Biological Monitoring Data Research Articles

A detailed methodology for estimating health-related hazards of workplace exposure to indoor formaldehyde vapours

A comprehensive risk assessment method was applied to examine the risks associated with airborne formaldehyde occupational exposure among hospital laboratory staff. The method assessed exposure levels and health impacts by integrating area and personal air sampling, biological monitoring, and self-reported health data. Samples were collected from 74 workplaces across various departments using NIOSH method 3500 and were analyzed via UV-vis spectrophotometry. The data showed significant differences in exposure levels between departments (p≤0.05) and confirmed the efficacy of the method in identifying risk differences. Despite average personal exposure levels being measured lower than occupational limits, individual assessments indicated that some participants surpassed these limits, emphasizing the necessity of personal monitoring for workers with higher risks. The high prevalence of respiratory symptoms, such as cough and wheezing among staff, indicated the need for further investigation and targeted interventions. Although estimated cancer and non-cancer risks were within safe thresholds, the study emphasized the importance of continuous exposure monitoring and the implementation of effective control measures in hospital laboratory departments with formaldehyde emission. This integrated method improved the reliability and generalizability of formaldehyde exposure risk assessments and aided in the development of safe occupational health practices.•The method integrated personal and area sampling with advanced calibration for precise occupational exposure evaluation in laboratories.•The method used of biomarkers to assess formaldehyde absorption in the body estimating both cancerous and non-cancerous health risks associated with occupational exposure.•Addressed traditional method limitations and integrated risk components to improve data reliability for workplace safety and health risk management.

АНАЛИЗ СТРУКТУРЫ ПРОМЫСЛА И ДИНАМИКИ РОСТА СТАДА СЕВЕРНОГО МОРСКОГО КОТИКА (CALLORHINUS URSINUS) ОСТРОВА ТЮЛЕНИЙ В ПЕРИОД ЭКСПЛУАТАЦИИ И ПОСЛЕ ЕЁ ЗАВЕРШЕНИЯ

Based on a long time series providing biological monitoring data of the Northern fur seal population on Tyuleny Island and detailed information on the harvesting characteristics of this population, we have analyzed trends in its growth. The research results of changes in the average reproductive success of bulls and the age structure of Northern fur seal males on Tyuleny Island are presented as indicators of harvesting impact on the reproductive potential of the population. Evidence has shown that selective harvesting was practiced, where most productive bulls were removed from the population. This led to a significant change in the age structure of the population and a significant reduction in the population growth. Despite a slight increase in the natural survival of almost all age groups, the population growth slowed down sharply. The proximity of the growth potential indicator to the critical unitary value revealed in recent years leads to a bleak forecast. Further growth in the Northern fur seal population, if any, will not be rapid. To accelerate it, it is necessary to provide a new natural restructuring of the population structure, improving its qualitative composition and increasing the proportion of males with high reproductive success.

Passive sampling of herbicides above sediments at sites with losses of submerged macrophytes in a mesotrophic lake

Declines of submerged macrophytes (SUM) were monitored in littoral zones of the deep, mesotrophic lake Suhrer See (Northern Germany) since 2017. Drastic losses coincided with intense agriculture in sandy sub-catchments and precipitation. All lines of evidence pointed to a causal connection with subsurface discharge indicating that herbicide application might have caused the effects. Passive sampling was applied in 2022 to elucidate, whether herbicides were really present at sites of losses and if so, in ecotoxicological relevant concentrations. Samplers were exposed on top of lake sediments in 2 m depth and under worst case conditions, i.e., at sites, known for losses of the whole functional group of SUM and at the beginning of the vegetation period. At this time, SUM diaspores were most vulnerable to repression of development and the subsurface discharge was high in the same instance. The potential ecotoxicological relevance of detected herbicide concentrations was assessed with a toxic units (TU) approach, with reference to acute effect concentrations (EC50 of green algae, 72 h, growth). The TU ranged from 0.001 to 0.03. Most concentrations exceeded the threshold of relevance set by an assessment factor of 1000, i.e., TU > 0.001. Locally applied herbicides acted by suppressing developmental stages, and the sum of TU exceeded 0.02 at all sites, mainly due to diflufenican. Not applied locally, terbuthylazine and its relevant metabolites, including terbutryn, acted by inhibiting photosynthesis, and the sum of TU reached 0.005. On this base, diflufenican was assessed to be likely a main stressor, all other detected herbicides to be potentially relevant. Uncertainties and knowledge gaps were specified. The result of the chemical risk assessment was counterchecked for consistence with biological monitoring data within a whole lake perspective. Concepts of empirical and advanced causal attribution methodology were applied to get a grip to the ecological causal field and to protection.

