Quantitative Microbial Risk Assessment for Agricultural ... · Quantitative Microbial Risk Assessment for Agricultural Irrigation: Ana Allende Practical Case in Murcia Region CEBAS-CSIC

Quantitative Microbial Risk Assessment

for Agricultural Irrigation:

Practical Case in Murcia RegionAna Allende

[email protected]

Reclaimed Water

https://www.indiamart.com/ https://www.governmenteuropa.eu/ May 2018

https://www.governmenteuropa.eu/

Reclaimed Water

https://www.indiamart.com/

Trevor Suslow

Reclaimed Water

https://www.indiamart.com/ https://sciencetrends.com/ Septiembre 2018

https://sciencetrends.com/

Reclaimed Water

https://www.indiamart.com/ https://www.politico.eu/ Junio 2018

https://www.politico.eu/

Microbial Risk

Fresh Fruits and Vegetables

Risk Factors

https://www.nestle.com/asset-library/

https://www.iagua.es/

European Commission

European Commission

https://www.indiamart.com/ http://h2orizon.es/

European Commission


European Commission

Plan de Gestión de Reutilización del Agua

Plan de Gestión de Reutilización del Agua

ANEXO II: Gestión de Riesgos

Descripción del sistema de reutilización

Identificación de los peligros potenciales

Identificación riesgos sobre el entorno, las poblaciones y los individuos

Realizar una evaluación de riesgos que contemple a la vez los riesgos medioambientales y los riesgos para la salud humana y animal

Si necesario medidas más estrictas que las establecidas en el Anexo I

Medidas preventivas

Asegurar un sistema adecuado de sistemas de control y procedimientos

Asegurar un sistema de control ambiental que detecte cualquier efecto negativo

Asegurar un sistema apropiado para la gestión de incidentes y emergencias


Risk Assessment

Risk Assessment

RISK ASSESSMENT

Hazard identification

Hazard characterization

Exposure assessment

Risk characterization

https://sciencefiles.org/


Hazard Identification

Pseudomonas syringae on a leaf surface. Image by J. Kremer and Sheng Yang He

Risk Assessment

Hazard identification is part of the process used to evaluate if any particular situation, item, thing, etc. may have the potential to cause harm

Hazard Characterization

Risk Assessment

Hazard characterization is a description of the relationship between levels of

a pathogen consumed (dose) and the probability of subsequent development and severity of illness or other adverse health outcome (response).

https://slideplayer.com/


Exposure Assessment

Risk Assessment

Exposure assessment is the process of estimating or measuring the magnitude,

frequency and duration of exposure to an agent, along with the number and

characteristics of the population exposed.

It describes the sources, pathways, routes, and the uncertainties in the assessment.


Research Project

Quantitative Microbial Risks Assessment Associated to the Use of Reclaimed Water for Irrigation

Esquema Campo de Cartagena

A: Entrada EDAR

B: Salida EDAR

E: Salida Boca de Riego

C: Salida Embalse EDAR

D: Salida Embalse Agricultor

F: Material vegetalG: Suelo

Case studies: UV-C light versus Sodium Hypochlorite Drip irrigation versus Surface Irrigation

Research Project


A: Entrada EDAR

B: Salida EDAR





Research Project


Prevalence / Concentration

Exposure Assessment

Risk Assessment

Exposure assessment is the process of estimating or measuring the magnitude,

frequency and duration of exposure to an agent, along with the number and

characteristics of the population exposed.

It describes the sources, pathways, routes, and the uncertainties in the assessment.



A: Entrada EDAR

B: Salida EDAR





Puntos de Muestreo

Entr

ada

Salid

a

Emba

lse E

DA

R

Emba

lse A

gric

ulto

r

Rie

go G

oteo

Plan

ta

Log

UF

C/1

00 m

L ó

g

0

2

4

6

8

10

90 % 90 % 95 % 100 %100 % 0%

Research Project


Prevalence / Concentration


A: Entrada EDAR

B: Salida EDAR





UV-C NaClO UV-C NaClO

Inpout

Treatments

Lo

g E

. co

li c

fu /

10

0 m

L

0

1

2

3

4

5

6

7

8

9

10

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

8

9

10


Outpout

Treatments

Lo

g E

. co

li c

fu/

10

0 m

L

0

1

2

3

4

5

6

7

8

9

10

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

8

9

10


Inpout

Treatments L

og

E.

