Science of the Total Environment 568 (2016) 66–74
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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Assessing the probability of infection by Salmonella due to sewage sludge
use in agriculture under several exposure scenarios for crops and
soil ingestion
Flávio Krzyzanowski Jr a,⁎, Marcelo de Souza Lauretto b, Adelaide Cássia Nardocci c,
Maria Inês Zanoli Sato d, Maria Tereza Pepe Razzolini c
a
Instituto Federal de Educação, Ciência e Tecnologia de Sao Paulo, R. Pedro Vicente 625, São Paulo, SP 01109-010, Brazil
EACH — Escola de Artes, Ciências e Humanidade, Universidade de Sao Paulo, R. Arlindo Bettio, 1000, São Paulo, SP 03828-000, Brazil
c
Faculdade de Saude Publica, Universidade de Sao Paulo, Av. Dr Arnaldo 715 1° andar, Sao Paulo, SP 01246-904, Brazil
d
CETESB — Companhia Ambiental do Estado de Sao Paulo, Av. Prof. Frederico Hermann Jr, 345, São Paulo, SP 05459-900, Brazil
b
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Nine scenarios for evaluating Salmonella
risk by using sewage sludge in soil
• Regrowth of Salmonella spp. in soil and
internalization in vegetables
• Annual risk for consumers of vegetables
and field workers
• Sanitary and health measures need to
be implemented.
a r t i c l e
i n f o
Article history:
Received 12 February 2016
Received in revised form 17 May 2016
Accepted 18 May 2016
Available online 7 June 2016
Editor: D. Barcelo
Keywords:
Risk assessment
Salmonella
a b s t r a c t
A deeper understanding about the risks involved in sewage sludge practice in agriculture is required. The aims of
the present study were to determine the annual risk of infection of consuming lettuce, carrots and tomatoes cultivated in soil amended with sewage sludge. The risk to agricultural workers of accidental ingestion of sludge or
amended soil was also investigated.
A Quantitative Microbial Risk Assessment was conducted based on Salmonella concentrations from five WWTPs
were used to estimate the probability of annual infection associated with crops and soil ingestion. The risk of infection was estimated for nine exposure scenarios considering concentration of the pathogen, sewage sludge dilution in soil, variation of Salmonella concentration in soil, soil attachment to crops, seasonal average
temperatures, hours of post-harvesting exposure, Salmonella regrowth in lettuce and tomatoes, Salmonella inhibition factor in carrots, crop ingestion and frequency of exposure, sludge/soil ingestion by agricultural workers
and frequency of exposure. Annual risks values varied across the scenarios evaluated. Highest values of annual
⁎ Corresponding author.
E-mail addresses: [email protected] (F. Krzyzanowski), [email protected] (M. de Souza Lauretto), [email protected] (A.C. Nardocci), [email protected] (M.I.Z. Sato),
[email protected] (M.T.P. Razzolini).
http://dx.doi.org/10.1016/j.scitotenv.2016.05.129
0048-9697/© 2016 Published by Elsevier B.V.
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
Sewage sludge
Crops
67
risk were found for scenarios in which the variation in the concentration of Salmonella spp. in both soil and crops
(scenario 1) and without variation in the concentration of Salmonella spp. in soil and variation in crops (scenario
3) ranging from 10−3 to 10−2 for all groups considered. For agricultural workers, the highest annual risks of infection were found when workers applied sewage sludge to agricultural soils (2.26 × 10−2). Sensitivity analysis
suggests that the main drivers for the estimated risks are Salmonella concentration and ingestion rate. These risk
values resulted from conservative scenarios since some assumptions were derived from local or general studies.
Although these scenarios can be considered conservative, the sensitivity analysis yielded the drivers of the risks,
which can be useful for managing risks from the fresh products chain with stakeholders' involvement.
© 2016 Published by Elsevier B.V.
1. Introduction
Sewage sludge has been recognized as a suitable component for soil
amendment, and its use has been intensified in recent decades worldwide (Egan, 2013). Moreover, its usage can minimize environmental
pollution in line with a global trend of exploiting some residuals generated in wastewater treatment plants (WWTP). Although in Brazil the
application of sewage sludge remains restricted, it has been seen as a
promising alternative for managing this residue. Brazilian sludge generation totaled around 372,000 tons/year in 2001 with the Sao Paulo Metropolitan Region alone responsible for 274,000 tons/year, representing
a major contributor to sewage sludge generation in Brazil (UNHabitat, 2008).
Although sewage sludge usage in agriculture is recognized worldwide, some issues regarding its quality and impact on human health
must be taken into account, such as the presence of pathogens, an
issue previously reported by several authors (Gerba and Smith,
2005; Jiménez et al., 2007; Pepper et al., 2008; Navarro et al.,
2009). Hence, this usage may represent a public health concern if
not applied properly.
Salmonella spp. are among the pathogens typically found in sewage sludge and their presence has been well documented in several
studies (Iranpour and Cox, 2006; Horswell et al., 2007; Sidhu and
Toze, 2009; Viau et al., 2011). These reports reveal that sludge sewage can potentially disseminate the pathogen if the sludge is applied
to agricultural fields without sanitary criteria or the establishment of
barriers, since this bacterium can remain viable in soil at 20 °C to 30 °C
for anything from 30 to 968 days (Heaton and Jones, 2008); under
certain conditions, such as moisture and carbon availability, even
regrowth can be observed (Eamens et al., 2006; Gibbs et al., 1997).
