A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil

Environmental Pollution xxx (xxxx) xxx

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A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil* Zijian Li School of Public Health (Shenzhen), Sun Yat-sen University, Guangdong 510275, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 April 2019 Received in revised form 28 September 2019 Accepted 9 October 2019 Available online xxx

In this study, a weather-based multicomponent model was developed based on the unique biostructures and metabolic processes of mushrooms to evaluate their uptake of pesticides from soils, and the effects of temperature and relative humidity on the bioaccumulation of pesticides in mushrooms was comprehensively quantified. Additionally, a new pseudo-partition coefficient between mushrooms and soils was introduced to assess the impacts of different physiochemical properties on the pesticide uptake process. The results indicate that, in general, the pseudo-partition coefficient increases as the relative humidity increases for both the air and soil according to Fick’s law of gas diffusion and the spatial competition of molecules, respectively. Meanwhile, the effect of temperature on the pesticide bioaccumulation process is more complex. For most pesticides (e.g., atrazine), the pseudo-partition coefficient that was computed from the transpiration component had a maximum value at a specific temperature due to the temperature dependency of the transpiration and biodegradation processes. For some pesticides (e.g., ethoprophos), the pseudo-partition coefficient of the air-deposition component had a maximum value at a certain temperature that was caused by the ratio of the soileair internal transfer energy and degradation activation energy of the pesticide. It was also concluded that for relatively low-volatility pesticides, transpiration dominated the bioaccumulation process; this was mainly determined from the pesticide water solubility. For nonbiodegradable pesticides (e.g., lindane), the computed coefficient values were relatively low due to their insolubility in water, which inhibits bioaccumulation in mushrooms and is one of the main reasons for their long-term persistence in soils. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Pesticide Pseudo-partition coefficient Weather-adjusted model Mushroom Bioaccumulation

1. Introduction Soil contamination from pesticides is a major environmental and health issue due to the foundational role of soils within ecosystems and the direct application of pesticides to agricultural fields (Chiaia-Hernandez et al., 2017; Fantke, 2019). After agricultural application, not only can pesticides in soils be transported into other environmental media (e.g., by volatilizing into the air, leaching into groundwater, or moving into surface waters), they can also bioaccumulate in living organisms through the food chain. Any of these transport pathways may ultimately threaten public health (Fantke et al., 2011a,b, 2013; Lavin and Hageman, 2013; Metcalf et al., 1971; Wu et al., 2017; Li, 2018a). As the starting point of the food chain, plants and fungi are crucial to the entire bioecological system, as they uptake nutrients

*

This paper has been recommended for acceptance by Charles Wong. E-mail address: [email protected].

from the soil and transfer chemicals and energy from the base of the food chain. Any pesticides taken up by soils, plants, or fungi may negatively affect the entire ecosystem (Arienzo et al., 2015; Rivera~ a et al., 2019). Therefore, the study and Becerril et al., 2017; Pen modeling of pesticide uptake are of great importance for quantifying, predicting, and controlling ecological health risks, including those presented to animals, including humans. Many practical and helpful uptake models have been developed to evaluate the transport of pesticides in plants and crops that mathematically described the absorption, diffusion, advection, transpiration, and other transport pathways of pesticides based on multicomponent models (Fantke et al., 2013; Juraske et al., 2009; Paterson et al., 1994; Trapp and Matthies, 1995; Trapp, 2007). Moreover, the dynamic uptake models of pesticides that are associated with human health risks for a life-cycle impact assessment proactively help us predict population risks posed by the agricultural application stage (Fantke et al., 2011a; 2013; Fantke and Jolliet, 2016). Mushrooms, especially the soil-based edible kind, play an

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important role in the food chain due to their nutritional value with regard to their carbohydrate, protein, and fat contents (Friedman, 2015). Many herbivores and omnivores, such as deer, rabbit, and rodents feed on mushrooms, especially in winter when many herbaceous plants become dry, withered, and may be covered by snow (Chen et al., 2011; Crawford, 1982; Oduguwa et al., 2008). Soil pollutants, such as pesticides and heavy metals, have been frequently detected in mushrooms worldwide, and the pollutants may raise environmental and human health risks (Balasubramanya and Kathe, 1996; Gałgowska et al., 2012; Liu et al., 2016). However, no studies have been conducted to model pesticide bioaccumulation in mushrooms, one of the most important environmental chemical processes to remediation and human health. Compared to herbaceous plants and crops, fungal enzymes in many mushrooms, which facilitate pesticide biodegradation and are temperature- and pesticide-dependent, complicate the uptake process. Additionally, the unique biostructures (e.g., umbrellashaped cap and porosity) of some mushrooms render the transpiration process and metabolism more sensitive to surrounding conditions (e.g., temperature and humidity) than in plants or crops (Xu et al., 2017). Thus, it is necessary to develop new models to understand the mechanism/s underlying pesticide bioaccumulation in mushrooms. Considering their unique biological characteristics, a weather-based multicomponent model was developed in this study to evaluate the pesticide uptake process in mushrooms. A new pseudo-partition coefficient (i.e., a thermodynamic and kinetic hybrid coefficient) between mushrooms and soil as a function of temperature and relative humidity was also introduced into the model. The impacts of different physiochemical properties of pesticides on the pseudo-partition coefficient were analyzed and quantified to further elucidate the bioaccumulation processes of individual pesticides. The ultimate aim of this study was to develop novel models for evaluating the uptake of pesticides by mushrooms from the soil.

partition processes, root absorption, etc.). When integrating these elements, a new systematic phenomenon could arise from the modeled pseudo-partition process (i.e., holism). Weather factors, including temperature and relative humidity of both the air and soil, were considered in the model framework, as they can significantly affect the transfer and fate of pesticide residues. Other specific weather conditions, such as snow, storms, frost, and sunshine, were not considered in this study to simplify the model, although they also influence mushroom metabolism, transpiration, and pesticide movement. It should be noted that using pesticides as a spray is one major source of pesticide release into the environment, and plays a prominent role in the uptake of pesticides by crops (Jacobsen et al., 2015). Therefore, for mushroom farming, the pesticides that are sprayed directly onto mushrooms must be considered in the model. Since this study was focused on the wild mushroom (i.e., part of the base of the food chain in ecosystems), the application of pesticides as a spray was omitted unless an aerial pesticide spray was used in a forest. 2.1. Uptake of pesticide from the soil by mushroom mycelia Mycelia play an important role in the diffusive transfer of pesticide residues from soil water into mushrooms due to their large surface-to-volume ratios, similar to those of plant root systems (Trapp, 2007), and are in concentration equilibrium. Thus, the concentration ratio of pesticide residues between soil water and mushroom mycelia in equilibrium can be expressed by the partition coefficient:

CMyc ¼ KMycW CW

where CMyc (mg kg1) is the pesticide concentration in mushroom mycelia and CW (mg L1) is the residue concentration in soil water; KMyc

2. Model development The multicomponent model for the uptake of pesticides by mushrooms from surface soils is shown in Fig. 1, and includes the distribution of pesticide residues between soils and surface waters, the transfer of pesticide into the air by volatilization, the uptake of pesticide residues via mushroom mycelia, the transfer of pesticides into mushrooms by transpiration, pesticide metabolism in mushrooms, and the exchange of pesticides between the air and mushroom surfaces. Some unit models for the fate and transport of pesticides are well established (e.g., the airewater and soilewater

(1)

1

- W

kg (mg ) is the partition coefficient of pesticide concenmg L1

trations between mushroom mycelia and soil water. This can be calculated by the plant tissue correction equation (Pussemier, 1991; Trapp and Matthies, 1995; Trapp, 2007):

KMycW ¼ WMyc þ

LMmyc ðKOW ÞcðMycÞ

rOc

(2)

where WMyc (L kg1) and LMyc (kg kg1) are the water and lipid contents of mushroom mycelia, respectively, c(Myc) is the dimensionless correction factor between mushroom mycelia lipids and octanol, and rOc (kg L1) is the octanol density. The octanolewater 1

L ). Once pesticide residues are partition coefficient is KOW (mg mg L1

absorbed by mushroom mycelia, a concentration equilibrium between the mycelia and mushroom tissue-bound fluids can be attained. Although the solubility of pesticide residues in solution could be affected by pH, salinity, oil content, and other chemicals, it is assumed that low concentrations of these chemicals in mushroom fluids do not change the solubility of pesticide residues. Thus, the partition coefficient between mycelia and tissue-bound fluids is approximately equal to KMyc-W, which is expressed as follows:

CMyc zKMycW CTF CTF z

CMyc ¼ CW KMycW

(3)

(4)

Fig. 1. Conceptual framework for the multicomponent pesticide uptake model adjusted by temperature (T) and relative humidity (RH).

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where CTF (mg L1) is the pesticide residue concentration in mushroom fluid, which is approximately equal to CW, and CW may be calculated from the partition coefficient between the soil and water, such that:

3

     RHM RHA 1 QW ðTA ; RHA Þ ¼ a  1  exp  b  273:15K 100 100   ðTA  273:15KÞ AM CF1

1

0

  C BCDS CBS CIW 1; 0  CDS  SðTS Þ; 0; CDS > SðTS Þ CW ¼ B or @K qW þqA KAW þ K A Kd Kd d d rS

   C C þ SðTS Þ 1  IW 1; 0  DS  SðTS Þ; 0; DS > SðTS Þ Kd Kd

(9) T where dm (mg d1) is the mass transport rate of pesticides in dt mushrooms via transpiration and QW ðTA ; RHA Þ (L d1) is the transpiration rate of mushrooms, which is proportional to the differM ence between their water activities (RH 100 , dimensionless) and the

(5) Kd ¼ Koc fOC

(6)

logKoc ¼ 0:69logKOW þ 0:22

(7)

where CDS (mg kg1) is the pesticide residue concentration in dry 1

kg ) is the partition coefficient between soil and soil and Kd (mg mg L1

water, which can be estimated from the organic carbonewater

A surrounding air (RH 100, dimensionless), and is a function of the air temperature (TA, K). The water volume is denoted as VW (L), a (cm d1) is the mass transfer coefficient on which the overall effect of temperature is adjusted in Eq (9), according to the secondary model by Mahajan et al. (2008), b (K) is the fitted coefficient from the experimental data (Mahajan et al., 2008), CF1 (1000L cm3 ) is the unit conversion factor, and AM (cm2) is the mushroom cap surface area that was obtained from the spherical shape equation of Mahajan et al. (2008), which is similar to the tomato surface area model of Sastry and Buffington (1983).

