Pollution Signatures Research Articles

Association of metabolic signatures of air pollution with MASLD: Observational and Mendelian randomization study

Background & AimsTo identify metabolic signatures associated with exposure to ambient air pollution and to explore their associations with risk of metabolic dysfunction-associated steatotic liver disease (MASLD). MethodsWe utilized data from the UK Biobank Cohort. Annual mean concentrations of PM2.5, PM10, NO2 and NOx were assessed for each participant using bilinear interpolation. The Elastic Net regression model was used to identify metabolites associated with four air pollutants and to construct metabolic signatures, respectively. Associations between air pollutants, metabolic signatures and MASLD were analyzed using Cox models. Mendelian randomization (MR) analysis was used to examine potential causality. Mediation analysis was employed to examine the role of metabolic signatures in the association between air pollutants and MASLD. ResultsA total of 244,842 participants from the UK Biobank were included in this analysis. We identified 87, 65, 76, and 71 metabolites as metabolic signatures of PM2.5, PM10, NO2, and NOx, respectively. Metabolic signatures were associated with risk of MASLD, with hazard ratios (HRs) and 95% confidence intervals (95% CIs) were 1.10 (1.06, 1.14), 1.06 (1.02, 1.10), 1.24 (1.20, 1.29) and 1.14 (1.10, 1.19). The four pollutants were associated with increased risk of MASLD, with HRs (95% CIs) of 1.03 (1.01, 1.05), 1.02 (1.01, 1.04), 1.01 (1.01, 1.02) and 1.01 (1.00, 1.01). MR analysis indicated an association between PM2.5, NO2 and NOx-related metabolic signatures and MASLD. Metabolic signatures mediated the association of PM2.5, PM10, NO2 and NOx with MASLD. ConclusionThere may be association between PM2.5, PM10, NO2 and NOx-related metabolic signatures and MASLD, and metabolic signatures mediate the increase of PM2.5, PM10, NO2 and NOx in the risk of MASLD. Impact and implicationsAir pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), however, the mechanism by which air pollution affects MASLD remains unclear. Our study used integrated serological metabolic data of 251 metabolites from a large-scale cohort study to demonstrate that metabolic signatures play a crucial role in the elevated risk of MASLD caused by air pollution. These results are relevant to patients and policymakers because they suggest that air pollution-related metabolic signatures are not only potentially associated with MASLD but also involved in mediating the process by which PM2.5, PM10, NO2, and NOx increase the risk of MASLD. Focusing on changes in air pollution-related metabolic signatures may offer a new perspective for preventing air pollution-induced MASLD and serve as protective measures to address this emerging public health challenge.

Early Onset of Lung Cancer in Small Areas as a Signature of Point Pollution Sources.

The impact of air pollution on lung cancer (LC) is difficult to detect in low-populated areas due to the potentially unfocused detection of pollutants and/or limited statistical power. This study identified and measured the harmful effect of pollution in small areas by considering the early onset of LC as a signature of pollution. This novel method requires a Bayesian standard curve calculated from the median age at LC onset and the corresponding median age of reference populations. Similar medians gathered from the area/s under investigation permits a probabilistic comparison with the standard curve. Statistically significant divergences can be interpreted as early or late LC onset. The method is exemplified in the Trieste municipality (northeast Italy) using data from the Friuli Venezia Giulia Cancer Registry (study population) and from the International Agency for Research on Cancer (reference population). Early LC onset has been observed near the pollution sources. Within 600 m of the iron foundry, onset ranged between 3.2 and 7.7 years earlier in men and between 11.7 and 16.8 years earlier in women. Near the shipyard, early onset was around 4 years in men and 7 years in women, while in the industrial area, early onset was 5 years in women only. Examining early LC onset may speed up the investigation of potential environmental hazards.

