Light at night (LAN) has received increasing attention for its potential health hazards to human and animals. However, to our knowledge, no study has explored the specific effects of bedroom nighttime light exposure on allostatic load (AL). To investigate the association between bedroom individual-level LAN exposure and AL among young adults, an integrative index manifests multiple system dysregulation. Using data from a cohort of 484 Chinese young adults aged 16–22 years. Bedroom light was objectively recorded at 1-min intervals for two nights using a portable illuminance meter. Fasting blood samples were collected at one-year follow-up for the detection of AL parameters. AL score was derived as sum of the top quartile of twelve physiological biomarkers in four systems: metabolic system (BMI, WC, TC, HDL, LDL, TG, HbA1c, INS, GLU); cardiovascular system (SBP, DBP); immune and inflammatory systems (hs-CRP), with HDL was lowest quartile. Univariate and multivariate linear regression models were used to evaluate the association between LAN intensity with AL score and separate AL parameters. The average age of subjects was 18.7 years, 64.3% were female. The mean AL score of LAN group (average LAN intensity ≥ 3lx) was significantly higher than Dim group (3.6 ± 2.6 vs. 2.7 ± 2.1; P = 0.007). For each 1 lx increase of LAN intensity was associated with 0.15-unit increase in AL score (95% CI: 0.06, 0.24; P = 0.001). Moreover, LAN group was associated with increased 1.01-unit in AL score (95% CI: 0.36–1.66; P = 0.003) compared to Dim group. Significant associations between bedroom LAN exposure with allostatic load and separate AL biomarkers were observed in our study. Keeping bedroom darkness at night may be a practicable option to reduce the wear of multiple body systems and improve human cardiometabolic health from early in life.

Lichens serve as important bioindicators of air pollution in cities. Here, we studied the diversity of epiphytic lichens in the urban area of Munich, Bavaria, southern Germany, to determine which factors influence species composition and diversity. Lichen diversity was quantified in altogether 18 plots and within each, five deciduous trees were investigated belonging to on average three tree species (range 1–5). Of the 18 plots, two were sampled in control areas in remote areas of southern Germany. For each lichen species, frequency of occurrence was determined in 10 quadrats of 100 cm² on the tree trunk. Moreover, the cover percentage of bryophytes was determined and used as a variable to represent potential biotic competition. We related our diversity data (species richness, Shannon index, evenness, abundance) to various environmental variables including tree traits, i.e. bark pH levels and species affiliation and air pollution data, i.e. NO₂ and SO₂ concentrations measured in the study plots. The SO₂ levels measured in our study were generally very low, while NO₂ levels were rather high in some plots. We found that the species composition of the epiphytic lichen communities was driven mainly by NO₂ pollution levels and all of the most common species in our study were nitrophilous lichens. Low NO₂ but high SO₂ values were associated with high lichen evenness. Tree-level lichen diversity and abundance were mainly determined by tree traits, not air pollution. These results confirm that ongoing NO₂ air pollution within cities is a major threat to lichen diversity, with non-nitrophilous lichens likely experiencing the greatest risk of local extinctions in urban areas in the future. Our study moreover highlights the importance of large urban green spaces for species diversity. City planners need to include large green spaces when designing urban areas, both to improve biodiversity and to promote human health and wellbeing.

Cyanobacterial blooms producing toxic metabolites occur frequently in freshwater, yet the environmental behaviors of complex cyanobacterial metabolites remain largely unknown. In this study, the seasonal and spatial variations of several classes of cyanotoxins (microcystins, cylindrospermopsins, saxitoxins) and taste-and-odor (T&O) compounds (β-cyclocitral, β-ionone, geosmin, 2-methylisoborneol) in Lake Taihu were simultaneously investigated for the first time. The total cyanotoxins were dominated by microcystins with concentrations highest in November (mean 2209 ng/L) and lowest in February (mean 48.7 ng/L). Cylindrospermopsins were abundant in May with the highest content of 622.8 ng/L. Saxitoxins only occurred in May (mean 19.2 ng/L) and November (mean 198.5 ng/L). Extracellular T&O compounds were most concentrated in August, the highest being extracellular β-cyclocitral (mean 240.6 ng/L) followed by 2-methylisoborneol (mean 146.6 ng/L). Environment variables play conflicting roles in modulating the dynamics of different groups of cyanotoxins and T&O compounds. Total phosphorus (TP), total nitrogen (TN), chlorophyll-a and cyanobacteria density were important factors affecting the variation of total microcystins, β-cyclocitral and β-ionone concentrations. In contrast, total cylindrospermopsins, 2-methylisoborneol and geosmin concentrations were significantly influenced by water temperature and TP. There was a significant and linear relationship between microcystins and β-cyclocitral/β-ionone, while cylindrospermopsins were positively correlated with 2-methylisoborneol and geosmin. The perceptible odors may be good indicators for the existence of cyanotoxins. Hazard quotients revealed that potential human health risks from microcystins were high in August and November. Meanwhile, the risks from cylindrospermopsins were at moderate levels. Cylindrospermopsins and saxitoxins were first identified in this lake, suggesting that diverse cyanotoxins might co-occur more commonly than previously thought. Hence, the risks from other cyanotoxins beyond microcystins shouldn't be ignored. This study also highlights that the necessity for further assessing the combination effects of these complex metabolites.

