Heavy metal content and health risk assessment of atmospheric particles in China: A meta-analysis

Atmospheric Heavy Metal Pollution Characteristics and Health Risk Assessment Across Various Type of Cities in China

This study investigates the spatiotemporal trends and health risks of nine atmospheric heavy metals (Pb, As, Mn, Ni, Cr, Cd, Zn, Cu, Fe) in PM2.5 across 50 Chinse cities, comparing resource-industrial cities (RICs) and general cities (GCs) before (2014-2018) and after (2019-2021) China's 2018 Air Pollution Prevention and Control Action Plan. Post-2018, concentrations of all metals except Fe declined significantly (33-77%), surpassing PM2.5 reductions (25%). Geospatial analysis revealed elevated heavy metal levels in northern and southern regions in China, aligning with industrial and mining hotspots. While RICs exhibited persistently higher metal concentrations than GCs, the inter-city gap narrowed post-2018, with RICs achieving greater reduction. Pre-2018, the combined non-carcinogenic hazard index (HI < 1) remained below safety thresholds, but the combined carcinogenic risk total (CRT) for children exceeded 10-4, driven primarily by As and Cr(VI). HIs were 1.5-2.0 times higher in RICs than in GCs. Post-2018, the CRT declined by 69.0-71.1%, aligning with reduced heavy metal levels. Despite improvements, CRTs necessitate targeted mitigation for As (contributing 81.1-86.2% to CRT) and Cr(VI) (11.7-14.0%). These findings validate the policy's effectiveness in curbing industrial and vehicular emissions but underscore the need for metal-specific controls in resource-intensive regions to safeguard child health.

Sources and distribution of potentially toxic elements in urban road dust: A comparative insights and risk assessment of two polluted cities

This study provides a comprehensive comparative analysis of seven potentially toxic elements (PTEs) from thirty samples using instrumental neutron activation analysis on the roadside dust samples collected from a south-western city (Khulna) and a highly urban megacity (Dhaka), Bangladesh. The mean concentrations (μg.g-1) of Cr, Mn, Fe, Co, Zn, As, and Sb in the analyzed dust samples were 67.5±33.2, 386±136, 25648±5334, 6.86±1.79, 98±63, 3.02±1.08, and 1.37±1.10, respectively in Khulna city and 66.7±6.9, 547±110, 25150±1723, 8.39±0.65, 125±17, 3.63±0.56, and 0.75±0.28, respectively, in Dhaka city, showing uneven distribution in both cities. PMF modelling and multivariate statistical approaches demonstrated that 65.68% anthropogenic and 34.32% geogenic sources for Khulna city, whereas 64.93% mixed (anthropogenic and geogenic) and 35.07% anthropogenic sources were the main contributors of measured elements in Dhaka city. In both cities, anthropogenic contributions were primarily linked to traffic emissions and industrial activity. Various geo-environmental indicators, including element-specific (Igeo, EF, CF), site-specific (Cd, mCd, PLI, NIPI), and ecological indices (Eri, RI), were used to assess the contamination characteristics of PTEs and contamination levels in both cities were in the following decreasing order: Sb >Zn >Cr >Fe >As >Mn >Co, whereas individual ecological risks were in the following decreasing order: Sb(34.28) >As(6.28) >Co(1.98) >Cr(1.47) >Zn(1.46) >Mn(0.50) in Khulna, and Sb(18.64) >As(7.56) >Co(2.43) >Zn(1.86) >Cr(1.45) >Mn(0.71) in Dhaka. The study site demonstrated lower potential ecological risks, even though non-carcinogenic and carcinogenic risks from various exposure pathways appeared minimal. Notably, children in both urban cities exhibited heightened vulnerability compared to adults.

Development of a comprehensive air risk index and its application to high spatial-temporal health risk assessment in a large industrial city

Particulate matter (PM) contains various hazardous air pollutants (HAPs) that can adversely affect human health, highlighting the need for an integrated index to represent the associated health risks. In response, this study developed a novel index, the comprehensive air-risk index (CARI), for Ulsan, the largest industrial city in South Korea. This index integrates toxicity-weighted concentrations of polycyclic aromatic hydrocarbons (PAHs) and heavy metals using their inhalation unit risks. CARI was categorized into four risk levels based on probabilistic health risks. Over eight years (2013-2020) in Ulsan, the risk from PAH exposure showed a decreasing trend, whereas the risk from heavy metals remained stable, reflecting different emission patterns and major source types. PAHs and heavy metals contributed 38.1% and 61.9% to CARI, respectively, highlighting the greater impact of heavy metals on human health. Unlike the monthly variations in PM2.5 concentrations, CARI values tended to increase in the summer and decrease in the spring and fall, indicating the impact of local emissions, particularly from petrochemical and non-ferrous industrial facilities. Moreover, a machine learning model enhanced the spatio-temporal resolution of CARI, showing that 'unhealthy' days were 2.4 times more frequent in industrial areas than in urban areas. In conclusion, CARI is a promising tool for assessing health risks in industrial cities and for developing risk-based management plans. Furthermore, we propose the development of a national-scale real-time CARI system by enhancing the spatio-temporal resolution of HAP data through the use of machine learning.

