Heavy metal pollution of water sources has raised global environmental sustainability concerns, calling for the development of high-performance materials for effective pollution treatment. Herein, we report a facile approach to synthesize carbonaceous nanofiber/NiAl layered double hydroxide (CNF/LDH) nanocomposites for high-efficiency elimination of heavy metals from aqueous solutions. The CNF/LDH nanocomposites were characterized by three-dimensional architectures formed by the gradual self-assembly of flower-like LDH on CNF. The nanocomposites exhibited excellent hydrophilicity and high structural stability in aqueous solutions, guaranteeing the high availability of active sites in these environments. High-efficiency elimination of heavy metal ions by the CNF/LDH nanocomposites was demonstrated by the high uptake capacities of Cu(II) (219.6 mg/g) and Cr(VI) (341.2 mg/g). The sorption isotherms coincided with the Freundlich model, most likely because of the presence of heterogeneous binding sites. The dominant interaction mechanisms consisted of surface complexation and electrostatic interaction, as verified by a combination of X-ray photoelectron spectroscopy and Fourier-transform infrared spectroscopy analyses and density functional theory calculations. The results presented herein confirm the importance of CNF/LDH nanocomposites as emerging and promising materials for the efficient removal of heavy metal ions and other environmental pollutants.

Nitrate is a ubiquitous aquatic pollutant that is commonly associated with eutrophication and dead zones in estuaries around the world. At high concentrations nitrate is toxic to aquatic life but at environmental concentrations it has also been purported as an endocrine disruptor in fish. To investigate the potential for nitrate to cause endocrine disruption in fish, we conducted a lifecycle study with fathead minnows (Pimephales promelas) exposed to nitrate (0, 11.3, and 56.5 mg/L (total nitrate-nitrogen (NO3-N)) from <24 h post hatch to sexual maturity (209 days). Body mass, condition factor, gonadal somatic index (GSI), incidence of intersex, and vitellogenin induction were determined in mature male and female fish and plasma 11-keto testosterone (11-KT) was measured in males only. In nitrate-exposed males both 11-KT and vitellogenin were significantly induced when compared with controls. No significant differences occurred for body mass, condition factor, or GSI among males and intersex was not observed in any of the nitrate treatments. Nitrate-exposed females also had significant increases in vitellogenin compared to controls but no significant differences for mass, condition factor, or GSI were observed in nitrate exposed groups. Estradiol was used as a positive control for vitellogenin induction. Our findings suggest that environmentally relevant nitrate levels may disrupt steroid hormone synthesis and/or metabolism in male and female fish and may have implications for fish reproduction, watershed management, and regulation of nutrient pollution.

PM₂.₅ is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM₂.₅. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM₂.₅ were collected at Tianjin, a megacity in northern China. The ambient PM₂.₅ dataset, source information, and gas-to-particle ratios (such as SO₂/PM₂.₅, CO/PM₂.₅, and NOx/PM₂.₅ ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM₂.₅ was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Previous studies have suggested that ambient temperature is associated with the mortality and morbidity of myocardial infarction (MI) although consistency among these investigations is lacking. We performed a meta-analysis to investigate the relationship between ambient temperature and MI. The PubMed, Web of Science, and China National Knowledge Infrastructure databases were searched back to August 31, 2017. The pooled estimates for different temperature exposures were calculated using a random-effects model. The Cochran's Q test and coefficient of inconsistency (I2) were used to evaluate heterogeneity, and the Egger's test was used to assess publication bias. The exposure-response relationship of temperature-MI mortality or hospitalization was modeled using random-effects meta-regression. A total of 30 papers were included in the review, and 23 studies were included in the meta-analysis. The pooled estimates for the relationship between temperature and the relative risk of MI hospitalization was 1.016 (95% confidence interval [CI]: 1.004–1.028) for a 1 °C increase and 1.014 (95% CI: 1.004–1.024) for a 1 °C decrease. The pooled estimate of MI mortality was 1.639 (95% CI: 1.087–2.470) for a heat wave. The heterogeneity was significant for heat exposure, cold exposure, and heat wave exposure. The Egger's test revealed potential publication bias for cold exposure and heat exposure, whereas there was no publication bias for heat wave exposure. An increase in latitude was associated with a decreased risk of MI hospitalization due to cold exposure. The association of heat exposure and heat wave were immediate, and the association of cold exposure were delayed. Consequently, cold exposure, heat exposure, and exposure to heat waves were associated with an increased risk of MI. Further research studies are required to understand the relationship between temperature and MI in different climate areas and extreme weather conditions.

