Data from three Swedish open-top chamber and four German FACE experiments were combined to derive response functions for elevated CO2 (eCO2) effects on Cd, Zn, Mn, protein, grain yield, grain mass and grain number of wheat. Grain yield and grain number were increased by ∼6% and ∼7%, respectively, per 100 ppm CO2; the former effect was linked to plant nitrogen status. Grain mass was not influenced by eCO2, whereas Cd concentration was reduced. Unlike Zn, Mn and protein, effects on Cd yield were not related to effects on grain yield. Yields of Mn, Zn and (weakly) protein were positively affected by eCO2. For protein, grain yield, grain mass and grain number, the results were consistent among the FACE and OTC experiments. A key conclusion was that yields of essential nutrients were enhanced (Mn > Zn > protein), although less than grain yield, which would not be expected from a simple dilution model.

Many wetlands support high biodiversity and are protected sites, but some are contaminated with radionuclides from routine or accidental releases from nuclear facilities. This radiation exposure needs to be assessed to demonstrate radiological protection of the environment. Existing biota dose models cover generic terrestrial, freshwater, and marine ecosystems, not wetlands specifically. This paper, which was produced under IAEA's Environmental Modelling for Radiation Safety (EMRAS) II programme, describes an evaluation of how models can be applied to radionuclide contaminated wetlands. Participants used combinations of aquatic and terrestrial model parameters to assess exposure. Results show the importance of occupancy factor and food source (aquatic or terrestrial) included. The influence of soil saturation conditions on external dose rates is also apparent. In general, terrestrial parameters provided acceptable predictions for wetland organisms. However, occasionally predictions varied by three orders of magnitude between assessors. Possible further developments for biota dose models and research needs are identified.

Aerosols from eight e-cigarettes at different nicotine levels and flavoring were characterized as particle number size distributions in the range 5.6–560 nm by FMPS and CPC. Results were used to provide dosimetry estimates applying the MMPD model.Particle number concentrations varied between 3.26 × 109 and 4.09 × 109 part cm−3 for e-liquids without nicotine and between 5.08 × 109 and 5.29 × 109 part cm−3 for e-liquids with nicotine. No flavor effects were detected on particle concentration data. Particle size distributions were unimodal with modes between 107–165 nm and 165–255 nm, for number and volume metrics, respectively.Averagely, 6.25 × 1010 particles were deposited in respiratory tree after a single puff. Highest deposition densities and mean layer thickness of e-cigarette liquid on the lung epithelium were estimated at lobar bronchi.Our study shows that e-cigarette aerosol is source of high particle dose in respiratory system, from 23% to 35% of the daily dose of a no-smoking individual.

Few studies have evaluated the effects of ambient air pollution and temperature in triggering out-of-hospital coronary deaths (OHCDs) in China. We evaluated the associations of air pollution and temperature with daily OHCDs in Shanghai, China from 2006 to 2011. We applied an over-dispersed generalized additive model and a distributed lag nonlinear model to analyze the effects of air pollution and temperature, respectively. A 10 μg/m3 increase in the present-day PM10, PM2.5, SO2, NO2 and CO were associated with increases in OHCD mortality of 0.49%, 0.68%, 0.88%, 1.60% and 0.08%, respectively. A 1 °C decrease below the minimum-mortality temperature corresponded to a 3.81% increase in OHCD mortality on lags days 0–21, and a 1 °C increase above minimum-mortality temperature corresponded to a 4.61% increase over lag days 0–3. No effects were found for in-hospital coronary deaths. This analysis suggests that air pollution, low temperature and high temperature may increase the risk of OHCDs.