coli

cfu

/1

00

mL

0

1

2

3

4

5

6

7

8

9

10

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

8

9

10


Outpout

Treatments

Lo

g E

. co

li c

fu/

10

0 m

L

0

1

2

3

4

5

6

7

8

9

10

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

8

9

10

Research Project

ENTRADA

SALIDA

E. coli reductions < 6 logs

Coliphages 3 - 2 logs



A: Entrada EDAR

B: Salida EDAR





Treatment UV-C NaClO

Salmonella 8% 0%

E. coli O157:H7 0% 0%

STEC 0% 6%

Norovirus I 80% 83%

Norovirus II 60% 71%

Hepatitis A 0% 0%

Treatment UV-C NaClO

Salmonella 100% 100%

E. coli O157:H7 100% 100%

STEC 100% 100%

Norovirus I 100% 100%

Norovirus II 100% 100%

Hepatitis A 0% 6%Research Project

ENTRADA

SALIDA



A: Entrada EDAR

B: Salida EDAR





Area 3

Lo

g E

. co

li c

fu/1

00

mL

0

1

2

3

4

5

6

7

Sampling Points

Out

pout

Res

ervo

ir_1

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

l

0

1

2

3

4

5

6

7

Area 2

Lo

g E

. co

li c

fu/

10

0 m

L

0

1

2

3

4

5

6

7

Sampling Points

Out

pout

Res

ervo

ir_1

Res

ervo

ir_2

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

Area 1

Lo

g E

. co

li c

fu/1

00

mL

0

1

2

3

4

5

6

7

Samplig points

Out

pout

Res

ervo

ir_1

Res

ervo

ir_2

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

7

Research Project



A: Entrada EDAR

B: Salida EDAR





Treatment UV-C NaClO2

Salmonella 0% 0%

E. coli O157:H7 0% 3%

STEC 0% 6%

Norovirus I 20% 0%

Norovirus II 0% 0%

Hepatitis A 0% 0%

Treatment Drip Furrow Spray

Salmonella 0% 0% 8%

E. coli O157:H7 0% 0% 0%

STEC 40% 0% 0%

Norovirus I 30% 0% 0%

Norovirus II 50% 0% 0%

Hepatitis A 0% 0% 0%

Irrigation Water

Plant

Research Project


Lo

g E

. co

li c

fu/1

00

mL

0

1

2

3

4

Irrigation system

Drip Furrow Spray

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

Lo

g E

. co

li c

fu/1

00

mL

0

1

2

3

4

Irrigation system

Drip Furrow Spray

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

Water

Research Project


Plant

Absence Presence

Lo

g E

. co

li c

fu/1

00

mL

0

1

2

3

4

5

6

7

8

Absence Presence

Lo

g T

. co

lip

ha

ges

pfu

/10

0 m

L

0

1

2

3

4

5

6

Research Project


Significance of indicator microorganisms to predict reference pathogens

E. coli / bacterial pathogens Coliphages / viruses

Research Project


Significance of indicator microorganisms to predict reference pathogens

Coliphages / Norovirus II

NOROVIRUS I (NVI)

Log T. coliphages pfu/100 ml

0 1 2 3 4 5 6 7 8

Log

gen

om

ic c

op

ies

/ 1

00

mL

0

1

2

3

4

5

6

7

8

Y=0.7275X+2.7567

R2=0.4784

Y=0.4078X+2.006

R2= 0.4116

NOROVIRUS II (NVII)

Log T. coliphages pfu/ 100 mL

0 1 2 3 4 5 6 7 8

Lo

g g

eno

mic

cop

ies

/10

0 m

L

0

1

2

3

4

5

6

7

8

Coliphages / Norovirus I

Exposure Assessment

Risk Assessment

Método Montecarlo: La simulación Montecarlo realiza el análisis de riesgo con la

creación de modelos de posibles resultados mediante la sustitución de un rango de valores —una distribución de probabilidad—


Redes Bayesianas: Las redes bayesianas modelan un fenómeno mediante un

conjunto de variables y las relaciones de dependencia entre ellas. Se puede

estimar la probabilidad posterior de las variables no conocidas, en base a las variables conocidas.

In collaboration with - Pablo S. Fernández Escámez- Alberto Garre- Jose Alberto Egea

Mathematical approach

https://www.europeanmedical-group.com/omnipresent/


Mathematical Modelling in Risk Assessment

Uncertainty

Variability

Probabilistic approach required

• Quantitative estimation of risk.

• Based on expert opinion and/or experimental data.

• Characterization of each step.

• Simulation of what-if scenarios.




Bayesian Believe Networks (BBN)

A

B

C

𝑃 𝐶 𝐴 =𝑃 𝐴 𝐶 ⋅ 𝑃(𝐶)

𝑃(𝐴)

C is conditionally independent of B.

Strengths Weaknesses

Model visualization Variable discretization required

Model can be updated when more data is available

Computationally expensive

Inclusion of expert opinion

Requires few hypotheses

Graphical representation understandable by “non-experts”

Adapted from Smid et al., Int J Food Microbiol (2010)




N1 N2 N3 N5 N6

Known

Estimated

N4

Outcome of presence/absence test

Microbial concentration




N1 N2 N3 N4

N1

P(N2|N1) Low Medium High

N2

Low 0.9 0.3 0.1

Medium 0.1 0.5 0.3

High 0 0.3 0.6

N3

P(N4|N3) Low Medium High

N4

Low 0.7 0.5 0.4

Medium 0.2 0.3 0.5

High 0.1 0.2 0.1




What-if scenarios

Probability estimates

Alternative hypothesis(e.g. high precipitation)

Network update

Preliminary Results & Outlook




Preliminary Results & Outlook

Simulations & What-if scenarios

INEDAR

1PLAN

T

INEDAR

2PLAN

T

QMRA


• To know the current situation in the Region of Murcia

• To predict the impact of intervention strategies

• To help managers in the decision-making process

• To fulfil with the requirements establish from the

European Commission

Quantitative Microbial Risk Assessment

for Agricultural Irrigation:

Practical Case in Murcia RegionAna Allende

[email protected]

Documents

Quantitative Microbial Risk Assessment for Agricultural ... · Quantitative Microbial Risk Assessment for Agricultural Irrigation: Ana Allende Practical Case in Murcia Region CEBAS-CSIC