Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.

This study modified a passive sampling technique similar to the US EPA Method 325 A/B method but extended to include more toxic volatile organic compounds (VOCs) under varied climate conditions to enhance field applicability. A mixing chamber was built to determine uptake rates (Us) for the target compounds. It was found that the Us of 27 air toxics previously reported in the literature agreed reasonably well with our findings within 18%, thus proving the chamber's integrity. To broaden the compound coverage, both Carbopack X and Carboxen 569 were studied for a suite of toxic VOCs to meet stringent quality control (QC) criteria of correlation coefficients (R-square), method detection limits (MDL), back diffusion (BD), storage stability, as well as a wide range of climate conditions in temperature and humidity. After excluding the species that failed to pass any of the QC criteria, Carbopack X was found to fit 50 air toxics, whereas Carboxen 569 held 37. After excluding the overlapped species, 61 toxic VOCs can be determined with robust Us for a broad range of climate conditions when the two sorbents are used in pairs. A one-week field measurement was conducted to compare with the online thermal desorption gas chromatography-mass spectrometry (TD-GC-MS) with hourly data resolution. The field passive sampling showed comparable results to the means of the online hourly measurements, despite the high variability of selected target compounds, such as toluene from 0.3 ppbv as the 5th percentile to the maximum of about 80 ppbv. Passive sampling clearly demonstrated the ability to smooth out concentration variability and thus the time-averaging strength of toxic VOCs, revealing its ideal role as an exposure monitor over time. The passive sampling method can be more desired than active sampling or online methods when the aim is simply the knowledge of prolonged time-averaged concentrations.

The stable stabilization of uranium (U) in iron (Fe) containing environments is restricted by the reoxidation of UO₂. In the current study, based on air reoxidation tests, we propose a novel two steps accumulation method to enrich microbial consortia from paddy soil. The constructed microbial consortia, denoted as the Fe–U bacteria, can co-precipitate U and Fe to form stable Fe–U solids. Column experiments running for 4 months demonstrated the production of U(IV)–O–Fe(II) precipitates containing maximum of 39.51% uranium in the presence of Fe–U bacteria. The reoxidation experiments revealed the U(IV)–O–Fe(II) precipitates were more stable than UO₂. 16S rDNA high throughput sequencing analysis demonstrated that Acinetobacter and Stenotrophomonas were responsible for Fe and U precipitation, while, Caulobacteraceae and Aminobacter were crucial for the formation of U(VI)-PO₄ chemicals. The proposed two steps accumulation method has an extraordinary application potential in stable immobilization of uranium in iron containing environments.

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.

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.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

The immense production of plastic polymers combined with their discordancy with nature has led to vast plastic waste contamination across the geosphere, from the oceans to freshwater reservoirs, wetlands, remote snowpacks, sediments, air and multiple other environments. These environmental pollutants include microplastics (MP), typically defined as small and fragmented plastics less than 5 mm in size, and nanoplastics (NP), particles smaller than a micrometer. The formation of micro and nanoplastics in aqueous media to date has been largely attributed to fragmentation of plastics by natural (i.e., abrasion, photolysis, biotic) or industrial processes. We present a novel method to create small microplastics (≲ 5 μm) and nanoplastics in water from a wide variety of plastic materials using a small volume of a solubilizer liquid, such as n-dodecane, in combination with vigorous mixing. When the suspensions or solutions are subjected to ultrasonic mixing, the particle sizes decrease. Small micro- and nanoparticles were made from commercial, real world and waste (aged) polyethylene, polystyrene, polycarbonate and polyethylene terephthalate, in addition to other plastic materials and were analyzed using dark field microscopy, Raman spectroscopy and particle size measurements. The presented method provides a new and simple way to create specific size distributions of micro- and nanoparticles, which will enable expanded research on these plastic particles in water, especially those made from real world and aged plastics. The ease of NP and small MP formation upon initial mixing simulates real world environments, thereby providing further insight into the behavior of plastics in natural settings.