Short-term effects of ambient PM1, PM2.5, and PM10 on internal metal/metalloid profiles in older adults: A distributed lag analysis in China

There is limited evidence linking exposure to ambient particulate matter (PM) with internal doses of metals and metalloids (metal(loid)s). This study aimed to evaluate the effects of short-term exposure to ambient PM on urine metal(loid)s among Chinese older adults. Biological monitoring data of 15 urine metal(loid)s collected in 3, 970 community-dwelling older adults in Fuyang city, Anhui Province, China, from July to September 2018, were utilized. PMs with an aerodynamic diameter ≤ 1 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10) up to eight days before urine collection were estimated by space–time extremely randomized trees (STET) model. Residential greenness was reflected by Normalized Difference Vegetation Index (NDVI). We used generalized additive model (GAM) combined with distributed lag linear/non-linear models (DLMs/DLNMs) to estimate the associations between short-term PM exposure and urine metal(loid)s. The results suggested that the cumulative exposures to PM1, PM2.5, or PM10 over two days (lag0-1 days) before urine collection were associated with elevated urine metal(loid)s in DLMs, while exhibited linear or “inverted U-shaped” relationships with seven urine metal(loid)s in DLNMs, including Gallium (Ga), Arsenic (As), Aluminum (Al), Magnesium (Mg), Calcium (Ca), Uranium (U), and Barium (Ba). Aforementioned results indicated robust rather than spurious associations between PMs and these seven metal(loid)s. After standardizations for three PMs, PM1 was the greatest contributor to U, PM2.5 made the greatest contributions to Ga, As, Al, and Ba, and PM10 contributed the most to Mg and Ca. Furthermore, the effects of three PMs on urine Ga, As, Al, Mg, Ca, and Ba were reduced when exposed to higher levels of NDVI. Overall, short-term exposures to ambient PMs contribute to elevated urinary metal(loid) levels in older adults, and three PMs exhibit various contributions to different urine metal(loid)s. Moreover, residential greenness may attenuate the effects of PMs on urine metal(loid)s.

Is Occupational Arsenic Exposure a Possible Causative Agent of Breast Cancer for a Young Female Laboratory Technician? A Case-Study

A 28-year-old female laboratory technologist who was exposed to highly concentrated inorganic arsenic for 7 years, 25 hours a week, presented with left breast cancer. When most epidemiologic evidence reported by the IARC supported the relationship between arsenic exposure and cancers of lung, skin and bladder; literature had documented increased risk of breast cancer in specific populational subgroups due to the estrogen-like activity of arsenic. The causal assessment of occupational cancer is challenging due to the lack of relevant data on the worker’s biological monitoring and environmental exposure monitoring data, together with the insufficient genetic composition information like BRCA1 gene. Moreover, the poor work practice and hygiene had made the exposure through dermal contact and digestion possible. The interpretation of work causal relationship while distinct occupational cancer from those of non-occupational must consider individual susceptibility as low-level short-period exposure might increase the risk for certain worker. Therefore, a systematically collected medical surveillance data along with industry hygiene data is highly recommended in order to assist in the refinement of human dose-response relationship of specific work carcinogen

From national monitoring to transnational indicators: reporting and processing of aquatic biology data under the European Environment Agency’s State of the Environment data flow

