Health Risks for Sanitation Service Workers along a Container-Based

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Ecotoxicology and Human Environmental Health

Health risks for sanitation service workers along a containerbased urine collection system and resource recovery value chain Heather N. Bischel, Lea Caduff, Simon Schindelholz, Tamar Kohn, and Timothy R Julian Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b01092 • Publication Date (Web): 13 May 2019 Downloaded from http://pubs.acs.org on May 14, 2019

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Environmental Science & Technology

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Title: Health risks for sanitation service workers along a container-based urine collection system and

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resource recovery value chain

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Authors: Heather N. Bischel,1,2,* Lea Caduff,3 Simon Schindelholz,1 Tamar Kohn,1 Timothy R.

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Julian3,4,5, *

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Affiliations: 1. School of Architecture, Civil, and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), CH 1015 Lausanne, Switzerland 2. Department of Civil & Environmental Engineering, University of California, Davis, California 95616, United States 3. Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH 8600 Dübendorf, Switzerland

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4. Swiss Tropical and Public Health Institute, CH 4002, Basel, Switzerland

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5. University of Basel, CH 4003, Basel, Switzerland

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*Correspondence to: Heather N. Bischel (email: [email protected], phone: +1 530 752 6772) and Timothy R. Julian (email: [email protected], phone: +41 58 765 5632)

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source-separation, microlevel activity time series, water sanitation and hygiene (WASH)

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Abstract

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Container-based sanitation (CBS) within a comprehensive service system value chain offers a low-

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cost sanitation option with potential for revenue generation, but may increase microbial health risks

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to sanitation service workers. This study assessed occupational exposure to rotavirus and Shigella

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spp. during CBS urine collection and subsequent struvite fertilizer production in eThekwini, South

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Africa. Primary data included high resolution sequences of hand-object contacts from annotated

Keywords: quantitative microbial risk assessment (QMRA), resource-oriented sanitation, urine

1 ACS Paragon Plus Environment


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video, and measurement of fecal contamination from urine and surfaces likely to be contacted. A

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stochastic model incorporated chronological surface contacts, pathogen concentrations in urine, and

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literature data on transfer efficiencies of pathogens to model pathogen concentrations on hands and

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risk of infection from hand-to-mouth contacts. The probability of infection was highest from

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exposure to rotavirus during urine collection (~10-1) and struvite production (~10-2), though risks

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from Shigella spp. during urine collection (~10-3) and struvite production (~10-4) were non-

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negligible. Notably, risk of infection was higher during urine collection than during struvite

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production due to contact with contaminated urine transport containers. In the scale-up of CBS,

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disinfection of urine transport containers is expected to reduce pathogen transmission. Exposure data

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from this study can be used to evaluate the effectiveness of measures to protect sanitation service

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workers.

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Introduction

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Wastewater treatment is shifting toward a new paradigm that recognizes the value of resource

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recovery.1 Simultaneously, the Sustainable Development Goals call for universal access to safely

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managed sanitation by 2030, which will require providing new sanitation services to more than

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4.5 billion people.2 Integrating these concepts to provide low-cost sanitation services that prioritize

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nutrient recovery can facilitate capture of nitrogen and phosphorus from human excreta otherwise

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lost to the environment.3

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Container-based sanitation (CBS) is a low-cost sanitation service that can incorporate nutrient

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recovery. In a CBS system, human excreta is collected at latrines in sealable containers. The

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containers are removed by sanitation service workers and transported to centralized facilities for

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treatment before disposal or further processing for resource recovery. When combined with urine-

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diverting toilets, CBS allows urine and feces to be collected and managed separately. CBS does not

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require expensive or infeasible sewer infrastructure and thus is amenable to small scale service


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providers, community based programs, and entrepreneurs.4–9 Human excreta may be processed into a

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variety of marketable products such as fertilizer or animal feed, whose sale can contribute to system

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finance.10,11

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One obstacle towards modernizing sanitation practices and integrating resource recovery is the need

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to assure human health and safety throughout the sanitation value chain. In the absence of a sewer

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system, CBS relies on service workers to manage excreta. Such interactions with excreta increases

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risks of enteric disease transmission (i.e., diarrheagenic E. coli, Shigella spp., norovirus, rotavirus,

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helminths).12–14 In the pursuit of economically attractive decentralized sanitation services like CBS,

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safe processes for collection, transport, and processing of excreta are critical to long term success.

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Risks to sanitation service workers along the value chain are likely driven by non-dietary ingestion

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exposures to pathogens via accidental ingestion, inhalation and hand-to-mouth contacts.15 Prior risk

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assessments on excreta reuse, conducted largely in the context of agricultural land application,

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assume risks are driven by accidental ingestion. For example, to assess risks to farmworkers from

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wastewater application to agricultural fields, estimates of accidental ingestion ranged from 1-10 ml

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of wastewater or 1-100 mg of wastewater-treated soil.16,17 Similarly, risk assessments of urine

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application to agriculture assumed 1 ml of urine is accidentally ingested per application.18,19

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Accidental ingestion is classified as a direct ingestion exposure route, in which exposures to

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contaminated sources (feces, urine) are direct. Although accidental ingestion is possible, a

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substantially more likely route of ingestion for daily activities is indirect ingestion, in which hand-to-

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mouth or hand to peri-oral (around the mouth) contacts are the source of exposure following hand

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contacts with contaminated sources.20 Indirect ingestion occurs when pathogens contact skin (from a

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pathogen source like urine or feces, or via an intermediate surface or liquid), then the skin contacts

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the mouth.



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Contributions from hand-to-mouth contacts are frequently neglected or aggregated into assumptions

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about accidental ingestion.17 Data on hand-to-mouth contact frequency rates are available for

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children in various settings,21–23 but fewer studies report adult hand-to-mouth contact frequency.20,24

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Due to the lack of adult hand-to-mouth contact frequency, studies estimating microbial risks from

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indirect (i.e., hand to peri-oral) contacts rely on simplifying assumptions. Other studies integrate

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activity data with risk assessments but rely on activity data from different populations, assuming

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activities amongst the two populations were similar. For example, Beamer et al. (2015) assumed the

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behaviors of adults in an office setting were most similar to children 7-12 years old outdoors, citing a

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lack of adult data as motivation for this assumption.25 Zhao et al. (2012) assumed hand-to-mouth

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contact frequency for U.S. adults based on a study of sedentary adults videotaped while working at a

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desk.26 Additional studies used videography in exposure assessments but did not use the data to

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calculate infection risks from hand-to-mouth contacts.27–30 The present study uses videography and

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associated annotation directly in a microbial risk assessment of the study population, providing an

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opportunity to identify contact events most responsible for subsequent infection risks.

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We apply this approach to evaluate occupational microbial health risks of container-based urine

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collection and subsequent struvite fertilizer production for nutrient recovery. These represent two

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primary components of a sanitation value chain within a decentralized network of container-based

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sanitation systems in the municipality of eThekwini, South Africa. Risks are evaluated within a

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quantitative microbial risk assessment (QMRA) framework, with an exposure assessment based on

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primary, site-specific data. The exposure assessment focuses on risks resulting from hand to peri-oral

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contacts, which are captured using time-lapse videographic data of sanitation workers during urine

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collection and struvite processing. Microbial and tracer sampling of both workers’ hands and

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frequently contacted surfaces was conducted in parallel. The video and sample data are integrated


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into a computer model to evaluate the sanitation workers’ infection risks from pathogens in urine

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over time. Rotavirus and Shigella spp. were selected as reference pathogens for the assessment due

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to their epidemiological importance in the study region and as contributors to diarrheal disease

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burden in developing countries more broadly.12,31 Globally, rotavirus and Shigella spp. were the two

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leading causes of diarrheal deaths in 2015, resulting in 165,000 to 241,000 and 85,000 to 278,700

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deaths, respectively.32 Quantifying risks and identifying factors driving risks informs control

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strategies to ensure safe management of a promising, low-cost, sanitation service with resource

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recovery.