1

kg ), the organic carbon content in partition coefficient, KOC (mg mg L1

1

soil, fOC (g g ), and KOW in Eqs (6) and (7) (Li, 2018b; Piwoni and Keeley, 1996). In some cases, the pesticide concentration in bulk soil samples (CBS, mg kg1) is considered, especially for regulatory and management processes (Li, 2018b). In such cases, CW can be calculated from the adjusted partition coefficient; qW (L L1) and qA (L L1) are the water and air contents in bulk soils, respectively. Henry’s constant is denoted as H, rS (kg L1) is the soil density, S(TS) (mg L1) is the solubility of pesticide residues in water as a function of temperature in the soilewater system (TS), IW(.) denotes the indicator function for calculating pesticide concentrations in water, which in Eq (5) is equal to 1 if the computed CW  S(TS); otherwise, it is 0. Thus, if the pesticideewater solution is unsaturated, CW can be calculated from the partition coefficient, and if the solution is saturated, CW will be equal to the solubility at the specific temperature.

2.2. Translocation of pesticides via transpiration Once a pesticide enters mushroom tissue-bound fluids, it can be further transferred to the mushroom cap through transpiration. The unique units of the mushroom structure, which include an umbrella-shaped cap, porous texture, and small cross-section, provide an efficient means of transpiration and water vapor escape (Xu et al., 2017), which are highly affected and controlled by the temperature and relative humidity of the surrounding atmosphere (Mahajan et al., 2008). Therefore, the model for the translocation of pesticides via transpiration was adjusted by temperature and relative humidity. The weather-adjusted pesticide translocation model for mushrooms was developed based on the mushroom transpiration rate and the fruit water vapor emission models that are associated with the surface area model (BenYehoshua, 1987; Mahajan et al., 2008; Sastry and Buffington, 1983):

dmT dVW ¼ C ¼ QW ðTA ; RHA ÞCW dt dt W

(8)

AM ¼ c MM

106 mg  kg

!d (10)

where MM (kg) is the mass of the mushroom, and c and d are the dimensionless fitted coefficients from the experimental data (Mahajan et al., 2008). It should be noted that some mushrooms can have special structures other than a spherical shape; thus, it is suggested that specific shape equations should be developed for different morphotypes. 2.3. Exchange of pesticides with the surrounding atmosphere Pesticides, especially those composed of volatile and semivolatile compounds, can volatilize from the soil into the surrounding atmosphere. Since mushrooms have a relatively small size and grow near the surface, it is important to consider the exchange of pesticides with the air. In this study, it was assumed that the pesticide concentration equilibrium between the soil and the air near the soil surface was reached. The diffusive mass flux of pesticide residues between mushrooms and the air is similar to that used in plant models, such as for leaves and fruits (Trapp and Matthies, 1995; Trapp, 2007), and may be expressed as follows:

  dmA C ¼ g CA  M AM CF1 dt KMA

(11)

A where dm (mg d1) is the diffusive mass flux of pesticide residues dt via exchange with the air, g (cm d1) is the conductance, CA (mg L1) and CM (mg kg1) are pesticide concentrations in the air and in the aboveground part of the mushroom, respectively. For most spherical shape mushrooms, the aboveground part is dominated by the cap. The partition coefficient between mushrooms and the air is 1

1

kg kg denoted as KMA (mg ), which can be estimated from KMW (mg ), mg L1 mg L1

the partition coefficient between mushrooms and water, and KAW (i.e., the dimensionless Henry’s law constant or the partition coefficient between air and water), such that:

KMA ¼

KMW KAW

(12)

Please cite this article as: Li, Z., A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil, Environmental Pollution, https://doi.org/10.1016/j.envpol.2019.113372

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KMW ¼ WM þ

LM ðKOW Þ

cðMÞ

(13)

rOc

where KMW can be further calculated from the tissue correction equation, which is similar to Eq (2). The water and lipid contents of the aboveground part of the mushroom are denoted as WM (L kg1) and LM (kg kg1), respectively, and c(M) is the dimensionless correction factor between mushroom lipids and octanol. It should be noted that KAW and KOW are also temperature dependent. Since c(M) in the equation for KMW (i.e., Eq. (13)), and the ratio in the derivation of KMA (Eq. (12)), might neutralize and balance the changes in partition coefficient that are caused by temperature, the KOW and KAW values at 298.15 K were used to simplify the calculation. Additionally, the volatilization of pesticides from mushroom surfaces into the air was found to be a relatively minor component of pesticide loss for most pesticides in the study. In this study, it was assumed that contaminated soil is the major source of pesticide residues in the surrounding atmosphere. Since the soileair partitioning process of pesticides, especially for volatile and semivolatile compounds, is governed by soil type, temperature, and relative humidity, which control the adsorption of pesticide to the soil (Davie-Martin et al., 2015), it was necessary to estimate the pesticide concentration in the air, while also considering the temperature and relative humidity of the soil (Hippelein and McLachlan, 2000).

    C 2fOC KOA DSA U exp KSA ðTS ; RHS Þ ¼ DS ¼ R CA rS    1 1  0:0437ðRHS  100Þ   TS 298:15K

(14)

1

kg where KSA ðTS ; RHS Þ (mg , unit conversion by rS ) is the partition mg L1

coefficient between soil and air, which is a function of the soil temperature (TS, K) and the relative humidity (RHS, %). The 1

L ), is 298.15 K, an DSA U octanoleair partition coefficient, KOA (mg mg L1

1

(kJ mol ) is the pesticide phase-transfer energy from the soil to the air. The ideal gas constant (R) was held at 0.008314 kJ K1 mol1 ((or 8.314 Pa m3 K1 mol1). In general, the TS of surface soils (5 cm) can be estimated from TA as follows (Islam et al., 2015):

TS  273:15K ¼ 0:9ðTA  273:15KÞ þ 3:83K

(15)

Similar to CW, CA can also be modeled using the indicator function to account for saturation:

Where M (g mol1) is the molecular mass of a pesticide, P(TA) (Pa) is the pesticide vapor pressure, which is also a function of TA, and CF2 1

mg g (1000 ) is the second unit conversion factor. 1000 L m3

2.4. Pesticide metabolism Pesticides can be biodegraded in mushrooms via fungal metabolic processes, including oxidative transformations and pesticide conjugation reactions, which provide an efficient xenobiotic transformation system due to the free radical-based peroxidase and the structure-modified conjugation (Van Eerd et al., 2003). Unlike some plants and photosynthetic bacteria, which use photosynthesis, mushroom mycelia absorb and digests food and nutrients externally; therefore, the photodegradation of pesticides was omitted for mushrooms in this study. Additionally, since most fungi are fast-growing species with high metabolic rates (Bano et al., 1988; Straatsma et al., 1994), only the mature stage was considered and the pesticide dilution caused by the growth of mushrooms was not considered (Trapp and Matthies, 1995). The activity of the enzymes that break down and biologically transform pesticides is controlled by temperature, so the temperature-dependent Arrhenius equation, assuming first-order kinetics, was applied in association with the activation energy, EA (kJ mol1) (European Food Safety Authority, 2008). Thus, the pesticide degradation rate in mushrooms can be modeled as a function of temperature:

  E kðTA Þ ¼ k0 exp  A RTA

(18)

where k(TA) (d1) is the biodegradation rate and k0 (d1) is the base rate. Assuming first-order kinetics, k0 can be calculated by the halflives of pesticides (DT50, d) and EA (European Food Safety Authority, 2008). It should be noted that although most degradation data were obtained from soil fungal or microbiological experiments, it was assumed that the pesticide biodegradation process could be applied to mushrooms since some enzyme-based fungal metabolic processes are similar and the physicochemical properties of pesticides. Further evaluation of pesticide degradation in mushrooms should be conducted. 3. Pseudo-partition coefficient 3.1. Steady-state pseudo-partition coefficient The mass balance of pesticide in the aboveground part of a mushroom can be expressed by combining the transpiration pro-

  CDS CDS CDS IA 1; 0   CðTA Þ; 0; > CðTA Þ KSA ðTS ; RHS Þ KSA ðTS ; RHS Þ KSA ðTS ; RHS Þ    CDS CDS  CðTA Þ; 0; > CðTA Þ þ CðTA Þ 1  IA 1; 0  KSA ðTS ; RHS Þ KSA ðTS ; RHS Þ

CA ¼

where C(TA) (mg L1) is the saturated vapor concentration of pesticide in the air as a function of TA, which can be calculated according to the ideal gas law (Van den Berg and Leistra, 2004; Wong et al., 2017):

CðTA Þ ¼

  MPðTA Þ CF2 RTA

(17)

(16)

cess (i.e., Eqs. (5) and (9)), the exchange with air (i.e., Eqs. (14) and (16)), and the pesticide metabolic reaction (i.e., Eq. (18)) as:

  dmM dCM dmT dmA ¼ MM ¼ þ  kðTA ÞCM MM dt dt dt dt

(19)

At steady state, this may be expanded as: Please cite this article as: Li, Z., A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil, Environmental Pollution, https://doi.org/10.1016/j.envpol.2019.113372

Z. Li / Environmental Pollution xxx (xxxx) xxx

loss of pesticide, respectively. These uptake and loss rate equations can be found in the Supporting Information (section S1). By inputting all of the parameters, the steady-state equation can be further expressed as follows:

  CDS I ð$Þ þ SðTS Þð1  IW ð$ÞÞ 0 ¼ QW ðTA ; RHA Þ Kd W   CDS I ð$Þ þ CðTA Þð1  IA ð$ÞÞ  þ gAM CF1 KSA ðTS ; RHS Þ A    CM   gAM CF1  kðTA ÞCM MM KMA

(20)

When CW  S(TS) and CA  C(TA), the steady-state equation is expressed as follows:

0 ¼ QW ðTA ; RHA Þ

5

    CDS CDS CM  gAM CF1 þ gAM CF1 Kd KSA ðTS ; RHS Þ KMA

 0  1 RHA M     a RH 100  100 AM CF1 1 B C 0¼@ A 1  exp  b  273:15K Kd       gAM CF1 rS DSA U  ðTA  273:15KÞ CDS þ exp  R 2fOC KOA   1 1  þ 0:0437ðRHS  100Þ  0:9TA þ 31:145K 298:15K      gAM CF1 E  CDS  CM  k0 MM exp  A CM KMA RTA (23)

 kðTA ÞMM CM (21) The equilibrium equation above can also be expressed as:

KPseudo ðTA ; RHA ; RHS Þ ¼  ¼

The generalized pseudo-partition coefficient between mushrooms and dry soil at the steady state, KPseudo ðTA ; RHA ; RHS Þ, as a function of TA, RHA, and RHS, assuming these three variables are independent, can be expressed as Eq (24).