Biological monitoring data from aquatic ecosystems are collected from European countries on a yearly basis by the European Environment Agency (EEA) through the Water Information System for Europe (WISE). The WISE-SoE (State of Environment) data flows provide indicators of pressures, states and impacts of surface waters and groundwaters on a pan-European scale. The WISE-2 Biology was established to obtain a harmonised flow of biology data reported annually as Ecological Quality Ratios (EQRs) from European surface waters, as a supplement to the mandatory 6-yearly reporting of ecological status of water bodies for the Water Framework Directive. The purposes of this paper are 1) to describe the compilation of national aquatic biology monitoring data indicators and to inform about the public availability of these data, 2) to give an overview of the reported data and indicate the potential for assessments based on these data, and 3) to illustrate the potential for further use of the underlying species abundance data in biodiversity research and assessment. WISE-2 data are reported for the following biological quality elements: phytoplankton, phytobenthos, macrophytes, macroalgae, angiosperms, benthic invertebrates and fish in rivers, lakes, transitional and/or coastal waters. The EQR values represent the deviation from reference conditions. The final processed and quality-checked data are published in EEA’s database Waterbase - Biology, which currently holds data from more than 13,000 waterbodies in 26 countries from the reporting years 2011–2021. Examples of time series aggregated by geographic regions give an indication of the type of trends that can be obtained from the reported data at the nEQR scale. However, the current results are representative only for certain geographic regions with high coverage of water bodies. Within the European research project EuropaBON (Europa Biodiversity Observation Network), the use of WISE-2 data can be leveraged to support biodiversity policy and conservation planning. EuropaBON’s online database provides an overview of how biodiversity monitoring schemes across Europe flows through different integration nodes, to produce Essential Biodiversity Variables and other policy-relevant indicators. Here, we use the EuropaBON visualisation tool to illustrate the WISE-2 as a European integration node for 157 biology datasets via the national integration nodes.

Analysis of Influencing Factors of Chlorophyll-a in Lake Taihu Based on Bayesian Network

Global warming has aggravated the problem of lake eutrophication. As a typical large, eutrophic, shallow lake in China, the issue of cyanobacterial harmful algal blooms (cyanoHABs) was particularly prominent in Lake Taihu. We took Lake Taihu as the study area, using the meteorological (temperature, wind speed, rainfall, and sunshine hours), water quality (total nitrogen, total phosphorus, conductivity, pH, and chemical oxygen demand), and biological (chlorophyll-a in phytoplankton) monitoring data from 1992 to 2020. We built a simulation model of chlorophyll-a based on the Bayesian network model with continuous variables to study the chlorophyll-a level of Lake Taihu under different meteorological and water quality conditions. The 75th percentile of chlorophyll-a concentration was used as the threshold to judge the risk of cyanobacterial bloom. When the probability of chlorophyll-a concentration below this threshold was greater than 75%, it was regarded as "low risk" of cyanobacterial bloom outbreak. The results showed that the average level of "temperature wind ratio" (ratio of air temperature to wind speed) in spring was 6.67℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.130 mg·L-1. The average "temperature wind ratio" level in summer was 10.52℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.257 mg·L-1. The average level of total phosphorus concentration in autumn was 0.154 mg·L-1, and the probability of high chlorophyll-a was less than 75% when the "temperature wind ratio" was less than 6.30℃·s·m-1. Based on the above research, the chlorophyll-a model constructed by the Bayesian network model with continuous variables was further used to simulate the nutrient control objectives under different climate change backgrounds. In order to control chlorophyll-a in Lake Taihu at the:"low risk" level of cyanoHABs, the target concentration thresholds of total phosphorus needed to be controlled under the climate level background from 1992 to 2000, 2001 to 2010, and 2011 to 2020 were given. From 1992 to 2000, the threshold value of total phosphorus concentration was 0.135 mg·L-1 in spring, 0.174 mg·L-1 in summer, and 0.171 mg·L-1 in autumn. From 2001 to 2010, the threshold value of total phosphorus concentration was 0.115 mg·L-1 in spring, 0.164 mg·L-1 in summer, and 0.162 mg·L-1 in autumn. From 2011 to 2020, the threshold value of total phosphorus concentration was 0.059 mg·L-1 in spring, 0.145 mg·L-1 in summer, and 0.145 mg·L-1 in autumn. The results showed that the control of cyanoHABs in eutrophic lakes required more stringent nutrient control strategies with global warming. It provided a reference for preventing and controlling cyanoHABs and eutrophication in Lake Taihu. Previous studies have used multiple regression models, hydrodynamic numerical models, and other methods to predict chlorophyll-a concentrations or cyanobacterial blooms in lakes. However, there has been no study on the prediction of cyanoHABs in lakes based on the Bayesian network model with continuous variables and the "dynamic" evaluation of nutrient thresholds. Therefore, based on the seasonal meteorological, water quality, and biological monitoring data of Lake Taihu from 1992 to 2020, the chlorophyll-a model of Lake Taihu was constructed for the first time based on the Bayesian network model with continuous variables to simulate the chlorophyll-a concentration of Lake Taihu under different climate indicators and total phosphorus concentrations. The weight of its influencing factors was also analyzed, and the nutrient control objectives under different climate scenarios were "dynamically" evaluated.