An on-site active solid-phase microextraction (SPME) sampling technique coupled with gas chromatography-mass spectrometry (GC–MS) for sampling and monitoring 16 polycyclic aromatic hydrocarbons (PAHs) and 8 organochlorine pesticides (OCPs) in seawater was developed. Laboratory experiments demonstrated that the sampling-rate calibration method was practical and could be used for the quantification of on-site sampling. The proposed method was employed for field tests which covered large amounts of water samples in the Pearl River Estuary in rainy and dry seasons. The on-site SPME sampling method can avoid the contamination of sample, the losses of analytes during sample transportation, as well as the usage of solvent and time-consuming sample preparation process. Results indicated that the technique with the designed device can address the requirement of modern environment water analysis. In addition, the sources, bioaccumulation and potential risk to human of the PAHs and OCPs in seawater of the Pearl River Estuary were discussed.

Quantifying relationships between species richness and single environmental factors is challenging as species richness typically depends on multiple environmental factors. Recently, various methods have been proposed to tackle this challenge. Using a dataset comprising field observations of grassland vegetation and measured pH values, we compared three methods for deriving species richness response curves. One of the methods estimates species richness close to the maximum species richness observed at the sites, whereas the other two provide estimates of the potential species richness along the environmental gradient. Our response curves suggest that potential species richness of grasslands is slightly more sensitive to acidification than realized plant species richness. However, differences in corresponding environmental quality standards (EQS) for acidification were small compared to intrinsic spatial differences in natural soil pH, indicating that natural background values are more important to consider in the derivation of EQS for pH than methodological differences between the three approaches.

Metal-based nanoparticles (MNPs) may be translocated and biochemically modified in vivo, which may influence the fate of MNPs in the environment. Here, synchrotron-based techniques were used to investigate the behavior of CuO NPs in rice plants exposed to 100 mg/L CuO NPs for 14 days. Micro X-ray fluorescence (μ-XRF) and micro X-ray absorption near edge structure (μ-XANES) analysis revealed that CuO NPs moved into the root epidermis, exodermis, and cortex, and they ultimately reached the endodermis but could not easily pass the Casparian strip; however, the formation of lateral roots provided a potential pathway for MNPs to enter the stele. Moreover, bulk-XANES data showed that CuO NPs were transported from the roots to the leaves, and that Cu (II) combined with cysteine, citrate, and phosphate ligands and was even reduced to Cu (I). CuO NPs and Cu-citrate were observed in the root cells using soft X-ray scanning transmission microscopy (STXM).

There was an increasing interest in reducing production and emission of air pollutants to improve air quality for animal feeding operations (AFOs) in the U.S. in the 21st century. Research was focused on identification, quantification, characterization, and modeling of air pollutions; effects of emissions; and methodologies and technologies for scientific research and pollution control. Mitigation effects were on pre-excretion, pre-release, pre-emission, and post-emission. More emphasis was given on reducing pollutant emissions than improving indoor air quality. Research and demonstrations were generally continuation and improvement of previous efforts. Most demonstrated technologies were still in a limited scale of application. Future efforts are needed in many fundamental and applied research areas. Advancement in instrumentation, computer technology, and biological sciences and genetic engineering is critical to bring major changes in this area. Development in research and demonstration will depend on the actual political, economic, and environmental situations.

This study investigates concentrations of 18 perfluoroalkyl substances (PFAS) in blood plasma of adult lactating Weddell seals (Leptonychotes weddellii) (n = 10) from McMurdo Sound, Antarctica. Perfluoroundecanoic acid (PFUnDA) was detected in all samples at concentrations ranging from 0.08 to 0.23 ng/ml. Perfluorooctane sulfonate (PFOS), perfluorohexanoate (PFHxA) and perfluorotridecanoate (PFTriDA) were sporadically detected, while the remaining compounds were below the limit of detection. This is the first report of detectible concentrations of PFASs in an endemic Antarctic marine mammal species. We suggest that the pollutants have been subjected to long range atmospheric transportation and/or derive from a local source. A review of these and published data indicate that perfluoroalkyl carboxylates (PFCAs) dominate in biotic PFAS patterns in species feeding south of the Antarctic Circumpolar Current (ACC), whereas PFOS was the major PFAS detected in species feeding predominantly north of the current.

Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates.