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.

Rice consumption is the major pathway for human methylmercury (MeHg) exposure in inland China, especially in mercury (Hg) contaminated regions. MeHg production, a microbially driven process, depends on both the chemical speciation of inorganic divalent mercury, Hg(II), that determines Hg bioavailability for methylation. Studies have shown that Hg(II) speciation in contaminated paddy soils is mostly controlled by natural organic matter and sulfide levels, which are typically thought to limit Hg mobility and bioavailability. Yet, high levels of MeHg are found in rice, calling for reconsideration of the nature of Hg species bioavailable to methylators in paddy soils. Here, we conducted incubation experiments using a multi-isotope tracer technique including ¹⁹⁸Hg(NO₃)₂, natural organic matter bond Hg(II) (NOM-¹⁹⁹Hg(II)), ferrous sulfide sorbed Hg(II) (≡FeS-²⁰⁰Hg(II)), and nanoparticulate mercuric sulfide (nano-²⁰²HgS), to investigate the relative importance of geochemically diverse yet relevant Hg(II) species on Hg methylation in paddy soils across a Hg concentration gradient. We show that methylation rates for all Hg(II) species tested decreased with increasing Hg concentrations, and that methylation rates using NOM-¹⁹⁹Hg(II) and nano-²⁰²HgS as substrates were similar or greater than rates obtained using the labile ¹⁹⁸Hg(NO₃)₂ substrate. ≡FeS-²⁰⁰Hg(II) yielded the lowest methylation rate in all sites, and thus the formation of FeS is likely a sink for labile ¹⁹⁸Hg(NO₃)₂ in sulfide-rich paddy soils. Moreover, the variability in the methylation data for a given site (1 to 5-fold variation depending on the Hg species) was smaller than what was observed across the Hg concentration gradient (10³–10⁴ fold variation between sites). These findings emphasize that at broad spatial scales, site-specific characteristics, such as microbial community structure, need to be taken into consideration, alongside the nature of the Hg substrate available for methylation, to determine net MeHg production. This study highlights the importance of developing site-specific strategies for remediating Hg pollution.

Lead (Pb) is a toxic metal in industrial production, which can seriously threat to human health and food safety. Thus, it is particularly crucial to reduce the content of Pb in the environment. In this study, raw fly ash (FA) was used to synthesise a new active silicate materials (IM) employing the low-temperature-assisted alkali (NaOH) roasting approach. The IM was further synthesised to form zeolite-A (ZA) using the hydrothermal method. The physicochemical characteristics of IM and ZA amendments before and after Pb²⁺ adsorption were analysed using the Scanning electron microscope-Energy Dispersive Spectrometer (SEM-EDS), Fourier Transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) apparatuses. The results revealed the considerably change in the microstructure and functional groups of IM and ZA amendments, conducive to Pb²⁺ removal. Moreover, a 3-year field experiment revealed that the IM and ZA significantly improved the growth of rice and reduced available Pb by 21%–26.8% and 9.7%–16.9%, respectively. After 3 years of remediation, the Pb concentration of the rice grain reached the national edible standard (≤0.2 mg kg⁻¹) of 0.171 mg kg⁻¹ and 0.179 mg kg⁻¹, respectively. Meanwhile, the concentration of acid-exchangeable Pb reduced, while those of reducible and residual fractions of Pb increased. There was no significant difference between the IM and ZA treatments. The potential mechanisms of remediation by the amendments were ion-exchange, complexation, precipitation, and electrostatic attraction. Overall, the results indicate that IM is suitable for the remediation of contaminated soil and promotes safe food production, and develops an environmentally friendly and cost-effective amendment for the remediation of polluted soil.

Rare earth elements (REEs) concentrated in soils have attracted increasing attention about their impact on soil health as emerging contaminants. However, the sources of REEs enriched in soils are diverse and need to be further investigated. Here, surface soil samples were collected from southern Jiangxi Province, China. REEs contents and soil physicochemical properties were determined, and cerium (Ce) and europium (Eu) anomalies were calculated. Moreover, we established a model to further identify the main sources of REEs accumulation in the studied soils. Results show that the abundance of soil REEs reveals larger spatial variation, suggesting spatially heterogeneous distribution of REEs. The median content of light REEs in soils (154.5 mg kg⁻¹) of the study area was higher than that of heavy REEs and yttrium (35.8 mg kg⁻¹). In addition, most of the soil samples present negative Ce anomalies and all the soil samples present negative Eu anomalies implying the combined effect of weathering and potential exogenous inputs on soil REEs. Positive matrix factorization modeling reveals that soil REEs content is primarily influenced by soil parent materials. Potential anthropogenic sources include mining-related leachate, traffic exhaust, and industrial dust. These results demonstrate that the identification of sources of soil REEs is an important starting point for targeted REEs sources management and regulation of excessive and potentially harmful REEs levels in the soil.