Coal combustion and passive smoking are two important contributors to indoor air pollution (IAP) in rural areas of northern China. Although the association between outdoor air pollutants and hypertension risk had been widely reported, fewer studies have examined the relationship between IAP and hypertension risk. This study evaluated the association between IAP and hypertension risk in housewives in rural areas of northern China and the potential mediation pathway of metal elements. Our cross-sectional study, conducted in Shanxi Province, China, enrolled 367 subjects without taking anti-hypertensive drugs, including 142 subjects with hypertension (case group) and 225 subjects without hypertension (control group). We collected information on energy use characteristics and lifestyle using questionnaires. An IAP exposure index was developed to indicate the population exposure to coal combustion and passive smoking. Scalp hair samples were collected from the housewives and various trace and major metal elements were measured. Our results revealed that the IAP index was positively correlated with systolic and diastolic blood pressure. A significant association between the IAP index and hypertension risk was found both without [odds ratio (95% confidence interval, CI) = 2.08 (1.30–3.31)] and with [OR (95% CI) = 2.52 (1.46–4.36)] adjustment for confounders. We also observed that the IAP index was positively correlated with the arsenic, lead, and rare earth element levels in hair samples, and negatively correlated with the levels of some other trace elements (i.e., chromium, cobalt, nickel, and tin) and alkaline earth elements (i.e., calcium, magnesium, and barium) with an overall p value of <0.01. We concluded that IAP may contribute to the development of hypertension in rural housewives in northern China, possibly by interfering with the uptake of metal elements.

CO2-driven acidification and emerging contaminants, such as pharmaceuticals, pose new threats for the maintenance of natural populations of marine organisms by interfering with their normal biochemical pathways and defences. The combined effects of seawater acidification, as predicted in climate change scenarios, and an emerging contaminant (the non-steroidal anti-inflammatory drug, NSAID, diclofenac) on oxidative stress-related parameters were investigated in the Mediterranean mussel Mytilus galloprovincialis and the Manila clam Ruditapes philippinarum. A flow-through system was used to carry out a three-week exposure experiment with the bivalves. First, the animals were exposed to only three pH values for 7 days. The pH was manipulated by dissolving CO2 in the seawater to obtain two reduced pH treatments (pH −0.4 units and pH −0.7 units), which were compared with seawater at the natural pH level (8.1). Thereafter, the bivalves were concomitantly exposed to the three experimental pH values and environmentally relevant concentrations of diclofenac (0.00, 0.05 and 0.50 μg/L) for an additional 14 days. The activities of superoxide dismutase, catalase and cyclooxygenase, and lipid peroxidation and DNA strand-break formation were measured in both the gills and digestive gland after 7, 14 and 21 days of exposure to each experimental condition. The results show that the biochemical parameters measured in both the mussels and clams were more influenced by the reduced pH than by the contaminant or the pH*contaminant interaction, although the biomarker variation patterns differed depending on the species and tissues analysed. Generally, due to increases in its antioxidant defence, M. galloprovincialis was more resistant than R. philippinarum to both diclofenac exposure and reduced pH. Conversely, reduced pH induced a significant decrease in COX activity in both the gills and digestive gland of clams, possibly resulting in the increased DNA damage observed in the digestive gland tissue.

As a result of legal protection and population recovery, European polecats (Mustela putorius) in Great Britain are expanding into areas associated with greater usage of second-generation anticoagulant rodenticides (SGARs). We analysed polecat livers collected from road casualties from 2013 to 2016 for residues of five SGARs. We related variation in residues to polecat traits and potential exposure pathways, by analysing stable isotopes of carbon (δ13C) and nitrogen (δ15N) in their whiskers. 54 of 68 (79%) polecats had detectable residues of at least one SGAR. Bromadiolone (71%) was the most frequently detected compound, followed by difenacoum (53%) and brodifacoum (35%). Applying historical limits of detection to allow comparison between these new data and previous assessments, we show that in the 25 years from 1992 to 2016 inclusive, the rate of detection of SGARs in polecats in Britain has increased by a factor of 1.7. The probability of SGAR detection was positively related to increasing values of δ15N, suggesting that polecats feeding at a higher trophic level were more likely to be exposed. Total concentrations of SGARs in polecats with detectable residues were higher in polecats collected in arable compared to pastoral habitats, and in the west compared to the east of Britain. The number of compounds detected and total concentrations of SGARs increased with polecat age. There was no evidence of regional or seasonal variation in the probability of detecting SGARs, suggesting that the current risk of exposure to SGARs does not vary seasonally and has increased (from that in the 1990s) throughout the polecat's range. We recommend quantification of current practices in rodenticide usage, particularly in the light of recent regulatory changes, to enable assessment and mitigation of the risks of secondary exposure to rodenticides in non-target wildlife.