Using long-term ecological monitoring to evaluate how climate and human-induced disturbances impact nekton communities in a Northern Gulf of Mexico estuary

Apalachicola Bay, a river-fed estuary in the northeast Gulf of Mexico is experiencing impacts from anthropogenic and climate-induced changes in freshwater inputs and sea level rise. These synergistic pressures have resulted in a historic oyster fishery collapse and the expansion of mangroves, which are displacing native salt marsh. Understanding how these natural and man-made-driven changes have affected the nekton community is essential for the sustainable management of this coastal region. To evaluate changes in nekton occurrence and distribution in Apalachicola Bay, we analyzed 20 years of biological monitoring data. Our results indicated that spatial variation in species’ populations was driven by differences in salinity, while seasonal changes were driven by temperature. Freshwater inflow is the primary driver of salinity, and these differences demonstrated that river flow reductions have the potential to alter nekton communities. The effects of climate-driven droughts and anthropogenic freshwater flow reductions on estuarine community structure highlight the importance of maintaining sufficient river flow for nursery areas, which provide ecosystem services for the region. Ultimately, these findings support the continued need for monitoring programs that track accelerating ecosystem change and provide a clearer understanding for how community composition will respond to global change.

Filling global gaps in monitoring data with local knowledge

Filling global gaps in monitoring data with local knowledge

The effects of Bythotrephes longimanus invasion on diets and growth of age‐0 yellow perch in Oneida Lake, New York

AbstractBythotrephes longimanus is a predatory zooplankton native to Eurasia known for its large caudal spine. This species can decrease Daphnia abundance and may decrease growth rates of native planktivorous fish including yellow perch (Perca flavescens). Bythotrephes was first documented in Oneida Lake, USA in 2019 and reached high densities in 2020 and 2021. This recent invasion created an opportunity to investigate at what length yellow perch feed on Bythotrephes, if age‐0 yellow perch select for Bythotrephes, and if yellow perch first‐year growth decreased. We found Bythotrephes in the stomachs of 23% (2020) and 35% (2021) of age‐1 and older yellow perch caught from June through September and 54% of the age‐0 yellow perch caught from June through October (2021). Age‐0 yellow perch started feeding on Bythotrephes at 27 mm and selected for Bythotrephes from June 24 through the autumn of 2021. Bythotrephes accounted for 54% of age‐0 diets by dry weight over the summer and fall. We used the extensive water quality and biological monitoring data from Oneida Lake to develop a multivariate linear regression model for age‐0 yellow perch October length from 1993 to 2021. Significant variables included were age‐0 yellow perch abundance (negative effect) and temperature, Daphnia biomass, and Bythotrephes presence, all positive effects. Contrary to our expectations, Bythotrephes were selected by age‐0 yellow perch from end of June through October, and contributed a substantial proportion of ingested mass by age‐0 perch, without negatively affecting age‐0 yellow perch growth.