Chemical composition, multiple sources, and health risks of PM2.5: A case study in Linyi, China's plate and logistics capital

Elucidating the chemical composition, sources, and health risks of fine particulate matter (PM2.5) is crucial for effectively preventing and controlling air pollution. This study collected PM2.5 samples in Linyi from November 10, 2021, to October 15, 2022, spanning the period of the 2022 Winter Olympics and Paralympics. The analysis focused on seasonal variations in the chemical composition of PM2.5, including water-soluble ions, inorganic elements, and carbonaceous aerosols. Results from the random forest model indicated that control measures during the Olympics and Paralympics reduced PM2.5 concentrations by 21.5% in Linyi. Organic matter was the dominant component of PM2.5, followed by NO3-, SO42-, and NH4+. Among secondary inorganic ions, SO42- exhibited the highest concentration in summer, while NO3- and NH4+ showed the lowest concentrations. The inorganic elements S, K, Fe, and Si had high mean annual concentrations, underscoring the need for targeted control measures for plate production, bulk coal burning, and biomass combustion in Linyi. The organic carbon (OC) to elemental carbon ratio (17.7-20.5) in Linyi was high, highlighting the importance of addressing secondary OC pollution. According to the positive matrix factorization model, coal burning, and the secondary formation processes of sulfate and nitrate were the dominant sources of PM2.5. Backward air mass trajectories revealed substantial contributions from the southeastern, local, and southwestern regions of Linyi. This suggests the need for enhanced regional joint prevention and control efforts between Linyi and neighboring cities, such as Rizhao and Jining in Shandong Province, as well as northern cities in Jiangsu Province. The highest non-carcinogenic and carcinogenic risks (CRs) were associated with As. coal burning posed significant noncarcinogenic risks and a moderate CR, contributing 41.7% and 44.0% of the total health risk, respectively. These findings are crucial for developing effective air pollution prevention and control strategies.

Kidney function mediates the association of per- and poly-fluoroalkyl substances (PFAS) and heavy metals with hepatic fibrosis risk

Heavy metals and per- and polyfluoroalkyl substances (PFAS) are significantly associated with the risk of hepatic fibrosis. However, the potential mediating effect of kidney function in the relationship between heavy metals, PFAS, and hepatic fibrosis risk remains unexplored. This research gap limits the development of hepatic fibrosis prevention and treatment strategies. To address this, this study conducts a cross-sectional analysis based on data from 10,870 participants in NHANES 2005-2018 to explore the relationship between heavy metals, PFAS, and the risk of hepatic fibrosis, as well as the mediating effect of kidney function. Participants with a Fibrosis-4 index <1.45 are defined as not having hepatic fibrosis in this study. Results from generalized linear regression models and weighted quantile sum regression models indicate that both individual and combined exposures to heavy metals and PFAS are positively associated with the risk of hepatic fibrosis. Nonlinear exposure-response functions suggest that there may be a threshold for the relationship between heavy metals (except mercury) and PFAS with the risk of hepatic fibrosis. Furthermore, heavy metals and PFAS increase the risk of kidney function impairment. After stratification by kidney function stage, the relationship between heavy metals (except lead) and proteinuria is not significant, while PFAS show a significant negative association with proteinuria. The decline in kidney function has a significant mediating effect in the relationship between heavy metals and PFAS and the risk of hepatic fibrosis, with mediation effect proportions all above 20%. The findings suggest that individual or combined exposure to heavy metals and PFAS does not increase the risk of hepatic fibrosis until a certain threshold is reached, and the mediating role of declining kidney function is very important. These results highlight the need to consider kidney function in the context of hepatic fibrosis risk assessment and management.

