Cardiovascular diseases is related to environmental pollution. Endosulfan is an organochlorine pesticide and its toxicity has been reported. However, the relationship between oxidative stress and autophagy induced by endosulfan and its underlying mechanism remain confusing. In this study, human umbilical vein endothelial cells (HUVECs) were chosen to explore the toxicity mechanism and were treated with 0, 1, 6, 12 μg/mL−1 endosulfan for 24 h, respectively. The present results showed that autophagy could be induced by endosulfan, which was verified by the monodansylcadaverine staining, autophagic ultrastructural observation, and LC3-I/LC3-II conversion. In addition, the levels of adenosine triphosphate (ATP), the mitochondria membrane potential (MMP) were significantly decreased in a dose-dependent way. The expression of proinflammatory cytokines (tumor necrosis factor α, interleukin-1β, and interleukin-6) were significantly elevated, and the index of endothelial function such as monocyte chemotactic protein 1 (MCP-1), intercellular cell adhesion molecule-1 (ICAM-1) increased. Moreover, endosulfan had an activation effect on the 5′AMP-activated protein kinase (AMPK)/rapamycin (mTOR) signaling pathway. Our findings demonstrated that endosulfan could induce oxidative stress and mitochondria injury, activate autophagy, induce inflammatory response, and eventually lead to endothelial dysfunction via the AMPK/mTOR pathway. This indicates that exposure to endosulfan is a potential risk factor for cardiovascular diseases.

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990–2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented.

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM10 and NO2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM10 and NO2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM10 and NO2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM10 and NO2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R2s of 0.45 and 0.82 for PM10 and NO2, respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries.

Emission from coal-fired power plants is one of the major anthropogenic sources of mercury (Hg) in the environment, because emitted Hg can be quickly deposited nearby the source, attention is paid to the effects of coal-burning facilities on levels of toxic methyl-mercury (MeHg) in biota near such sources. Since rice is an agricultural crop that can bio-accumulate MeHg, the potential effects of a large Hg-emitting coal-fired power plant in Hunan Province, China on both inorganic Hg (Hg(II)) and MeHg distributions in rice was investigated. Relatively high MeHg (up to 3.8 μg kg−1) and Hg(II) (up to 22 μg kg−1) concentrations were observed in rice samples collected adjacent to the plant, suggesting a potential impact of Hg emission from the coal fired power plant on the accumulation of Hg in rice in the area. Concentrations of MeHg in rice were positively correlated with soil MeHg, soil S, and gaseous elemental Hg (GEM) in ambient air. Soil MeHg was the most important factor controlling MeHg concentrations in rice. The methylation of Hg in soils may be controlled by factors such as the chemical speciation of inorganic Hg, soil S, and ambient GEM.

To characterize air pollution and determine its source distribution in Qingdao, Shandong Province, we analyzed hourly national air quality monitoring network data of normal pollutants at nine sites from 1 November 2015 to 31 January 2016. The average hourly concentrations of particulate matter <2.5 μm (PM2.5) and <10 μm (PM10), SO2, NO2, 8-h O3, and CO in Qingdao were 83, 129, 39, 41, and 41 μg m⁻³, and 1.243 mg m⁻³, respectively. During the polluted period, 19–26 December 2015, 29 December 2015 to 4 January 2016, and 14–17 January 2016, the mean 24-h PM2.5 concentration was 168 μg m⁻³ with maximum of 311 μg m⁻³. PM2.5 was the main pollutant to contribute to the pollution during the above time. Heavier pollution and higher contributions of secondary formation to PM2.5 concentration were observed in December and January. Pollution pathways and source distribution were investigated using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses. A cluster from the west, originating in Shanxi, southern Hebei, and west Shandong Provinces, accounted for 44.1% of the total air masses, had a mean PM2.5 concentration of 134.9 μg m⁻³ and 73.9% trajectories polluted. This area contributed the most to PM2.5 and PM10 levels, >160 and 300 μg m⁻³, respectively. In addition, primary crustal aerosols from desert of Inner Mongolia, and coarse and fine marine aerosols from the Yellow Sea contributed to ambient PM. The ambient pollutant concentrations in Qingdao in winter could be attributed to local primary emissions (e.g., coal combustion, vehicular, domestic and industrial emissions), secondary formation, and long distance transmission of emissions.

