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Benchmarks are important to measure and aid in improve outcomes for surgical procedures. However, best achievable results that have been validated internationally for transsphenoidal surgery are not available. Therefore, we aimed to establish robust, standardized outcome benchmarks for transsphenoidal surgery of pituitary adenomas. A total of 2862 transsphenoidal tumor resections from 12 high-volume centers in 4 continents were analyzed. Patients were risk stratified and the median values of each center’s outcomes were established. The outcome benchmark was defined as the 75th percentile of all median values for a particular outcome as defined by Staiger et al. Out of 2862 patients, 1201 (41.9%) defined the benchmark cohort. The proportion of benchmark cases contributing to the final cohort ranged across centers between 22.1% to 59.7%. Within the benchmark cases, 928 (73.3%) patients underwent microscopic (MTS) and 263 (21.9%) patients endoscopic endonasal resection (EES). The overall postoperative complication rate was 18.9% with an in-hospital mortality between 0.0-0.8%. Benchmark cutoffs were ≤ 3.3% for reoperation rate, ≤ 4.6% for cerebrospinal fluid leak requiring intervention, and ≤ 15.3% for transient diabetes insipidus. At 6 months follow-up, benchmark cutoffs were calculated as follows: readmission rate: ≤ 7.1%, new hypopituitarism ≤ 15.5%, new neurological deficit ≤ 1.2%, tumor remnant ≤ 25.5%. This analysis defines benchmark values for transsphenoidal resection of pituitary adenomas targeting morbidity, mortality, surgical and tumor-related outcomes. The benchmark cutoffs can be used to assess different centers, patients’ populations, and novel surgical techniques.

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

It is well recognized that there are currently limitations in the spatial and temporal resolution of environmental exposure models due to significant variabilities and uncertainties in model inputs and parameters. Here we present the updated Pangea multi-scale multimedia model based on the more spatially resolved, catchment-based hydrological HydroBASINS dataset covering the entire globe. We apply it to predict spatially-explicit exposure concentrations of linear alkylbenzene sulphonate (LAS) and triclosan (TCS) as two chemicals found in homecare (HC) and personal care (PC) products in river catchments across Asia, and test its potential for identifying/prioritizing catchments with higher exposure concentrations. In addition, we also identify the key parameters in the model framework driving higher concentrations and perform uncertainty analyses by applying Monte Carlo simulations on emissions and other non-spatial model inputs.The updated combination of Pangea with the HydroBASINS hydrological data represents a substantial improvement from the previous model with the gridded hydrological dataset (WWDRII) for modelling substance fate, with higher resolution and improved coverage in regions with lower flows, with the results demonstrating good agreement with monitored concentrations for TCS in both the freshwater (R2 = 0.55) and sediment (R2 = 0.81) compartments. The ranking of water basins by Predicted Environmental Concentrations (PECs) was similar for both TCS and LAS, with highest concentrations (Indus, Huang He, Cauvery, Huai He and Ganges) being one to two orders of magnitude greater than the water basins with lowest predicted PECs (Mekong and Brahmaputra). Emissions per unit volume of each catchment, chemical persistence, and river discharge were deemed to be the most influential factors on the variation of predicted PECs. Focusing on the Huang He (Yellow River) water basin, uncertainty confidence intervals (factor 31 for LAS and 6 for TCS) are much lower than the variability of predicted PECs across the Huang He catchments (factors 90,700 for LAS and 13,500 for TCS). Keywords: Multimedia modelling, LAS, Triclosan, Household chemicals, Ecosystem exposure

Studies on the presence and fate of household and personal care chemicals (HPCCs) in wastewater treatment plants (WWTPs) are important due to their increasing consumption worldwide. The seasonal patterns and removal mechanisms of HPCCs are not well understood for WWTPs that apply different treatment technologies. To answer these questions, the sewage and sludge samples were taken from 10 typical WWTPs in Northeast China. Levels of UV filters in the influents in the warm season were significantly greater than that of the cold season (p 0.05). Significant seasonal differences were found for the removals of many HPCCs. Results revealed that the highest removal efficiencies were found for linear alkylbenzene sulphonates with values ranging from 97.2% to 99.7%, and the values were 50.0%-99.9% for other HPCCs. The SimpleTreat model demonstrated that the studied WWTPs were operating with high efficiency at the time of sampling. The sorption of HPCCs to sludge can be strongly associated with their physicochemical parameters. Mass balance calculation suggested that sorption was the dominant mechanism for the removal of antimicrobials, while degradation and/or biotransformation were the other mechanisms for removing the most HPCCs in the WWTPs. This study real the factors influencing the seasonal patterns and removal mechanisms which imply the need for further studies to fully understands the plant and human health implications as sludge could be used in the municipal land application of biosolids.