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Methods

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Overall Approach

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Study Site. The eThekwini Water and Sanitation (EWS) unit manages a large network of urine

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diverting dry toilets (UDDTs) in rural townships within the municipality, which also includes the

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large coastal city of Durban. Primary data was collected in August 2014 within the scope of VUNA

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(Valorisation of Urine Nutrients in Africa), a research project to optimize urine collection and

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treatment for fertilizer production.33 UDDTs at households that participated in the study were

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equipped with 20-L plastic jerry cans to capture the urine outside of the dry toilet superstructure. The

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containers were collected by EWS staff approximately weekly.34 Staff transferred urine by pouring

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into receiving tanks in municipality vans or trucks and returned the urine containers to the UDDTs.

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The urine receiving tanks were then transported to a centralized research facility (Newlands Mashu)

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where urine was processed for nutrient recovery using struvite reactors35 or biological nitrification

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reactors.36,37 The struvite production protocol35 entailed filling 40L reactors with urine using a

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submersion pump in the receiving tanks, dosing magnesium in the reactors, gently mixing the

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contents, and filtering the mixture through a cloth bag to capture the struvite. In accordance with

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EWS training, study participants generally wore facemasks and rubber or latex gloves during 5 ACS Paragon Plus Environment


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observation. Human subjects approval was obtained from the University of KwaZulu Natal

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Biomedical Research Ethics Committee (BREC, BE147/13) and informed consent was obtained

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from all study participants.

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Modeling approach. Health risks to sanitation service workers were assessed following the World

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Health Organization QMRA framework, which includes problem formulation, exposure assessment,

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health effects assessment, and risk characterization and management.38 The problem formulation

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identified urine exposure as the primary risk, given widespread presence of pathogens in urine. A

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previous hazard assessment of urine collected in this region identified several pathogens of

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concern.12 Rotavirus and Shigella spp. were selected as reference viral and bacterial pathogens,

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respectively, for the model because they were frequently detected, present significant health risks in

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developing countries, and dose-response curves are available. Because direct contact of urine with

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the mouth (i.e., via accidental ingestion during a spill) was assumed to be rare or non-existent, the

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exposure assessment focused on indirect contact. Indirect contact was modeled as pathogens

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contacting skin either from urine or via a surface assumed to be contaminated with urine prior to skin

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contacting the mouth.

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Exposure assessment

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Videography. First person videography was used to collect data on the frequency, duration, and

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chronology of hand-object (i.e., surfaces, opposite hand, face, mouth) contacts. Portable cameras

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(GoPro Hero3 White Edition, GoPro, Inc., San Mateo, CA) with supplementary battery packs were

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worn by study participants on the head, such that the camera’s field of view captured the subject’s

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hand movements as well as their peri-oral region. Study participants (all trained municipality staff)

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were asked to conduct their typical urine collection and struvite production procedures. Over three

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days, 12.6 person-hours of video were collected during urine collection to capture a minimum of 30


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household visits for urine collection by three study participants. Additionally, 11.6 person-hours of

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video were collected over two days during struvite production to capture the production of at least 10

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batches of struvite by two study participants.

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Video translation. Video Translator for the Personal Computer (VTDPC, Version 1.0.0.0 © 2014

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Arizona Board of Regents on behalf of The University of Arizona) was provided by Robert A.

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Canales to extract micro-level activity time series (MLATS) data from the first-person video.39

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MLATS are temporal sequences of every object the hands contact over the duration of a video-

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recorded event, with sub-second resolution. The VTDPC templates included 18 object classes for

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urine collection (Table S2) and 7 object classes for struvite production (Table S3). The object classes

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were decided based on preliminary video review, in which objects with similar surface types (e.g.,

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metal, cloth, skin) and expected contamination levels were categorized into object classes. Whether

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or not an object was visibly wet (urine-contaminated) on contact was also tracked for each object

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contacted. See Supporting Information Methods for further details. Summary results of surface

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contact frequencies and durations are presented as mean (µ) ± standard deviation (σ) [range].