Setting an Environmental Baseline for the Deep-Sea Slope Offshore Northwestern Cuba (Southeastern Gulf of Mexico) Using Sediments and Nematode Diversity

Deep-sea offshore northwestern Cuba is the less studied zone of the Gulf of Mexico (GoM). Our study aimed to set an environmental baseline and investigate a potential west-east gradient of sediment properties and nematode diversity along the northwestern Cuba. Sediments were collected by multicorer at nine sites in the insular slope between 974 and 1682 m depth. Sediment texture and composition showed a west-east gradient caused likely by narrowing of shelf width and increasing of downslope transport of terrigenous material. We found clear signatures of heavy metal pollution likely derived from Havana City but also from open-sky mining (Castellanos mining complex) and port dredging operations (Mariel). Nematode assemblages were dominated by the deep-sea genera: Acantholaimus, Metadasynemella, Desmodorella, Cervonema, Daptonema, Halalaimus, and Pselionema. α-diversity was about 20–50 genera and γ-diversity of about 100 genera. The β-diversity was substantial likely because small-scale patchiness of resources and heavy metals stress that increased assemblage variability. Individual weight decreased with depth indicating food-supply limitation in the deep sea; also, heavy metals were negatively correlated with weight suggesting deleterious effects on the growing. Our findings indicate that heavy metal pollution has reached deep-sea sediments and corroborate the long-range effects of anthropogenic activities on deep sea. Taxonomic diversity and biological traits (life strategy and trophic composition) constituted a powerful bioindicator of benthic health and benchmark for future potential disturbances in the region. Northwestern Cuba slope is still an understudied portion of the GoM and more research is needed to fully understand its biodiversity and biogeochemical patterns.

Multi-omic signatures of traffic related air pollution (TRAPs) in London

Multi-omic signatures of traffic related air pollution (TRAPs) in London

Multi-Dimensional Surface Water Quality Analyses in the Manawatu River Catchment, New Zealand

Land Use and Land Cover (LULC) properties give vital information about pollution signatures in rivers, and they help develop best management practices (BMPs) for effective water resource management. This work employs multivariate statistical methods, receptor modeling, connectivity analysis, and univariate trend analysis to investigate pollution sources across spatiotemporal scales in the Manawatu River, New Zealand. A positive matrix factorization (PMF) method was applied to interpret possible contamination sources. A 25-year dataset (1989–2014) comprising 12 water quality variables from three sites was used. Runoff connectivity analyses identified high-producing grassland (HG) as the most dominant pollution class in all sub-catchments. Univariate analyses revealed that nutrients and sediments were higher than in the initial monitoring years. The PMF analysis found possible pollutants causing impairment, which required attention from waste managers. PMF also showed that point, natural, and agricultural sources significantly contributed to pollution downstream of the river. In the midstream, the erosion, point, and agricultural sources were significant contributing factors. Agricultural pollution and soil erosion were the main contributors to the upstream sub-catchment area. This study suggests that BMPs with a high retention capacity are needed in specific locations in the catchment area to filter high concentrations of pollutants generated.

A novel method to estimate the aggregate pollution index in a typical limestone mining and cement producing environment.

This study presents a novel method to estimate the aggregate pollution index (API) within the residential communities around a limestone mining and cement producing environment. The ranges for the air quality index (AQI), pollution load index in the topsoil (PLIt), pollution load index in the subsoil (PLIs), heavy metal pollution index in the water (HPI), and radiological external hazard index (Hex) were 5.99 to 57.97, 1.65E-07 to 3.6E-04, 1.7E-08 to 3.5E-04, 52.17 to 1053.13, and 0.0694Bq.kg-1 to 0.550, respectively. The AQI, PLIt, PLIs, HPI, and Hex revealed nonuniform patterns of variations across the communities, though there was a significant correlation between the PLIt and the PLIs, and between the HPI and the Hex; and a moderate correlation between the HPI and the AQI, between the HPI and the PLIt, and between the HPI and the PLIs. The measured quality indicators (MQI), and the calculated pollution indices (CPI) were subjected to the multivariate analysis. The principal components (PC) split the ten communities the same way in the CPI and the MQI. The API, using the PC, ranged from 3 to 9. The Afami, Balogun, and Akinbo communities had the highest API, while the Ewekoro and Itori communities had the lowest. The CPI was 41% of the MQI, with respect to the within-cluster variance, indicating that the CPI-based clustering was more reliable. Both the CPI and the MQI attributed a unique pollution signature to the Ewekoro community, while the remaining nine communities shared a common pollution status with the Ibese community.

The Historic Built Environment As a Long-Term Geochemical Archive: Telling the Time on the Urban "Pollution Clock".