The long-term associations between multiple metals and incident diabetes are uncertain. We aimed to examine the relationship between plasma concentrations of 23 metals and the incidence of type 2 diabetes among Chinese senior adults. We quantified fasting plasma concentrations of 23 metals by inductively coupled plasma mass spectrometry among 1039 incident diabetes cases and 1039 controls (age and sex matched) nested in a prospective study, the Dongfeng-Tongji cohort. Both cases and controls were free of diabetes at baseline (2008–2010), incident diabetes were identified using the following criteria: fasting glucose ≥ 7.0 mmoL/l; or hemoglobin A1c (HbA1c) ≥ 6.5%; or self-reported physician diagnosis of diabetes or use of anti-diabetic medication during the follow-up visits in 2013. In the conditional logistic regression models, the multivariable adjusted ORs (95% CIs) of diabetes across quartiles (Q1–Q4) of metal concentrations were as follows: titanium, 1.00, 0.92, 1.31, 1.38 (1.00–1.91, Ptrend = 0.011); selenium, 1.00, 1.08, 1.45, 1.27 (0.93–1.74, Ptrend = 0.05); and antimony, 1.00, 0.79, 0.77, 0.60 (0.44–0.83, Ptrend = 0.002). Arsenic was significantly associated with diabetes in the crude model (ORs comparing extreme quartiles 1.30; 1.02–1.65; Ptrend = 0.006), but was not significant after adjustment for socio-demographic factors. No significant associations were found for other metals. In conclusion, titanium and selenium were positively while antimony was negatively associated with incident diabetes.

The exponential production and use of plastics has generated increasing environmental release over the past decades, and microplastics (MPs) have been reported across all the oceans. Field studies have documented the occurrence of MPs in several species, but important knowledge gaps still remain. In the present study, we characterized the distribution of MPs in ten sediment-dwelling and epibenthic species representative of different habitat, feeding modes and trophic levels within the inner Oslofjord (Oslo, Norway), an area subjected to moderate anthropogenic pressures. Analysed species included fish, bivalves, echinoderms, crustaceans and polychaetes. MPs were present in all the species with a frequency up to 65% of positive individuals for some species. In most cases, 1 or 2 MPs were found per individual, but some organisms contained up to 7 particles. A total of 8 polymer typologies were identified, with PE and PP being the most common according to our extraction protocol. MP sizes ranged from 41 μm to lines as long as 9 mm. Our results indicate that occurrence of MPs in analysed biota is not influenced by organism habitat or trophic level, while characteristics and typology of polymers might be significantly affected by feeding mode of organisms.

Artificial lighting at night has becoming a new type of pollution posing an important anthropogenic environmental pressure on organisms. The objective of this research was to examine the potential association between nighttime artificial light pollution and nest densities of the three main sea turtle species along Florida beaches, including green turtles, loggerheads, and leatherbacks. Sea turtle survey data was obtained from the “Florida Statewide Nesting Beach Survey program”. We used the new generation of satellite sensor “Visible Infrared Imaging Radiometer Suite (VIIRS)” (version 1 D/N Band) nighttime annual average radiance composite image data. We defined light pollution as artificial light brightness greater than 10% of the natural sky brightness above 45° of elevation (>1.14 × 10⁻¹¹ Wm⁻²sr⁻¹). We fitted a generalized linear model (GLM), a GLM with eigenvectors spatial filtering (GLM-ESF), and a generalized estimating equations (GEE) approach for each species to examine the potential correlation of nest density with light pollution. Our models are robust and reliable in terms of the ability to deal with data distribution and spatial autocorrelation (SA) issues violating model assumptions. All three models found that nest density is significantly negatively correlated with light pollution for each sea turtle species: the higher light pollution, the lower nest density. The two spatially extended models (GLM-ESF and GEE) show that light pollution influences nest density in a descending order from green turtles, to loggerheads, and then to leatherbacks. The research findings have an implication for sea turtle conservation policy and ordinance making. Near-coastal lights-out ordinances and other approaches to shield lights can protect sea turtles and their nests. The VIIRS DNB light data, having significant improvements over comparable data by its predecessor, the DMSP-OLS, shows promise for continued and improved research about ecological effects of artificial light pollution.

