Nynke Hofstra

Impacts of climate change on the microbial safety of pre-harvest leafy green vegetables as indicated by Escherichia coli O157 and Salmonella spp.

The likelihood of leafy green vegetable (LGV) contamination and the associated pathogen growth and survival are strongly related to climatic conditions. Particularly temperature increase and precipitation pattern changes have a close relationship not only with the fate and transport of enteric bacteria, but also with their growth and survival. Using all relevant literature, this study reviews and synthesises major impacts of climate change (temperature increases and precipitation pattern changes) on contamination sources (manure, soil, surface water, sewage and wildlife) and pathways of foodborne pathogens (focussing on Escherichia coli O157 and Salmonella spp.) on pre-harvested LGVs. Whether climate change increases their prevalence depends not only on the resulting local balance of the positive and negative impacts but also on the selected regional climate change scenarios. However, the contamination risks are likely to increase. This review shows the need for quantitative modelling approaches with scenario analyses and additional laboratory experiments. This study gives an extensive overview of the impacts of climate change on the contamination of pre-harvested LGVs and shows that climate change should not be ignored in food safety management and research.

Exploring global Cryptosporidium emissions to surface water.

The protozoan parasite Cryptosporidium is a major cause of diarrhoea worldwide. This paper presents the first model-based inventory with 0.5 by 0.5 degree resolution of global Cryptosporidium emissions for the year 2000 from humans and animals to surface water. The model is based on nutrient distribution modelling, because the sources and transport of oocysts and nutrients to the surface water are comparable. Total emissions consist of point source emissions from wastewater and nonpoint source emissions by runoff of oocysts in manure from agricultural lands. Results indicate a global emission of 3 × 10(17) oocysts per year, with comparable contributions from point and nonpoint sources. Hot-spot areas for point sources are big cities in China, India and Latin America, while the area with the largest nonpoint source emissions is in China. Uncertainties in the model are large. Main areas for further study are (i) excretion rates of oocysts by humans and animals, (ii) emissions of humans not connected to sewage systems, and (iii) retention of oocysts to determine surface water pathogen concentrations rather than emissions. Our results are useful to health organisations to identify priority areas for further study and intervention.

Global occurrence and emission of rotaviruses to surface waters.

Group A rotaviruses (RV) are the major cause of acute gastroenteritis in infants and young children globally. Waterborne transmission of RV and the presence of RV in water sources are of major public health importance. In this paper, we present the Global Waterborne Pathogen model for RV (GloWPa-Rota model) to estimate the global distribution of RV emissions to surface water. To our knowledge, this is the first model to do so. We review the literature to estimate three RV specific variables for the model: incidence, excretion rate and removal during wastewater treatment. We estimate total global RV emissions to be 2 × 1018 viral particles/grid/year, of which 87% is produced by the urban population. Hotspot regions with high RV emissions are urban areas in densely populated parts of the world, such as Bangladesh and Nigeria, while low emissions are found in rural areas in North Russia and the Australian desert. Even for industrialized regions with high population density and without tertiary treatment, such as the UK, substantial emissions are estimated. Modeling exercises like the one presented in this paper provide unique opportunities to further study these emissions to surface water, their sources and scenarios for improved management.

Impacts of population growth, urbanisation and sanitation changes on global human Cryptosporidium emissions to surface water

Cryptosporidium is a pathogenic protozoan parasite and is a leading cause of diarrhoea worldwide. The concentration of Cryptosporidium in the surface water is a determinant for probability of exposure and the risk of disease. Surface water concentrations are expected to change with population growth, urbanisation and changes in sanitation. The objective of this paper is to assess the importance of future changes in population, urbanisation and sanitation on global human emissions of Cryptosporidium to surface water. The GloWPa-Crypto H1 (the Global Waterborne Pathogen model for Human Cryptosporidium emissions version 1) model is presented and run for 2010 and with scenarios for 2050. The new scenarios are based on the Shared Socio-economic Pathways (SSPs) developed for the climate community. The scenarios comprise assumptions on sanitation changes in line with the storylines and population and urbanisation changes from the SSPs. In SSP1 population growth is limited, urbanisation large and sanitation and waste water treatment strongly improve. SSP1* is the same as SSP1, but waste water treatment does not improve. SSP3 sees large population growth, moderate urbanisation and sanitation and waste water treatment fractions that are the same as in 2010. Total global Cryptosporidium emissions to surface water for 2010 are estimated to be 1.6×1017 oocysts per year, with hotspots in the most urbanised parts of the world. In 2050 emissions are expected to decrease by 24% or increase by 52% and 70% for SSP1, SSP3 and SSP1* respectively. The emissions increase in all scenarios for countries in the Middle East and Africa (MAF) region, while emissions in large parts in Europe decrease in scenarios SSP1 and SSP3. Improving sanitation by connecting the population to sewers, should be combined with waste water treatment, otherwise (SSP1*) emissions in 2050 are expected to be much larger than in a situation with strong population growth and slow development of safe water and improved sanitation (SSP3). The results show that population increase, urbanisation and changes in sanitation should be considered when water quality and resulting health risks are estimated by water managers or public health specialists.