Most bitumen in the Alberta oil sands (Canada) is extracted by thermal in-situ recovery. Despite the widespread use of in-situ bitumen extraction, little information is available on the release of petroleum hydrocarbons by this method to adjacent land and water. Here we analyzed the composition and abundance of parent and alkylated polycyclic aromatic compounds (PACs) in 11 radiometrically-dated lake sediment cores collected near in-situ operations at Cold Lake Alberta to assess potential petroleum contamination sources to surrounding lakes over the past century. Like open-pit mining areas, alkylated PACs in Cold Lake sediments were elevated compared to unsubstituted parent PACs and increased coeval with the onset of bitumen extraction in the area. Diagnostic ratios and pyrogenic indices showed that PAC sources to these lake sediments were dominantly pyrogenic, likely from historic forest fires, however they shifted to more petrogenic sources coeval with expanding oil sands extraction at Cold Lake. PACs in sediment from regional lakes are weakly correlated to their proximity to in-situ oil wells, once corrected for lake area. These results suggest that in-situ operations, via diesel-fueled vehicular emissions and the combustion of natural gas for steam generation, are a source of PACs to nearby lakes, but PACs did not exceed Canadian sediment quality guidelines for the protection of aquatic life.

Ozone levels have been declining in the Los Angeles, CA, USA area for the last four decades, but there was a recent uptick in the 4th highest daily maximum 8-h (MDA8) ozone concentrations from 2014 to 2018 despite continued reductions in the estimated precursor emissions. In this study, we assess the emissions and meteorological impacts on the 4th highest MDA8 ozone concentrations to better understand the factors affecting the observed MDA8 ozone using a two-step generalized additive model (GAM)/least squares approach applied to the South Coast Air Basin (SoCAB) for the 1990 to 2019 period. The GAM model includes emissions, meteorological factors, large-scale climate variables, date, and the interactions between meteorology and emissions. A least squares method was applied to the GAM output to better capture the 4th highest MDA8 ozone. The resulting two-step model had an R² of 0.98 and a slope of 1 between the observed and predicted 4th highest MDA8 ozone. Emissions and the interactions between the maximum temperature and emissions explain most of the variation in the peak MDA8 ozone concentrations. Declining emissions have lowered the 4th highest MDA8 ozone concentration. Meteorology explains the higher than expected 4th-high, ozone levels observed in 2014–2018, indicating that meteorology was a stronger forcer than the continued reductions in emissions during that time period. The model was applied to estimate future ozone levels. Meteorology developed from climate modeling of the representative concentration pathway (RCP) scenarios, and two sets of emissions were used in the application. The modeling results indicated climate trends will push ozone levels slightly higher if no further emissions reductions are realized and that of two emissions trajectories modeled, the more stringent is required to reliably meet the federal ozone standard given annual meteorological variability.

Atmospheric nitrogen dioxide (NO₂) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO₂ monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO₂ from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO₂ with high accuracy, with a coefficient of determination (R²) of 0.89 and a root mean squared error of 5.8 μg/m³ for sample-based 10-fold cross-validation. Based on the surface NO₂ concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO₂ pollution in north China. We found substantial drops in surface NO₂ concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO₂ exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO₂ among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO₂ concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO₂ dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Dyslipidemia may be a potential mechanism linking air pollution to adverse cardiovascular outcomes and this may differ among obese and normal-weight populations. However, the joint effect of multiple air pollutants on lipid profiles and the role of each pollutant are still unclear. This panel study aims to investigate and compare the overall associations of major air pollutants with lipid parameters in obese and normal-weight adults, and assess the relative importance of each pollutant for lipid parameters. Forty-four obese and 53 normal-weight young adults were recruited from December 2017 to June 2018 in Beijing, China. Their fasting blood was collected and serum lipid levels were measured in three visits. Six major air pollutants were included in this study, which were PM₂.₅, PM₁₀, NO₂, SO₂, O₃ and CO. Bayesian kernel machine regression (BKMR) was implemented to estimate the joint effect of the six air pollutants on various lipid parameters. We found that decreased high-density lipoprotein cholesterol (HDL-C) in the obese group and increased low-density lipoprotein cholesterol (LDL-C) and non-HDL-C in the normal-weight group were associated with the exposure to the mixture of six air pollutants above. Significant increases in total cholesterol (TC)/HDL-C and non-HDL-C/HDL-C were observed in both groups, and the effect was stronger in obese group. Of the six air pollutants above, O₃ had the largest posterior inclusion probability in above lipid indices, ranging from 0.75 to 1.00. In the obese group, approximately linear exposure-response relationships were observed over the whole range of logarithmic O₃-8 h max concentration, while in the normal-weight group, these relationships existed when the logarithmic concentration exceeded about 2.8. Therefore, lipid profiles of obese adults may be more sensitive to air pollution and this study highlights the importance of strengthening emissions control efforts for O₃ in the future.