In this study, concentrations of NOₓ, SO₂, O₃ and fine particles (PM₂.₅) were measured at three monitoring stations in Beijing during 2015. For extreme haze episodes during 25 Nov. - 3 Dec. 2015, observation data confirmed that high concentrations of NOₓ promoted the conversion of SO₂ to sulfate. Annual data confirmed that this is an important mechanism for the occurrence of heavy haze during winter in Beijing. Furthermore, in situ perturbation experiments in a potential aerosol mass (PAM) reactor were carried out at Shengtaizhongxin (STZX) station during both clean and polluted days. The concentrations of SO₄²⁻, NH₄⁺, NO₃⁻ and organic aerosol were positively related to the concentration of added NO₂. These results provide definitive evidence that NO₂ can promote the conversion of SO₂ to sulfate. At the same time, we found that NO₂ can promote the formation of NH₄⁺ and organic compounds in the aerosols. Our results illustrate that strengthened controls of nitrogen oxides is a key step in reducing the fine particles level in China.

Polycyclic Aromatic Hydrocarbons (PAHs) are among the most ubiquitous environmental pollutants of high global concern. PAHs belong to a diverse family of hydrocarbons with over one hundred compounds known, each containing at least two aromatic rings in their structure. Due to hydrophobic nature, PAHs tend to accumulate in the aquatic sediments, leading to bioaccumulation and elevated concentrations over time. In addition to their well-manifested mutagenic and carcinogenic effects in humans, they pose severe detrimental effects to aquatic life. The high eco-toxicity of PAHs has attracted a number of reviews, each dealing specifically with individual aspects of this global pollutant. However, efficient management of PAHs warrants a holistic approach that combines a thorough understanding of their physico-chemical properties, modes of environmental distribution and bioaccumulation, efficient detection, and bioremediation strategies. Currently, there is a lack of a comprehensive study that amalgamates all these aspects together. The current review, for the first time, overcomes this constraint, through providing a high level comprehensive understanding of the complexities faced during PAH management, while also recommending future directions through potentially viable solutions. Importantly, effective management of PAHs strongly relies upon reliable detection tools, which are currently non-existent, or at the very best inefficient, and therefore have a strong prospect of future development. Notably, the currently available biosensor technologies for PAH monitoring have not so far been compiled together, and therefore a significant focus of this article is on biosensor technologies that are critical for timely detection and efficient management of PAHs. This review is focussed on inland aquatic ecosystems with an emphasis on fish biodiversity, as fish remains a major source of food and livelihood for a large proportion of the global population. This thought provoking study is likely to instigate new collaborative approaches for protecting aquatic biodiversity from PAHs-induced eco-toxicity.

Plant uptake factors (PUFs) are of great importance in human cadmium (Cd) exposure risk assessment while it has been often treated in a generic way. We collected 1077 pairs of vegetable-soil samples from production fields to characterize Cd PUFs and demonstrated their utility in assessing Cd exposure risks to consumers of locally grown vegetables. The Cd PUFs varied with plant species and pH and organic matter content of soils. Once normalized PUFs against soil parameters, the PUFs distributions were log-normal in nature. In this manner, the PUFs were represented by definable probability distributions instead of a deterministic figure. The Cd exposure risks were then assessed using the normalized PUF based on the Monte Carlo simulation algorithm. Factors affecting the extent of Cd exposures were isolated through sensitivity analyses. Normalized PUF would illustrate the outcomes for uncontaminated and slightly contaminated soils. Among the vegetables, lettuce was potentially hazardous for residents due to its high Cd accumulation but low Zn concentration. To protect 95% of the lettuce production from causing excessive Cd exposure risks, pH of soils needed to be 5.9 and above.