Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM₂.₅), though it is important to mitigate the estimation bias of PM₂.₅ due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM₂.₅ levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM₂.₅ levels during 2013–2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R² of 0.95. Subsequently, the second random-forest submodel (named PM₂.₅-submodel) was trained to estimate the PM₂.₅ levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM₂.₅ levels, and the covariates, and achieved a cross-validation R² of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM₂.₅ levels could be overestimated by 34.6% if the PM₂.₅ values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM₂.₅ from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.

Most cities in China experience frequent PM₂.₅ pollution, in relation to unfavorable planetary boundary layer (PBL) conditions. Partly due to the limited appropriate PBL observations, the explicit relationships between PBL structure/process and PM₂.₅ pollution in China are not yet clearly understood. Using the fine-resolution sounding measurements from 2014 to 2017, the relationships between boundary layer height (BLH) and PM₂.₅ pollution in China were systematically examined. Four regions of interest (ROIs) featured with dense population and heavy pollution were studied and compared, including Northeast China (NEC), North China Plain (NCP), East China (EC), and Sichuan Basin (SCB). From 2014 to 2017, the heaviest PM₂.₅ pollution happened in NCP with an annual average concentration of 84 μg m⁻³, followed by NEC (60 μg m⁻³), SCB (57 μg m⁻³), and EC (54 μg m⁻³). Correlation analyses revealed a significant anti-correlation between BLH and daily PM₂.₅ concentrations across China, independent of ROIs. During an annual cycle, the pollution was heaviest in winter, followed by fall and spring, and reached its minimum in summer. Such a seasonal variation of pollution was not only modulated by the emissions, but also the seasonal shifts of BLH. The low BLH in winter was often associated with strong near-surface thermal stability. Moreover, certain synoptic conditions in winter can exacerbate the pollution, leading to concurrent drops of BLH and synchronous increases of PM₂.₅ concentration in different cities of a ROI. In NCP and SCB, the mountainous terrains could further worsen the pollution by blocking effects and lee eddies.

Colloids are ubiquitous in soils, and it has been reported that colloids can act as carriers to increase the mobility of poorly soluble contaminants in subsurface environments. Addition of sulfur (S) fertilizer greatly influences on heavy metal behavior in paddy soil, while the influence of S fertilizer on Cu migration and transformation in colloids of soil pore water has not yet been studied. The influence of S fertilizer (S⁰ and Na₂SO₄) applied in paddy soils on Cu migration and transformation in colloids of soil pore water from the rice rhizosphere region was explored in this study. The speciation of Cu in colloids of soil pore water from the rice rhizosphere region was explored by advanced synchrotron-based X-ray absorption near-edge spectroscopy (XANES) techniques. The morphology of colloids was characterized by field emission scanning electron microscopy coupled to energy dispersive X-ray spectroscopy (SEM-EDX). At a depth of 20 cm, the concentration of Cu in colloids of the rhizosphere soil pore water in the control was 2.4- and 6.5- fold higher than that in treatments of S⁰ and Na₂SO₄, respectively. The colloids in soil pore water were all positively charged, ranging from 2.4 to 7.8 mV, and the size of colloids was 440–740 nm. The proportion of Fe in colloids in the rhizosphere region decreased with S fertilizer application, while the proportions of C and O increased. Sulfur fertilizer application, increased the proportion of Cu-Cysteine, while the proportion of Cu₂S decreased in soil colloids. In conclusion, application of sulfur fertilizer in paddy soil decreased the Cu concentration in soil pore water and colloids of the rhizosphere region, thereby decreasing the vertical migration of Cu in soil pore water.

Perfluorooctanoic acid (PFOA) is widely distributed in various environmental media and is toxic to organisms. This study demonstrated that PFOA induces hepatotoxicity in the frog and evaluated the role of CYP3A and the Nrf2-ARE signaling pathway in regulating responses to PFOA-induced hepatotoxicity. Rana nigromaculata were exposed to 0, 0.01, 0.1, 0.5, or 1 mg/L PFOA solutions in a static-renewal system for 14 days. Liver tissue samples were collected 24 h after the last treatment. Hepatic histology was observed by HE staining and transmission electron microscopy. The oxidative stress levels in the liver were measured. The expression levels of CYP3A, Nrf2, NQO1, and HO-1 mRNA were measured by quantitative reverse transcription–polymerase chain reaction. PFOA-treated frog liver tissue exhibited diffuse cell borders, cytoplasmic vacuolization, broken nuclei, nuclear chromatin margination, and swollen mitochondria. In addition, the livers of PFOA-treated frogs showed a significantly elevated content of reactive oxygen species, malondialdehyde, glutathione and glutathione S-transferase activity compared to the livers of control frogs. However, the glutathione peroxidase activities concomitantly decreased in PFOA-treated frogs compared to those in the control group. Furthermore, compared with control frogs, the expression levels of CYP3A, Nrf2, and NQO1 mRNA significantly increased in PFOA-treated frogs. HO-1 mRNA expression remarkably increased only in groups treated with 0.5 or 1 mg/L PFOA. Our results indicate that PFOA induces hepatotoxicity in a dose-dependent manner. Furthermore, the results of the comparison analysis between different gender groups illustrated that PFOA is more toxic to female frogs than male frogs. Our results demonstrated that PFOA causes liver damage and that CYP3A enhances PFOA-induced female frogs hepatotoxicity are more virulent than male through biotransformation, and the activation of the Nrf2-ARE pathway is induced to protect against hepatotoxicity in Rana nigromaculata, all of which provide the scientific basis for the protection of amphibians against environmental contaminants.