CM kþ ðT ; RH Þ þ kþ ðT ; RHS Þ ¼ W A A  A S kA þ kD ðTA Þ CDS

    1     gAM CF1 E aAM CF1 RHM RHA 1 ðTA  273:15KÞ  1  exp  þ k0 MM exp  A b  273:15K KMA RTA Kd 100 100        gAM CF1 rS DSA U 1 1 þ 0:0437ðRHS  100Þ  þ exp  R 0:9TA þ 31:145K 298:15K 2fOC KOA

(24)

3.1.1. Pseudo-partition coefficient for low-volatility pesticides    k EA ), a When k ðTA Þ  0:01 (i.e., gAKMMACF1  0:01k0 MM exp  RT A

0 ¼ Transpiration uptake rate  CDS þ Air deposition rate  CDS Air loss rate  CM  Metabolism clearance rate  CM

A

D

þ   0 ¼ kþ W ðTA ; RHA ÞCDS þ kA ðTS ; RHS ÞCDS  kA CM  kD ðTA ÞCM

(22) 1 where kþ W ðTA ; RHA Þ (kg d ) is the pesticide uptake rate by water

1 transpiration as a function of TA and RHA, and kþ A ðTS ; RHS Þ (kg d ) is the pesticide deposition rate through the air flux as a function of TS

pesticide has a relatively high degradation rate and a low volatility. In such cases, the degradation process dominates the total loss of pesticides in a mushroom. The pseudo-partition coefficient at the steady state can be approximated for relatively low-volatility pesticides as follows:

CM kþ ðT ; RHA Þ þ kþ A ðTS ; RHS Þ z W A k CDS D ðTA Þ          aAM CF1 RHM RHA EA E 1 ðTA  273:15KÞ  exp  exp A  ¼ b  273:15K Kd k0 MM 100 100 RTA RTA        gAM CF1 rS E DSA U 1 1 þ 0:0437ðRHS  100Þ  exp A  þ R 0:9TA þ 31:145K 298:15K 2fOC KOA k0 MM RTA KPseudo ðTA ; RHA ; RHS Þ ¼

and RHS, which can be further expressed as a function of TA and RHS 1 based on Eq (15). Additionally, k A (kg d ) is the pesticide loss rate via volatilization from the surface of the mushroom cap, and it is  assumed that k A is independent of temperature; kD ðTA Þ (kg d-1) is the pesticide clearance rate through fungal metabolism as a function of TA; the “þ” and “-” signs in these rates indicate the gain and

(25)

3.1.2. Pseudo-partition coefficient for low-degradability pesticides    k EA ), a When k ðTA Þ  100 (i.e., gAKMMACF1  100k0 MM exp  RT A D

A

pesticide has a relatively low degradation rate and a high volatility

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from the mushroom surface, and the air conduction process is considered to greatly contribute to the loss of pesticides in a mushroom. The pseudo-partition coefficient at steady state can for relatively low-degradability pesticides can be approximately expressed as follows:

  C kþ ðT ; RHA Þ þ kþ aKMA A ðTS ; RHS Þ KPseudo ðTA ; RHA ; RHS Þ ¼ M z W A ¼  kA CDS gKd       RHM RHA 1 ðTA 273:15KÞ   1  exp  b  273:15K 100 100      KMA rS DSA U þ exp  R 2fOC KOA    1 1  þ 0:0437ðRHS  100Þ  0:9TA þ 31:145K 298:15K (26) As expressed in Eqs. (24)e(26), KPseudo ðTA ; RHA ; RHS Þ

kg1 (mg ) mg kg1

defines the pseudo-partition coefficient between mushrooms and dry soils at steady state when the pesticide residue solution is not saturated in either water or air. Compared to the thermodynamic partition coefficient at equilibrium, the pseudo-partition coefficient kinetically describes the chemical ratio between the environment and the mushroom, which helps to quantify and predict the amount of pesticide that is absorbed in the mushroom. Similarly, the pseudo-partition coefficients when pesticide residue solutions are saturated can be expressed based on Eq (20), including K SPseudo SðWÞ

(saturated with water and air), K Pseudo (saturated with water and SðAÞ

unsaturated with air), and K Pseudo (unsaturated with water and saturated with air), which are discussed in section S2 of the Supporting Information.

3.2. Standard-state pseudo-partition coefficient The standard state of KPseudo ðTA ; RHA ; RHS Þ, K 0Pseudo , was introduced to compare pseudo-partition coefficients among pesticides, for which T 0A ¼ 25  C (298.15 K), there is 1 atm of pressure, RH 0S ¼ 100% (saturated soil), and the RH 0A is estimated as follows:

RH0A ¼

VD0A VD0S

 100%

(27)

where VD0A (g m3) is the actual vapor density and is calculated via Eq (28) when the atmosphere contains the average mass of water vapor (i.e., 0.25% at 298.15 K and 1 atm; Wallace and Hobbs, 2006). The saturated vapor density, VD0S (g m3) at 298.15 K is 23 g m3 (Georgia State University, 2016).

VD0A ¼

conditions. Based on Eq (28), the VD0A ¼ 2.96 g m3 and the RH 0A ¼ 12.87%. Therefore, the standard state of the pseudo-partition coefficient between mushrooms and dry soil is K 0Pseudo ð298:15K; 12:87%; 100%Þ. There are two purposes for setting a standard state for KPseudo ðTA ; RHA ; RHS Þ. First, like the “ordinary” partition coefficient that is thermodynamically determined (e.g., KOW), this particular state for the pseudo-partition coefficient that is thermodynamically and kinetically controlled could be approximately applied as a reference value for chemicals when weather information (i.e., T and RH) is unavailable. Secondly, pseudo-partition coefficients at a standard state could be used to compare the bioaccumulation factors in mushrooms for different pesticides. It must be noted that bioaccumulation factors could be very different under alternative states. For some geographical regions with extreme climates, an alternative standard state for KPseudo ðTA ; RHA ; RHS Þ can also be established using the pseudo-partition coefficient model.

4. Model application 4.1. Analysis of KPseudo ðTA ; RHA ; RHS Þ as a function of TA, RHA, and RHS Table 1 summarizes all of the parameters used to calculate the KPseudo ðTA ; RHA ; RHS Þ models. A spherical umbrella shape was assumed for the mushrooms, and a spherical model was applied to calculate AM (Ben-Yehoshua, 1987; Mahajan et al., 2008), which has also been used for other spherical products, such as tomatoes (Sastry and Buffington, 1983). The shapes of mushroom caps (pilei) may vary due to genetic and environmental effects, and include cylindrical, bell-shaped, conical, convex, flat, infundibuliform (cone-shaped), depressed, and knobbed morphologies (Canadian Nature Photographer, 2013). The topology of mushrooms associated with surface area, structure, size, and function should be further studied and quantified for different types of mushrooms. Some site-specific parameters, such as rS and fOC, are generally estimated (Vik et al., 1999; Zhang et al., 2009), and could be adjusted through in situ measurements from different locations. The c(M) factor in Eq (13) was estimated using cut bean roots (Trapp and Pussemier, 1991; Trapp, 2007), and the DSA U values of pesticides were estimated from vapor pressure (P, Pa or mmHg) for predicting volatilization of pesticides from soils (Davie-Martin et al., 2012; Hippelein and McLachlan, 2000). For pesticidespecific values, KMA values were determined by KOW and calculated using Eq (13) and KAW by Eq (12). Finally, the Kd values were calculated from fOC and the organic carbonewater partition coefficient, which could be further derived from KOW (Falandysz et al., 2008; Li, 2018b; Ouzouni and Riganakos, 2007). Pesticide-specific values, including the calculated values, are shown in Table S1.

g 0:25%  29 mol MWVP 0:25%  MMAir g ! ¼ ¼ z2:96 3 3 atm 1mol8:206105 mmolK 298:15K VAir m 0 1molRT P 0A

A

where MWVP (g) is the mass of actual water vapor per mol, VAir (m3) is the volume of air per mol, MMAir (g mol-1) is the molar mass of the air,

P 0A

(28)

1atm

(atm) is the atmospheric pressure under standard-state

4.1.1. Pesticides with relatively high degradability and low volatility When



kA  kD ðTA Þ

 0:01 and pesticides have relatively high degrad-

ability and low volatility, k D ðTA Þ dominates the total loss of

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7

Table 1 Input values of the KPseudo ðTA ; RHA ; RHS Þ models. Parameter

Symbol

Unit

Value

Reference

Mass of mushroom Surface area of mushroom Mass transfer coefficient (to incorporate the overall effect of temperature) Fitted coefficient Relative humidity of mushroom internal atmosphere Water content of mushroom Lipid content of mushroom Octanol density Correction factor Conductance Soil density Organic carbon content of soil Ideal gas constant Unit conversion factor (first) Unit conversion factor (second)

MM AM a b RHM WM LM

kg cm2 cm d1 K % L kg1 kg kg1 kg L1 dimensionless cm d1 kg L1 g g1 kJ K1 mol1 L cm3

