Suchergebnisse

AbstractTemporal variations in concentrations of pathogenic microorganisms in surface waters are well known to be influenced by hydrometeorological events. Reasonable methods for accounting for microbial peaks in the quantification of drinking water treatment requirements need to be addressed. Here, we applied a novel method for data collection and model validation to explicitly account for weather events (rainfall, snowmelt) when concentrations of pathogens are estimated in source water. Online in situ β‐d‐glucuronidase activity measurements were used to trigger sequential grab sampling of source water to quantify Cryptosporidium and Giardia concentrations during rainfall and snowmelt events at an urban and an agricultural drinking water treatment plant in Quebec, Canada. We then evaluate if mixed Poisson distributions fitted to monthly sampling data ( = 30 samples) could accurately predict daily mean concentrations during these events. We found that using the gamma distribution underestimated high Cryptosporidium and Giardia concentrations measured with routine or event‐based monitoring. However, the log‐normal distribution accurately predicted these high concentrations. The selection of a log‐normal distribution in preference to a gamma distribution increased the annual mean concentration by less than 0.1‐log but increased the upper bound of the 95% credibility interval on the annual mean by about 0.5‐log. Therefore, considering parametric uncertainty in an exposure assessment is essential to account for microbial peaks in risk assessment.

AbstractThe identification of appropriately conservative statistical distributions is needed to predict microbial peak events in drinking water sources explicitly. In this study, Poisson and mixed Poisson distributions with different upper tail behaviors were used for modeling source water Cryptosporidium and Giardia data from 30 drinking water treatment plants. Small differences (<0.5‐log) were found between the "best" estimates of the mean Cryptosporidium and Giardia concentrations with the Poisson–gamma and Poisson–log‐normal models. However, the upper bound of the 95% credibility interval on the mean Cryptosporidium concentrations of the Poisson–log‐normal model was considerably higher (>0.5‐log) than that of the Poisson–gamma model at four sites. The improper choice of a model may, therefore, mislead the assessment of treatment requirements and health risks associated with the water supply. Discrimination between models using the marginal deviance information criterion (mDIC) was unachievable because differences in upper tail behaviors were not well characterized with available data sets (). Therefore, the gamma and the log‐normal distributions fit the data equally well but may predict different risk estimates when they are used as an input distribution in an exposure assessment. The collection of event‐based monitoring data and the modeling of larger routine monitoring data sets are recommended to identify appropriately conservative distributions to predict microbial peak events.

AbstractThe coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose–response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co‐exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source‐to‐outcome continuum. Cross‐cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

The presence of SARS-CoV-2 RNA in wastewater was first reported in March 2020. Over the subsequent months, the potential for wastewater surveillance to contribute to COVID-19 mitigation programmes has been the focus of intense national and international research activities, gaining the attention of policy makers and the public. As a new application of an established methodology, focused collaboration between public health practitioners and wastewater researchers is essential to developing a common understanding on how, when and where the outputs of this non-invasive community-level approach can deliver actionable outcomes for public health authorities. Within this context, the NORMAN SCORE "SARS-CoV-2 in sewage" database provides a platform for rapid, open access data sharing, validated by the uploading of 276 data sets from nine countries to-date. Through offering direct access to underpinning meta-data sets (and describing its use in data interpretation), the NORMAN SCORE database is a resource for the development of recommendations on minimum data requirements for wastewater pathogen surveillance. It is also a tool to engage public health practitioners in discussions on use of the approach, providing an opportunity to build mutual understanding of the demand and supply for data and facilitate the translation of this promising research application into public health practice. ; All authors wish to thank the WWTP operators for providing samples. LL, AH and MV would like to acknowledge the VINNOVA (Swedish Governmental Agency for Innovation Systems) DRIZZLE – Centre for Stormwater Management (Grant no. 2016-05176) and the technical expertise provided by the Stormwater&Sewers network, Nireas-International Water Research Center of the University of Cyprus would like to thank the Sewerage Board of Limassol-Amathus (SBLA), the Sewerage Board of Nicosia (SBN) and the Paralimni Sewerage Board (PSB) for the provision of influent samples, for the purpose of performing this work. MPD wishes to thank COVIDBENS Inv04020 financed by EDAR Bens S.A, A Coruña, FYL wishes to thank Z Cetecioglu Gurol (KTH) and P Haglund (Umeå University) and TM would like to acknowledge financial support from APVV-19-0250, PP-COVID-20-0019, ASS8 and VIR-SCAN. Authors from the Univ Jaume LB, FH, MB and RdL acknowledge the financial support from Dirección General del Agua, Generalitat Valenciana, to develop the project "Covid_Wastewater", as well as the help E. Santateresa and N. Zamorano from FACSA, for the invaluable support in performing this work. RdL. was funded through a Beatriz Galindo Fellowship of the Ministerio de Educación y Formación Profesional, Spanish Government (BEAGAL18/00042). TM wishes to thank the generous support of the Operational Program Integrated Infrastructure for the project "Strategic research in the field of SMART monitoring, treatment and preventive protection against coronavirus (SARS-CoV-2) ", Project no. 313011ASS8 (co-financed by the European Regional Development Fund) and the project VIR-SCAN - Wastewater monitoring data as an early warning tool to alert COVID-19 in the population (EOSCsecretariat.eu has received funding from the European Union's Horizon Program call H2020-INFRAEOSC- 05-2018-2019, grant Agreement number 831644). SK (IBISS) acknowledges the financial support from Ministry of Education, Science and Technological Development of Republic of Serbia grant No 451-03-9/2021-14/ 200007.