Productive coastal and estuarine habitats can be degraded by contaminants including persistent organic pollutants (POPs) such as PCBs, dioxins, and organochlorine insecticides to the extent of official designation as contaminated sites. Top-predatory wildlife may continue to use such sites as the habitat often appears suitable, and thus bioaccumulate POPs and other contaminants with potential consequences on their health and fitness. Victoria and Esquimalt harbours are located on southern Vancouver Island, British Columbia (BC) and are federally designated contaminated sites due mainly to past heavy industrial activities, such as from shipyards and sawmills. We collected scat samples from river otters (Lontra canadensis) throughout an annual cycle, and combined chemical analysis with DNA genotyping to examine whether the harbour areas constituted a contaminant-induced ecological trap for otters. We confirmed spatial habitat use by radio telemetry of a subsample of otters. Fifteen percent of otter scat contained PCB concentrations exceeding levels considered to have adverse effects on the reproduction of mink (Neovison vison), and there were significant positive correlations between concentrations of PCBs and of thyroid (T3) and sex (progesterone) hormones in fecal samples. Radio telemetry data revealed that otters did not show directional movement away from the harbours, indicating their inability to recognize the contaminated site as a degraded habitat. However, analysis and modeling of the DNA genotyping data provided no evidence that the harbour otters formed a sink population and therefore were in an ecological trap. Despite the highly POP-contaminated habitat, river otters did not appear to be adversely impacted at the population level. Our study demonstrates the value of combining chemical and biological technologies with ecological theory to investigate practical conservation problems.

The last decades have seen the increasing prevalence of thyroid disorders. These augmentations could be the consequence of the increasing contamination of the environment by chemicals that may disrupt the thyroid function. Indeed, in vitro studies have shown that many chemicals contaminating our environment and highlighted in human serum, are able to interfere with the thyroid function. Given the crucial importance of thyroid hormones on neurodevelopment in fetus and newborns, the influence of these pollutants on newborn thyroid homeostasis is a major health concern. Unfortunately, the overall evidence for a deleterious influence of environmental pollutants on thyroid remains poorly studied. Therefore, we assessed the contamination by polychlorinated biphenyls (PCBs), organochlorine pesticides and perfluorinated compounds (PFC) in 221 cord blood samples collected in Belgium between 2013 and 2016. Our results showed that compared to previous studies performed on newborns recruited in Belgium during the two last decades, the present pollutant contamination is declining. Multivariate statistical analyses pointed out a decrease of thyroid stimulating hormone (TSH) level in male newborns with detectable level of 4,4′- dichlorodiphenyldichloroethylene (4,4′-DDE) in comparison with those with no detectable level (p = 0.025). We also highlighted a negative association between perfluorononanoic acid (PFNA) concentration and TSH in male newborns (p = 0.018). Logistic regression showed increased odds ratio for presentation of hypothyroid in mother for each one unit augmentation of log natural concentration of PFOA (OR = 2.30, [1.18–4.5]) and PFOS (OR = 2.03 [1.08–3.83]). Our findings showed that the residual contamination by PFCs and organochlorine pollutants in cord blood are correlated with thyroid hormone in the newborns and the risk of hypothyroid in mothers.

Hyperspectral imaging technology has been investigated as a possible way to detect microplastics contamination in soil directly and efficiently in this study. Hyperspectral images with wavelength range between 400 and 1000 nm were obtained from soil samples containing different materials including microplastics, fresh leaves, wilted leaves, rocks and dry branches. Supervised classification algorithms such as support vector machine (SVM), mahalanobis distance (MD) and maximum likelihood (ML) algorithms were used to identify microplastics from the other materials in hyperspectral images. To investigate the effect of particle size and color, white polyethylene (PE) and black PE particles extracted from soil with two different particle size ranges (1–5 mm and 0.5–1 mm) were studied in this work. The results showed that SVM was the most applicable method for detecting white PE in soil, with the precision of 84% and 77% for PE particles in size ranges of 1–5 mm and 0.5–1 mm respectively. The precision of black PE detection achieved by SVM were 58% and 76% for particles of 1–5 mm and 0.5–1 mm respectively. Six kinds of household polymers including drink bottle, bottle cap, rubber, packing bag, clothes hanger and plastic clip were used to validate the developed method, and the classification precision of polymers were obtained from 79% to 100% and 86%–99% for microplastics particle 1–5 mm and 0.5–1 mm respectively. The results indicate that hyperspectral imaging technology is a potential technique to determine and visualize the microplastics with particle size from 0.5 to 5 mm on soil surface directly.

This study investigated the treatment performance and nitrogen removal mechanism of highly alkaline ammonia-stripped digestate effluent in horizontal subsurface flow constructed wetlands (CWs). A promising nitrogen removal performance (up to 91%) was observed in CWs coupled with intensified configurations, i.e., aeration and effluent recirculation. The results clearly supported that the higher aeration ratio and presence of effluent recirculation are important to improve the alkalinity and pollutant removal in CWs. The influent pH (>10) was significantly decreased to 8.2–8.8 under the volumetric hydraulic loading rates of 0.105 and 0.21 d−1 in the CWs. Simultaneously, up to 91% of NH4+-N removal was achieved under the operation of a higher aeration ratio and effluent recirculation. Biological nitrogen transformations accounted for 94% of the consumption of alkalinity in the CWs. The significant enrichment of δ15N-NH4+ in the effluent (47–58‰) strongly supports the occurrence of microbial transformations for NH4+-N removal. However, relatively lower enrichment factors of δ15N-NH4+ (−1.8‰ to −11.6‰) compared to the values reported in previous studies reflected the inhibition effect of the high pH alkaline environment on nitrifiers in these CWs.

Maternal arsenic exposure leads to adverse birth outcomes, but the critical window of this susceptibility keeps unclear. To determine whether the associations between maternal arsenic exposure and birth outcomes were trimester-specific, we conducted a birth cohort study of 1390 women from 2014 to 2016 in Wuhan, China. We examined associations between total urinary arsenic concentrations in three trimesters and birth weight, birth length and the risk of small for gestational age (SGA), and the differences of these associations across trimesters using generalized estimating equations. Maternal urinary arsenic concentrations varied across trimesters and were weakly correlated. Arsenic concentrations in the 3rd trimester, but not in the 1st and 2nd trimesters, were associated with birth outcomes. For each doubling of arsenic levels in the 3rd trimester, birth weight was decreased 24.27 g (95% confidence interval (CI): −46.99, −1.55), birth length was decreased 0.13 cm (95% CI: −0.22, −0.04), and the risk for SGA birth was increased 25% (95% CI: 1.03, 1.49). Further, stratified analyses indicated that these associations were only observed in female infants. Our findings indicate maternal arsenic levels in the 3rd trimester seemed to have significant impacts on birth outcomes, and also emphasize the public health interventions relevance to arsenic exposure in late pregnancy.

Few studies have estimated historical exposures to PM₁₀ at a national scale in China using satellite-based aerosol optical depth (AOD). Also, long-term trends have not been investigated.In this study, daily concentrations of PM₁₀ over China during the past 12 years were estimated with the most recent ground monitoring data, AOD, land use information, weather data and a machine learning approach.Daily measurements of PM₁₀ during 2014–2016 were collected from 1479 sites in China. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data, land use information, and weather data were downloaded and merged. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed and their predictive abilities were compared. The best model was applied to estimate daily concentrations of PM₁₀ across China during 2005–2016 at 0.1⁰ (≈10 km).Cross-validation showed our random forests model explained 78% of daily variability of PM₁₀ [root mean squared prediction error (RMSE) = 31.5 μg/m³]. When aggregated into monthly and annual averages, the models captured 82% (RMSE = 19.3 μg/m³) and 81% (RMSE = 14.4 μg/m³) of the variability. The random forests model showed much higher predictive ability and lower bias than the other two regression models. Based on the predictions of random forests model, around one-third of China experienced with PM₁₀ pollution exceeding Grade Ⅱ National Ambient Air Quality Standard (>70 μg/m³) in China during the past 12 years. The highest levels of estimated PM₁₀ were present in the Taklamakan Desert of Xinjiang and Beijing-Tianjin metropolitan region, while the lowest were observed in Tibet, Yunnan and Hainan. Overall, the PM₁₀ level in China peaked in 2006 and 2007, and declined since 2008.This is the first study to estimate historical PM₁₀ pollution using satellite-based AOD data in China with random forests model. The results can be applied to investigate the long-term health effects of PM₁₀ in China.