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Pathogen Contamination. The pathogen surface density (genome copies cm-2) for each object class

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i that was contacted by the study participants was calculated indirectly. Specifically, surface density

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was assumed to be a function of the equivalent volume of urine on a surface and the concentration of

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the pathogen in the urine as follows:

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eq(1)

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where 𝜌𝐴𝑃,𝑖(genome copies cm-2) is the surface density of pathogen P on object class i, V/Aurine,i (ml

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cm-2) is the volume of urine (ml) per unit surface area (cm2) on object class i, and CP,urine (genome

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copies ml-1) is the concentration of the pathogen in the urine.

𝜌𝐴𝑃,𝑖 = 𝐶𝑃,𝑢𝑟𝑖𝑛𝑒𝑉/𝐴𝑢𝑟𝑖𝑛𝑒,𝑖

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The volume of urine per unit surface area (𝑉/𝐴𝑢𝑟𝑖𝑛𝑒,𝑖) was estimated using E. coli contamination as

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an indicator. This approach is based on the concept of human fecal equivalents,40–42 which uses

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relative levels of E.. coli in environmental samples and in feces as a proxy to estimate pathogen

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concentrations. Fecal-contaminated urine was assumed the main source of E. coli on the surfaces

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tested. Specifically, urine volume per unit surface area was estimated using the ratio of E. coli

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contamination on a surface (object class i) to the E. coli contamination in urine:

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eq(2)

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where 𝜌𝐴𝐸.𝑐𝑜𝑙𝑖, 𝑖 is the surface density of E. coli, or number of E. coli per unit surface area (colony

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forming units, CFU cm-2) and CE.coli,Urine is the concentration of E.coli in urine (CFU ml-1) as

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measured on the same day. E. coli contamination was measured in urine and on a range of surfaces

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using E. coli / total coliform petrifilm dryplates (3M, St. Paul, MN), according to manufacturer’s

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recommendations. A total 42 surfaces were sampled using polyester-tipped swabs pre-wetted with

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sterile ¼-strength Ringer’s solution (see Supporting Information: Methods for further details).

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𝜌𝐴𝐸.𝑐𝑜𝑙𝑖, 𝑖𝑖

𝑉/𝐴 𝑈𝑟𝑖𝑛𝑒,𝑖 = 𝐶𝐸.𝑐𝑜𝑙𝑖,𝑈𝑟𝑖𝑛𝑒

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The approach for measuring pathogen contamination on surfaces was modified for object types that

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were both observed as wet (i.e., visibly urine-contaminated) and on which E. coli or a tracer for urine

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contamination were measured for validation, as described in the following section. Specifically, the

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estimate of the surface density of a pathogen on the object (𝜌𝐴𝑃,𝑖) was modified from eq(1) to assume

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pathogen contamination was a function of pathogen concentration in the urine and the amount of

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urine on the surface, or:

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eq(3)

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where CP,urine is the concentration of the pathogen in the urine (genome copies/ml, which is

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equivalent to genome copies / cm3), and h is the film thickness of urine on the surface (2.2 × 10-3 cm)

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based on the average of high and low values for water on hands and corrected for the density of

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urine.43

𝜌𝐴𝑃,𝑖 = 𝐶𝑃,𝑢𝑟𝑖𝑛𝑒ℎ


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Validation of urine volume and hand contamination. During video translation, surfaces were

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categorized as visibly wet (urine-contaminated) or dry based on observation. A tracer exposure study

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was conducted using a method modified from a previous study44 to validate visibly observed urine

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exposure. Use of tracers also allowed quantitative estimates of the volume of urine on workers’

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hands and on the struvite reactor surfaces after struvite production. In brief, 250 L urine was spiked

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with Brilliant Blue (BB) food coloring to achieve an approximate 10-3 M starting concentration.

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Contact with urine during struvite production resulted in transfer of dyed urine to the hands of study

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participants, who were provided with white cotton gloves over latex gloves, or to reactor surfaces.

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The amount of urine on the hands and the surfaces was then estimated by quantifying the BB

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spectrophotometrically (629 nm, Orberco SP 600 Spectrophotometer, Orbeco-Hellige Inc. /

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Tintometer GmbH, Dortmund, Germany).