Several outbreaks associated with vegetable consumption and
Salmonella spp. presence have been reported (Brandl, 2006; Hirneisen
et al., 2012).
According to Francis et al. (2012), people’ eating habits are shifting
toward a healthier lifestyle. These changes include a higher intake of
vegetables. Furthermore, studies have shown that climate change,
which can cause droughts, floods and higher temperatures, may also increase the capacity of Salmonella spp. to infect pre-harvest leafy green
vegetables and even increase their internalization (Liu et al., 2013; Ge
et al., 2012).
A deeper understanding about the risks involved in this practice is
required. The aims of the present study were to better clarify this important issue and determine the annual risk of infection of consuming lettuce, carrots and tomatoes (three of the most consumed vegetables in
Brazil) cultivated in soil amended with sewage sludge. The risk to agricultural workers of accidental ingestion of sludge or amended soil was
also investigated.
2. Material and methods
In order to assess the risk of infection by Salmonella spp. in sewage
sludge, nine different scenarios were devised representing the following
conditions:
a) Consumption of lettuce, tomatoes and carrots:
• presence or absence of variation in Salmonella spp. concentration in
amended soils;
• presence or absence of Salmonella spp. regrowth in crops.
b) Accidental ingestion of soil or sludge by agricultural workers:
• Ingestion of amended soil in presence or absence of variation in Salmonella spp. concentration in amended soils;
• Direct ingestion of sewage sludge.
Table 1 summarizes all nine scenarios devised.
2.1. Concentrations of Salmonella spp. in raw sludge (C)
Data on the identification and concentration of Salmonella spp. in the
five WWTPs was drawn from the study carried out by Krzyzanowski et
al. (2014) in which quantification and characterization was performed
according to EPA Method 1682 (USEPA, 2006): Salmonella spp. in sewage sludge by Modified Semisolid Rappaport-Vassiliadis (MSRV)
Medium.
Data on Salmonella concentrations were clustered due to the different concentration patterns found in the five WWTPs (Table 2). The clustering was based on significance tests, where WTTPs were considered as
equivalent when differences in Salmonella concentrations were not significant at a level of 0.1. Due to the high occurrence of samples with leftcensored data (concentrations below the detection limit — DL) and ties,
which preclude the use of tests based on normal distribution assumptions, a signal test proposed by Putter (1955) was applied (ANU — asymptotic uniformly most powerful nonrandomized test) for its
conceptual simplicity and good empirical results (Coakley and Heise,
1996). WWTPs 1, 3 and 5 were grouped together, while WWTPs 2 and
Table 1
Description of the different scenarios devised in this study for evaluating annual risk of infection by Salmonella through ingestion of crops or by accidental ingestion of soil/sewage
sludge.
Scenario Risk of infection related to
Variation in Salmonella
concentration in soil
Regrowth of
Salmonella in
crops
1
2
3
4
5
6
7
Present
Present
Absent
Absent
Present
Absent
Present
Present
Absent
Present
Absent
Inhibition
Inhibition
NA
Absent
NA
NA
NA
8
9
Lettuce/tomatoes
Lettuce/tomatoes
Lettuce/tomatoes
Lettuce/tomatoes
Carrots
Carrots
Ingestion of soil + sludge
by agricultural workersa
Ingestion of soil + sludge
by agricultural workersa
Direct ingestion of sludge
by agricultural workersb
NA: not applicable.
a
Workers who directly handle amended soil.
b
Workers who directly apply sewage sludge in soil to be amended.
68
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
Table 2
Inhibition of S. enterica growth by epiphytic microorganisms of carrots according to Liao
(2007).
Epiphytic microorganisms: S. entericaa
Inhibition of S. enterica (log CFU/disk)
0:1
1:1
10:1
100:1
7.81 ± 0.42 (control)
6.75 ± 0.22
5.46 ± 0.35
5.49 ± 0.23
Adapted from Liao (2007).
a
Initial concentration of S. enterica of 3.78 log CFU/disk.
4 showed no similarities with any others and were therefore treated
separately. Concentrations data were modelled by the maximum likelihood method adapted for left-censored data, using the R Package
“fitdistrplus” (DELIGNETTE-MULLER and DUTANG, 2015; R Core Team,
2015), with four initial candidate distribution families: triangular, lognormal, gamma and weibull. Fig. 1 depicts the empirical distributions
based on samples (dotted curves) and the best-fit distributions for
WTTP groups (continuous curves). It can be seen that samples from
WWTP 1, 3 and 5 contained the lowest concentrations (several below
the DL), while WWTP 2 samples exhibited the highest concentrations.
Table 3 shows the best-fit distributions for the WWTP groups together with the respective estimated parameters.
2.2. Sewage sludge dilution in soil (D)
For dilution of sewage sludge in soil, the triangular distributions calculated by Magalhães (2012) (Table 3) were adopted as dilution factors
on the basis of the recommendations of application of nitrogen, phosphorus and potassium in cultured tubers, leafy vegetables and fruit proposed by Novais (2010) apud Magalhães (2012) and Ribeiro et al.
