Emerging evidence witnesses the association of air pollution exposure with sleep disorders or the risk of obstructive sleep apnea (OSA); however, the results are not consistent. OSA patients with or without a low arousal threshold (LAT) have different pathology and therapeutic schemes. No study has evaluated the potential diverse effects of air pollution on the phenotypes of OSA. The current study aimed to evaluate the associations of short-term and long-term exposure to air pollution with sleep-disordered measures and OSA phenotypes. This cross-sectional study consisted of 4634 participants from a sleep center in Taipei from January 2015 to April 2019. The personal exposure to ambient PM₂.₅ and NO₂ was assessed by a spatial-temporal model. Overnight polysomnography was used to measure the sleep parameters. According to a developed clinical tool, we defined the low arousal threshold (LAT) and identified the OSA patients with or without LAT. We applied a generalized linear model and multinomial logistic regression model to estimate the change of sleep measures and risk of the OSA phenotypes, respectively, associated with an interquartile range (IQR) increment of personal pollution exposure after adjusting for the essential confounders. In the single-pollutant model, we observed the associations of NO₂ with sleep-disordered measures by decreasing the total sleep time, sleep efficiency, extending the time of wake after sleep onset, and the association of NO₂ with the increased risk of LAT OSA by around 15%. The two-pollutant model with both long-term and short-term exposures confirmed the most robust associations of long-term NO₂ exposure with sleep measures. An IQR increment of NO₂ averaged over the past year (6.0 ppb) decreased 3.32 min of total sleep time and 0.85% of sleep efficiency. Mitigating exposure to air pollution may improve sleep quality and reduce the risk of LAT OSA.

Air pollution has been an important risk factor for female reproductive health. However, epidemiological evidence of ambient air pollution on the predictor for ovarian reserve (antral follicle count, AFC) is deficient. We aim to comprehensively evaluate the association of long-term exposure to ambient air pollution with AFC among women of reproductive age in Shanxi of north China. 600 women with spontaneous menstrual cycle, not using controlled ovarian stimulation, were enrolled in the retrospective study. Two distinct periods of antral follicle development were designed as exposure windows. Generalized linear model was employed to estimate the change of AFC associated with exposure of atmospheric pollutants (SO₂, NO₂, PM₁₀, PM₂.₅, CO and O₃). Stratification analysis based on age (<30, ≥30 years), university degree (yes, no), years of exposure (2013–2016, 2017–2019) and duration of infertility (<2, 2–5, >5 years) along with two pollutants model were employed to further illustrate the association. We found every 10 μg/m³ increase in SO₂ concentration level during the entire development stage of antral follicle was associated with a −0.01 change in AFC (95% confidence interval: −0.016, −0.002) adjusting for the confounders including age, BMI, parity and infertility diagnosis factors. The significant association of increased SO₂ level with decreased AFC was particularly observed during the early transition from primary follicle to preantral follicle stage by 10 μg/m³ increase in SO₂ exposure level with a −0.01 change (95% CI: -0.015, −0.002) in AFC. The negative association was pronounced among women aged ≥30 years old, and also significant in two pollutants model after adjusting the confounders. No significant associations between other air pollutants and AFC were observed. Our finding suggests that long-term exposure to air pollutant SO₂ is associated with lower AFC, raising our concern that atmospheric SO₂ exposure may have potential adverse impact on women ovarian reserve.

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%–52.0%), 32.3% (95% CI: 22.5%–42.4%), and 14.2% (7.9%–20.5%) in the number of COVID-19 cases for every 10-μg/m³ increase in long-term exposure to NO₂, PM₂.₅, and PM₁₀, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

Previous studies assessing excessive proliferation of phytoplankton (EPP) in lakes are generally based on single investigation and focused on limited environmental factors; meanwhile, less attention has been paid to lakes susceptibility to EPP. Here, we identify the priority of lakes for EPP control in a basin by assessing EPP in multiple lakes and identify the key factors related to lakes’ vulnerability to EPP. Field measurements, as well as multi-source survey data acquisition were conducted for 63 shallow lakes in the middle-lower Yangtze River basin. Resource-use efficiency by phytoplankton (RUE) was then used to represent lake susceptibility to EPP. Generalized linear models were used to assess the relative importance of environmental factors for RUE. We found that most lakes (76.19 %) were not suitable for recreation, due to health concern attributed to irritative or allergenic risk caused by EPP. Phosphorus was the primary limiting nutrient for EPP (74.60 % of lakes) which should be limited to < 0.09 mg/L. The linear model that included latitude, particulate matter 10, and precipitation explained 27.60 % of the variation of RUETP among lakes. In contrast, the linear model that included ozone, Secchi depth, and wind speed explained 19.41 % of the variation of RUETN among lakes. The key factor related to RUETP and RUETN was particulate matter 10 and ozone, respectively, both of which potentially increase RUE or reflect it. Our results suggest that integrating multiple survey datasets is critical for lakes EPP assessment in a basin, while lakes impacted by air pollution are a high priority for EPP control.