Refinement and calibration of a human PBPK model for the plasticiser, Di-(2-propylheptyl) phthalate (DPHP) using in silico, in vitro and human biomonitoring data.

An existing physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was refined to improve the simulations of the venous blood concentrations of the primary monoester metabolite, mono-(2-propylheptyl) phthalate (MPHP). This was considered a significant deficiency that should be addressed because the primary metabolite of other high molecular weight phthalates has been associated with toxicity. The various processes that influence the concentration of DPHP and MPHP in blood were re-evaluated and modified. A few simplifications of the existing model were made, including the removal of enterohepatic recirculation (EHR) of MPHP. However, the primary development was describing the partial binding of MPHP to plasma proteins following uptake of DPHP and metabolism in the gut affording better simulation of the trends observed in the biological monitoring data. Secondly, the relationship between blood concentrations and the urinary excretion of secondary metabolites was explored further because the availability of two data streams provides a better understanding of the kinetics than reliance on just one. Most human studies are conducted with few volunteers and generally with the absence of blood metabolite measurements which would likely imply an incomplete understanding of the kinetics. This has important implications for the "read across" approach proposed as part of the development of New Approach Methods for the replacement of animals in chemical safety assessments. This is where the endpoint of a target chemical is predicted by using data for the same endpoint from another more "data rich" source chemical. Validation of a model parameterized entirely with in vitro and in silico derived parameters and calibrated against several data streams would constitute a data rich source chemical and afford more confidence for future evaluations of other similar chemicals using the read-across approach.

Application of All-Ages Lead Model Based on Monte Carlo Simulation of Preschool Children’s Exposure to Lead in Guangdong Province, China

Introduction: Lead (Pb) poisoning in children is a major public health issue worldwide. The physiologically based pharmacokinetic model (PBPK model) has been extensively utilized in Pb exposure risk assessment and can connect external exposure with biological monitoring data. This study aimed to combine a Monte Carlo simulation with the all-ages lead model (ALLM) to quantify the heterogeneity and uncertainty of certain parameters in the population. The parameters of the all-ages lead model based on Monte Carlo simulation (ALLM + MC) were localized in Guangdong Province. Our study discusses the practicability of the application of the localized ALLM + MC in Guangdong Province. Methods: A local sensitivity analysis was used to assess the impact of pharmacokinetic parameters on the prediction of blood lead level (BLL). Environmental Pb concentration, exposure parameters, and sensitive parameters were included in the ALLM + MC, and the differences between the ALLM- and the ALLM + MC-predicted values were compared. Additionally, we localized the exposure parameters in the ALLM + MC and used them to evaluate BLL in preschool children from Guangdong Province. Finally, we compared the predictive values to those observed in the literature. Results: The predictive values of ALLM and ALLM + MC had a significant correlation (r = 0.969, p < 0.001). The predictive value of ALLM was included in the ALLM + MC prediction range. Moreover, there were no significant differences between the predictive and the observed values of preschool children from Guangdong Province (z = −0.319, p = 0.749). Except for children aged 5–6, the difference between the predictive and the observed values was less than 1 μg/dL. The root mean square error (RMSE) and the mean deviation (RMD) of ALLM and ALLM + MC were reduced by 24.73% and 32.83%, respectively. Conclusions: The localized ALLM + MC is more suitable for predicting the BLL of preschool children in Guangdong Province, which can be used to explain the heterogeneity and uncertainty of parameters in the population. The ALLM + MC has fewer time, space, and financial restrictions, making it more appropriate for determining the BLLs in large populations. The use of ALLM + MC would improve the feasibility of regular and long-term blood Pb detection.

Direct habitat descriptors improve the understanding of the organization of fish and macroinvertebrate communities across a large catchment.