(1999) apud Magalhães (2012). This author considered the incorporation of sewage into the ground to about 0.20 m in depth and a soil density of 1.5 g/cm3 (Andreoli et al., 2001).
For the scenario of direct ingestion of sludge by agricultural workers
(scenario 9), D = 1 was considered.
2.3. Variation in Salmonella spp. concentration in soil (VS)
In this study, a model of variation in Salmonella spp. concentration
was built on the basis of an experiment conducted by Eamens et al.
(2006) assessing regrowth and die-off patterns of Salmonella spp. after
sewage sludge application on soil. In their work, the experiment
entailed applying dewatered sludge generated at the Cronulla WWTP,
Sydney (Australia), to agricultural land at Gouburn, NSW, and measuring the concentrations of the bacteria monthly over a period of
68 months. Regrowth/die-off factors were computed from the raw
data kindly provided by Dr. Eamens, by dividing the geometric mean
of concentrations on each sampling day by the geometric mean of initial
concentrations in sewage sludge immediately before its application
onto the soil. These empirical factors were fitted considering four candidate distributions using the maximum likelihood method: triangular,
gamma, log-normal and weibull. Table 3 shows the best-fit distribution
and the respective estimated parameters.
For scenarios which do not consider Salmonella spp. variation in soil,
VS = 1 is assumed.
2.4. Soil attachment to crops (SA)
The amount of soil that attaches to lettuce and carrots was estimated
based on two different studies. The first of these was the study of Gale
(2003), based on self-reported experience of farmers and assumes
higher quantities of attached soil on both lettuce and carrots than
those reported by the second study of Magalhães (2012). For devising
the scenario, both studies were considered (the higher values from
Gale and the lower ones from Magalhães, 2012) using a uniform distribution as shown in Table 3. To the best of our knowledge, there are no
estimates available in the literature for soil attachment to tomatoes. A
uniform distribution was assumed ranging from 0.1 g to 0.3 g of soil attached to tomato plants via different routes such as wind, flies and
splashed water promoted by rain or irrigation.
2.5. Seasonal average temperatures (ST)
Daily data on mean temperatures for 2011 and 2012 were obtained
from the Institute of Astronomy, Geophysics and Atmospheric Sciences
of the University of São Paulo (IAG, 2013). A normal distribution was assumed for each season of the year (summer, autumn, winter, spring),
using the sample mean and standard deviation estimates (Table 3).
2.6. Hours of post-harvesting exposure (H)
This parameter was estimated from a survey conducted with
CEAGESP — central state supply,1 in which CEAGESP's managers were
asked to inform the delay between the harvesting and delivery of the
Fig. 1. Empirical (dotted lines) and fitted (solid lines) distributions of Salmonella spp.
concentrations in sewage sludge, by WWTP cluster.
1
Government department in charge of storing and distributing crops in the Metropolitan Region of São Paulo.
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
69
Table 3
Parameters for exposure to Salmonella in crops, sewage sludge and soil amended with sewage sludge.
Subsection/variable
Variable details
Distributions Parameters
References
2.1 Salmonella (MPN/g) in sewage sludge (C)
WWTP 2
WWTP 4
W eibull
Triangle
Authors
WWTP 1 3 5
Scenarios of crop
ingestion
Gamma
Triangle
Shape = 1.349; scale = 4. 642
Min = 1.8 × 10−2; mode = 2.0
× 10−2; max = 2.2
2.2 Sewage dilution on soil (D)
Lettuce
Carrot
Tomato
Scenarios of soil/sewage
sludge ingestion
2.3 Variation of Salmonella concentration in soil (VS)
2.4 Soil attachment to crops (g) (SA)
2.5 Seasonal average temperatures (°C) (ST)
2.6 Hours of post-harvest exposure (h) (H)
2.7 Salmonella regrowth in lettuce and tomatoes
Amended
soil
Sewage
sludge
Scenarios of variation in soil
Triangle
Lognormal
Scenarios of no variation in soil
Lettuce
Carrot
Tomato
Summer
NA
Uniform
Normal
Eamens et al. 2006
Authors
Gale (2003)
Magalhães (2012)
Authors
IAG (2013)
Fall
= 3.1102
Mean (μ) = 18.682; Std Dev (σ)
Winter
= 3.1826
Mean (μ) = 17.205; Std Dev (σ)
NA
Weight (W) (g)
N max (CFU/g)
Lettuce
Tomato
Lettuce
Uniform
NA
Lettuce
Tomato
= 2.886
Min = 24; max = 29
400
100
√μ = 0.0178 (T + 4.6); ln(λ) =
CEAGESP
Authors
Sant'Ana et al.
(2012b)
0.0118 (T − 32.3)
√μ = 0.026 × (T-0.107); ln(λ) = Wenjing (2010);
USFDA (2007)
3.4
NA
3 × 103
108
NA
Uniform
Triangle
150
Min = 2; max = 3
Min = 20; mode = 30; max =
Adults
Carrot ingestiona
Men
50
Min = 24; mode = 25; max =
Women
56
Min = 10; mode = 22; max =
Men
48
Min = 30; mode = 55; max =
Women
91
Min = 30; mode = 45.5; max =
Annual frequency (day/years)
Sludge/soil ingestion
Annual frequency
Amended
soil
Sewage
sludge
Magalhães (2012)
Andreoli et al.
(2001)
NA
Weight, W (g)
Inhibition factor, IF
Lettuce ingestiona
Tomato ingestiona
2.10 Sludge/soil ingestion by agricultural workers (g) (SI)
Annual frequency of exposition for agricultural workers
(days) (F)
Mean (μ) = 0.7367; Std Dev (σ)
= 4.2121
VS = 1
Min = 0.55; max = 0.8
Min = 1.7; max = 3.0
Min = 0.1; max = 0.3
Mean (μ) = 22.252; Std Dev (σ)
= 1.9929
Mean (μ) = 20.127; Std Dev (σ)
Tomato
2.9 Ingestion rate of crops (g/day) (CI) Annual frequency of
crop ingestion (days) (F)
Authors
Spring
μ (growth rate), λ
2.8 Inhibition of Salmonella growth in carrots (1/log) (IF)
Min = 0.0018; mode = 0.0044;
max = 0.0128
Min = 0.0012; mode = 0.0036;
max = 0.0094
Min = 0.0015; mode = 0.0039;
max = 0.0121
Min = 0.0015; mode = 0.0039;
max = 0.0121
D=1
NA
Uniform
NA
60
F = 365
Min = 0.1; max = 0.625
240
120
Manios et al. (2013)
Asplund and Nurmi
(1991)
Authors
Liao (2007)
Carlos et al. (2008)
Authors
U.S. EPA, (2011)
Bastos et al. (2009)
NA = not applicable.
a
Child ingestion rate for lettuce (32.2 g/d); carrots (43.3 g/d) and tomatoes (44 g/d) according to Voci 2006.
vegetables to public markets. According to the managers interviewed,
this delay ranges from between 24 and 29 h (oral communication).
Based on this information, a uniform distribution within this time interval was employed to estimate the post harvesting time during which a
crop is exposed to unrefrigerated (abuse) temperatures (Table 3).
2.7. Salmonella spp. regrowth in lettuce and tomatoes
The model for Salmonella regrowth in lettuce and tomatoes
proposed by Baranyi and Roberts (1994) was adopted. Theses
authors introduced a general model for post-harvest regrowth of microorganisms in crops, given by Eq. (1) as follows:
expðμ ðT ÞAðt ÞÞ−1
; Aðt Þ
ln ðNðt ÞÞ ¼ ln ðN0 Þ þ μ ðT ÞAðt Þ− ln 1 þ
expðN max −N0 Þ
1
expð−μ ðT Þt Þ þ q0
1
ln
;
¼tþ
; q0 ¼
μ ðT Þ
exp½μ ðT ÞλðT Þ−1
1 þ q0
ð1Þ
where: N0 = initial concentration of the microorganism in crop (CFU/g);
t = post-harvest exposure time (h); T = average environmental
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F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
temperature (°C); N(t) = final concentration of the microorganism in
crop (CFU/g) after exposure time; μ(T) = maximum growth rate at temperature T (log CFU/g/h); Nmax = maximum concentration of the microorganism in crop (UFC/g); λ(T) = lag time at temperature T (h).
Assumptions for the parameters Nmax, μ(T), λ(T) are given in Table 3.
The initial concentration of bacteria carried by the soil that adheres
to different crops (N0) was estimated by the equation:
N0 ¼ C D VS SA=W;
ð2Þ
where W is the crop weight, whose assumptions are given in Table 3.
2.8. Salmonella spp. inhibition factor in carrots (IF)
Several studies have reported that carrots have a microbial inhibition factor (Viswanathan and Kaur, 2001; Liao, 2007; Sant'Ana et al.,
2012a). Table 2 shows the results of an experiment carried out by Liao
(2007) proving that the epiphytic microorganisms of carrots inhibit
Salmonella enterica, which is associated with the concentration of
epiphytic microorganisms. Thus, taking into account the low concentration of Salmonella spp. after the dilution of sewage in soil and the natural
higher concentration of the epiphytic microorganisms present in
carrots, a log decay of about 2 to 3 log in the total amount of Salmonella
spp. internalized by carrots was assumed (Table 3).
The final concentration of Salmonella spp. in carrots (CFU/g) after exposure time t is given by N(t) = N0 IF, where N0 is the initial Salmonella
spp. concentration in carrots, given by Eq. (2), and IF ≤ 1 is the inhibition
factor.
2.9. Crop ingestion (CI) and frequency of exposure (F)
Rates of lettuce, tomatoes and carrots ingestion by adults were obtained from a survey carried out by Carlos et al. (2008) in São Paulo
City. In the survey, 1562 adults were asked to report what they had
eaten during the last 24 h. A triangle distribution was applied involving
the lowest, medium and biggest portion ingested, for both women and
men. Ingestion rates of children were obtained from a dissertation published by Voci (2006). In this case, no distribution was applied as the authors could not assess statistical intervals (Table 3).
A conservative scenario was assumed for daily crop consumption,
i.e., a frequency of exposure of F = 365.
2.10. Sludge/soil ingestion by agricultural workers (SI) and frequency of exposure (F)
The estimates for soil ingestion rate per exposure were based on U.S.
EPA (2011), p. 5–45). A uniform distribution was considered, whose parameters are given in Table 3.