2.22  102 4.68  101 2.12  101 2.78  102 100 0.90 0.04 0.82 0.75 8640 1.84 1.23  102 0.008314 1.00  103 1.00

Mahajan et al. (2008) Mahajan et al. (2008) Mahajan et al. (2008) Mahajan et al. (2008) Ben-Yehoshua (1987); Mahajan et al. (2008) Falandysz et al. (2008) Ouzouni and Riganakos (2007) Trapp (2007); Trapp and Pussemier (1991) Trapp and Matthies (1995) Trapp and Matthies (1995) Vik et al. (1999); Zhang et al. (2009) Vik et al. (1999); Zhang et al. (2009) NA NA NA

e

e

e

e

e

e

e

e

e e e e e e

e e e e e e

rOc

c(M) g

rS fOC R CF1 CF2

Pesticide-specific parameters Octanolewater partition coefficient

KOW

Octanoleair partition coefficient

KOA

Mushroomeair partition coefficient

KMA

Soilewater partition coefficient

Kd

Internal energy of soil to air phase transfer Vapor pressure Rate coefficient Activation energy Henry’s constant Henry’s constant (dimensionless)

DSA U P k0 EA H KAW

mg g1 L m3 mg L1 mg L1 mg L1 mg L1 mg kg1 mg L1 mg kg1 mg L1 kJ mol1 Pa d1 kJ mol1 atm-m3 mol1 dimensionless

Note: NA indicates that references are not available; KOW, KOA, P, H, and KAW values were taken at 298.15 K; pesticide-specific values are summarized in Table S1.

pesticides in mushrooms. The KPseudo ðTA ; RHA ; RHS Þ model can be expressed by Eq (25), which is further expressed as the sum of the transpiration-contributed component, K TranP Pseudo ðTA ;RHA Þ, and the airdeposition-contributed component, K AirD Pseudo ðTA ; RHs Þ:

KPseudo ðTA ;RHA ;RHS Þ¼

þ kþ W ðTA ;RHA Þ kA ðTS ;RHS Þ þ ¼K TranP  Pseudo ðTA ;RHA Þ kD ðTA Þ k D ðTA Þ

þK AirD Pseudo ðTA ;RHs Þ (29) Based on Eq (15), TS has been converted to TA. Atrazine was used as an example in this study to explore the KPseudo ðTA ; RHA ; RHS Þ values for pesticides with a relatively high degradability and low volatility. The computed



kA  kD ðTA Þ

value for

atrazine at 298.15 K was 4.32  103, due to the low volatility (i.e., P ¼ 2.89  107 mm Hg and the KAW ¼ 9.65  108) (U.S. EPA, 2018; U.S. National Library of Medicine, 2018) and the relatively high degradability (Kaufman and Blake, 1970). It should be noted that  the ratio of k A and kD ðTA Þ can vary with TA, and 298.15 K was used in this study to approximate the overall ratio on the interval. There-

15 K AirD exp½0:0437ðRHS  100Þ Pseudo ðTA ; RHS Þ ¼ 3:24  10    8321 1 1   1:28  104  exp TA 0:9TA þ 31:145 298:15

(31) K TranP Pseudo ðTA ; RHA Þ

K AirD Pseudo ðTA ; RHs Þ

Fig. 2 illustrates and as a function of TA (273.15 K < TA  308.15 K) and RHA (0%  RHA  100%) and of TA and RHS (0%  RHS  100%), respectively. The interval of TA was selected based on normal mushroom transpiration and metabolic activities, and the intervals of RHA and RHS were selected based on the physical possibilities. For the transpiration component, K TranP Pseudo ðTA ; RHA Þ decreased in the interval of RHA as the result of QW ðTA ; RHA Þ in Eq (9), which is proportional to the difference between RHM and RHA based on Fick’s law of gas diffusion (Mahajan et al., 2008), and k D ðTA Þ, which is independent of RHA. Therefore, K TranP Pseudo ðTA ; RHA Þ and KPseudo ðTA ; RHA ; RHS Þ increased when the surrounding air became drier (i.e., more atrazine residues accumulated in mushroom at steady state in dry weather). Meanwhile, the effect of TA on K TranP Pseudo ðTA ; RHA Þ was more complex because TA determines

AirD fore, the K TranP Pseudo ðTA ; RHA Þ and K Pseudo ðTA ; RHs Þ values of atrazine in Eq (29) were calculated as follows:

 TranP both kþ W ðTA ; RHA Þ and kD ðTA Þ. At TA ¼ 278.44 K, the K Pseudo ðTA ; RHA Þ of atrazine reached its maximum value series, and when RHA ¼ 0%,

    8321 exp  exp TA

the maximum value of K TranP Pseudo ð278:44; 0Þ was 21.19, which can also



RHA 1  10 100    8321   0:197ðTA  273:15Þ TA

K TranP Pseudo ðTA ; RHA Þ ¼ 3:44

12

(30)

be evaluated by the first partial derivative

vK TranP Pseudo ðTA ;RHA Þ , as vTA

described

in section S3.1. The transpiration uptake rate and metabolic clearance rate equations (see section S3.1) further illustrate the mechanism of the temperature effect on K TranP Pseudo ðTA ; RHA Þ. When the interval of TA in-

 creases, both kþ W ðTA ; RHA Þ and kD ðTA Þ increase; however, when

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AirD Fig. 2. Pseudo-partition coefficients for atrazine computed from the transpiration (K TranP Pseudo ðTA ; RHA Þ) and air-deposition (K Pseudo ðTA ; RHs Þ) components.

TA  278.44 K, the rate of increase for kþ W ðTA ; RHA Þ is larger than for k D ðTA Þ.

This is because the second partial derivative of

kþ W ðTA ; RHA Þ

with respect to TA is negative, and kþ W ðTA ; RHA Þ takes the limit value as TA approaches infinity. Meanwhile, k D ðTA Þ exhibits the opposite phenomenon, in which the second partial derivative is always positive, and there is no limit as TA increases. Additionally, as shown in Fig. S.2, the monotonic increase of kþ W ðTA ; RHA Þ is convergent, while that of k D ðTA Þ is not. For the air-deposition contribution in Eq (31), Fig. 2 indicates that K AirD Pseudo ðTA ; RHs Þ increases as the intervals of both independent variables, TA and RHS, increase. The monotonically increasing property with respect to RHS can be directly observed from the AirD K AirD Pseudo ðTA ; RHs Þ equation, which indicates that K Pseudo ðTA ; RHs Þ increases exponentially over the interval of RHS. This phenomenon can be further tracked to KSA ðTS ; RHS Þ in Eq (14) (i.e., KSA ðTS ; RHS Þ exponentially decreases as RHS increases, indicating that more pesticide enters the surrounding atmosphere from the soil when soil moisture increases). High soil moisture results in the presence of more water molecules in the surrounding air, which spatially compete with pesticide molecules to be adsorbed into soil particles through hydrogen bonds (Davie-Martin et al., 2015; Goss et al., 2004). With respect to temperature, the first partial derivative, vK AirD Pseudo ðTA ;RHS Þ , vTA

shown in section S.3.2, also illustrates the exponen-

The normal distributions of the three variables, assuming independence, were selected based on real situations: TA ~ N (298.15, 1); RHA ~ N (50, 10); RHS ~ N (40, 5) (COURT and WACO, 1965; Dutta et al., 2017; Wilson et al., 2003). Although in real situations there could be interactions among TA, RHA, and RHS, these variables are always strongly affected by different external factors (e.g., solar radiation and greenhouse gases affect TA, geographic locations and winds affect RHA, and soil types and groundwater levels affect RHS), which could substantially weaken the interactions among them. Fig. 3 illustrates the simulated results for the KPseudo ðTA ; RHA ; RHS Þ of atrazine with 10,000 iterations. The arithmetic mean is 2.26, and the 95th percentile of the simulated distribution is 3.10, which indicates that under the selected real conditions, atrazine levels in mushrooms are generally less than three times as high as in surface soils 95% of the time. The sensitivity test of variables, which was based on tornado analysis, indicated that RHA (84.84%) and TA (15.13%) contribute much more than RHS (0.02%), and the KPseudo ðTA ; RHA ; RHS Þ values decrease as RHA and TA increase within their distributions. This is because atrazine has a low volatility and the RHS-independent K TranP Pseudo ðTA ; RHA Þ dominates the total result, which can be further explained by the probabilistic simulations of K TranP Pseudo ðTA ; RHA Þ 4 (mean: 2.26; 95th: 3.11) and K AirD Pseudo ðTA ; RHs Þ (mean: 3.85  10 ; 95th: 5.42  104), as shown in section S.3.3.

tially increasing property of K AirD Pseudo ðTA ; RHs Þ over the same interval.

 Fig. S.5 (y-axis in log scale) and S.6 for the kþ A ðTA ; RHS Þ and kD ðTA Þ of

atrazine further indicate that the increasing rate of kþ A ðTA ; RHS Þ is greater than that of k D ðTA Þ as TA increases. This trend can be further explained by comparing the rate of change for the atrazine energy barrier terms of the TS-dependent soileair partitioning coefficient in Eq (14), which is associated with the linear relationship of TSeTA !  DSA U¼106kJ mol1 1 1 in Eq (15), such that   R 0:9TA þ31:145K 298:15K , and the pesticide degradation rate equation in Eq (18),  !  EA ¼69kJ mol1 1 TA . This indicates that it was easier for pesticide R molecules to move across the energy barrier of DSA U than EA as TA increased on the interval in this study. To further evaluate and compare the contributions of K TranP Pseudo ðTA ; RHA Þ and K AirD Pseudo ðTA ; RHs Þ to KPseudo ðTA ; RHA ; RHS Þ, Monte Carlo simulations were conducted, which were associated with the sensitivity analysis of variables (XLSTAT, Addinsoft Corp., France).