This study introduces a novel methodology for utilizing historic built environments as reliable long-term geochemical archives, addressing a gap in the reconstruction of past anthropogenic pollution levels in urban settings. For the first time, we employ high-resolution laser ablation mass spectrometry for lead isotope (206Pb/207Pb and 208Pb/206Pb) analysis on 350-year-old black crust stratigraphies found on historic built structures, providing insights into past air pollution signatures. Our findings reveal a gradual shift in the crust stratigraphy toward lower 206Pb/207Pb and higher 208Pb/206Pb isotope ratios from the older to the younger layers, indicating changes in lead sources over time. Mass balance analysis of the isotope data shows black crust layers formed since 1669 primarily contain over 90% Pb from coal burning, while other lead sources from a set of modern pollution including but not limited to leaded gasoline (introduced after 1920) become dominant (up to 60%) from 1875 onward. In contrast to global archives such as ice cores that provide integrated signals of long-distance pollution, our study contributes to a deeper understanding of localized pollution levels, specifically in urban settings. Our approach complements multiple sources of evidence, enhancing our understanding of air pollution dynamics and trends, and the impact of human activities on urban environments.

Distribution of stable isotopes of Mo and W from a river to the ocean: signatures of anthropogenic pollution

Molybdenum and tungsten are redox-sensitive elements, and their stable isotope ratios have attracted attention as paleoceanographic proxies. However, our knowledge of the distribution of stable Mo and W isotopes in the modern hydrosphere remains limited. In this study, we provided the concentrations and isotope ratios of dissolved Mo and W in the oceans (the North Pacific and Indian Oceans), marginal seas (the East China Sea and Sea of Japan), and a river-estuary system in Japan (from the Uji-Yodo rivers to Osaka Bay). In the North Pacific and Indian Oceans, the W concentration was 48.2 ± 6.2 pmol/kg (ave ± 2sd, n = 109), δ186/184W was 0.52 ± 0.06 ‰, the Mo concentration was 105.1 ± 8.0 nmol/kg, and δ98/95Mo was 2.40 ± 0.06 ‰. The results indicate that W has the constant concentration and isotopic composition in the modern ocean as well as Mo. In the East China Sea and the Sea of Japan, the W concentration and δ186/184W in the upper water (< 1000 m depth) were different from those in the ocean (W = 56 ± 18 pmol/kg, δ186/184W = 0.45 ± 0.06 ‰, n = 24). However, the concentrations in deeper water were congruent with those in the oceans (W = 49.9 ± 7.6 pmol/kg, δ186/184W = 0.50 ± 0.02 ‰, n = 7). The Mo concentration was 105.4 ± 3.1 nmol/kg and δ98/95Mo was 2.36 ± 0.03 ‰ (n = 31) throughout the water column, congruent with those in the ocean. In the Uji River-Yodo River-Osaka Bay system, the W concentration reached 1074 pmol/kg and δ186/184W reached 0.20 ‰. We propose that the enrichment of W with a low δ186/184W in the river-estuary system and marginal seas is caused by anthropogenic pollution. Anthropogenic Mo pollution was not detected in marginal seas. However, the Mo concentration and δ98/95Mo showed high anomalies above the mixing line of river water and seawater in the lower Yodo River and Osaka Bay, implying possible anthropogenic pollution of Mo in the metropolitan area.

Comprehensive characterization of per- and polyfluoroalkyl substances in wastewater by liquid chromatography-mass spectrometry and screening algorithms

Per- and polyfluoroalkyl substances (PFASs) constitute a large category of synthetic environmental pollutants, many of which remain unknown and warrant comprehensive investigation. This study comprehensively characterized PFASs in fluorinated-industrial wastewater by nontarget, quasi-target and target analyses using liquid chromatography-high-resolution mass spectrometry and data-processing algorithms. The algorithms based on characteristic in-source neutral losses and isotopologue distributions were applied to screening and identifying PFASs, while semiquantitative and quantitative analyses were utilized to determine their concentrations and distributions in the wastewater. In total, 175 formulae of PFASs, including traditional, little-known and unknown species were identified and further ascertained in terms of concentrations and distributions. The total concentrations of PFASs in the wastewater were 5.3–33.4 μg mL−1, indicating serious pollution of PFASs. This study not only provides an efficient approach for screening and identification of unknown PFASs, but also presents a practicable and simple way to comprehensively depict environmental pollution signatures of PFASs.

Nontarget analysis and comprehensive characterization of halogenated organic pollutants by GC-Q-Orbitrap-HRMS in association with chromatogram segmentation and Cl/Br-specific screening algorithms

Nontarget analysis enables high-efficiency screening and identification of halogenated organic pollutants (HOPs) in complex matrices irrespective of lacking authentic standards, particularly for novel and emerging species, thereby realizing comprehensive component characterization of HOPs. Notwithstanding, nontarget analysis and comprehensive characterization of HOPs remain on the way to improvement. In this study, we implemented nontarget analysis of HOPs in fly ash, egg and sediment using gas chromatography quadrupole-orbitrap high-resolution mass spectrometry with the aid of chromatogram segmentation and Cl/Br-specific screening algorithms, and further performed comprehensive characterization of components and distribution of HOPs. In total, 122 HOP formulas were identified and tentatively assigned with structures, of which 28 were found in ≥ two matrices. Taking isomers into account, in total 1059 HOP congeners were found. Based on the identification and semiquantification results, the chemical components and concentration profiles of HOPs were preliminarily clarified, and accordingly the overall pollution signatures of HOPs were sketched. The total concentrations of HOPs in the fly ash, egg and sediment were 4.7, 41.2 and 750.8 μg g−1, respectively. Organochlorines were the most abundant among the categories classified by halogen types, and halogenated benzenes, halogenated dioxins, halogenated biphenyls/terphenyls and halogenated polycyclic aromatic hydrocarbons (H-PAHs) were the predominant of the structurally classified categories. Moreover, dozens of formulas of novel/little-known HOPs such as mix-chlorinated/brominated PAHs with ≥4 aromatic rings and polychlorinated terphenyls were identified. This study presents an accurate and high-performance nontarget analysis method for HOPs in complex matrices, and yields new cognitions on the pollution status of HOPs from an overall perspective.

Respiratory Deposition Dose of PM2.5 and PM10 Before, During and After COVID-19 Lockdown Phases in Megacity-Delhi, India

Considerable changes in particulate matter (PM) during COVID-19 lockdown in major cities around the World demand changes in exposure assessment studies of PM. The present study shows variations in respiratory deposition dose (RDD) of both fine (PM2.5) and coarse (PM10) particles before, during and after Covid-19 lockdown phases at three sites (with different pollution signatures) in Delhi—Alipur, Okhla and Pusa Road. Exposure assessment study showed mean PM2.5 RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 2.41(± 1.20) and 0.84(± 0.42) for Alipur, 2.71(± 1.60) and 0.94(± 0.56) for Okhla, and 2.54(± 1.28) and 0.88(± 0.44) for Pusa road, which decreased drastically during Lockdown 1(L1) as 0.85(± 0.35) and 0.30(± 0.12) for Alipur, 0.83(± 0.33) and 0.29(± 0.11) for Okhla, and 0.68(± 0.28) and 0.23(± 0.10) for Pusa road, respectively. Mean PM10 RDD (± S.D.) (µg/min) for walk and sit mode during before lockdown (BL) as 3.90 (± 1.73) and 1.36 (± 0.60) for Alipur, 4.74 (± 2.04) and 1.65 (± 0.71) for Okhla, and 4.25 (± 1.69) and 1.48 (± 0.59) for Pusa Road, respectively which decreased drastically during Lockdown 1(L1) as 2.19 (± 0.95) and 0.76 (± 0.33) for Alipur, 1.73 (± 0.67) and 0.60 (± 0.23) for Okhla and, 1.45 (± 0.50) and 0.50 (± 0.17) for Pusa Road, respectively. Significant decrease in RDD concentrations (Both PM2.5 and PM10) than that of BL phase have been found during Lockdown 1(L1) phase and other successive lockdown and unlock phases—Lockdown 2(L2), Lockdown 3(L3), Lockdown 4(L4) and Unlock1 (UL1) phases. Changes in RDD values during lockdown phases were affected by lesser traffic emission, minimized industrial activities, biomass burning activities, precipitation activities, etc. Seasonal variations of RDD showed Delhites are found exposed to more fine and coarse particles’ RDD (walk and sit modes) before and after lockdown, i.e. during normal days than during lockdown phases showing potential health effects. People in sit condition found less exposed to fine and coarse RDD comparison to those in walk condition both during normal and lockdown days.