The assumed frequency of exposure (F) for agricultural workers was
based on BASTOS et al. (2009), which estimated 120 days of exposure
during sewage sludge application into soil, and 240 days of exposure
to amended soil.
2.11. Exposure dose (d)
The dose from crop ingestion was estimated directly as the product
of the concentration of Salmonella spp. in the crop and the ingestion
rate:
d ¼ Nðt Þ CI
ð3Þ
For agricultural workers, doses were calculated as follows:
d ¼ C D VS SI;
ð4Þ
where, in the scenario of direct ingestion of sewage sludge, D = 1 and
VS = 1.
2.12. Dose-response parameter
The dose-response parameter for Salmonella spp. is a Beta-Poisson
distribution whose parameters are α = 0.3126 and N50 = 2.36 × 104
(Haas et al., 1999).
2.13. Infection risk, uncertainty and sensitivity analyses
For risk assessment and uncertainty analysis, a Monte Carlo simulation was performed, where each iteration consisted of drawing F independent values of C(i), D(i), VS(i), SA(i), ST(i), H(i), IF(i), CI(i), SI(i) (i = 1, 2,
… , F), according to the distributions and parameters described in
Table 3, and then computing the exposure dose d(i) according to Eqs.
(3) and (4).
The infection risk per exposure is given by the Beta-Poisson distribution,
"
#
ðiÞ
−α
d 1=α
ðiÞ
P d
¼ 1− 1 þ
2 −1
:
N50
ð5Þ
The annual infection risk is computed according to Eq. (2) (Haas et
al., 1999), as follows.
h
i
365
ðiÞ
:
P a ¼ 1−∏d¼1 1−P d
ð6Þ
This procedure was repeated 20,000 times to provide the distribution for annual risk and the corresponding values of empirical median
and 95% quantile.
In order to assess the influence of parameters C, D, VS, SA, ST, H, IF, CI
and SI on the risks, a sensitivity analysis was performed, based on the
rank correlation coefficient between each parameter and the estimated
daily risks (Haas et al. 1999, p. 340–341).
3. Results and discussion
Table 4 shows the results of the estimated annual risk of infection by
Salmonella spp. for intake of lettuce and tomatoes, according to gender
(male and female) and age group — children and adults. Table 5
shows the estimated risk for the intake of carrots.
Observing Table 4, it can be noted that the estimated risks in scenarios 1 and 3 for the ingestion of lettuce and tomatoes grown in sewage
sludge from WWTP 2 and WWTP 4, for both the median values as
well as the values of the upper 95% CI, reached values in the order of
10−2, which is high considering that many people consume vegetables,
including sensitive populations. In Brazil, there is no value for tolerable
annual risk and therefore the value recommended by USEPA (10− 4)
was used as a basis for comparison.
Scenarios 1 and 3, unlike scenarios 2 and 4, consider the variation in
concentration of Salmonella spp. in vegetables. Comparing the differences in the annual risk of infection in these scenarios, it is clear that
this variation was the main determinant of the higher risks obtained
in scenarios 1 and 3. This is also evident in the sensitivity analysis
(Fig. 2), which highlights the major influence of temperature and
post-harvesting time (the main parameters of the regrowth model in
vegetables) on estimated risks.
Internalization may occur even when Salmonella spp. is present at
very low concentrations, as shown by the study of Manios et al.
(2013) with internalization from a low concentration of Salmonella
Typhimurium (1 to 4 cells). The authors reported the internalization
and proliferation of this strain in lettuce leaves reaching a final concentration of approximately 104 CFU/g of lettuce. In two other studies conducted to evaluate the internalization and growth of Salmonella spp. in
tomatoes, Asplund and Nurmi (1991) observed the growth of Salmonella
Enteritidis, S. Typhimurium and S. Infantis in pieces of tomato starting
from initial concentrations of 1.2 × 10−1 cells/g; 0.7 × 101 cells/g and
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
71
Table 4
Annual risks of infection by Salmonella spp. through the ingestion of lettuce and tomatoes in scenarios 1, 2, 3 and 4.
Median
Lettuce
WWTP 2
WWTP 4
WWTP 1, 3, 5
Tomatoes
WWTP 2
WWTP 4
WWTP 1, 3, 5
Adults
Children
Adults
Children
Adults
Children
Men
Women
Children
Men
Women
Children
Men
Women
Children
95% quantiles
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 1
Scenario 2
Scenario 3
Scenario 4
1.10 × 10−2
1.07 × 10−2
1.93 × 10−3
1.87 × 10−3
8.82 × 10−5
8.73 × 10−5
1.44 × 10−2
1.13 × 10−2
1.10 × 10−2
2.53 × 10−3
1.93 × 10−3
1.87 × 10−3
1.04 × 10−4
8.13 × 10−5
7.77 × 10−5
4.02 × 10−5
3.90 × 10−5
6.99 × 10−6
6.84 × 10−6
3.65 × 10−7
3.62 × 10−7
7.72 × 10−5
5.96 × 10−5
5.78 × 10−5
1.34 × 10−5
1.03 × 10−5
1.01 × 10−5
6.91 × 10−7
5.27 × 10−7
5.23 × 10−7
1.77 × 10−2
1.72 × 10−2
3.08 × 10−3
2.98 × 10−3
1.81 × 10−4
1.74 × 10−4
2.49 × 10−2
1.92 × 10−2
1.89 × 10−2
4.36 × 10−3
3.37 × 10−3
3.27 × 10−3
2.22 × 10−4
1.75 × 10−4
1.70 × 10−4
5.90 × 10−5
5.72 × 10−5
1.02 × 10−5
9.86 × 10−6
6.55 × 10−7
6.35 × 10−7
1.13 × 10−4
8.72 × 10−5
8.50 × 10−5
1.95 × 10−5
1.50 × 10−5
1.47 × 10−5
1.26 × 10−6
9.69 × 10−7
9.42 × 10−7
2.79 × 10−2
2.60 × 10−2
4.50 × 10−3
4.69 × 10−3
4.56 × 10−4
4.12 × 10−4
4.76 × 10−2
3.67 × 10−2
3.57 × 10−2
9.33 × 10−3
7.48 × 10−3
6.43 × 10−3
6.92 × 10−4
5.82 × 10−4
4.49 × 10−4
7.04 × 10−5
6.79 × 10−5
1.22 × 10−5
1.19 × 10−5
1.21 × 10−6
1.10 × 10−6
1.37 × 10−4
1.07 × 10−4
1.03 × 10−4
2.38 × 10−5
1.80 × 10−5
1.74 × 10−5
2.27 × 10−6
1.74 × 10−6
1.70 × 10−6
2.46 × 10−2
2.39 × 10−2
4.29 × 10−3
4.16 × 10−3
4.06 × 10−4
4.10 × 10−4
5.24 × 10−2
4.04 × 10−2
4.02 × 10−2
9.42 × 10−3
7.23 × 10−3
6.98 × 10−3
8.05 × 10−4
6.00 × 10−4
5.69 × 10−4
6.39 × 10−5
6.18 × 10−5
1.09 × 10−5
1.06 × 10−5
9.37 × 10−7
8.99 × 10−7
1.24 × 10−4
9.49 × 10−5
9.25 × 10−5
2.12 × 10−5
1.63 × 10−5
1.59 × 10−5
1.83 × 10−6
1.40 × 10−6
1.36 × 10−6
Scenario 1: variation in the concentration of Salmonella spp. in both soil and crops.
Scenario 2: variation in the concentration of Salmonella spp. in soil but no variation in crops.
Scenario 3: without variation in the concentration of Salmonella spp. in soil and variation in crops.
Scenario 4: without variation in the concentration of Salmonella spp. in both soil and crops.
0.5 × 101 cells/g and rising to a final concentration of 2.0 × 106 cells/g;
2.3 × 106 cells/g; 7.1 × 106 cells/g, respectively, in pieces of tomato kept
at 21 °C for 24 h. Also, Beuchat and Mann (2008) demonstrated that,
starting from an inoculum of Salmonella Typhimurium with an initial
low concentration of 1.2 cells/g of tomatoes, kept at a temperature of
21 °C, observed a final concentration after 14 days of bacteria of approximately 108 cells/g.
Thus, as temperature is one of the most important growth factors,
measures to control bacterial regrowth in vegetables at a temperature
level of between 4 and 10 °C during transportation should prove an effective barrier. As cited previously in this study, climate change, a phenomenon intensifying worldwide, can also contribute to the
phenomenon of internalization through the stress that drought and excess water causes in the immune system of plants (Ge et al., 2012; Liu et
al., 2013).
The variation in concentration of Salmonella spp. in the soil is the
only factor that differs between scenarios 1 and 3 (considered only in
scenario 1) and scenarios 2 and 4 (considered only in scenario 2). Comparison of the results reveals a slightly lower annual risk of infection in
scenarios where this variable is considered. In fact, the variation in concentration of Salmonella spp. in soil contributed to a decrease in the estimated annual risks, since there was generally a die-off effect in the
bacteria concentration — as shown in Table 3 (item 2.3), where the
mean of variation rates equals 0.7367 (less than 1).
A surprising result was that even considering DL values for Salmonella
concentration (WWTP 1,3,5), the risk values for the upper 95% CI found
Table 5
Annual risks of infection by Salmonella spp. through the ingestion of carrots in scenarios 5
and 6.
Median
95% quantiles
Scenario 5
WWTP 2
WWTP 4
WWTP 1, 3, 5
Men
Women
Children
Men
Women
Children
Men
Women
Children
−6
1.13 × 10
8.67 × 10−7
1.42 × 10−6
1.97 × 10−7
1.48 × 10−7
2.42 × 10−7
9.89 × 10−9
7.59 × 10−9
1.24 × 10−8
Scenario 6
−6
1.68 × 10
1.28 × 10−6
2.08 × 10−6
2.90 × 10−7
2.21 × 10−7
3.59 × 10−7
1.85 × 10−8
1.41 × 10−8
2.28 × 10−8
Scenario 5
−6
2.12 × 10
1.59 × 10−6
2.64 × 10−6
3.67 × 10−7
2.74 × 10−7
4.52 × 10−7
3.43 × 10−8
2.84 × 10−8
4.39 × 10−8
Scenario 6
1.87 × 10−6
1.44 × 10−6
2.32 × 10−6
3.22 × 10−7
2.46 × 10−7
3.97 × 10−7
2.86 × 10−8
2.20 × 10−8
3.49 × 10−8
Scenario 5: variation in the concentration of Salmonella spp. in soil, inhibition in carrots.
Scenario 6: without variation in the concentration of Salmonella spp. in soil, inhibition in
carrots.
were not negligible for adults (scenarios 1 and 3) or children (scenario 3)
as shown in Table 4, since elderly and children are the most sensitive
populations. Regarding tomatoes, the highest annual risks were found
in scenarios 1 and 3, both for estimated annual risks in terms of medians
as well as the upper 95% CI. The highest annual risks were found in the
latter case.
The higher annual risk of infection related to the ingestion of tomatoes may be caused by both the higher rates of intake, especially by
the male population, and also by the higher maximum growth rate of
Salmonella spp. observed in tomatoes (108 CFU/g) when compared
with rates for lettuce (103 CFU/g) (Sant'Ana et al., 2012b; MANIOS et
al., 2013). To the best of our knowledge, this is the first study examining
tomatoes in a scenario involving QMRA. Data are lacking concerning the
true amount of soil that attaches to tomatoes and there is a poor understanding of the different pathways that could transport Salmonella spp.
to the vegetable. The variation in concentration of Salmonella spp. in tomatoes was also the factor that most contributed to the high risks,
around 2 logs above the levels established by the USEPA, as was also observed for the estimated annual risks of infection for lettuce ingestion.
For lettuce, annual risk values varied for each scenario and between
adult and child populations as shown in Table 4. Mara and Horan (2002)
obtained annual risk for ingestion of lettuce grown in soil amended with
sewage sludge of 2 × 10−5 i.e. 2 to 3 log lower than the values obtained
in the present study for scenarios 1 and 3 for WWTP 2. In Brazil, Bastos
et al. (2009) conducted a simulation study to estimate the risk (daily
and annual risks) of Salmonella infection related to ingestion of lettuce
and carrots and also to accidental ingestion of soil particles treated
with sewage sludge. The authors determined an annual risk of infection
by Salmonella spp. for the intake of lettuce of 1.7 × 10−5. The scenario
outlined by Bastos et al. and Mara and Horan (2002) was very similar
and thus similar values of risk can be expected. In the present study
however, the four scenarios devised to evaluate the annual risk considered both internalization phenomena and variation in Salmonella concentration in soil, parameters not assessed by Bastos et al. and Mara
and Horan (2002).
These elevated annual risks of infection for ingestion of both tomatoes and lettuces are consistent with the literature (Beuchat 2002;
Brandl, 2006; Heaton and Jones, 2008). The cited authors highlighted
that the ingestion of lettuce and tomatoes is strongly associated with
outbreaks involving Salmonella spp. worldwide. Brandl (2006) compiled and analyzed data from food outbreaks whose etiologic agent
was bacteria of the genus Salmonella and concluded that lettuce and tomatoes were one of the vegetables most commonly implicated in the
occurrence of these outbreaks.
72
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
Fig. 2. Sensitivity analysis of the simulated parameters for annual risk of infection by Salmonella spp. due to ingestion of lettuce and tomatoes.
The sensitivity analysis for lettuce and tomatoes (Fig. 2) highlights
that, besides seasonal average temperature and post-harvesting time,
Salmonella concentration in sludge is also a relevant parameter, reinforcing the need for appropriate sludge treatment.
The annual risks of infection for the intake of carrots, presented in
Table 5, were below 10−5 irrespective of scenario, gender or age. Sensitivity analysis (Fig. 3) highlighted the high negative correlation between
the inhibition factor and estimated risk. These results are in agreement
with data on outbreaks involving this root (Beuchat, 2002; Heaton and
Jones, 2008; Berger et al., 2010). Several studies (Nguyen-the and Lund,
1991; Liao, 2007; Noriega et al., 2010; Sant'Ana et al., 2012a.) have detected inhibition of Salmonella spp. growth by the bacteria present in
the vegetable. This phenomenon might explain the low values of annual
risk of infection obtained for the intake of carrots. Gale (2005) used the
Quantitative Microbial Risk Assessment (QMRA) approach to estimate
the annual risks of infection by Salmonella associated with the ingestion
of tubers and roots, obtaining a value of 7.9 × 10−9. The authors did not
consider the inhibition that the natural epiphytic microbiota present in
carrots exerts on Salmonella spp., instead assessing washing after harvesting. Several studies have shown that washing crops after harvesting
is not effective in decreasing contamination by microorganisms since
bacteria can invade stomata, wounds present on different surfaces of
the vegetable and further contaminate the internal tissues and vascular
system of the crops (Deering et al., 2012; Berger et al., 2010; Beuchat
and Scouten, 2002). For this reason, log decay, or something similar,
as a result of washing or peeling crops was not considered in the present
scenarios.
Bastos et al. (2009), akin to the above-cited studies, also estimated
the annual risk of infection of consuming carrots grown in soil treated
with sewage sludge at similar Salmonella spp. concentrations to those
found in the sludge of the present study. The annual value of estimated
risk of infection was 4.6 × 10− 4. It is noteworthy that Bastos et al.
(2009), in contrast to the present study, did not consider the role
which the inhibition factor of epiphytic microbiota plays in the population of Salmonella spp. present in carrots, and therefore determined
higher values for the annual risk of infection by consuming carrots.