4.1.2. Pesticides with relatively moderate degradability and volatility 

 When 0:01  k ðTA Þ  100, both k A and kD ðTA Þ are considered to k

D

A

be the major sources of pesticide loss in mushroom (i.e., when pesticides have relatively moderate degradability and volatility). The KPseudo ðTA ; RHA ; RHS Þ model can be expressed from Eq (24) as follows:

KPseudo ðTA ; RHA ; RHS Þ ¼

kþ kþ ðT ; RH Þ W ðTA ; RHA Þ þ A S  S   kA þ kD ðTA Þ kA þ kD ðTA Þ

(32)

Trifluralin, a soil-incorporated herbicide that is applied in agriculture to control weeds and is frequently detected in soil, is volatile, especially in moist soils with P values of 4.58  105 mmHg and KAW values of 4.21  103 (Parochetti and Hein, 1973; U.S. EPA, k



2018; U.S. National Library of Medicine, 2018). The computed k ðTA D

AÞ

value for trifluralin at 298.15 K is 7.89; therefore, the transpiration and air-deposition components are expressed as:

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Fig. 3. Monte Carlo simulations (10,000 iterations) and sensitivity analysis of the KPseudo ðTA ; RHA ; RHS Þ values of atrazine.

4 K TranP Pseudo ðTA ; RHA Þ ¼ 1:00  10 0



RHA 1 100

TA ¼ 291.55 K



to

the

first

partial

derivative

of

average curvature of the slopes of the trifluralin K TranP Pseudo ðTA ; RHA Þ surface is lower than that of atrazine, which indicates that the

1

B C 1 C  B @ Af1  exp½ 3:44  103 þ 6:67  105 exp  6305 TA  0:197ðTA  273:15Þg (33) 4 exp½0:0437ðRHS  100Þ K AirD Pseudo ðTA ; RHS Þ ¼ 5:82  10 1 0  C B 1 Cexp   B A @ 3:44  103 þ 6:67  105 exp  6305 TA   1 1  1:05  104  0:9  TA þ 31:145 298:15

K TranP Pseudo ðTA ; RHA Þ

according

K TranP Pseudo ðTA ; RHA Þ of TA at RHA ¼ 0% (see section S4.1). However, the

K TranP Pseudo ðTA ; RHA Þ of trifluralin has a lower sensitivity for TA. This trend is also observable from the sensitivity analysis (Fig. S.15) because the relatively high volatility increases the total loss rate of trifluralin in mushrooms, which balances the degradation and uptake rates from transpiration. Fig. S.10 (y-axis in log scale) S.11  further illustrate the impact of k A on kLoss , which buffers the change

 TranP in ratio of kþ W ðTA ; RHA Þ and kLoss (i.e., K Pseudo ðTA ; RHA Þ) and reduces

the curvature of the slopes of the trifluralin K TranP Pseudo ðTA ; RHA Þ surface. The overall shape of K AirD Pseudo ðTA ; RHs Þ on the selected intervals for

(34)

K AirD Pseudo ðTA ; RHs Þ

trifluralin is similar to that of atrazine because K AirD Pseudo ðTA ; RHs Þ exponentially increases with RHS; furthermore, analysis of the TA  effect from kþ A ðTA ; RHS Þ and kLoss in Fig. S.13 (y-axis in log scale) and S.14 indicates that the constant rate of k A significantly reduces the increasing rate of k Loss as TA increases, rendering the

þ

kA ðTA ;RHS Þ  kLoss

(i.e.,

K AirD Pseudo ðTA ;

Fig. 4 illustrates the and functions for trifluralin on the selected intervals. Similar to atrazine,

RHs Þ) for trifluralin more sensitive to TA than that of atrazine. The sensitivity analysis shown in Fig. S.16 further dem-

K TranP Pseudo ðTA ; RHA Þ deceases as RHA increases according to Fick’s law,

onstrates that the K AirD Pseudo ðTA ; RHs Þ for trifluralin is more sensitive to TA (i.e., 15.56% of the contribution) compared to atrazine (i.e.,

and

K TranP Pseudo ðTA ; RHA Þ

also has a maximum value series at

AirD Fig. 4. Pseudo-partition coefficients for trifluralin computed from the transpiration (K TranP Pseudo ðTA ; RHA Þ) and air-deposition (K Pseudo ðTA ; RHs Þ) components.

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AirD Fig. 5. Pseudo-partition coefficients for ethoprophos computed from the transpiration (K TranP Pseudo ðTA ; RHA Þ) and air-deposition (K Pseudo ðTA ; RHs Þ) components.

2.28%). In contrast to atrazine, the simulated K TranP Pseudo ðTA ; RHA Þ (mean: 1.29  102; 95th: 1.71  102) and K AirD Pseudo ðTA ; RHs Þ (mean: 1.31  102; 95th: 1.89  102) values for trifluralin are very close because trifluralin has a relatively large log Kd value of 1.99 computed from the log KOW value (5.34), as compared to atrazine (log Kd ¼ 0.11 and log KOW ¼ 2.61) (U.S. National Library of Medicine, 2018). The relatively large log Kd value of trifluralin causes more residues to be absorbed into the soil than in soil waters, which results in fewer residues accumulating in the mushrooms through the transpiration process. The effect of temperature on KPseudo ðTA ; RHA ; RHS Þ, K TranP Pseudo ðTA ; RHA Þ, and K AirD Pseudo ðTA ; RHs Þ for pesticides with relatively moderate degradability and volatility is more complex. Ethoprophos, which is a common nematicide that is applied for the control of microscopic worms in soils (Karpouzas and Walker, 2000), has a P value of 3.80  104 mmHg and KAW of 6.95  106 (U.S. EPA, 2018; U.S. National Library of Medicine, 2018). The computed



kA  kD ðTA Þ

value for

ethoprophos at 298.15 K is 2.70  102, and the equations of the transpiration and air-deposition components are given as follows:

  RHA 3 K TranP 1 Pseudo ðTA ; RHA Þ ¼ 1:62  10 100 0

1

B C 1 C  B @ Af1  exp½ 1:13  104 þ 7:54  1010 exp  8962 TA  0:197ðTA  273:15Þg (35) 5 exp½0:0437ðRHS  100Þ K AirD Pseudo ðTA ; RHS Þ ¼ 5:40  10 1 0  C B 1  C B Aexp @ 1:13  104 þ 7:54  1010 exp  8962 TA   1 1  9:49  103  0:9  TA þ 31:145 298:15

(36)

The overall computed K TranP Pseudo ðTA ; RHA Þ values for ethoprophos, which dominate the KPseudo ðTA ; RHA ; RHS Þ values, are significantly less than those of atrazine and trifluralin due to the large Kd value. This indicates that greater ethoprophos residues are absorbed by

soil, whereas fewer residues are uptaken into mushrooms via transpiration from soil waters as compared to the other pesticides. The shape of the K TranP Pseudo ðTA ; RHA Þ surface for ethoprophos is more similar to that atrazine than trifluralin (Fig. 5) because the TAdependent k D ðTA Þ dominates the total rate of pesticide loss, though  k D ðTA Þ and kA are both considered to be major sources, opposite to the pattern shown for trifluralin. This can be further illustrated  through the analysis of k D ðTA Þ and kA , as shown in section S5.1. Interestingly, for ethoprophos, K AirD Pseudo ðTA ; RHs Þ is not an increasing function of TA over the selected interval, and there is a maximum value series at TA ¼ 284.82 K (see section S5.2). This is because k D ðTA Þ dominates the total loss rate, and the DSA U value (78.9 kJ mol1) is very close to the EA value (74.5 kJ mol1) of

ethoprophos (i.e., DESAAU ¼ 1:06), which drives the term     EA DSA U 1 1   close to the critical point RTA R 0:9TA þ31:145K 298:15K when k A can be neglected at higher temperatures. For atrazine

(DESAAU ¼ 1:54) and trifluralin (DESAAU ¼ 1:66), the large ratio of DSA U to

EA causes K TranP Pseudo ðTA ; RHA Þ to monotonically increase over the selected interval of TA. 4.1.3. Pesticides with relatively low degradability Some persistent organic pollutant (POP) pesticides, such as the 12 initial POPs and the new POPs in the Stockholm Convention (Jennings and Li, 2015a,b), significantly increase environmental and human health risks due to their relatively low biodegradability and long-term persistence (Mostafa et al., 1987; Rigas et al., 2005). Though laboratory experiments with optimized conditions (e.g., incubation temperature, reaction pH, liquid medium, lignocellulose systems, etc.) have achieved the breakdown of POP pesticides, such as dichlorodiphenyltrichloroethane (DDT), dichlorodiphenyldichloroethylene (DDE), aldrin, dieldrin, and lindane, via the extracellular enzymes of fungi isolated from their growth environment, these pesticides are relatively resistant to microbial biodegradation under natural conditions (Bumpus et al., 1993; Rigas et al., 2005; ndez et al., 2008; Tu et al., 1968; Xiao et al., 2011). Robles-Herna Moreover, there is a gap in the data used in systematic DT50eTA studies for POPs (European Food Safety Authority, 2008). Therefore, the k D ðTA Þ models were neglected for the POP pesticides, since this study was focused on mushroom metabolism under natural conditions; Eq (26) was applied to evaluate the KPseudo ðTA ; RHA ; RHS Þ for the POPs. AirD Fig. 6 illustrates the K TranP Pseudo ðTA ; RHA Þ and K Pseudo ðTA ; RHs Þ

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AirD Fig. 6. Pseudo-partition coefficients for lindane computed from the transpiration (K TranP Pseudo ðTA ; RHA Þ) and air-deposition (K Pseudo ðTA ; RHs Þ) components.

functions for lindane over the selected intervals, which are expressed in Eqs (37) and (38) according to the transformed KPseudo ðTA ; RHA ; RHS Þ model in Eq (26). The models for POP pesticides are more straightforward than those for pesticides with a relatively high biodegradability due to the lack of a TA-dependent degradation

term.

Therefore,

both

K TranP Pseudo ðTA ; RHA Þ

and

K AirD Pseudo ðTA ; RHs Þ are increasing functions of the independent variables on their intervals, which indicates that the bioaccumulation of POP pesticides exponentially increases as TA increases.