Influence of climate variables on the concentration of Escherichia coli in the Rhine, Meuse, and Drentse Aa during 1985–2010

This study evaluates the relationship between the climate variables temperature and precipitation and the concentration of Escherichia coli bacteria in the Rhine, Meuse, and Drentse Aa for the period 1985–2010. Data from 4,679 E. coli concentration measurements spread over a total of 13 locations in these three river systems were used in this study. The variables water temperature, precipitation, and river discharge were correlated with E. coli measurements. Water temperature was found to correlate negatively, and this is in line with expectations that higher temperature increases microorganism die-off. Precipitation and discharge were found to correlate positively, and this is in line with expectations that runoff from agricultural lands brings along pathogens from manure and increases the chance of sewer overflows. The data of the Meuse were fit to a linear model that explained E. coli concentrations from a time component, the climate variables and a locations dummy variable, in order to assess the relative contribution of the different variables. This model had an R2 of 0.49, meaning that climate variables and location can account for nearly half of the observed variation in E. coli concentrations in surface water, even when other factors, such as land-use variables, are not taken into account. The effect of the different climate variables was found to differ with scale, with temperature being relatively important at a local scale, and discharge being mainly of importance at larger scales. From our results, we expect that climate change, mainly the projected increased precipitation, may increase E. coli concentrations overall. Other waterborne pathogens that follow similar transmission pathways as E. coli may be similarly impacted by climate change.

The Impact of Environmental Variables on Faecal Indicator Bacteria in the Betna River Basin, Bangladesh

Environmental variables influence Faecal Indicator Bacteria (FIB) in surface water. Understanding that influence is important, because presence of FIB, which are an indication of faecal contamination, means that harmful pathogens could be present that could also be influenced by environmental variables. Although some recent studies have focused on this topic, most of this work has been conducted in developed countries. Similar studies in developing countries and in a (sub)tropical climate are lacking. In this study we assess the influence of environmental variables on fluctuations in FIB concentrations of the Betna River in southwest Bangladesh that floods almost every year. Monthly water samples from five locations along Betna River were tested for FIB (E. coli and enterococci) in 2014–2015. A linear regression model was developed to assess the effect of the environmental variables on FIB concentrations. The study revealed increased FIB concentrations during wet weather conditions. Precipitation and water temperature were positively correlated with FIB concentrations. Water temperature was positively correlated, because the warm May to September period coincides with frequent precipitation. Precipitation increases manure release from land to surface water. The regression model explains nearly half of the variability in FIB concentrations (R2 of 0.46 for E. coli and 0.48 for enterococci). This study indicates that increased precipitation combined with higher water temperature, as is expected in this region with climate change, likely increases FIB concentrations. Waterborne pathogens are expected to respond similarly to these environmental changes, indicating that disease outbreaks could well become more frequent and severe.

Impacts of Climate and Management Variables on the Contamination of Preharvest Leafy Greens with Escherichia coli

The observed seasonality of foodborne disease suggests that climatic conditions play a role and that changes in the climate may affect the presence of pathogens. However, it is hard to determine whether this effect is direct or whether it works indirectly through other factors, such as farm management. This study aimed to identify the climate and management variables that are associated with the contamination (presence and concentration) of leafy green vegetables with E. coli. This study used data about E. coli contamination from 562 leafy green vegetables (lettuce and spinach) samples taken between 2011 and 2013 from 23 open-field farms in Belgium, Brazil, Egypt, Norway, and Spain. Mixed-effect logistic and linear regression models were used to study the statistical relationship between the dependent and independent variables. Climate variables and agricultural management practices together had a systematic influence on E. coli presence and concentration. The variables important for E. coli presence included the minimum temperature of the sampling day (odds ratio = 1.47), region, and application of inorganic fertilizer. The variables important for concentration (R(2) = 0.75) were the maximum temperature during the 3 days before sampling and the region. Temperature had a stronger influence (had a significant parameter estimate and the highest R(2)) than did management practices on E. coli presence and concentration. Region was a variable that masked many management variables, including rainwater, surface water, manure, inorganic fertilizer, and spray irrigation. Climate variables had a positive relationship with E. coli presence and concentration. Temperature, irrigation water type, fertilizer type, and irrigation method should be systematically considered in future studies of fresh produce safety.