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BB was quantified on hands by removing the cotton gloves after struvite production and extracting

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BB into distilled water (Figure 1). BB was quantified from surfaces by swabbing (approximately 100

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cm2 per surface) with cotton-tipped swabs and extracting BB in 10 ml of 1/4-strength Ringer’s

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solution. The BB concentration measured in the urine and the extracts were then used to calculate the

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volume of urine that had come in contact with the hand or surface, as follows:

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eq(4)

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where VUrine,i is the volume (ml) of urine on object i, CBB,i is the concentration of BB measured in the

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object i sample extract (mol L-1), Vextract is the volume of distilled water used to extract BB from

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gloves or surface (ml), Vdiluent is the total volume of other liquids added to the extract (urine or other)

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during the period of use (ml), and CBB,urine is the concentration of BB measured in the urine (mol L-1).

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Spike tests of urine containing BB onto cotton gloves indicated sufficient recovery of BB. See

𝑉𝑈𝑟𝑖𝑛𝑒,𝑖 =

𝐶𝐵𝐵,𝑖(𝑉𝑒𝑥𝑡𝑟𝑎𝑐𝑡 + 𝑉𝑑𝑖𝑙𝑢𝑒𝑛𝑡) 𝐶𝐵𝐵,𝑢𝑟𝑖𝑛𝑒



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Supporting Information Methods, Figure S4 and S5, and Table S4 for further details on method

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development for the validation of urine exposure.

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Model

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Dose. Calculations of ingested pathogen dose from indirect contacts with urine required modeling

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the pathogen concentration on the hand through time (e.g., Figure 2). Changes in concentration on

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the hand over time are influenced by the difference in concentration between the object and the hand

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during the contact event at a point in time, so transfer can occur in both directions. The concentration

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of pathogen P on hand H (right or left) was thus calculated as previously described, using a

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stochastic-mechanistic framework30,45:

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eq(5)

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𝜌𝐴 𝑃,𝐻(𝑡) = 𝜌𝐴 𝑃,𝐻(𝑡 ― ∆𝑡) + 𝑇𝑃,i→H𝑓i→H(𝜌𝐴 𝑃,𝑖 ― 𝜌𝐴 𝑃,𝐻(𝑡 ― ∆𝑡))

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where 𝜌𝐴 𝑃,𝐻(t) is the surface density (genome copies/ cm2) of the pathogen P on the hand H at time t,

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Δt is the time step between two subsequent contacts (which is non-uniform), TP,iH is the transfer

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efficiency (%) of pathogen P from object class i to the hand H, fiH is the fractional surface area, or

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the fraction of the hand’s total surface area, in contact with object class i during a contact event, and

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𝜌𝐴 𝑃,𝑖is the surface density of pathogen P on object i (genome copies/ cm2). The parameter values in

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the model were described by probability distribution functions (PDFs) drawn from primary data

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collection on activity data and pathogen contamination determined in the present study. Transfer

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efficiencies and fractional surface areas of contact were drawn from literature (Supporting

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Information Methods and Table S5 and S6). In this generalized equation, the hand may be gloved or

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not-gloved, with model input parameters adjusted accordingly.

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The model was implemented using a Monte Carlo framework to account for parameter uncertainty

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and variability as previously described.45,29 For each period of observation, pathogen contamination


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on hands and ingested dose were simulated 1000 times. For comparison, Julian et al. (2018) showed

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convergence of the median simulation value for non-pathogenic E. coli contamination on hands of

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Vietnamese farmers using a similar framework within 100 simulations29. Within each simulation,

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activity data were fixed (using the exact chronology of each video segment) while pathogen

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contamination, transfer efficiencies, and fractional surface area varied randomly from within the

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descriptive PDFs. Several assumptions are implicit in the model construct that are consistent with

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previous implementations of similar models.29,30,46 First, the concentration distribution of the

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pathogen on each object is assumed constant in time, unless the object is the other hand of the study

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participant. Second, the transfer efficiency was assumed the same in both directions (hand-to-object

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or object-to-hand). Third, inactivation of pathogens on surfaces or the hand was not included in the

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model because prior modeling work on pathogen transfer between surfaces showed contributions of

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inactivation are negligible in the time frame (

Health Risks for Sanitation Service Workers along a Container-Based

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