ZnO nanoparticles (ZnO-NPs) are emerging contaminants that raise the concerns of potential risk in the aquatic environment. It has been estimated that the environmental ZnO-NPs concentration is 76 μg/l in the aquatic environment. Our aim was to determine the aquatic toxicity of ZnO-NPs with chronic exposure at environmentally relevant concentrations using the nematode Caenorhabditis elegans. Two simulated environmentally relevant mediums—moderately hard reconstituted water (EPA water) and simulated soil pore water (SSPW)—were used to represent surface water and pore water in sediment, respectively. The results showed that the ZnO-NPs in EPA water has a much smaller hydrodynamic diameter than that in SSPW. Although the ionic release of Zn ions increased time-dependently in both mediums, the Zn ions concentrations in EPA water increased two-fold more than that in SSPW at 48 h and 72 h. The ZnO-NPs did not induce growth defects or decrease head thrashes in C. elegans in either media. However, chronic exposure to ZnO-NPs caused a significant reduction in C. elegans body bends in EPA water even with a relatively low concentration (0.05 μg/l); similar results were not observed in SSPW. Moreover, at the same concentrations (50 and 500 μg/l), body bends in C. elegans were reduced more severely in ZnO-NPs than in ZnCl2 in EPA water. The ATP levels were consistently and significantly decreased, and ROS was induced after ZnO-NPs exposure (50 and 500 μg/l) in EPA water. Our results provide evidences that chronic exposure to ZnO-NPs under environmentally relevant concentrations causes metabolic and locomotive toxicities implicating the potential ecotoxicity of ZnO-NPs at low concentrations in aquatic environments.

Twelve different soil types that represent the soil compartments of the Czech Republic were fortified with three antibiotics (clindamycin (CLI), sulfamethoxazole (SUL), and trimethoprim (TRI)) to investigate their fate. Five metabolites (clindamycin sulfoxide (CSO), hydroxy clindamycin sulfoxide (HCSO), S-(SDC) and N-demethyl clindamycin (NDC), N4-acetyl sulfamethoxazole (N4AS), and hydroxy trimethoprim (HTR)) were detected and identified using HPLC/HRMS and HRPS in the soil matrix in this study. The identities of CSO and N4AS were confirmed using commercially available reference standards.The parent compounds degraded in all soils. Almost all of the metabolites have been shown to be persistent in soils, with the exception of N4AS, which was formed and degraded completely within 23 days of exposure. The rate of degradation mainly depended on the soil properties.The PCA results showed a high dependence between the soil type and behaviour of the pharmaceutical metabolites.The mentioned metabolites can be formed in soils, and the most persistent ones may be transported to the ground water and environmental water bodies. Because no information on the effects of those metabolites on living organism are available, more studies should be performed in the future to predict the risk to the environment.

Concentrated animal-feeding operations (CAFOs) are considered a source of airborne human pathogens and antibiotic resistance genes. Although bacterial abundance and diversity have been well studied, limited information on the size distribution of bioaerosols has prevented a clear understanding of the health effects of exposure to bioaerosols from CAFOs. Here, different sizes of particles were sampled from the inside and outside of atmospheric environments of layer and broiler feeding operations using 8-stage Andersen samplers. The quantitative real-time polymerase chain reaction (qPCR) and 16S rDNA-based sequencing were used to analyze the characteristics of biological abundance and diversity, respectively, according to size. The results indicated that size-related differences occurred in terms of airborne bacterial richness, diversity, and concentration at poultry-feeding operations. The richness of biological genera in the urban atmospheric environment was lower than in concentrated poultry-feeding operations. The biological diversity of airborne bacterial genera, including genera associated with potential pathogens, varied according to size. The bacterial lineages of bioaerosols present in the 7 size stages for layers clustered apart from those for broilers, suggesting that the type of poultry house is a more important factor than the particle size in shaping the microbial communities. In most cases, the concentrations of the 16S rDNA, Escherichia coli, tetW, and tetL genes increased as the particle size increased, with the geometric mean diameters varying from 4.7 to 5.8 μm. These results regarding the size-related differences in the diversity and abundance of bioaerosols will facilitate a better understanding of the potential health impact on both poultry and humans working in such environments.

Polybrominated diphenyl ethers (PBDEs) and hexa-brominated biphenyls (Hexa-BBs) are bioaccumulative and aggregate in the food chain. Therefore, background monitoring and risk assessment for dietary intake are necessary. In present study, a systematic sampling method was first used to collect the high fat content foodstuff such as poultry, livestock, eggs, fish, other seafood, dairy products, and the infant foods and then foodstuff with high consumption in seven categories of 600 food samples. After integrating four years of background surveys of PBDE levels (2010–2013) and one year of that of Hexa-BBs (2013), the highest estimated daily intake (EDI) of PBDEs for Taiwanese food consumption was found in 0- to 3-year-olds (mean = 9.38 ng kg−1 bw d−1, the 95% upper limit of Monte Carlo Simulation (MCS P95) was 21.52 ng kg−1 bw d−1), and the lowest in 16- to 18-year-old girls (mean = 3.35 ng kg−1 bw d−1, MCS P95 was 6.53 ng kg−1 bw d−1). Moreover, the highest of EDI of Hexa-BBs was found in 0–3 years old (mean = 0.007 ng kg−1 bw d−1, MCS P95 = 0.019 ng kg−1 bw d−1), and lowest in 17–18 years old female (mean = 0.002 ng/kg/day, MCS P95 = 0.005 ng kg−1 bw d−1). This study suggests that the large MOEs (>2.5) for the four important congeners BDE-47, -99, −153, and −209, indicate that the dietary exposures are not probably a significant health concern for Taiwanese.

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was −1.19 points (95% CI −1.94, −0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.