Global Cryptosporidium loads from livestock manure

Understanding the environmental pathways of Cryptosporidium is essential for effective management of human and animal cryptosporidiosis. In this paper we aim to quantify livestock Cryptosporidium spp. loads to land on a global scale using spatially explicit process-based modeling, and to explore the effect of manure storage and treatment on oocyst loads using scenario analysis. Our model GloWPa-Crypto L1 calculates a total global Cryptosporidium spp. load from livestock manure of 3.2 × 1023 oocysts per year. Cattle, especially calves, are the largest contributors, followed by chickens and pigs. Spatial differences are linked to animal spatial distributions. North America, Europe, and Oceania together account for nearly a quarter of the total oocyst load, meaning that the developing world accounts for the largest share. GloWPa-Crypto L1 is most sensitive to oocyst excretion rates, due to large variation reported in literature. We compared the current situation to four alternative management scenarios. We find that although manure storage halves oocyst loads, manure treatment, especially of cattle manure and particularly at elevated temperatures, has a larger load reduction potential than manure storage (up to 4.6 log units). Regions with high reduction potential include India, Bangladesh, western Europe, China, several countries in Africa, and New Zealand.

Present and Future Human Emissions of Rotavirus and Escherichia coli to Uganda’s Surface Waters

Rotavirus (RV) and diarrheagenic are waterborne pathogens commonly causing diarrhea in children below five years old worldwide. Our study is a first step toward a loads-concentrations-risk modeling and scenario analysis framework. We analyzed current and future human RV and indicator (EC) emissions from sanitation facilities to surface waters in Uganda using two process-based models. Emissions were estimated for the baseline year 2015 and for three scenarios in 2030 using population, excretion rates, sanitation types, and wastewater treatment. The first model is a downscaled GloWPa-Rota H1 version, producing emissions at a 1-km resolution. The second model is newly developed for Kampala and adds emissions from pit latrines and septic tanks excluded in the first model. The scenarios Business as Usual, Industrious, and Low Emissions reflect government prospects in sanitation coverage and wastewater treatment. For the first model, 6.14 × 10 RV particles d and 1.31 × 10 EC colony-forming units (CFU) d are emitted to surface waters in 2015. The RV emissions are expected to increase in 2030 by 75% for Business as Usual and 212% for Industrious and decrease by 58% in Low Emissions. Emissions from the second model are higher for Kampala than in the first model, at 3.74 × 10 vs. 5.95 × 10 RV particles d and 8.18 × 10 vs. 1.75 × 10 EC CFU d in 2015, most of which come from the onsite-not-contained category. Simulated emissions for Kampala show the importance of including onsite sanitation in our modeling. Our study is replicable in other locations and helps identify key emission sources, their hotspots, and the importance of wastewater treatment. The scenarios can guide future sanitation safety planning.

Microbial Water Quality: Monitoring and Modeling

Microbial water quality lies in the nexus of human, animal, and environmental health. Multidisciplinary efforts are under way to understand how microbial water quality can be monitored, predicted, and managed. This special collection of papers in the was inspired by the idea of creating a special section containing the panoramic view of advances and challenges in the arena of microbial water quality research. It addresses various facets of health-related microorganism release, transport, and survival in the environment. The papers analyze the spatiotemporal variability of microbial water quality, selection of predictors of the spatiotemporal variations, the role of bottom sediments and biofilms, correlations between concentrations of indicator and pathogenic organisms and the role for risk assessment techniques, use of molecular markers, subsurface microbial transport as related to microbial water quality, antibiotic resistance, real-time monitoring and nowcasting, watershed scale modeling, and monitoring design. Both authors and editors represent international experience in the field. The findings underscore the challenges of observing and understanding microbial water quality; they also suggest promising research directions for improving the knowledge base needed to protect and improve our water sources.