Geo-Temporal Signatures of Physicochemical and Heavy Metals Pollution in Groundwater of Khulais Region—Makkah Province, Saudi Arabia

Seawater has intruded into many of Saudi Arabia’s Red Sea coastal aquifers, with varying degrees of extension depending on location, hydrogeology, and population density. This study aimed to evaluate and comprehend the processes that influence the hydrogeochemical characteristics of the coastal aquifer in Saudi Arabia’s Khulais region. Groundwater samples were taken from nineteen locations during the winter and summer of 2021, and data from major ions and trace elements were examined and interpreted using ArcGIS software. The total dissolved solids (TDS) concentrations ranged between 480 and 15,236 mg/L and 887–18,620 mg/L in winter and summer, respectively. Groundwater TDS concentration was observed to be influenced by groundwater flow, lithogenic, anthropogenic, and seawater intrusion in this study (2021) when compared to 2016. The concentration of nitrate (NO3−) and strontium (Sr) in most samples exceeds the drinking guidelines. The occurrence of high concentrations of bromide (Br), Fluoride (F), Iron (Fe) (winter and summer) and Aluminum (Al), Boron (B), Chromium (Cr), Nickel (Ni), lead (Pb), cadmium (Cd), cobalt (Co), copper (Cu) and manganese (Mn) (winter) was also exhibited and observed up to more than drinking and irrigation limits. The central part of the study area was affected by seawater intrusion. The hydraulic conductivity of the topsoil was measured, and it ranged from 0.24 to 29.3 m/day. Based on electrical conductivity (EC) and sodium absorption ratio, most aquifer samples were unsuitable for irrigation (SAR).

Using Noise Pollution Data for Traffic Prediction in Smart Cities: Experiments Based on LSTM Recurrent Neural Networks

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic information is key to managing traffic issues. Many approaches that use traffic time series data to predict traffic flow have been proposed. In addition to traffic- specific parameters, some other features (called signatures) may be associated with road traffic, i.e., air and noise pollution. In this paper, we show how noise pollution and traffic time-series data were used to train Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which led to better traffic prediction on major roads in Madrid. This approach has already been used with pollution signatures. This work addresses a new potential investigation path closely related to the use of signature profiles and Artificial Intelligent techniques as a way to reduce the specialization of sensing infrastructure.

Metabolomic signatures of the short-term exposure to air pollution and temperature

BackgroundShort-term exposures to air pollution and temperature have been reported to be associated with inflammation and oxidative stress. However, mechanistic understanding of the affected metabolic pathways is still lacking and literature on the short-term exposure of air-pollution on the metabolome is limited. ObjectivesWe aimed to determine changes in the plasma metabolome and associated metabolic pathways related to short-term exposure to outdoor air pollution and temperature. MethodsWe performed mass-spectrometry based untargeted metabolomic profiling of plasma samples from a large and well-characterized cohort of men (Normative Aging Study) to identify metabolic pathways associated with short-term exposure to PM2.5, NO2, O3, and temperature (one, seven-, and thirty-day average of address-specific predicted estimates). We used multivariable linear mixed-effect regression and independent component analysis (ICA) while simultaneously adjusting for all exposures and correcting for multiple testing. ResultsOverall, 456 white men provided 648 blood samples, in which 1158 metabolites were quantified, between 2000 and 2016. Average age and body mass index were 75.0 years and 27.7 kg/m2, respectively. Only 3% were current smokers. In the adjusted models, NO2, and temperature showed statistically significant associations with several metabolites (19 metabolites for NO2 and 5 metabolites for temperature). We identified six metabolic pathways (sphingolipid, butanoate, pyrimidine, glycolysis/gluconeogenesis, propanoate, and pyruvate metabolisms) perturbed with short-term exposure to air pollution and temperature. These pathways were involved in inflammation and oxidative stress, immunity, and nucleic acid damage and repair. ConclusionsThis is the first study to report an untargeted metabolomic signature of temperature exposure, the largest to report an untargeted metabolomic signature of air pollution, and the first to use ICA. We identified several significant plasma metabolites and metabolic pathways associated with short-term exposure to air pollution and temperature; using an untargeted approach. Those pathways were involved in inflammation and oxidative stress, immunity, and nucleic acid damage and repair. These results need to be confirmed by future research.

