China accounts for more than half of the world's vegetable production, and identifying the contribution of vegetable production to nitrous oxide (N₂O) emissions in China is therefore important. We performed a meta-analysis that included 153 field measurements of N₂O emissions from 21 field studies in China. Our goal was to quantify N₂O emissions and fertilizer nitrogen (N) based-emission factors (EFs) in Chinese vegetable systems and to clarify the effects of rates and types of N fertilizer in both open-field and greenhouse systems. The results indicated that the intensive vegetable systems in China had an average N₂O emission of 3.91 kg N₂O-N ha⁻¹ and an EF of 0.69%. Although the EF was lower than the IPCC default value of 1.0%, the average N₂O emission was generally greater than in other cropping systems due to greater input of N fertilizers. The EFs were similar in greenhouse vs. open-field systems but N₂O emissions were about 1.4 times greater in greenhouses. The EFs were not affected by N rate, but N₂O emissions for both open-field and greenhouse systems increased with N rate. The total and fertilizer-induced N₂O emissions, as well as EFs, were unaffected by the type of fertilizers in greenhouse system under same N rates. In addition to providing basic information about N₂O emissions from Chinese vegetable systems, the results suggest that N₂O emissions could be reduced without reducing yields by treating vegetable systems in China with a combination of synthetic N fertilizer and manure at optimized economic rates.

Organic fertilizers, such as digestates and manure, are increasingly applied in agricultural systems because of the benefits they provide in terms of plant nutrients and soil quality. However, there are few investigations of N₂O emissions following digestate application to agricultural soils using process-based models. In this study, we modified the UK-DNDC model to include digestate applications to soils by adding digestate properties to the model and considering the effect of organic fertilizer pH. Using the modified model, N₂O emissions were simulated from two organic fertilizers (digested food waste and livestock slurry) applied to three farms in the United Kingdom: one growing winter wheat at Wensum (WE) and two grasslands at Pwllpeiran (PW) and North Wyke (NW). The annual cumulative gross (i.e. not excluding control emission) N₂O emissions were calculated using MATLAB trapezoidal numerical integration. The relative errors of the modeled annual cumulative emissions to the measured emissions ranged from −5.4% to 48%. Two-factor models, including linear, exponential and hyperbola responses, correlating total N loading and soil clay content to calculations of N₂O emissions and N₂O emission factors (EFs) were developed for calculations of emission fluxes and EFs. The squares of the correlation coefficients of the measured and two-factor linear modeled emissions were 0.998 and 0.999 for digestate and slurry, respectively, and the corresponding squares of correlation coefficients of the EFs were 0.998 and 0.938. The two-factor linear model also predicted that the EFs increased linearly with decreasing clay content and the maximum EFs for digestate and slurry were 0.95 and 0.76% of total N applied, respectively. This demonstrates that the modified UK_DNDC is a good tool to simulate N₂O emission from digestate and slurry and to calculate UK EFs using TIER 3 methodology..

Herbicides are increasingly recognised as sources of water pollution. Glyphosate-based herbicides (GBHs) are widely used because of their low cost and high effectiveness. By measuring the photosynthetic efficiency of Fucus virsoides fronds exposed to a GBH (Roundup® Power 2.0), we investigated the effect of a continuous exposure (6 days) and the potential of recovery after a short exposure (24 h). Both experiments were carried out combining GBH with and without nutrient enrichment, simulating a runoff event. A factorial experimental design allowed us to assess the potential of interactions between GBH and nutrients, which are likely to co-occur in coastal areas. Our results show deleterious effects of GBH at low concentration on F. virsoides, independently from the duration of exposure and the presence of nutrients.

A 120-day field study was carried out near Lake Taihu to evaluate the bioaccumulation of microcystin (MC) congeners in a soil-plant system, as well as to assess human health risk when consuming edible plants irrigated with MCs-contaminated water. Natural cyanobacteria bloom-containing lake water (lake water) and half-diluted natural cyanobacteria bloom-containing lake water with tap water (half-lake water) were used to irrigate lettuce and rice. An additional treatment involving fertilization with a cyanobacteria bloom was applied just to the lettuce experiment. MCs in soils, roots, leaves and grains (rice) were detected. In the soil-lettuce system, the three MC congeners in soils fertilized with a cyanobacteria bloom were not detected. The highest concentrations of MCs detected in soils, lettuce roots and leaves were 24.8 (MC-LR 10.1, MC-RR 10.5, MC-YR 4.2) μg kg−1, 424 (MC-LR 168, MC-RR 194, MC-YR 61.5) μg kg−1 and 183 (MC-LR 78.0, MC-RR 76.8, MC-YR 28.1) μg kg−1, respectively, in the lake water treatment. In the soil-rice system, the highest concentration of MCs was accumulated in roots 1504 (MC-LR 634, MC-RR 573, MC-YR 297) μg kg−1, in the lake water treatment. However, the concentration of MCs that accumulated in grains was extremely low with a total MCs concentration of 5.2 (MC-LR 2.1, MC-RR 2.0, MC-YR 1.1) μg kg−1 in the lake water treatment. According to the estimated daily intake (EDI) value, fertilizing with an appropriate amount (0.2% or less, w/w, dry weight (DW)) of a cyanobacteria bloom, as well as consuming rice irrigated with lake water would not pose a threat to human health. However, the EDI values for both adults and children reached tolerable daily intake (TDI) value, assuming they consumed lettuce irrigated with lake water. Results obtained from the growth and yield indicators suggest that MCs bioaccumulation in edible plants is not necessarily coupled with phytotoxic effects.

The effects of ambient temperature on stroke death in China have been well addressed. However, few studies are focused on the attributable burden for the incident of different types of stroke due to ambient temperature, especially in Beijing, China. We purpose to assess the influence of ambient temperature on hospital stroke admissions in Beijing, China. Data on daily temperature, air pollution, and relative humidity measurements and stroke admissions in Beijing were obtained between 2013 and 2014. Distributed lag non-linear model was employed to determine the association between daily ambient temperature and stroke admissions. Relative risk (RR) with 95% confidence interval (CI) and Attribution fraction (AF) with 95% CI were calculated based on stroke subtype, gender and age group. A total number of 147, 624 stroke admitted cases (including hemorrhagic and ischemic types of stroke) were documented. A non-linear acute effect of cold temperature on ischemic and hemorrhagic stroke hospital admissions was evaluated. Compared with the 25th percentile of temperature (1.2 °C), the cumulative RR of extreme cold temperature (first percentile of temperature, −9.6 °C) was 1.51 (95% CI: 1.08–2.10) over lag 0–14 days for ischemic type and 1.28 (95% CI: 1.03–1.59) for hemorrhagic stroke over lag 0–3 days. Overall, 1.57% (95% CI: 0.06%–2.88%) of ischemic stroke and 1.90% (95% CI: 0.40%–3.41%) of hemorrhagic stroke was attributed to the extreme cold temperature over lag 0–7 days and lag 0–3 days, respectively. The cold temperature's impact on stroke admissions was found to be more obvious in male gender and the youth compared to female gender and the elderly. Exposure to extreme cold temperature is associated with increasing both ischemic and hemorrhagic stroke admissions in Beijing, China.

Tin is a naturally occurring heavy metal that occurs in the environment in both inorganic and organic forms. Human exposure to tin is almost ubiquitous; however, surprisingly little is known about factors affecting environmental tin exposure in humans. This study analyzed demographic, socioeconomic and lifestyle factors associated with total urinary tin levels in adults (N = 3522) and children (N = 1641) participating in the National Health and Nutrition Examination Survey (NHANES) 2011–2014, a nationally representative health survey in the United States. Urinary tin levels, a commonly used biomarker of environmental tin exposure, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Detection frequencies of tin were 87.05% in adults and 91.29% in children. Median and geometric mean levels of urinary tin in the adult population were 0.42 μg/L and 0.49 μg/L, respectively. For children, median and geometric mean levels of urinary tin were 0.60 μg/L and 0.66 μg/L, respectively. Age was identified as an important factor associated with urinary tin levels. Median tin levels in the ≥60 year age group were almost 2-fold higher than the 20–39 year age group. Tin levels in children were 2-fold higher than in adolescents. Race/ethnicity and household income were associated with tin levels in both adults and children. In addition, physical activity was inversely associated with urinary tin levels in adults. These results demonstrate that total tin exposures vary across different segments of the general U.S. population. Because the present study does not distinguish between organic and inorganic forms of tin, further studies are needed to better characterize modifiable factors associated with exposures to specific tin compounds, with the goal of reducing the overall exposure of the U.S. population.

This paper summarizes the historical and scientific foundations of the Linear No-Threshold (LNT) cancer risk assessment model. The story of cancer risk assessment is an extraordinary one as it was based on an initial incorrect gene mutation interpretation of Muller, the application of this incorrect assumption in the derivation of the LNT single-hit model, and a series of actions by leading radiation geneticists during the 1946–1956 period, including a National Academy of Sciences (NAS) Biological Effects of Atomic Radiation (BEAR) I Genetics Panel (Anonymous, 1956), to sustain the LNT belief via a series of deliberate obfuscations, deceptions and misrepresentations that provided the basis of modern cancer risk assessment policy and practices. The reaffirming of the LNT model by a subsequent and highly influential NAS Biological Effects of Ionizing Radiation (BEIR) I Committee (NAS/NRC, 1972) using mouse data has now been found to be inappropriate based on the discovery of a significant documented error in the historical control group that led to incorrect estimations of risk in the low dose zone. Correction of this error by the original scientists and the application of the adjusted/corrected data back to the BEIR I (NAS/NRC, 1972) report indicates that the data would have supported a threshold rather than the LNT model. Thus, cancer risk assessment has a poorly appreciated, complex and seriously flawed history that has undermined policies and practices of regulatory agencies in the U.S. and worldwide to the present time.

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health.

The temporal and spatial trends in sediment of 22 poly- and perfluorinated (PFAS) compounds were investigated in the southern Great Lakes Erie and Ontario as well as Lake St. Clair. Surface concentrations measured by Ponar grab samples indicated a trend for greater concentrations near to urban sites. Mean concentrations ∑22PFAS were 15.6, 18.2 and 19 ng g−1 dm for Lakes St. Clair, Erie and Ontario, respectively. Perfluoro-n-butanoic acid (PFBA) and Perfluoro-n-hexanoic acid (PFHxA) were frequently determined in surface sediment and upper core samples indicating a shift in use patterns. Where PFBA was identified it was at relatively great concentrations typically >10 ng g−1 dm. However as PFBA and PFHxA are less likely to bind to sediment they may be indicative of pore water concentrations Sedimentation rates between Lake Erie and Lake Ontario differ greatly with greater rates observed in Lake Erie. In Lake Ontario, in general concentrations of PFAS observed in core samples closely follow the increase in use along with an observable change due to regulation implementation in the 1970s for water protection. However some of the more water soluble PFAS were observed in deeper core layers than the time of production could account for, indicating potential diffusion within the sediment. Given the greater sedimentation rates in Lake Erie, it was hoped to observe in greater resolution changes since the mid-1990s. However, though some decrease was observed at some locations the results are not clear. Many cores in Lake Erie had clearly observable gas voids, indicative of gas ebullition activity due to biogenic production, there were also observable mussel beds that could indicate mixing by bioturbation of core layers.