool is provided with an infinite number of parallel service tables, the number of the shower service tables is determined by the number of shower nozzles (36 male shower baths and 26 female shower baths), namely, queuing is needed when the number of the shower baths is more than the number of the shower nozzles; the customer receives the service individually; the service time is smooth and follows a lognormal distribution. Natatorium M queuing and service rules: the queuing phenomenon only occurs in the shower room, the queuing rule is a waiting system, and the service rule is a first-come first-serve.

And acquiring data parameters. The natatorium aspect provides a gender-specific patron arrival sequence of 15 weekdays within 4 months of 2016. And then calculating a typical customer arrival time sequence by adopting an averaging method. Next, fitting a typical customer arrival time series using gaussian function (equation 24) and fourier function (equation 25), respectively, to obtain a customer arrival rate time function (fig. 3, table 8):

TABLE 8 customer arrival Rate function parameters

The questionnaire obtained the length of time that the customer was performing in the natatorium M and fitted to a lognormal distribution (table 9). The first parameter of the lognormal distribution is the mean and the second parameter is the standard deviation.

TABLE 9 customer stay time (min times)^-1)

Secondly, calculating a time function G of the number of the customers related to the pollution source by realizing the algorithm described in the steps 201 to 203 through mathematical software according to the logic structure and the data parameters in the first step₁(t)、G₄(t)、G₆(t)：

(c) Solving the pollutant concentration function C of each room in the formulas 13-20 through mathematical software (such as MATLAB)_i(t) of (d). In this example, the Monte Carlo method was used to repeat the above steps 5000 times to form 95% confidence intervals of the contaminant concentration at the same time, compared to the measured air concentration (FIGS. 4-5). The verification result shows that the method has good accuracy.

Claims

1. A prediction method for volatile organic pollutants in indoor air of wading public places is characterized by comprising the following steps:

in the above formulas 1 and 2: v_iIs the room volume; t is time; q. q.s_{i_j}、q_{j_i}Respectively, the ventilation rate of entering a room with serial number j from a room with serial number i and the ventilation rate of entering a room with serial number i from a room with serial number j; j ranges from 1 to n +1, wherein n is the total number of rooms, n +1 represents outdoor space, and j is not equal to i; wherein S in the above formula 1_i(t) is a function of the rate of release of contaminants from the room having the source of contamination; i represents the serial number of any room with a pollution source, and j represents the serial number of the room with the serial number i in an air circulation relationship; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i; wherein i in the above formula 2 represents the serial number of any one contamination source-free room, and j represents the air flow with the room having the serial number iThe room number of the communication relation; c_i(t) is a function of the air concentration of the pollutants in the room with serial number i; c_j(t) is a function of the air concentration of the contaminant in the room in air flow relationship with the room numbered i;

in equation 3, i represents the room number where the completely mixed stream pollution source under consideration is located; k is a radical of_L,refIs the liquid phase mass transfer coefficient of the reference mass; d_l,refIs the liquid phase diffusion coefficient of the reference substance; d_g,iAnd D_l,iGas phase and liquid phase diffusion coefficients of the contaminants released by the contamination source, respectively; mu.s_w,iIs the water viscosity; rho_a,iIs the air density; mu.s_a,iIs an air viscosity; p is mechanical work power of the water surface occupation behavior and a constant; v_w,iIs the volume of the pool; v. of_iThe water surface wind speed is a pollution source;

in formula 4: i represents the room serial number of the considered piston flow pollution source; q_L,iPiston flow pollution source flow in room with serial number i;

the logic structure comprises:

the data parameters comprise:

determining the stay time of the customer;