  RHA K TranP ðT ; RH Þ ¼ 0:48 1  f1  exp½ A A Pseudo 100  0:197ðTA  273:15Þg

values

(mean:

0.24;

95th:

0.32)

are

much

larger

than

2 2 K AirD Pseudo ðTA ; RHs Þ (mean: 3.87  10 ; 95th: 5.58  10 ) for the selected distributions of these variables, which are illustrated in

Figs. S.26 and S.27, even though the K AirD Pseudo ðTA ; RHs Þ values are higher when RHS approaches 100%. Although lindane has poor solubility in water (7.3 mg L1 at 298.15 K) and a relatively high KOW (log KOW ¼ 3.72 at 298.15 K) (U.S. National Library of Medicine, 2018; Yalkowsky and Dannenfelser, 1992), the extremely high computed KOA (log KOW ¼ 7.40 at 298.15 K) value demonstrates that the air-deposition process is not the major pathway for the bioaccumulation of lindane in mushrooms when compared to transpiration.

(37)

 K AirD ðT ; RH Þ ¼ 0:44 exp½0:0437ðRH  100Þexp S S Pseudo A   1 1   1:06  104 0:9  TA þ 31:145 298:15

4.2. Analysis of standard-state K 0Pseudo The KPseudo ðTA ; RHA ; RHS Þ values for the 40 pesticides in the

(38)

standard state (i.e., K 0Pseudo ðTA ¼ 298:15K;RA ¼ 12:87%; RS ¼ 100%Þ) were estimated using the models expressed in Eqs. (24)e(26) and

The nonbiodegradable property of lindane further affects the response of KPseudo ðTA ; RHA ; RHS Þ to these variables. The sensitivity analysis shown in Fig. S.25 indicates that RHA accounts for nearly

are summarized in Table 2, including K 0TranP Pseudo ðTA ¼ 298:15K;

the entire contribution because the simulated K TranP Pseudo ðTA ; RHA Þ

RA ¼ 12:87%Þ and K 0AirD Pseudo ðTA ¼ 298:15K; RS ¼ 100%Þ, which were computed from the transpiration and air-deposition components, respectively. The other pesticide-specific parameters are

Table 2 Estimated K 0Pseudo values under standard state conditions. Pesticide

CAS No.

K 0Pseudo

Pesticides with relatively high degradability and low volatility Alachlor 15972-60-8 0.59 Atrazine 1912-24-9 3.93 Bentazon 25057-89-0 2.13 Carbaryl 63-25-2 4.27 Chlorotoluron 15545-48-9 6.49 Chlorsulfuron 64902-72-3 5.55 Cyanazine 21725-46-2 2.12 Florasulam 145701-23-1 281.28 Imazamox 114311-32-9 19.71 Imidacloprid 138261-41-3 295.52 Isoproturon 34123-59-6 1.02 Isoxaben 82558-50-7 1.02 Linuron 330-55-2 1.49 Methomyl 16752-77-5 11.60 Metolachlor 51218-45-2 1.55 Metribuzin 21087-64-9 12.16

K 0TranP Pseudo

K 0TranP Pseudo %

K 0AirD Pseudo

K 0AirD Pseudo %

k A k D ð298:15KÞ

0.58 3.93 2.13 4.26 6.49 5.55 2.12 281.27 19.71 295.52 1.02 1.02 1.49 11.60 1.55 12.16

99.76% 99.87% 99.90% 99.79% 99.99% 100.00% 100.00% 100.00% 100.00% 100.00% 99.99% 99.97% 99.78% 100.00% 99.67% 99.99%

1.39E-03 5.17E-03 2.20E-03 9.08E-03 5.86E-04 1.72E-09 3.98E-06 3.47E-03 4.07E-11 1.27E-06 6.82E-05 2.86E-04 3.33E-03 5.18E-04 5.12E-03 1.50E-03

0.24% 0.13% 0.10% 0.21% 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.03% 0.22% 0.00% 0.33% 0.01%

2.31E-03 4.32E-03 2.21E-03 6.16E-03 4.07E-04 7.31E-10 2.30E-06 1.00E-05 2.10E-12 4.67E-08 6.99E-05 5.95E-04 4.48E-03 2.24E-05 6.70E-03 4.57E-04 (continued on next page)

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Table 2 (continued ) Pesticide

CAS No.

K 0Pseudo

Oxamyl 23135-22-0 26.84 Prometryne 7287-19-6 1.49 Propiconazole 60207-90-1 1.42 Simazine 122-34-9 9.23 Terbuthylazine 5915-41-3 0.57 Triadimefon 43121-43-3 0.34 Triasulfuron 82097-50-5 46.49 Pesticides with relatively moderate degradability and volatility Chloridazon 1698-60-8 36.13 Cyprodinil 121552-61-2 0.44 Ethofumesate 26225-79-6 4.35 Ethoprophos 13194-48-4 0.22 Pendimethalin 40487-42-1 0.10 Propachlor 1918-16-7 1.32 Propyzamide 23950-58-5 0.74 Triallate 2303-17-5 0.31 Trifluralin 1582-09-8 0.20 Pesticides with relatively low degradability and high volatility Aldrin 309-00-2 0.17 DDT (p,p’-) 50-29-3 0.47 Dieldrin 60-57-1 0.46 Endrin 72-20-8 0.62 Heptachlor 76-44-8 0.14 Heptachlor Epoxide 1024-57-3 0.32 Lindane 58-89-9 0.93 Mirex 2385-85-5 2.57

K 0TranP Pseudo

K 0TranP Pseudo %

K 0AirD Pseudo

K 0AirD Pseudo %

k A k ð298:15KÞ D

26.81 1.49 1.42 9.22 0.57 0.34 46.49

99.89% 99.66% 99.96% 99.93% 99.29% 100.00% 100.00%

3.02E-02 5.13E-03 6.05E-04 6.62E-03 4.08E-03 1.39E-05 2.62E-05

0.11% 0.34% 0.04% 0.07% 0.71% 0.00% 0.00%

1.28E-04 8.49E-03 1.14E-03 3.62E-03 6.25E-03 1.32E-05 2.76E-06

36.12 0.43 4.27 0.19 0.05 1.05 0.48 0.12 0.02

99.95% 97.79% 98.16% 89.81% 53.13% 79.64% 64.07% 39.47% 11.35%

1.87E-02 9.65E-03 8.01E-02 8.87E-03 8.38E-18 2.68E-01 2.67E-01 1.85E-01 1.75E-01

0.05% 2.21% 1.84% 4.10% 0.00% 20.36% 35.93% 60.53% 88.65%

6.13E-02 1.72E-02 8.05E-02 2.70E-02 3.27E-02 1.87E-01 7.61E-01 1.59Eþ00 7.89Eþ00

0.07 0.39 0.26 0.40 0.01 0.13 0.42 0.00

40.84% 82.38% 57.57% 64.80% 7.26% 39.45% 44.61% 0.15%

1.01E-01 8.31E-02 1.94E-01 2.18E-01 1.26E-01 1.92E-01 5.17E-01 2.57Eþ00

59.16% 17.62% 42.43% 35.20% 92.74% 60.55% 55.39% 99.85%

NA NA NA NA NA NA NA NA

summarized in Table S1, including the log KMA, log KOA, log KOW, log KAW, P, DSA U, and EA. The distribution of K 0Pseudo values is shown in Fig. 7. During the analysis of K 0Pseudo , the weather conditions were held constant to help evaluate the impact of pesticide physiochemical properties on their bioaccumulation in mushrooms. A total of 23 pesticides have k



A the computed values of k ð298:15KÞ < 0.01, and the model in Eq (25) D

was applied. The estimated K 0Pseudo values for these pesticides are generally higher than for the other two groups, even though they have relatively high rates of biodegradation, because they are more soluble in water (i.e., they have relatively low Kd and KOW values).

For example, imidacloprid and florasulam have K 0Pseudo values greater than 280, which indicates that the levels of accumulated pesticide residues in mushrooms can theoretically be close to 300 times those in soils. The low Kd values of imidacloprid and florasulam, as estimated from their KOW values, lead to more residues that are soluble in soil water, which further facilitates the pesticide uptake of mushrooms via transpiration. In contrast, POP pesticides have high Kd and KOW values (i.e., low water solubility and high lipid solubility), which hinders their bioaccumulation in mushrooms. In addition to their limited biodegradability, the low K 0Pseudo values as a result of low KOW values for POP pesticides may also be a major reason for their prolonged persistence in soils. For most pesticides,

Fig. 7. Standard-state pseudo-partition coefficients (K 0Pseudo ) computed from different groups of pesticides.

Please cite this article as: Li, Z., A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil, Environmental Pollution, https://doi.org/10.1016/j.envpol.2019.113372

Z. Li / Environmental Pollution xxx (xxxx) xxx

the K 0TranP values account for a large proportion of the total Pseudo K 0Pseudo values due to their low volatility. However, the K 0AirD Pseudo values for mirex and heptachlor take up over 90% of the total K 0Pseudo values, which not only results from the relatively high volatility (i.e., log KAW > 2.0) but also their insolubility in water (i.e., log KOW > 6.0). Therefore, air-deposition is the major uptake mechanism of pesticides by mushrooms, and this process can be significantly affected by soil conditions (e.g., RHS and TS). To further quantify the impact of pesticide physiochemical properties on K 0Pseudo values, a Monte Carlo analysis associated with sensitivity testing was conducted for the three groups of pesticides according to the K 0Pseudo models (see section S.7.2). The parametric distributions of pesticide-specific variables were fitted using the data shown in Table S1. It should be noted that the parameters of the distributions were obtained from a limited dataset. Although the data sample was small, it was sufficient for running a sensitivity analysis to estimate the average impact of physiochemical properties. For the selected pesticides with a relatively high degradability and low volatility (Fig. S.28), Kd contributed most to K 0Pseudo values (83.60%) because the values for these pesticides were relatively low, 0 which led to large variations in their K 0TranP Pseudo and K Pseudo values. Both KOA and DSA U had very slight impacts on the results (0.01% and 0.02%, respectively) because these pesticides exhibit very low 0 volatility, and K 0AirD Pseudo accounts for a tiny percent of the total K Pseudo

values, while the K 0AirD Pseudo values of all these pesticides make up less than 1% of the total. For pesticides with relatively moderate degradability and volatility (Fig. S29), KOA has the greatest impact on the K 0Pseudo values. Nearly half of these pesticides have Log KOA values of less than 8.00, and the average of the Kd values is 25.28, which significantly increases the proportion of K 0AirD Pseudo in the total K 0Pseudo . For pesticides with relatively low degradability and high volatility (Fig. S30), pesticide volatilization from the surface of the mushroom is the only clearance pathway; therefore, K 0Pseudo is very sensitive to the KMA values, with a contribution of 49.41%. In contrast, most POP pesticides have very low solubility in water (i.e., high Kd and KOW values), which decreases the proportion of K 0TranP Pseudo in the total K 0Pseudo and causes KOA to have a major impact (41.95%) on the results. It should be noted that the Monte Carlo simulation averages over the input space. In some circumstances, pesticidespecific analysis for K 0Pseudo values should be conducted.