Water pollution are among the most critical problems in China and emerging contaminants in surface water have attracted rising attentions in recent years. There is great interest in China's future environmental quality as the national government has committed to a major action plan to improve surface water quality. This study presents methodologies to rank the importance of socioeconomic and environmental drivers to the chemical concentration in surface water during 2000–2030. A case study is conducted on triclosan, a home and personal care product (HPCP) ingredient. Different economic and discharge flow scenarios are considered. Urbanization and wastewater treatment connection rates in rural and urban areas are collected or projected for 2000–2030 for counties across China. The estimated usage increases from ca. 86 to 340 t. However, emissions decreases from 76 to 52 t during 2000–2030 under a modelled Organisation for Economic Co-operation (OECD) economic scenario because of the urbanization, migration and development of wastewater treatment plants/facilities (WWTPs). The estimated national median concentration of triclosan ranges 1.5–8.2 ng/L during 2000–2030 for different scenarios. It peaks in 2009 under the OECD and three of the Intergovernmental Panel on Climate Change (IPCC), A2, B1 and B2 economic scenarios, but in 2025 under A1 economic scenario. Population distribution and surface water discharge flow rates are ranked as the top two drivers to triclosan levels in surface water over the 30 years. The development of urban WWTPs was the most important driver during 2000–2010 and the development of rural works is projected to be the most important in 2011–2030. Projections suggest discharges of ingredients in HPCPs - controlled by economic growth - should be balanced by the major expenditure programme on wastewater treatment in China. Keywords: WWTPs, GDP, Urbanization rates, Population, Surface water concentration, China

This paper analyzes spatially ecosystem exposure to home and personal care (HPC) chemicals, accounting for market data and environmental processes in hydrological water networks, including multi-media fate and transport. We present a global modeling framework built on ScenAT (spatial scenarios of emission), SimpleTreat (sludge treatment plants), and Pangea (spatial multi-scale multimedia fate and transport of chemicals), that we apply across Asia to four chemicals selected to cover a variety of applications, volumes of production and emission, and physico-chemical and environmental fate properties: the anionic surfactant linear alkylbenzene sulphonate (LAS), the antimicrobial triclosan (TCS), the personal care preservative methyl paraben (MeP), and the emollient decamethylcyclopentasiloxane (D5). We present maps of predicted environmental concentrations (PECs) and compare them with monitored values. LAS emission levels and PECs are two to three orders of magnitude greater than for other substances, yet the literature about monitored levels of LAS in Asia is very limited. We observe a good agreement for TCS in freshwater (Pearson r=0.82, for 253 monitored values covering 12 streams), a moderate agreement in general, and a significant model underestimation for MeP in sediments. While most differences could be explained by uncertainty in both chemical/hydrological parameters (DT50water, DT50sediments, Koc, foc, TSS) and monitoring sites (e.g. spatial/temporal design), the underestimation of MeP concentrations in sediments may involve potential natural sources. We illustrate the relevance of local evaluations for short-lived substances in fresh water (LAS, MeP), and their inadequacy for substances with longer half-lives (TCS, D5). This framework constitutes a milestone towards higher tier exposure modeling approaches for identifying areas of higher chemical concentration, and linking large-scale fate modeling with (sub) catchment-scale ecological scenarios; a major limitation in model accuracy comes from the discrepancy between streams routed on a gridded, 0.5°×0.5° global hydrological network and actual locations of streams and monitoring sites.

Many regulatory screening level exposure assessments are based on simple large scale conceptual scenarios. However, exposure, and therefore risks associated with chemicals, are characterised by high spatial variability. The Scenario assembly tool (ScenAT) is a global screening level model to enable spatially resolved local predictions of environmental concentrations of home and personal care chemicals. It uses the European Union Technical Guidance Document (TGD) equation to predict local scale freshwater concentrations (predicted environmental concentrations - PECs) of chemicals discharged via wastewater. ScenAT uses Geographic Information System (GIS) layers for the underlying socio-economic (population) and environmental parameters (per capita water use, sewage treatment plant connectivity, dilution factor). Using a probabilistic approach, we incorporate sources of uncertainty in the input data (tonnage estimation, removal in sewage treatment plants and seasonal variability in dilution factors) for two case-study chemicals: the antimicrobial triclosan (TCS) and the anionic surfactant linear alkylbenzene sulphonate (LAS). We then compare model estimates of wastewater and freshwater concentrations of TCS and LAS to UK monitoring data. Comparison showed that modeled PECs were on average higher than mean measured data for TCS and LAS by a factor 1.8 and 1.4, respectively. Considering the uncertainty associated with both model and monitoring data, the use of a probabilistic approach using the ScenAT model for screening assessment is reasonable. The combination of modelled and monitoring data enables the contextualisation of monitoring data. Spatial PECs can be used to identify areas of elevated concentration for further refined assessment.

SESAMe v3.3, a spatially explicit multimedia fate model for China, is a tool suggested to support quantitative risk assessment for national scale chemical management. The key advantage over the previous version SESAMe v3.0 is consideration of spatially varied environmental pH. We evaluate the model performance using estimates of emission from total industry usage of three UV filters (benzophenone-3, octocrylene, and octyl methoxycinnamate) and three antimicrobials (triclosan, triclocarban, and climbazole). The model generally performs well for the six case study chemicals as shown by the comparison between predictions and measurements. The importance of accounting for chemical ionization is demonstrated with the fate and partitioning of both triclosan and climbazole sensitivity to environmental pH. The model predicts ionizable chemicals (triclosan, climbazole, benzophenone-3) to primarily partition into soils at steady state, despite hypothetically only being released to freshwaters, as a result of agricultural irrigation by freshwater. However, further model calibration is needed when more field data becomes available for soils and sediments and for larger areas of water. As an example, accounting for the effect of pH in the environmental risk assessment of triclosan, limited freshwater areas (0.03% or ca. 55 km(2)) in mainland China are modeled to exceed its conservative environmental no-effect threshold. SESAMe v3.3 can be used to support the development of chemical risk assessment methodologies with the spatial aspects of the model providing a guide to the identification regions of interest in which to focus monitoring campaigns or develop a refined risk assessment.