In large-scale aquatic ecological studies, direct habitat descriptors (e.g. water temperature, hydraulics in river reaches) are often approximated by coarse-grain surrogates (e.g. air temperature, discharge respectively) since they are easier to measure or model. However, as biological variability can be very strong at the habitat scale, surrogate variables may have a limited ability to capture all of this variability, which may lead to a lesser understanding of the ecological processes or patterns of interest. In this study, we aimed to compare the capacity of direct habitat descriptors vs. surrogate environmental variables to explain the organization of fish and macroinvertebrate communities across the Loire catchment in France (105 km2). For this purpose, we relied on high-resolution environmental data, extensive biological monitoring data (>1000 sampling stations) and multivariate analyses. Fish and macroinvertebrate abundance datasets were considered both separately and combined to assess the value of a cross-taxa approach. We found that fish and macroinvertebrate communities exhibited weak concordance in their organization and responded differently to the main ecological gradients. Such variations are probably due to fundamental differences in their life-history traits and mobility. Regardless of the biological group considered, direct habitat descriptors (water temperature and local hydraulic variables) consistently explained the organization of fish and macroinvertebrate communities better than surrogate descriptors (air temperature and river discharge). Furthermore, the organization of fish and macroinvertebrate communities was slightly better explained by the combination of direct or surrogate environmental variables when the two biological groups were considered together than when considered separately. Tied together, these results emphasize the importance of using a cross-taxa approach in association with high-resolution direct habitat variables to more accurately explain the organization of aquatic communities.

Exposure to Antineoplastic Drugs in Occupational Settings: A Systematic Review of Biological Monitoring Data.

The high toxicity of antineoplastic drugs (ADs) makes them dangerous not only for patients, but also for exposed workers. Therefore, the aim of this review was to provide an updated overview of the biological monitoring of occupational AD exposure in order to extrapolate information useful to improve risk assessment and management strategies in workplaces. Several studies demonstrated that remarkable portions of healthcare workers may have traces of these substances or their metabolites in biological fluids, although with some conflicting results. Nurses, directly engaged in AD handling, were the occupational category at higher risk of contamination, although, in some cases, personnel not involved in AD-related tasks also showed quantifiable internal doses. Overall, further research carried out on greater sample sizes appears necessary to gain deeper insight into the variability retrieved in the reported results. This may be important to understand the impact of the extent of ADs use, different handling, procedures, and cleaning practices, spill occurrence, training of the workforce, as well as the adoption of adequate collective and personal protective equipment in affecting the occupational exposure levels. This may support the achievement of the greatest clinical efficiency of such therapies while assuring the health and safety of involved workers.

Use of Site-Specific Data for Modeling Selenium Bioaccumulation by Terrestrial Animals.

We developed a bioaccumulation model from an extensive set of monitoring data to predict selenium (Se) concentrations in biota within a terrestrial system (Kesterson Reservoir, CA). The model uses water-extractable Se and total Se concentrations in soil to estimate the expected mean and ranges of Se concentrations in biota at Kesterson for future scenarios. Biological monitoring data collected at Kesterson from 1989 to 1994 were used to parameterize the initial model. The model was tested and updated with additional sample results from 1995 through 2001 biological monitoring and validated and calibrated using Se concentrations from sampling conducted in 2004 and 2006. Minor adjustments were made to the model based on each additional year's results, and the model was used in 2014 to assess whether there were continuing threats to wildlife at Kesterson. The model predicts Se concentrations in small mammals, bird blood, and bird eggs in common species found at Kesterson. This model was used for the final assessment of Kesterson in 2014 and performed well, but there was variability in results, probably due to differences in individual diets and feeding ranges of animals. The model has been further refined since 2014, as we describe here. The model performs well for predicting central tendency and is conservative as the predicted upper limits of the biotic exposure distributions were mostly similar or higher than the measured. The trophic and tissue transfer factors and regression equations should be applicable to other Se-contaminated sites; adjusting weighting factors based on diet and range allows the model to be adapted and used at other sites.

Biological Monitoring: Evidence for Reductions in Occupational Exposure and Risk.