Due to the severe degradation and environmental pollution of coastal wetlands by human activities, they have gradually become an important source of greenhouse gases (GHGs) emissions, so exploring the characteristics of their emission is important to reduce greenhouse gas emissions from coastal wetlands. In this study, the dynamics of methane (CH₄) and carbon dioxide (CO₂) emissions were investigated in five kinds of typical tidal flats from the Yellow River delta wetland during the years 2011–2013, and the influences of water level and salinity on their emissions were explored in laboratory experiments. The mean fluxes of CO₂ and CH₄ were −20.98 to 68.12 mg m⁻² h⁻¹ and −0.12 to 0.44 mg m⁻² h⁻¹ across all seasons in the five kinds of representative tidal flats. The highest and lowest mean fluxes of CO₂ were mainly observed during summer and winter, respectively, whereas the seasons with the highest and lowest mean fluxes of CH₄ varied according to the type of tidal flat. The results showed that the summer season and the mud flat environment had the largest contributions to greenhouse gas emissions. In laboratory experiments, the largest sequestration fluxes of CO₂ and CH₄ were observed with +4/+2 cm and −4 cm water levels, respectively, indicating that a moderately high water level was beneficial for CO₂ sequestration but led to the increase of CH₄ emission. In the study of salinity, we found that the largest sequestration fluxes of CO₂ and CH₄ were both detected at 24 g L⁻¹ salinity, indicating that high salinity level was advantageous for CO₂ and CH₄ sequestration in the five simulation devices. Furthermore, a carbon cycle pathway of coastal wetlands was proposed, which could have a vital significance for research into the global carbon cycle. We can reduce GHG emissions by protecting the coastal wetlands and lessening human activities.

Faced with the unavoidable reality of the emission of pollutant gases by vessels both while sailing and when performing in-port manoeuvres, the international community has devised an extensive set of rules to limit greenhouse gas emissions and the emission of other pollutants which are bad for our health. In order to make these reductions in the emissions, the areas addressed are the engine regime or speed control, the quality of the fuel used, the state of conservation of the vessel and its hull or the time taken to perform the manoeuvres of mooring and unmooring. One factor which is having a strong influence on this last aspect is the installation in commercial ports of Automatic Mooring Systems using suction cups (AMS). These devices, which help to reduce considerably the time required to perform the mooring and unmooring manoeuvres, allow the times taken in operations for making steady a vessel to land and of releasing it to sail away to be reduced from some tens of minutes to a few seconds. The aim of this work is to verify the effect of the AMS on the emission of pollutant gases in the surroundings of the installations devoted to Ro-Ro/Pax vessel traffic. In particular, will focus on the CO2 emissions produced by vessels during mooring operations using two different calculation methodologies (EPA and ENTEC), first when using traditional mooring methods as a means of securing the vessel to the dock and second when using only the AMS, to finally carry out a comparison of the results. Will conclude with a discussion on the values of the reduction in emissions obtained and the advantages of installing AMS in commercial ports. In the RoRo/Pax terminals in which the AMS is installed and operating, a reduction in CO2 emissions of 97% has been estimated.

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

Aerosol samples (as PM₁₀, n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, NH₄⁺, F⁻, Cl⁻, NO₃⁻, SO₄²⁻), and carbon. Vital ratios like NO₃⁻/SO₄²⁻, EC/OC, K⁺/EC, K⁺/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable.