13

such as KOA and KOW, must be adjusted before model simulation. Due to their unique biostructures, such as umbrella-shaped caps and porous tissues, as well as the complex metabolic processes of mushrooms (e.g., temperature-sensitive fungal enzymes), it might be difficult to collect real data to examine the estimated distribution coefficients by controlling model variables (i.e., TA , RHA , and RHs ) because many secondary variables (i.e., T- and RH-dependent transpiration rates, T-dependent degradation rates, and T- and RHdependent soileair coefficients) will be influence the pseudopartition coefficient as a result of joint probability, which might be one of the reasons no studies have been performed to model pesticide bioaccumulation in mushrooms until now. In this study, I tried to develop a comprehensive mathematical model that could theoretically describe and explore the mechanisms of pesticide bioaccumulation in mushrooms in order to help researchers better understand the complex pesticide bioaccumulation process. For estimating real pesticide levels, the model should be further validated using real data. 6. Conclusions In this study, I explored and developed a weather-based, multicomponent model for evaluating the uptake of pesticides by mushrooms from soil. The pseudo-partition coefficient, KPseudo ðTA ; RHA ; RHS Þ, was introduced to connect the pesticide residue levels between soils and mushrooms under steady-state conditions, which allowed further modeling of the impacts of surrounding weather conditions (i.e., temperature and humidity) on pesticide bioaccumulation in mushrooms. For relatively low-volatility pesticides, the transpiration-contributed K TranP Pseudo dominates the bioaccumulation process, which is mainly determined by pesticide water solubility. For some volatile and semivolatile pesticides, the air-deposition-contributed K AirD Pseudo becomes more important, and the effect of TA on the total KPseudo ðTA ; RHA ; RHS Þ becomes more complex depending upon the values of DSA U and EA. For nonbiodegradable pesticides (e.g., POPs), the computed KPseudo ðTA ; RHA ; RHS Þ values are relatively low due to their insolubility in water, which hinders bioaccumulation in mushrooms and should be one of the major reasons for their persistence in soils. Due to the limitations of real data, the models developed in this study can be useful as mathematical tools to better quantify the uptake of pesticides by mushrooms, and further our understanding of the mechanisms of pesticide bioaccumulation.

5. Model limitations

Declaration of competing interest

Since the pseudo-partition coefficient was thermodynamically and kinetically determined, and included many variables (e.g., weather factors and pesticide physiochemical properties), it might be difficult to conduct experiments by controlling the variables. To make the predicted results more realistic, the model developed in this study can incorporate Monte Carlo probabilistic assessments into future studies. For other specific weather conditions (e.g., snow, storms, etc.), indicator functions can be added to the uptake  and loss submodels (e.g., kþ W ðTA ; RHA Þ, kD ðTA Þ, etc.). For example, if mushrooms are covered by snow, the air-deposition process might be temporarily blocked, and by using the indicator function, the airdeposition submodel could be eliminated. For some geographic regions experiencing extreme climate events (e.g., high temperatures), the model could also be used, but the physiological factors for mushrooms must be considered for the transpiration component (e.g., the transpiration system for mushrooms might be damaged), and some temperature-dependent partition coefficients,

The author has declared that is no conflict of interest in this paper. Acknowledgments This study was supported by Sun Yat-sen University (grant 58000-18841211). The author very much appreciates Kimberly Hageman and Cleo Davie-Martin for the discussion about the internal energy of soil to air phase transfer. Also, the author gratefully acknowledges the anonymous reviewers for their careful reading of the manuscript and their valuable comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2019.113372.

Please cite this article as: Li, Z., A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil, Environmental Pollution, https://doi.org/10.1016/j.envpol.2019.113372

14

Z. Li / Environmental Pollution xxx (xxxx) xxx

References Arienzo, M., Albanese, S., Lima, A., Cannatelli, C., Aliberti, F., Cicotti, F., Qi, S., De Vivo, B., 2015. Assessment of the concentrations of polycyclic aromatic hydrocarbons and organochlorine pesticides in soils from the Sarno River basin, Italy, and ecotoxicological survey by Daphnia magna. Environ. Monit. Assess. 187 (2), 52. Balasubramanya, R.H., Kathe, A.A., 1996. An inexpensive pretreatment of cellulosic materials for growing edible oyster mushrooms. Bioresour. Technol. 57 (3), 303e305. Bano, Z., Rajarathnam, S., Steinkraus, K.H., 1988. Pleurotus mushrooms. Part II. Chemical composition, nutritional value, post-harvest physiology, preservation, and role as human food. Crit. Rev. Food Sci. Nutr. 27 (2), 87e158. Ben-Yehoshua, S., 1987. Transpiration, water stress, and gas exchange. In: Weichmann, J. (Ed.), Postharvest Physiology of Vegetables. Marcel Dekker Inc, New York, pp. 113e170. Bumpus, J.A., Powers, R.H., Sun, T., 1993. Biodegradation of DDE (1, 1-dichloro-2, 2bis (4-chlorophenyl) ethene) by Phanerochaete chrysosporium. Mycol. Res. 97 (1), 95e98. Canadian Nature Photographer, 2013. Tips for Identifying and Photographing Mushrooms. https://www.canadiannaturephotographer.com/mushroom_ photography.html. (Accessed 11 January 2019). Chen, S.N., Nan, F.H., Chen, S., Wu, J.F., Lu, C.L., Soni, M.G., 2011. Safety assessment of mushroom b-glucan: subchronic toxicity in rodents and mutagenicity studies. Food Chem. Toxicol. 49 (11), 2890e2898. €chter, D., Steinlin, C., Camenzuli, L., Chiaia-Hernandez, A.C., Keller, A., Wa Hollender, J., Krauss, M., 2017. Long-term persistence of pesticides and TPs in archived agricultural soil samples and comparison with pesticide application. Environ. Sci. Technol. 51 (18), 10642e10651. COURT, A., WACO, D., 1965. Means and midranges of relative humidity. Mon. Weather Rev. 93 (8), 517e522. Crawford, H.S., 1982. Seasonal food selection and digestibility by tame white-tailed deer in central Maine. J. Wildl. Manag. 974e982. Davie-Martin, C.L., Hageman, K.J., Chin, Y.P., 2012. An improved screening tool for predicting volatilization of pesticides applied to soils. Environ. Sci. Technol. 47 (2), 868e876. , V., Fujita, Y., 2015. Influence of Davie-Martin, C.L., Hageman, K.J., Chin, Y.P., Rouge temperature, relative humidity, and soil properties on the soileair partitioning of semivolatile pesticides: laboratory measurements and predictive models. Environ. Sci. Technol. 49 (17), 10431e10439. Dutta, P.N., Karlo, T., Dutta, P., 2017. Some features of surface air temperature: a statistical viewpoint. Environ. Ecol. Res. 5 (5), 367e376, 2017. European Food Safety Authority (EFSA), 2008. Opinion on a request from EFSA related to the default Q10 value used to describe the temperature effect on transformation rates of pesticides in soil-Scientific Opinion of the Panel on Plant Protection Products and their Residues (PPR Panel). EFSA J. 6 (1), 622. Falandysz, J., Kunito, T., Kubota, R., Gucia, M., Mazur, A., Falandysz, J.J., Tanabe, S., 2008. Some mineral constituents of parasol mushroom (Macrolepiota procera). J. Environ. Sci. Health Part B 43 (2), 187e192. Fantke, P., 2019. Modelling the environmental impacts of pesticides in agriculture. In: Assessing the Environmental Impact of Agriculture. Fantke, P., Jolliet, O., 2016. Life cycle human health impacts of 875 pesticides. Int. J. Life Cycle Assess. 21 (5), 722e733. Fantke, P., Charles, R., de Alencastro, L.F., Friedrich, R., Jolliet, O., 2011a. Plant uptake of pesticides and human health: dynamic modeling of residues in wheat and ingestion intake. Chemosphere 85 (10), 1639e1647. n, A., Friedrich, R., Jolliet, O., 2011b. Dynamic multicrop Fantke, P., Juraske, R., Anto model to characterize impacts of pesticides in food. Environ. Sci. Technol. 45 (20), 8842e8849. Fantke, P., Wieland, P., Wannaz, C., Friedrich, R., Jolliet, O., 2013. Dynamics of pesticide uptake into plants: from system functioning to parsimonious modeling. Environ. Model. Softw 40, 316e324. Friedman, M., 2015. Chemistry, nutrition, and health-promoting properties of Hericium erinaceus (lion’s mane) mushroom fruiting bodies and mycelia and their bioactive compounds. J. Agric. Food Chem. 63 (32), 7108e7123. Gałgowska, M., Pietrzak-Fie cko, R., Felkner-Po zniakowska, B., 2012 Nov 1. Assessment of the chlorinated hydrocarbons residues contamination in edible mushrooms from the North-Eastern part of Poland. Food Chem. Toxicol. 50 (11), 4125e4129. Georgia State University, 2016. Saturated Vapor Pressure Density for Water. http:// hyperphysics.phy-astr.gsu.edu/hbase/Kinetic/watvap.html. (Accessed 31 January 2019). Goss, K.U., Buschmann, J., Schwarzenbach, R.P., 2004. Adsorption of organic vapors to air-dry soils: model predictors and experimental validation. Environ. Sci. Technol. 38 (13), 3667e3673. Hippelein, M., McLachlan, M.S., 2000. Soil/air partitioning of semivolatile organic compounds. 2. Influence of temperature and relative humidity. Environ. Sci. Technol. 34 (16), 3521e3526. Islam, K.I., Khan, A., Islam, T., 2015. Correlation between atmospheric temperature and soil temperature: a case study for Dhaka, Bangladesh. Atmos. Clim. Sci. 5 (03), 200. Jacobsen, R.E., Fantke, P., Trapp, S., 2015. Analysing half-lives for pesticide dissipation in plants. SAR QSAR Environ. Res. 26 (4), 325e342. Jennings, A.A., Li, Z., 2015a. Residential surface soil guidance applied worldwide to