Microplastics have attracted much attention in recent years because they are able to interact with other pollutants including pesticides, with implications for the potential risks to biota. However, the sorption behavior of pesticides on microplastics, especially on biodegradable microplastics which are promising alternatives to conventional polymers, has been insufficiently studied. In this study, triadimefon and difenoconazole were selected as model triazole fungicides, and their sorption behavior on a typical biodegradable microplastics (PBS: polybutylene succinate) and two conventional polyethylene (PE) and polyvinyl chloride (PVC) microplastics was investigated with batch experiments in an aqueous solution. PBS presented the highest sorption capacity for triadimefon (104.2 ± 4.8 μg g⁻¹) and difenoconazole (192.8 ± 2.3 μg g⁻¹), which was 1.8- and 1.3-fold that on PE and 4.4- and 7.4-fold that of PVC, respectively. The results of sorption kinetic and isotherm modeling were better fit by a pseudo-second order model and linear model, respectively. More importantly, the effects of environmental factors (pH, salinity and dissolved organic matter) on the sorption behavior were investigated. Fungicide sorption on PBS was generally not affected by salinity, pH or dissolved organic matter. However, in contrast, salinity and dissolved organic matter both significantly decreased sorption on PE and PVC. The results showed that not only the sorption capacities of biodegradable microplastics but also their responses to environmental factors are quite different from those of conventional microplastics. This finding highlights the importance of the role played by biodegradable microplastics in the accumulation and transportation of organic pollutants.

Although association between polycyclic aromatic hydrocarbons (PAHs) exposure and mitochondrial DNA copy number (mtDNAcn) was researched by traditional linear model extensively, most of these studies analyzed independent effect of each PAHs metabolite and adjust for the confounding other metabolites concomitantly, without considering others interactions. As a complex organic pollutant, a reasonable statistical method is needed to study toxic effects of PAHs.Therefore, we aimed to conduct a novel statistical approach, Bayesian Kernel Machine Regression (BKMR), to explore the effect of PAHs exposure on mtDNAcn among coke oven workers. In this cross-sectional study, the concentrations urinary of PAHs metabolites were measured using high performance liquid chromatography mass spectrometry (HPLC-MS). The mtDNAcn was measured using real-time quantitative polymerase chain reaction (RT-PCR) in peripheral blood of 696 Chinese coke oven workers. The relationship of urinary of PAHs metabolites and mtDNAcn were evaluated by BKMR model. And the results showed a significant negative effect of PAHs metabolites on mtDNAcn when PAHs metabolites concentrations were all above 35th percentile compared to the median and the statistically significant negative single-exposure effect of 2-OHNAP and 2-OHPHE on mtDNAcn when all of the other PAHs are fixed at a particular threshold (25th, 50th, 75th percentile). The changes in log 2-OHNAP and 2-OHPHE from the 25th to the 75th percentile when other PAHs metabolites were at the 50th percentile were associated with change in mtDNAcn of −0.082 (−0.021, −0.124) and −0.048 (−0.021, −0.090) respectively. And evidence of a linear effect of urinary 2-OHNAP and 2-OHPHE were found. Finally, our findings suggested that PAHs cumulative exposures and particularly single-exposure of 2-OHNAP and 2-OHPHE might compromise mitochondrial function by decreasing mtDNAcn in Chinese coke oven workers.

Telomeres are located at the end of eukaryotic chromosomes and vulnerable to exogenous chemical compounds. Exposure to coke oven emissions (COEs) leads to a dose-related telomere damage, and such chromosomal damage might trigger the cGAS/STING signaling pathway which plays an important role in immune surveillance. However, the relationship between the genetic variations in the cGAS/STING signaling pathway and telomere damage in the COEs-exposure workers has not been investigated. Therefore, we recruited 544 coke oven workers and 238 healthy control participants, and determined the level of COEs exposure, concentration of urinary 1-hydroxypyrene (1-OHPYR), genetic polymorphisms and telomere length. The results showed that the telomere length significantly decreased from the control-to high-exposure groups as defined by the external exposure level (P < 0.05). The results also indicated that STING rs7447927 CC, cGAS rs34413328 AA, and cGAS rs610913 AA could inhibit telomere shortening in the exposure group (P < 0.05), and cGAS rs34413328, urine 1-OHPYR and cumulative exposure dose (CED) had a significant association with telomere length by generalized linear model. In conclusion, telomere shortening was a combined consequence of short-term exposure, long-term exposure, and genetic variations among the COEs-exposure workers.