Air pollution remains the major environmental problem globally. There is extensive evidence showing that the variety of air pollutants from environmental and occupational exposures cause adverse effects to our health. The clinical symptoms of those effects may present at a late stage, so surveillance is difficult to manage. Several biomarkers have been used for the early detection of health issues following exposure to air pollution, including the use of telomere length which indicates cellular senescence in response to oxidative stress. Oxidative stress is one of the most plausible mechanisms associated with exposure to air pollutants. Some specific contexts including age groups, gender, ethnicity, occupations, and health conditions, showed significant alterations in telomere length after exposure to air pollutants. Several reports demonstrated both negative and positive associations between telomere length and air pollution, the studies using different concentrations and exposure times to air pollution on the study of telomere lengths. Surprisingly, some studies reported that low levels of exposure to air pollutants (lower than regulated levels) caused the alterations in telomere length. Those findings suggest that telomere length could be one of most practical biomarkers in air pollution surveillance. Therefore, this review aimed to summarize and discuss the relationship between telomere length and exposure to air pollution. The knowledge from this review will be beneficial for the planning of public health to reduce health problems in the general population, particularly in vulnerable people, who still live in areas with high air pollution.

Environmentally persistent free radicals (EPFRs) have aroused widespread concern due to their potential adverse health effects. Research on EPFRs in road dust is still very limited. In this study, 86 road dust samples were collected using vacuum sampling in a rapidly developing city in central China. The pollution characterization and health risk of EPFRs in the urban road dust were then systematically analyzed. The results showed the average concentrations of EPFRs in urban road dust and fraction of particle with aerodynamic diameters lower than 10 μm (PM₁₀) were 2.24 × 10¹⁷ to 3.72 × 10¹⁹ spins·g⁻¹ and 6.02 × 10¹⁷ to 1.41 × 10²⁰ spins g⁻¹, respectively. The concentrations of EPFRs in dust from expressways, arterial roads, and secondary trunk roads were significantly higher than those found in the remaining road types. The g-factors of 2.0032–2.0039 indicated that the EPFRs have consisted of oxygen-centered and carbon-centered radicals or carbon-centered radicals with nearby oxygen or halogen atoms. Moreover, three decay patterns of EPFRs were observed: a fast decay followed by a slow decay, a single slow decay, and the slowest decay. In addition, a comparative evaluation was made for probabilistic risk assessments of exposure to the EPFRs in road dust and the PM₁₀ fraction. Compared with road dust, the probability of the number of equivalent cigarettes to exceed the 100 and 200 cigarettes for inhaling EPFRs in the PM₁₀ fraction increased by 27.0% and 25.0%, respectively. The simulation results showed the PM₁₀ fraction were primarily deposited in the upper respiratory tract regions (57.1%) and pulmonary regions (28.8%). The findings of this study suggest a potential risk of EPFRs in inhalable particles and provide a new insight for further exploration of the EPFRs in fine particles of road dust.

Particulate matter with a diameter of less than 2.5 μm or PM2.5 is recognized worldwide as a cause of public health problems, mainly associated with respiratory and cardiovascular diseases. There is accumulating evidence to show that exposure to PM2.5 has a crucial causative role in various neurological disorders, the main ones being dementia and Alzheimer's disease (AD). PM2.5 can activate glial and microglial activity, resulting in neuroinflammation, increased intracellular ROS production, and ultimately neuronal apoptosis. PM2.5 also causes the alteration of neuronal morphology and synaptic changes and increases AD biomarkers, including amyloid-beta and hyperphosphorylated-tau, as well as raising the levels of enzymes involved in the amyloidogenic pathway. Clinical trials have highlighted the correlation between exposure to PM2.5, dementia, and AD diagnosis. This correlation is also displayed by concordant evidence from animal models, as indicated by increased AD biomarkers in cerebrospinal fluid and markers of vascular injury. Blood-brain barrier disruption is another aggravated phenomenon demonstrated in people at risk who are exposed to PM2.5. This review summarizes and discusses studies from in vitro, in vivo, and clinical studies on causative relationships of PM2.5 exposure to AD-related neuropathology. Conflicting data are also examined in order to determine the actual association between ambient air pollution and neurodegenerative diseases.