the pesticides added to the Stockholm Convention in 2009 and 2011. J. Environ. Manag. 160, 226e240. Jennings, A.A., Li, Z., 2015b. Residential surface soil guidance values applied worldwide to the original 2001 Stockholm Convention POP pesticides. J. Environ. Manag. 160, 16e29. ~ oz, P., Anto  n, A., 2009. Uptake and persistence Juraske, R., Castells, F., Vijay, A., Mun of pesticides in plants: measurements and model estimates for imidacloprid after foliar and soil application. J. Hazard. Mater. 165 (1e3), 683e689. Karpouzas, D.G., Walker, A., 2000. Aspects of the enhanced biodegradation and metabolism of ethoprophos in soil. Pest Manag. Sci. former. Pestic. Sci. 56 (6), 540e548. Kaufman, D.D., Blake, J., 1970. Degradation of atrazine by soil fungi. Soil Biol. Biochem. 2 (2), 73e80. Lavin, K.S., Hageman, K.J., 2013. Contributions of long-range and regional atmospheric transport on pesticide concentrations along a transect crossing a mountain divide. Environ. Sci. Technol. 47 (3), 1390e1398. Li, Z., 2018a. A Bayesian generalized log-normal model to dynamically evaluate the distribution of pesticide residues in soil associated with population health risks. Environ. Int. 121, 620e634. Li, Z., 2018b. A Health-Based Regulatory Chain Framework to Evaluate International Pesticide Groundwater Regulations Integrating Soil and Drinking Water Standards. Environment international. Liu, T., Zhang, C., Peng, J., Zhang, Z., Sun, X., Xiao, H., Sun, K., Pan, L., Liu, X., Tu, K., 2016. Residual behaviors of six pesticides in shiitake from cultivation to postharvest drying process and risk assessment. J. Agric. Food Chem. 64 (47), 8977e8985. Mahajan, P.V., Oliveira, F.A.R., Macedo, I., 2008. Effect of temperature and humidity on the transpiration rate of the whole mushrooms. J. Food Eng. 84 (2), 281e288. Metcalf, R.L., Sangha, G.K., Kapoor, I.P., 1971. Model ecosystem for the evaluation of pesticide biodegradability and ecological magnification. Environ. Sci. Technol. 5 (8), 709e713. Mostafa, I.Y., El-Arab, A.E., Zayed, S.M.A.D., 1987. Fate of 14C-lindane in a rice-fish model ecosystem. J. Environ. Sci. Health Part B 22 (2), 235e243. Oduguwa, O.O., Edema, M.O., Ayeni, A.O., 2008. Physico-chemical and microbiological analyses of fermented corn cob, rice bran and cowpea husk for use in composite rabbit feed. Bioresour. Technol. 99 (6), 1816e1820. Ouzouni, P.K., Riganakos, K.A., 2007. Nutritional value and metal content of Greek wild edible fungi. Acta Aliment. 36, 99e110. Parochetti, J.V., Hein, E.R., 1973. Volatility and photodecomposition of trifluralin, benefin, and nitralin. Weed Sci. 21 (5), 469e473. Paterson, S., Mackay, D., McFarlane, C., 1994. A model of organic chemical uptake by plants from soil and the atmosphere. Environ. Sci. Technol. 28 (13), 2259e2266. ~ a, N., Knudsen, M.T., Fantke, P., Anton, A., Hermansen, J.E., 2019. Freshwater Pen ecotoxicity assessment of pesticide use in crop production: testing the influence of modeling choices. J. Clean. Prod. 209, 1332e1341. Piwoni, M.D., Keeley, J.W., 1996. Basic Concepts of Contaminant Sorption at Hazardous Waste Sites. Environmental Assessment Sourcebook, p. 9. Pussemier, L., 1991. Model calculations and measurements of uptake and translocation of carbamates by bean plants. Chemosphere 22 (3e4), 327e339. Rigas, F., Dritsa, V., Marchant, R., Papadopoulou, K., Avramides, E.J., Hatzianestis, I., 2005. Biodegradation of lindane by Pleurotus ostreatus via central composite design. Environ. Int. 31 (2), 191e196. guet, J., Kuszala, C., Rivera-Becerril, F., van Tuinen, D., Chatagnier, O., Rouard, N., Be Soulas, G., Gianinazzi-Pearson, V., Martin-Laurent, F., 2017. Impact of a pesticide cocktail (fenhexamid, folpel, deltamethrin) on the abundance of Glomeromycota in two agricultural soils. Sci. Total Environ. 577, 84e93. Robles-Hern andez, L., Gonz alez-Franco, A.C., Crawford, D.L., Chun, W.W., 2008. Review of environmental organopollutants degradation by white-rot basidiomycete mushrooms. Tecnociencia Chihuahua 2 (2), 32e40. Sastry, S.K., Buffington, D.E., 1983. Transpiration rates of stored perishable commodities: a mathematical model and experiments on tomatoes. Int. J. Refrig. 6 (2), 84e96. Straatsma, G., Samson, R.A., Olijnsma, T.W., Den Camp, H.J.O., Gerrits, J.P., Van Griensven, L.J., 1994. Ecology of thermophilic fungi in mushroom compost, with emphasis on Scytalidium thermophilum and growth stimulation of Agaricus bisporus mycelium. Appl. Environ. Microbiol. 60 (2), 454e458. Trapp, S., Matthies, M., 1995. Generic one-compartment model for uptake of organic chemicals by foliar vegetation. Environ. Sci. Technol. 29 (9), 2333e2338. Trapp, S., 2007. Fruit tree model for uptake of organic compounds from soil and air. SAR QSAR Environ. Res. 18 (3e4), 367e387. Trapp, S., Pussemier, L., 1991. Model calculations and measurements of uptake and translocation of carbamates by bean plants. Chemosphere 1991 (22), 327. Tu, C.M., Miles, J.R.W., Harris, C.R., 1968. Soil microbial degradation of aldrin. Life Sci. 7 (6), 311e322. U.S. EPA, 2018. Regional Screening Levels (RSLs) - Generic Tables Chemical Specific Parameters. https://www.epa.gov/risk/regional-screening-levels-rsls-generictables. (Accessed 11 January 2019). U.S. National Library of Medicine, 2018. National Center for Biotechnology Information - Chemicals & Bioassays. https://www.ncbi.nlm.nih.gov/guide/ chemicals-bioassays/. (Accessed 11 January 2019). Van den Berg, F., Leistra, M., 2004. Improvement of the Model Concept for Volatilisation of Pesticides from Soils and Plant Surfaces in PEARL; Description and User’s Guide for PEARL 2.1, pp. 1eC1. Van Eerd, L.L., Hoagland, R.E., Zablotowicz, R.M., Hall, J.C., 2003. Pesticide metabolism in plants and microorganisms. Weed Sci. 51 (4), 472e495.

Please cite this article as: Li, Z., A new pseudo-partition coefficient based on a weather-adjusted multicomponent model for mushroom uptake of pesticides from soil, Environmental Pollution, https://doi.org/10.1016/j.envpol.2019.113372

Z. Li / Environmental Pollution xxx (xxxx) xxx Vik, E.A., Breedveld, G., Farestveit, T., 1999. Guidelines for the Risk Assessment of Contaminated Sites. 1999. Norwegian Climate and Pollution Agency, pp. 1e107 (82-7655-192-0). Wallace, John M., Hobbs, Peter V., 2006. Atmospheric Science: an Introductory Survey, vol. 92. Elsevier. Wilson, D.J., Western, A.W., Grayson, R.B., Berg, A.A., Lear, M.S., Rodell, M., Famiglietti, J.S., Woods, R.A., McMahon, T.A., 2003. Spatial distribution of soil moisture over 6 and 30 cm depth, Mahurangi river catchment, New Zealand. J. Hydrol. 276 (1e4), 254e274. Wong, H.L., Garthwaite, D.G., Ramwell, C.T., Brown, C.D., 2017. How does exposure to pesticides vary in space and time for residents living near to treated orchards? Environ. Sci. Pollut. Control Ser. 24 (34), 26444e26461. Wu, X., Davie-Martin, C.L., Steinlin, C., Hageman, K.J., Cullen, N.J., Bogdal, C., 2017.

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Understanding and predicting the fate of semivolatile organic pesticides in a Glacier-Fed lake using a multimedia chemical fate model. Environ. Sci. Technol. 51 (20), 11752e11760. Xiao, P., Mori, T., Kamei, I., Kondo, R., 2011. Metabolism of organochlorine pesticide heptachlor and its metabolite heptachlor epoxide by white rot fungi, belonging to genus Phlebia. FEMS Microbiol. Lett. 314 (2), 140e146. Xu, N., Hu, X., Xu, W., Li, X., Zhou, L., Zhu, S., Zhu, J., 2017. Mushrooms as efficient solar steam-generation devices. Adv. Mater. 29 (28), 1606762. Yalkowsky, S.H., Dannenfelser, R.M., 1992. Aquasol Database of Aqueous Solubility. College of Pharmacy, University of Arizona, Tucson, AZ. Zhang, P., Aagaard, P., Nadim, F., Gottschalk, L., Haarstad, K., 2009. Sensitivity analysis of pesticides contaminating groundwater by applying probability and transport methods. Integr. Environ. Assess. Manag. 5 (3), 414e425.

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