Air pollution exposure is a public health emergency, which attributes globally to an estimated seven million deaths on a yearly basis We are all exposed to air pollutants, varying from ambient air pollution hanging over cities to dust inside the home. It is a mixture of airborne particulate matter and gases that can be subdivided into three categories based on particle diameter. The smallest category called PM₀.₁ is the most abundant. A fraction of the particles included in this category might enter the blood stream spreading to other parts of the body. As air pollutants can enter the body via the lungs and gut, growing evidence links its exposure to gastrointestinal and respiratory impairments and diseases, like asthma, rhinitis, respiratory tract infections, Crohn's disease, ulcerative colitis, and abdominal pain. It has become evident that there exists a crosstalk between the respiratory and gastrointestinal tracts, commonly referred to as the gut-lung axis. Via microbial secretions, metabolites, immune mediators and lipid profiles, these two separate organ systems can influence each other. Well-known immunomodulators and gut health stimulators are probiotics, prebiotics, together called synbiotics. They might combat air pollution-induced systemic inflammation and oxidative stress by optimizing the microbiota composition and microbial metabolites, thereby stimulating anti-inflammatory pathways and strengthening mucosal and epithelial barriers. Although clinical studies investigating the role of probiotics, prebiotics, and synbiotics in an air pollution setting are lacking, these interventions show promising health promoting effects by affecting the gastrointestinal- and respiratory tract. This review summarizes the current data on how air pollution can affect the gut-lung axis and might impact gut and lung health. It will further elaborate on the potential role of probiotics, prebiotics and synbiotics on the gut-lung axis, and gut and lung health.

Spatial patterns and background ranges of naturally occurring radionuclides (NORs) (i.e. U-238, Th-232, K-40) and Cs-137 were studied in the urban soils of Yerevan, the capital city of Armenia. Multifractal Inverse Distance Weighting (MIDW) was used to generate and analyze distribution patterns of radionuclide activities. Based on Fourier transformation of radioactivity data, a spectral analysis was also applied to separate, where possible, background/baseline patterns from local anomalies: two ranges of background values were found to characterise the Yerevan territory. Specifically, in the south and south-east of Yerevan, the lower background ranges of U-238, Th-232 and K-40 comprised in the intervals 2.60–36.42 Bq/kg, 4.04–30.63 Bq/kg and 147.7–396.7 Bq/kg, respectively, were observed in association with the presence of sedimentary formations. In contrast, the higher ones were found, instead, in the central and northern parts of the city where andesite-basalt lavas and ignimbrite tuffs occur. Here, the background values rise to 142.4 Bq/kg, 138.76 Bq/kg and 1502 Bq/kg, respectively. As for the distribution of artificial Cs-137, its baseline levels in Yerevan seem to depend mostly on the global radioactive fallout and some local technogenic sources. Its distribution patterns partially differ from those of NORs. In the framework of this paper, Radium equivalent activity (RaEq), outdoor absorbed dose rate in air (ODRA) and annual effective dose equivalent (AEDEs) were also determined and mapped. They show a good coincidence of their spatial variations with those of NORs. The Monte Carlo simulation was used to assess excess lifetime cancer risk from a stochastic perspective. The related sensitivity analysis revealed that, among NORs, U-238 and Th-232 give the greatest contribution to the total variance (45.7% 42.8%, respectively). In comparison, K-40 has the lowest share (11.3%). Regarding Cs-137, a highly negligible contribution to the onset of health risks (accounting for 0.02%) was observed.

The influences of weather and air pollutants on chronic obstructive pulmonary disease (COPD) have been well-studied. However, the heterogeneous effects of different influenza viral infections, air pollution and weather on COPD admissions and re-admissions have not been thoroughly examined. In this study, we aimed to elucidate the relationships between meteorological variables, air pollutants, seasonal influenza, and hospital admissions and re-admissions due to COPD in Hong Kong, a non-industrial influenza epicenter. A total number of 507703 hospital admissions (i.e., index admissions) and 301728 re-admission episodes (i.e., episodes within 30 days after the previous discharge) for COPD over 14 years (1998–2011) were obtained from all public hospitals. The aggregated weekly numbers were matched with meteorological records and outdoor air pollutant concentrations. Type-specific and all-type influenza-like illness positive (ILI+) rates were used as proxies for influenza activity. Generalized additive models were used in conjunction with distributed-lag non-linear models to estimate the associations of interest. According to the results, high concentrations of fine particulate matter, oxidant gases, and cold weather were strong independent risk factors of COPD outcomes. The cumulative adjusted relative risks exhibited a monotone increasing trend except for ILI+ B, and the numbers were statistically significant over the entire observed range of ILI+ total and ILI+ A/H3N2 when the reference rate was zero. COPD hospitalization risk from influenza infection was higher in the elderly than that in the general population. In conclusion, our results suggest that health administrators should impose clean air policies, such as strengthening emissions control on petrol vehicles, to reduce pollution from oxidant gases and particulates. An extension of the influenza vaccination program for patients with COPD may need to be encouraged: for example, vaccination may be included in hospital discharge planning, particularly before the winter epidemic.