Exposure to environmental metals has been reported to be associated with airway inflammation. Fractional exhaled nitric oxide (FeNO) is an important inflammatory biomarker of the airway. However, the associations between metal exposures and FeNO change and the underlying mechanisms remain unclear. To investigate the associations between urinary metals and FeNO, and the potential role of Club cell secretory protein (CC16), a lung epithelial biomarker, in these associations. We conducted a cross-sectional study from the Wuhan-Zhuhai cohort and measured eight urinary metals, plasma CC16 and FeNO among 3067 subjects by using inductively coupled plasma-mass spectrometry, enzyme-linked immunosorbent assay kit and Nano Coulomb Nitric Oxide Analyzer, respectively. Mixed linear models were used to quantify dose-relationships between urinary metals and FeNO, as well as urinary metals and plasma CC16. The potential role of plasma CC16 in the associations between urinary metals and FeNO was estimated using mediationanalyses. After adjusting for covariates, one percent increase in urinary vanadium, nickel or antimony was associated with a respective 6.60% (95% CI: 3.52%, 9.68%), 2.18% (0.45%, 3.91%), 4.87% (1.47%, 8.27%) increase in FeNO level. The adverse associations were much stronger among participants with low concentration of plasma CC16 than those with high CC16 level. Moreover, plasma CC16 decreased monotonically with increasing quartiles of urinary vanadium, nickel or antimony. Mediation analyses found that CC16 mediated the associations between urinary metals and FeNO by 5.64%, 39.06% and 25.18% for vanadium, nickel and antimony respectively. CC16 plays an important role in airway inflammation. General population with lower plasma CC16 concentration is more likely to suffer from airway inflammation when exposed to high levels of vanadium, nickel or antimony.

A mass balance approach to quantify methane (CH4) emission of four co-located landfills by means of airborne measurements and dispersion modelling was proposed and assessed. By flying grids at different heights above the landfills, atmospheric CH4 densities and wind components were measured along the edges and inside the study atmospheric volume, in order to calculate mass flows in the along- and across-wind directions. A steady-state Gaussian dispersion model was applied to build the concentration fields associated to unit emission from each landfill, while the contribution of each one to the total emission was assessed using a General Linear Model approach, minimizing the difference between measured and modeled mass flows. Results showed that wind spatial and temporal variability is the main factor controlling the accuracy of the method, as a good agreement between measured and modeled mass flows was mainly found for flights made in steady wind conditions. CH4 emissions of the entire area ranged from 213.5 ± 33.5 to 317.9 ± 90.4 g s−1 with a mean value of 252.5 ± 54.2 g s−1. Contributions from individual sources varied from 17.5 to 40.1 g m−2 day−1 indicating a substantial heterogeneity of the different landfills, which differed in age and waste composition. The proposed method was validated against tower eddy covariance flux measurements made at one of the landfills, revealing an overall agreement within 20%.

We examine the spatio-temporal trends of mercury, a well-known global legacy contaminant, in eleven fish species across all of the Canadian Great Lakes. These particular fish species are selected based on their ecological, commercial, and recreational importance to the biodiversity and fishing industry of the Great Lakes. We present a two-pronged Bayesian methodological framework to rigorously assess mercury temporal trends across multiple fish species and locations. In the first part of our analysis, we develop dynamic linear models to delineate the total mercury levels and rates of change, while explicitly accounting for the covariance between fish length and mercury levels in fish tissues. We then use hierarchical modelling to evaluate the spatial variability of mercury contamination between nearshore and offshore locations, as well as to examine the hypothesis that invasive species have induced distinct shifts on fish mercury contamination trends. Our analysis suggests that the general pattern across the Great Lakes was that the elevated mercury concentrations during the 1970s had been subjected to a declining trend throughout the late 1980s/early 1990s, followed by a gradual stabilization after the late 1990s/early 2000s. The declining trend was more pronounced with top fish predators, whereas benthivorous fish species mainly underwent wax-and-wane cycles with a weaker evidence of a long-term declining trend. Historically contaminated regions, designated as Areas of Concern, and bays receiving riverine inputs are still characterized by mercury concentrations that can lead to consumption restrictions. Lake Erie displayed the lowest mercury levels across all the fish species examined. However, several species of commercial importance showed a reversing (increasing) trend in the 2000s, although their current levels do not pose any major concerns for consumption advisories. These recent trend reversals can be linked with systematic shifts in energy trophodynamics along with the food web alterations induced from the introduction of non-native species, and the potentially significant fluxes from the atmosphere.