For agricultural workers (Table 6), the highest annual risks of infection by Salmonella spp. were found for when workers applied sewage
sludge to agricultural soils (2.26 × 10−2), identified at the 95% quantile
(WWTP 2) in scenario 9. Sensitivity analysis (Fig. 4) suggests that the
main drivers for the estimated risks are Salmonella concentration and
ingestion rate. The rate of variation in concentration in soil also has a
major influence on the risk values (scenarios 7 and 8), due to the dieoff effect in most cases.
Westrell et al.(2004) established a number of scenarios involving
different microorganisms and estimated the risk of infection in
cases of exposure of WWTP workers and for workers who apply the
sludge onto farmland in Sweden. In the case of infection caused by
Fig. 3. Sensitivity analysis of the simulated parameters for the annual risk of infection by Salmonella spp. due to ingestion of carrots.
F. Krzyzanowski Jr et al. / Science of the Total Environment 568 (2016) 66–74
73
Table 6
Annual risks of infection by Salmonella spp. for agricultural workers due to accidental ingestion of soil amended with sewage sludge or direct ingestion of sewage sludge.
Medians
WWTP 2
WWTP 4
WWTP 1, 3, 5
95% quantiles
Scenario 7
Scenario 8
Scenario 9
Scenario 7
Scenario 8
Scenario 9
1.68 × 10−4
2.90 × 10−5
1.39 × 10−6
2.53 × 10−4
4.37 × 10−5
2.79 × 10−6
1.98 × 10−2
3.45 × 10−3
2.16 × 10−4
3.24 × 10−4
5.72 × 10−5
6.04 × 10−6
2.83 × 10−4
4.84 × 10−5
4.39 × 10−6
2.26 × 10−2
3.91 × 10−3
3.90 × 10−4
Scenario 7: Accidental ingestion of amended soil with variation in Salmonella spp. concentration.
Scenario 8: Accidental ingestion of amended soil without variation in Salmonella spp. concentration.
Scenario 9: Accidental direct ingestion of sludge.
Fig. 4. Sensitivity analysis of the simulated parameters for the annual risk of infection by Salmonella spp. for agricultural workers.
Salmonella spp. due to accidental ingestion of sewage sludge by farmworkers, the annual risk obtained was the highest among all of the exposure scenarios evaluated, with a median value of 2 × 10− 2 and
upper 95% CI of 3.6 × 10−1. The median value is the same order of magnitude as the results obtained in the present study in scenario 9 for
WWTP 2. The similarity in both studies is explained by the fact that
the decay of Salmonella spp. concentration in sewage sludge was not
considered whereas there was a difference for accidental rate of ingestion, which was 2 g/day in the study by Westrell et al. (2004). Bastos
et al. (2009) also estimated the annual risk of Salmonella infection in exposed workers in the field. The exposure scenario devised considered an
accidental ingestion rate of both sludge as well as soil amended with
sewage sludge of 10 mg/day. The median annual risk of infection obtained for workers handling amended soil was 5.6 × 10−5 while the risk for
appliers of sewage sludge was 2.4 × 10−2, similar values to those of the
present study.
Viau et al. (2011) conducted a review of studies employing the
QMRA approach to survey risks involving Salmonella spp. In conclusion,
the authors reported that the annual probability of Salmonella infection
in descending order were: accidental direct ingestion of sewage sludge,
inhalation of bioaerosols from contaminated sludge, ingestion of contaminated ground water, eating contaminated food. For direct ingestion
of sewage sludge, the annual probability of infection was in the order of
10−3 but negligible for food intake.
The first observation is congruent with the present results whereas
the latter finding of very low annual risks associated with food intake,
conflicts with the high estimated annual risk for ingestion of lettuce
and tomatoes found in the present study. This disparity could be explained by the fact that the studies reviewed by Viau et al. (2011), and
likewise the studies discussed earlier in this section, failed to consider
bacterial regrowth in vegetables.
and 3 for both lettuce and tomatoes. These risk values resulted from
conservative scenarios since some assumptions were derived from
local or general studies, such as the frequency of vegetable intake or accidental ingestion of sewage sludge by workers. However, these scenarios highlighted the need for investment toward obtaining data to refine
exposure scenarios. Although these scenarios can be considered conservative, the sensitivity analysis yielded the drivers of the risks, which can
be useful for managing risks from the fresh products chain with stakeholders' involvement. The driver post-harvest exposure (H) for example, can be managed by refrigerated transportation of the products,
thereby reducing risks.
Funding
This study was supported by the FAPESP — São Paulo Research Foundation for Financial Support [2010/05664-6]. The author was partially
supported by the CNPq [scholarship 300642/2013-0 to MTPR].
We would like to thank The Coordination of Improvement of Higher
Education-Capes for providing FKJ with the scholarship.
Acknowledgements
We would like to thank Dr. G.J. Eamens from NSW Agriculture, Elizabeth Macarthur Agricultural Institute, Camden (Australia), for kindly
providing the raw data for Salmonella spp. regrowth in soil gathered
from his study. We also would like to kindly thank Dr. Gertjan Medema
from KWR Watercycle Research Inst. and Water Management of Civil
Engineering and Geosciences, Delft University of Technology (The Netherlands), for supporting Dr. Flávio Krzyzanowski Jr. during his staying in
TU Delft.
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