Aims: The aim of this publication is to explore occupational exposure trends from biological monitoring data collected over a period of more than 20 years. The data is stored within the HSE database, which holds more than 950,000 results from 120,000 workers in 8,000 companies. The data were collated for all biological monitoring results for lead, mercury, benzene, and hexamethylene diisocyanate exposures where there have been some regulatory drivers within the reported time period of the data searched. Methods: Relevant results from sample analysed were extracted from the database and categorised by year from 1996 to the end of 2019 for individual blood lead results and individual urine results for mercury, benzene, and hexamethylene diisocyanate. Results were classed by broad occupational sector where possible. Data were reported graphically by analytical biomarker result (as 90th percentile (P90)) and number of samples per year as well as with overall summary statistics. To look at longer-term trends, results were also evaluated as P90 over 6-year periods. Results: In the period 1996–2019, 37,474 blood lead, 11,723 urinary mercury, 9,188 urinary S-phenylmercapturic acid (SPMA, benzene metabolite) and 21,955 urinary hexamethylene diamine (HDA, metabolite of hexamethylene diisocyanate, HDI) samples were analysed and reported. Over the time period the blood lead concentrations saw the P90 reduce from 53 μg/dl 1996) to 24 μg/dl in 2019; the P90 urinary mercury levels reduced from 13.7 μmol/mol creatinine to 2.1 μmol/mol creatinine and the P90 urinary SPMA levels reduced from 133.7 μmol/mol creatinine to 1.7 μmol/mol creatinine. For HDI the P90 results reduced from 2 µmol HDA/mol creatinine in 1996–2000 to 0.7 in 2005–2010 but levels have since increased to 1.0 µmol HDA/mol creatinine (2016–2019). Conclusion: There is strong evidence of reductions in exposure of GB workers to lead, benzene and mercury from the data presented here. These reductions may reflect the impact of national, regional and global regulatory action to reduce exposures however, the loss of high exposure industries (from either GB as a whole or just this dataset i.e., samples are being sent elsewhere) and the increase in automation or substitution also need to be considered as potential factors. The results for HDI show that whilst interventions can reduce exposures significantly, such initiatives may need to be refreshed at intervals to maintain the reductions in exposure. We have observed that exposures move between sectors over time. Waste and recycling (lead, mercury) and tunnelling through contaminated land (benzene) were sectors or tasks associated with significant exposures and may be increasingly areas of concern.

Impact of MenAfriVac on Meningococcal A Meningitis in Cameroon: A Retrospective Study Using Case-by-Case-Based Surveillance Data from 2009 to 2015.

Meningococcal meningitis is a public health concern in Africa. Conjugated vaccine against serogroup A Neisseria meningitidis (MenAfriVac) was used in mass vaccination and was proved to have a good impact in the meningitis belt. There is a lack of information about the impact of this intervention in Cameroon after mass vaccination was undertaken. This study aimed at filling the gap in its unknown impact in Cameroon. A retrospective longitudinal study using biological monitoring data of case-by-case-based surveillance for meningitis was obtained from the National Reference Laboratories from 1 January 2009 to 20 September 2015. Immunization coverage data were obtained from Regional Public Health Delegations where immunizations took place. We compared the risks of vaccine serogroup occurrence before and after vaccinations and calculated the global impact using Halloran's formula. Annual cases of meningitis A decreased gradually from 92 in 2011 to 34 in 2012 and then to 1 case in 2013, and since 2014, no cases have been detected. The impact was estimated at 14.48% (p=0.41) in 2012 and then at 98.63% (p < 0.0001) after the end of vaccinations in 2013. This survey confirms the effectiveness of the MenAfriVac vaccine in Cameroon as expected by the WHO. The surveillance must be pursued and enhanced to monitor coming immunizations measures with multivalent conjugated vaccines for this changing threat.

Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data.

A physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the biokinetics in humans after single oral doses. The model was parameterized with in vitro and in silico derived parameters and uncertainty and sensitivity analysis was used during the model development process to assess structure, biological plausibility and behaviour prior to simulation and analysis of human biological monitoring data. To provide possible explanations for some of the counter-intuitive behaviour of the biological monitoring data the model included a simple lymphatic uptake process for DPHP and enterohepatic recirculation (EHR) for DPHP and the mono ester metabolite mono-(2-propylheptyl) phthalate (MPHP). The model was used to simultaneously simulate the concentration-time profiles of blood DPHP, MPHP and the urinary excretion of two metabolites, mono-(2-propyl-6-hydroxyheptyl) phthalate (OH-MPHP) and mono-(2-propyl-6-carboxyhexyl) phthalate (cx-MPHP). The availability of blood and urine measurements permitted a more robust qualitative and quantitative investigation of the importance of EHR and lymphatic uptake. Satisfactory prediction of blood DPHP and urinary metabolites was obtained whereas blood MPHP was less satisfactory. However, the delayed peak of DPHP concentration relative to MPHP in blood and second order metabolites in urine could be explained as a result of three processes: 1) DPHP entering the systemic circulation from the lymph, 2) rapid and very high protein binding and 3) the efficiency of the liver in removing DPHP absorbed via the hepatic route. The use of sensitivity analysis is considered important in the evaluation of uncertainty around in vitro and in silico derived parameters. By quantifying their impact on model output sufficient confidence in the use of a model should be afforded. This approach could expand the use of PBPK models since parameterization with in silico techniques allows for rapid model development. This in turn could assist in reducing the use of animals in toxicological evaluations by enhancing the utility of “read across” techniques.

Setting Priorities in River Management Using Habitat Suitability Models

Worldwide river systems are under pressure from human development. River managers need to identify the most important stressors in a stream basin, to propose effective management interventions for river restoration. In the European Union, the Water Framework Directive proposes the ecological status as the management endpoint for these interventions. Many decision support tools exist that use predictive water quality models to evaluate different river management scenarios, but only a few consider a river’s ecological status in this analysis explicitly. This paper presents a novel method, which combines abiotic monitoring data and biological monitoring data, to provide information and insight on why the ecological status does not reach the good status. We use habitat suitability models as a decision support tool, which can identify the most important stressors in river systems to define management scenarios. To this end, we disassemble the ecological status into its individual building blocks, i.e., the community composition, and we use habitat suitability models to perform an ecological gap analysis. In this paper, we present our method and its underlying ecological concepts, and we illustrate its benefits by applying the method on a regional level for Flanders using a biotic index, the Multimetric Macroinvertebrate Index Flanders (MMIF). To evaluate our method, we calculated the number of correctly classified instances (CCI = 47.7%) and the root-mean-square error (RMSE = 0.18) on the MMIF class and the MMIF value. Furthermore, there is a monotonic decreasing relationship between the results of the priority classification and the ecological status expressed by the MMIF, which is strengthened by the inclusion of ecological concepts in our method (Pearson’s R2 −0.92 vs. −0.87). In addition, the results of our method are complementary to information derived from the legal targets set for abiotic variables. Thus, our proposed method can further optimize the inclusion of monitoring data for the sake of sustainable decisions in river management.

Low-cost biomonitoring and high-resolution, scalable models of urban metal pollution

As the global toll on human lives and ecosystems exacted by urban pollution grows, planning tools still lack the resolution to identify priority sites where toxic pollution can be most efficiently averted at a spatial scale that matches funding and management. Here we tackle this gap by demonstrating novel scalable methods to monitor and predict urban metal pollution at high resolution (<5 m) across large areas (10,000–100,000 km2) to guide pollution reduction and stormwater management. We showcase and calibrate predictive models of Zn, Cu, and a synthetic index of pollution for the Puget Sound region of Washington State, U.S., a densely urbanized yet important ecosystem of conservation interest, and exemplify their transferability across the entire United States. We leveraged widely and freely available datasets of car traffic characteristics and land use as predictor variables and trained the models with biological monitoring data of metal concentrations in epiphytic moss from >100 trees based on new rapid and low-cost protocols introduced in this study. Our model predictions, showing that 50% of the total Cu and Zn pollution across the Puget Sound watershed is deposited over only 3.3% of the land area, will allow cities to effectively and efficiently target toxic hotspots.