Triclosan (TCS) is an organic compound with a wide range of antibiotic activity and has been widely used in items ranging from hygiene products to cosmetics; however, recent studies suggest that it has several adverse effects. In particular, TCS can be passed to both fetus and infants, and while some evidence suggests in vitro neurotoxicity, there are currently few studies concerning the mechanisms of TCS-induced developmental neurotoxicity. Therefore, this study aimed to clarify the effect of TCS on neural development using zebrafish models, by analyzing the morphological changes, the alterations observed in fluorescence using HuC-GFP and Olig2-dsRED transgenic zebrafish models, and neurodevelopmental gene expression. TCS exposure decreased the body length, head size, and eye size in a concentration-dependent manner in zebrafish embryos. It increased apoptosis in the central nervous system (CNS) and particularly affected the structure of the CNS, resulting in decreased synaptic density and shortened axon length. In addition, it significantly up-regulated the expression of genes related to axon extension and synapse formation such as α1-Tubulin and Gap43, while decreasing Gfap and Mbp related to axon guidance, myelination and maintenance. Collectively, these changes indicate that exposure to TCS during neurodevelopment, especially during axonogenesis, is toxic. This is the first study to demonstrate the toxicity of TCS during neurogenesis, and suggests a possible mechanism underlying the neurotoxic effects of TCS in developing vertebrates.

This study investigates the influence of small-scale sediment transport on glyphosate and AMPA redistribution on the soil surface and on their off-site transport during water erosion events. Both a smooth surface (T1) and a surface with “seeding lines on the contour” (T2) were tested in a rainfall simulation experiment using soil flumes (1 × 0.5 m) with a 5% slope. A dose of 178 mg m⁻² of a glyphosate-based formulation (CLINIC®) was applied on the upper 0.2 m of the flumes. Four 15-min rainfall events (RE) with 30-min interval in between and a total rainfall intensity of 30 mm h⁻¹ were applied. Runoff samples were collected after each RE in a collector at the flume outlet. At the end of the four REs, soil and sediment samples were collected in the application area and in four 20 cm-segments downslope of the application area. Samples were collected according to the following visually distinguished soil surface groups: light sedimentation (LS), dark sedimentation (DS), background and aggregates.Results showed that runoff, suspended sediment and associated glyphosate and AMPA off-site transport were significantly lower in T2 than in T1. Glyphosate and AMPA off-site deposition was higher for T2 than for T1, and their contents on the soil surface decreased with increasing distance from the application area for all soil surface groups and in both treatments. The LS and DS groups presented the highest glyphosate and AMPA contents, but the background group contributed the most to the downslope off-site deposition.Glyphosate and AMPA off-target particle-bound transport was 9.4% (T1) and 17.8% (T2) of the applied amount, while water-dissolved transport was 2.8% (T1) and 0.5% (T2). Particle size and organic matter influenced the mobility of glyphosate and AMPA to off-target areas. These results indicate that the pollution risk of terrestrial and aquatic environments through runoff and deposition can be considerable.

This study conducted a field campaign to collect atmospheric deposition samples of heavy metals and arsenic, a metalloid element with typical chemical-physical characteristics (HMA), from 12 sampling sites and water samples from 37 rivers across the Bohai Sea (BS) and North Yellow Sea (NYS) in China. The HMA budgets in the BS and NYS were quantified by a budget model, which was developed based on the HMA inputs from atmospheric deposition and riverine discharge, sequestration to sediment, and interexchange among the BS's four subareas and the NYS. Statistical analyses of 76 deposition samples and 109 water concentration samples showed that atmospheric deposition was a main pathway of Pb entering the BS and NYS, whereas riverine discharge dominated the input of Cr, Cu, Zn, Cd, and As into the marine environment. Modeled results showed that the fractions of HMA in the water bodies compared with their total burdens were 86.6 ± 4.55% in the Liaodong Bay, 60.5 ± 10.5% in the Bohai Bay, 20.9 ± 9.05% in the Laizhou Bay, 95.1 ± 2.06% in the Central BS, and 94.3 ± 1.93% in the NYS. The lowest fraction of HMA in the Laizhou Bay was attributed to high sedimentation rates and higher suspended particulate matter concentrations due to inputs from the Yellow River. The modeled 1-, 10- and 100- year mass budgets indicated that the Liaodong Bay in the north of the BS was a sink of HMA, the Bohai Bay and Laizhou Bay in the west and south of the BS acted as sources, and the Central BS and NYS were a transition area for most HMA.