The Nanfei River was one of dominant inflowing rivers of the fifth largest freshwater Chaohu Lake in China, which had been subjected to increasing nutrients and contaminants from population expansion, rapid industrialization and agricultural intensification in recent decades. In present study, surface sediment from the Nanfei River was collected to investigate the anthropogenic impact on distribution and bioavailability of heavy metals. Possible Cd sources along the river were constrained by using Cd isotope signatures and labile concentrations of heavy metals in sediment were determined through the DGT technique for risk assessment. Results showed that Cd in river sediment showed greatest enrichment (EF 0.8–9.4), indicating massive pollution from anthropogenic activities. Among the various possible Cd source materials, urban road dust, industrial soil and chicken manure, displayed higher Cd abundance and enrichment that might contribute to Cd accumulation in river sediment. Cadmium isotopic composition in river sediment was ranged from −0.21 ± 0.01‰ to 0.13 ± 0.03‰, whereas yielded relative variation from −0.31 ± 0.02‰ to 0.23 ± 0.01‰ in source materials. Accordingly, Cd sources along the river were constrained, i.e. traffic and industrial activities in the upper and middle reaches whereas agricultural activities in the lower reaches. Furthermore, the evaluation on ecological risk of heavy metals in sediment on basis of SQGs and DGT-labile concentrations demonstrated that Pb and Zn might pose higher risk on aquatic species. The present study confirmed that Cd isotopes were promising source tracer in environmental studies.

Traffic contributes to fine particulate matter (PM₂.₅) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM₂.₅ emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM₂.₅ over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM₂.₅, increasing at 6.1% year⁻¹, more than twice that of EC (2.9% year⁻¹). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015–2021.48 studies identified the sources of PM₂.₅ and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12–51% and 8–80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM₂.₅. The remaining sources for ambient PM₂.₅, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4–69%, 4–49%, 1–53%, 7–25% and 3–29%, respectively. For PM₁₀, the most dominant source was dust with 7–95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002–2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0–178.5 years) and 34.4 years (4.0–64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5–83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

This international scale study measured the prevalence of indoor microplastics (MPs) in deposited dust in 108 homes from 29 countries over a 1-month period. Dust borne MPs shape, colour, and length were determined using microscopy and the composition measured using μFTIR. Human health exposure and risk was assessed along with residential factors associated with MPs via a participant questionnaire. Samples were categorised according to each country's gross national income (GNI). Synthetic polymers dominated in low income (LI) (39%) and high income (HI) (46%) while natural fibres were the most prevalent in medium income (MI) (43%) countries. Composition and statistical analysis showed that the main sources of MPs and dust were predominantly from indoor sources. Across all GNI countries, greater vacuuming frequency was associated with lower MPs loading. High income country samples returned higher proportions of polyamides and polyester fibres, whereas in LI countries polyurethane was the most prominent MPs fibre. Exposure modelling showed infants (0–2 years) were exposed to the highest MPs dose through inhalation (4.5 × 10⁻⁵ ± 3 × 10⁻⁵) and ingestion (3.24 × 10⁻² ± 3.14 × 10⁻²) mg/kg-Bw/day. Health risk analysis of constituent monomers of polymers indicates cancer incidence was estimated at 4.1–8.7 per million persons across age groups. This study's analysis showed socio-economic factors and age were dominant variables in determining dose and associated health outcomes of MPs in household dust.

Per- and polyfluoroalkyl substances (PFAS) have been produced and used in a broad range of products since the 1950s. This class, comprising of thousands of chemicals, have been used in many different products ranging from firefighting foam to personal care products and clothes. Even at relatively low levels of exposure, PFAS have been linked to various health effects in humans such as lower birth weight, increased serum cholesterol levels, and reduced antibody response to vaccination. Human biomonitoring data demonstrates ubiquitous exposure to PFAS across all age groups. This has been attributed to PFAS-contaminated water and dietary intake, as well as inadvertent ingestion of indoor dust for adults and toddlers. In utero exposure and breast milk have been indicated as important exposure pathways for foetuses and nursing infants. More recently, PFAS have been identified in a wide range of products, many of which come in contact with skin (e.g., cosmetics and fabrics). Despite this, few studies have evaluated dermal uptake as a possible route for human exposure and little is known about the dermal absorption potential of different PFAS. This article critically investigates the current state-of-knowledge on human exposure to PFAS, highlighting the lack of dermal exposure data. Additionally, the different approaches for dermal uptake assessment studies are discussed and the available literature on human dermal absorption of PFAS is critically reviewed and compared to other halogenated contaminants, e.g., brominated flame retardants and its implications for dermal exposure to PFAS. Finally, the urgent need for dermal permeation and uptake studies for a wide range of PFAS and their precursors is highlighted and recommendations for future research to advance the current understanding of human dermal exposure to PFAS are discussed.

Silicosis is a disease mainly caused by pulmonary interstitial fibrosis caused by long-term inhalation of dust with excessively high content of free SiO₂. Transdifferentiation of lung fibroblasts into myofibroblasts is an important cellular basis for silicosis, but the key transcription factors (TFs) involved in this process are still unclear. In order to explore the biological regulation of transcription factor PPARγ/LXRα in silica-induced pulmonary fibrosis, this study explored the molecular mechanism of PPARγ/LXRα involved in regulating transcription factors related to SiO₂-induced lung injury at the cellular level and in animal models. ChIP-qPCR detected that PPARγ directly regulated the transcriptional activity of the LXRα gene promoter, while the PPARγ agonist RSG increased the expression of LXRα. In addition, we demonstrated in the cell model that upregulation of LXRα can inhibit silica-mediated fibroblast transdifferentiation, accompanied by an increase in the expression of SREBF1, PLTP and ABCA1. The results of LXRα silencing experiment matched those of overexpression experiment. These studies explored the role of LXRα in plasticity and phenotypic transformation between lung fibroblasts and myofibroblasts. Therefore, inhibiting or reversing the transdifferentiation of lung fibroblasts to myofibroblasts by intervening PPARγ/LXRα may provide a new therapeutic target for the treatment of silicosis.

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.

Carcinogenic and neurotoxic polybrominated diphenyl ethers (PBDEs) are environmentally ubiquitous and have been widely investigated. However, little is understood regarding their pollution status, sources, and potential risk to persons in public transportation microenvironments (PTMs). We collected 60 dust samples from PTMs and then selected four materials typical of bus interiors to determine the sources of PBDEs in dust using principal component analysis coupled with Mantel tests. We then evaluated the risk of PBDEs to public health using Monte Carlo simulations. We found that PBDE concentrations in dust were 2-fold higher in buses than at bus stops and that brominated diphenyl ether (BDE)-209 was the main pollutant. The number of buses that passed through a bust stop contributed to the extent of PBDE pollution, and the primary potential sources of PBDEs in dust were plastic handles and curtains inside buses; BDE-209 and BDE-154 were the main contributors of pollution. We found that health risk was 8-fold higher in toddlers than in adults and that the reference doses of PBDEs in dust were far below the United States Environmental Protection Agency limits. Our findings provide a scientific basis that may aid in preventing PBDE pollution and guiding related pollution management strategies in PTMs.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.