Distribution characteristics, chemical speciation and human health risk assessment of metals in surface dust in Shenyang City, China

Surface dust samples were collected in 2018 at 30 different sites in Shenyang City, China. Concentrations of chromium (Cr), arsenic (As), cupper (Cu), lead (Pb), zinc (Zn), nickel (Ni), mercury (Hg) and cadmium (Cd) were determined, and chemical speciation of metals were analyzed. Mean metal concentrations followed the order Zn > Cu > Pb > Cr > Ni > As > Cd > Hg. Although concentrations of metals in dust samples were lower than previously reported, a measurable metal pollution signature in dust still exists. Metals (except for As), are influenced by anthropogenic activities. Spatial variation in mean metal dust concentrations and speciation varied significantly and were influenced by land-use and industrial activities. Source appointment Results revealed that pollution of surface dust was derived from three main source profiles (industrial and traffic pollution, coal combustion, and urban pollution), contributing to 90.7% of the variance. Health risk assessment indicated that risks were low, but children were susceptible to metals in surface dust. Results indicated oral ingestion was the dominant exposure pathway. Non-carcinogenic Cr and Pb and carcinogenic As were the main metal contaminants in surface dust.

EPIGENETIC MARKERS OF THE INFLUENCE OF PARTICULATE MATTER WITH DIFFERENT AERODYNAMIC DIAMETERS ON HUMAN HEALTH: A REVIEW

In this review article we provide an overview of epigenetic markers associated with the effects of particulate matter with different aerodynamic diameters, namely, PM0,1, PM, PM10 and PM2,5-10. The developed list of more than 150 epigenetic signatures of environmental pollutants on different human health conditions may contribute to the development of novel methodological approaches to early detection of diseases in individuals exposed to particulate matter. In addition, this work can become a prerequisite for the development of fundamentally new air purification systems.

The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China

Abstract. The Taklamakan desert is an important dust source for the global atmospheric dust budget and a cause of the dust weather in East Asia. The characterization of Taklamakan dust in the source region is still very limited. To fill this gap, the DAO (dust aerosol observation) was conducted in April 2019 in Kashi, China. The Kashi site is about 150 km from the western rim of the Taklamakan desert and is strongly impacted by desert dust aerosols, especially in spring time, i.e., April and May. According to sun–sky photometer measurements, the aerosol optical depth (at 500 nm) varied in the range of 0.07–4.70, and the Ångström exponent (between 440 and 870 nm) in the range of 0.0–0.8 in April 2019. In this study, we provide the first profiling of the 2α+3β+3δ parameters of Taklamakan dust based on a multiwavelength Mie–Raman polarization lidar. For Taklamakan dust, the Ångström exponent related to the extinction coefficient (EAE, between 355 and 532 nm) is about 0.01 ± 0.30, and the lidar ratio is found to be 45 ± 7 sr (51 ± 8–56 ± 8 sr) at 532 (355) nm. The particle linear depolarization ratios (PLDRs) are about 0.28–0.32 ± 0.07 at 355 nm, 0.36 ± 0.05 at 532 nm and 0.31 ± 0.05 at 1064 nm. Both lidar ratios and depolarization ratios are higher than the typical values of Central Asian dust in the literature. The difference is probably linked to the fact that observations in the DAO campaign were collected close to the dust source; therefore, there is a large fraction of coarse-mode and giant particles (radius >20 µm) in the Taklamakan dust. Apart from dust, fine particles coming from local anthropogenic emissions and long-range transported aerosols are also non-negligible aerosol components. The signatures of pollution emerge when dust concentration decreases. The polluted dust (defined by PLDR532≤0.30 and EAE355-532≥0.20) is featured with reduced PLDRs and enhanced EAE355−532 compared to Taklamakan dust. The mean PLDRs of polluted dust generally distributed in the range of 0.20–0.30. Due to the complexity of the nature of the involved pollutants and their mixing state with dust, the lidar ratios exhibit larger variabilities compared to those of dust. The study provides the first reference of novel characteristics of Taklamakan dust measured by Mie–Raman polarization lidar. The data could contribute to complementing the dust model and improving the accuracy of climate modeling.

Metabolomics signatures of the short-term exposure to air pollution and temperature

Background/Aim:Short-term exposures to air pollution and temperature have been reported to be associated with inflammation and oxidative stress. However, mechanistic understanding of the affected metabolic pathways is still lacking and the existing literature on the short-term exposure of air-pollution on the metabolome is limited. We aimed to determine the changes in the blood metabolome and the associated pathways related to short-term exposure to outdoor air pollution and temperature. Methods:We performed mass-spectrometry based untargeted metabolomic profiling of plasma samples from a large and well-characterized cohort of men (the Normative Aging Study) to identify metabolic pathways associated with short-term exposure to PM2.5, NO2, O3, and temperature (one, seven-, and thirty-day average of central site monitors). We used multivariable linear mixed-effect regression and independent component analysis (ICA) while simultaneously adjusting for all exposures and correcting for multiple testing. Results:Overall, 456 white men provided 648 blood samples, in which 1,158 metabolites were quantified, between 2000 and 2016. Average age and body mass index were 75.0 years and 27.7 kg/m2, respectively. Only 3% were current smokers. In the adjusted models, PM2.5, NO2, and temperature showed multiple statistically significant associations with several metabolites (40 metabolites for PM2.5, 100 metabolites for NO2, 1 metabolite for O3, and 14 metabolites for temperature). We identified eight metabolic pathways (glycerophospholipid, sphingolipid, glutathione, beta-alanine, pyrimidine, butanoate, propanoate, and methionine, cysteine, SAM and taurine metabolisms) perturbed with short-term exposure to air pollution and temperature. These pathways were involved in inflammation and oxidative stress, immunity, and nucleic acid damage and repair. Conclusions:We identified several significant metabolites and metabolic pathways associated with short-term exposure to air pollution and temperature; using an untargeted approach. This is the first study to report an untargeted metabolomic signature of temperature exposure, the first to use ICA, and the largest study to derive a metabolomic signature of air pollution.

Short-term stability of particles in the WD J0914+1914 white dwarf planetary system

ABSTRACT Nearly all known white dwarf planetary systems contain detectable rocky debris in the stellar photosphere. A glaring exception is the young and still evolving white dwarf WD J0914+1914, which instead harbours a giant planet and a disc of pure gas. The stability boundaries of this disc and the future prospects for this white dwarf to be polluted with rocks depend upon the mass and orbit of the planet, which are only weakly constrained. Here, we combine an ensemble of plausible planet orbits and masses to determine where observers should currently expect to find the outer boundary of the gas disc. We do so by performing a sweep of the entire plausible phase space with short-term numerical integrations. We also demonstrate that particle-star collisional trajectories, which would lead to the (unseen) signature of rocky metal pollution, occupy only a small fraction of the phase space, mostly limited to particle eccentricities above 0.75. Our analysis reveals that a highly inflated planet on a near-circular orbit is the type of planet which is most consistent with the current observations.

Traffic influences nutritional quality of roadside plants for monarch caterpillars

Roadside habitats are increasingly being targeted for restoration and conservation. Roadside habitats often exhibit altered soil and plant chemistry due to pollution from maintenance (e.g. de-icing salt), car deterioration, and exhaust. Roadside plants may attract animals due to elevated levels of sodium or nitrogen, but high concentrations of heavy metals and sodium can be toxic, potentially setting an ecological trap. In this study, we determine how roads influence the chemistry of common milkweed (Asclepias syriaca) as it is the primary roadside host plant for the declining monarch butterfly (Danaus plexippus) in the eastern United States. Even though road salt is applied during the winter, we detect enhanced sodium along roads the following growing season. Road salts increase soil sodium, which in turn elevates host-plant foliar sodium (occasionally to toxic levels in <10% of plants) and sodium content in monarch caterpillars feeding on these plants. Sodium levels of milkweed leaves are highest close to the edge of busy roads. Some heavy metals (lead, zinc) are also elevated in roadside soils or plants. Nitrogen content was affected by adjacent agricultural use, but not traffic volume or proximity to a road. Other potential road pollutants (e.g. nickel) were not elevated in soil or plants. Despite a clear signature of road pollution in the chemistry of milkweed, most plants are likely still suitable for developing monarchs. Nonetheless, restoration investments in snowy regions should prioritize sites with lower-traffic density that are further from the road edge to minimize toxic impacts of high sodium. To extend this research to other insects of conservation concern, future work should characterize the nutritional quality of nectar, pollen, and other species of host-plants in roadside habitats.