Mapping the constituent preference of tree species for capturing particulate matter on leaf surfaces using single-particle mass spectrometry and supervised machine learning

Respiratory health is negatively influenced by the dimensions and constituents of particulate matter (PM). Although mass concentration is widely acknowledged to be key to assessing dust retention by urban trees, the role of plant leaves in filtering PM from the urban atmosphere, particularly regarding the particle dimensions and chemical constituents of retained PM on the leaf, remains elusive. Here we combined single-particle aerosol mass spectrometry and a particle resuspension chamber to investigate how urban tree species capture PM constituents. Results indicate that leaves are efficient in capturing relatively larger particles (1.0-2.0 μm). Compositionally, airborne particles were mostly composed of elemental carbon (EC, 20%), organic carbon (OC, 17%), and secondary reaction products (13%). However, leaf surfaces revealed a preference for retaining crustal species, comprising 55% of captured particulates. Notably, specific tree species demonstrated varied affinities for different PM constituents: Osmanthus fragrans Lour. predominantly captured levoglucosan (LEV), indicative of its efficiency against biomass burning particles, whereas Cinnamomum camphora (L.) J.Presl and Sabina chinensis var. kaizuca W.C.Cheng & W.T.Wang were more effective in capturing heavy metals (HMs). XGBoost modelling identified indicator ions, e.g., CN-, NO3-, NO2-, PO3-, with SHAP values surpassing 0.035, suggesting a preferential adsorption of these ions among different tree species. These findings demonstrate that the particulate capture efficiency of urban tree species varies with species-specific leaf properties, particularly in their ability to selectively adsorb particles containing hazardous constituents such as LEV and HMs. This study provides a scientific basis for the strategic selection of tree species in urban forestry initiatives aimed at improving air quality and public health.

Particulate matter 2.5 accelerates aging: Exploring cellular senescence and age-related diseases

Exposure to Particulate matter 2.5 (PM2.5) accelerates aging, causing declines in tissue and organ function, and leading to diseases such as cardiovascular, neurodegenerative, and musculoskeletal disorders. PM2.5 is a major environmental pollutant and an exogenous pathogen in air pollution that is now recognized as an accelerator of human aging and a predisposing factor for several age-related diseases. In this paper, we seek to elucidate the mechanisms by which PM2.5 induces cellular senescence, such as genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, and mitochondrial dysfunction, and age-related diseases. Our goal is to increase awareness among researchers within the field of the toxicity of environmental pollutants and to advocate for personal and public health initiatives to curb their production and enhance population protection. Through these endeavors, we aim to promote longevity and health in older adults.

Investigating the filtration performance and service life of vehicle cabin air filters in China

To protect occupants in vehicle cabin environments from the health risks of high concentrations of particulate matter (PM), it is important to install vehicle cabin air filter (VCAF) to eliminate PM. In this study, we investigated the filtration performance of 22 VCAFs. Results showed that the minimum average filtration efficiency was 56.1 % for particles with a diameter of 0.1-0.3 μm, a pressure drop of 33.2-250 Pa at air velocity of 2.5 m/s, and the dust-holding capacity ranged from 5.8 to 19.4 g. In addition, as the filter area increased from 0.23 m2 to 0.50 m2, the filtration efficiency for particles with a diameter of 0.1-0.3 μm increased from 56.7 % to 77.5 %, the pressure drop decreased from 96.1 to 62.5 Pa, and the dust holding capacity increased 2.7 times. Furthermore, we compared the service life of VCAF from 31 major Chinese cities and found that the service life varied greatly from maximum of 1730 h for Haikou to minimum of 352 h for Shijiazhuang. Considering occupant health risks, Beijing requires that VCAFs have PM2.5 filtration efficiency at least 88.1 %, and Liaoning requires minimum of 97.5 %. Hence, choosing the appropriate VCAF based on the atmospheric environment of different cities deserves our attention.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Effects of atmospheric deposition on heavy metal contamination in paddy field systems under different functional areas in ChangZhuTan, Hunan Province, China

In recent decades, the problem of heavy metal contamination in rice paddies has attracted widespread attention. However, most studies on heavy metal contamination in paddy fields are biased towards soil and/or rice plants, without taking atmospheric deposition into account. In this study, atmospheric deposition, paddy soil, and rice samples were collected from three functional areas (area proximity to factories, along the roadside, and suburban) in ChangZhuTan, Hunan Province. The pollution characterization, translocation, and health risk of heavy metals were reassessed. The findings revealed that Cd and As contamination in the study area's soils was more severe, with point exceedance rates reaching 70 % and 35.9 %, respectively. The highest concentrations of As, Ni, Cd, and Pb in atmospheric deposition were found along the roadside, with 1.42 μg/m2/day, 3.21 μg/m2/day, 0.34 μg/m2/day, and 8.28 μg/m2/day, respectively. In area proximity to factories, As and Ni in atmospheric deposition showed to be lowest, whereas Cd and Pb concentrations showed lowest in suburban areas. Furthermore, the accumulation of Cd and Pb in rice grains in regions proximity to factories was significantly higher than in other regions. The human health risk assessment indicated the health risk caused by rice intake in areas proximity to factories was the highest and requires attention, which was mainly due to Cd accumulation, with HQ value reached 3.19. Correlation tests indicate that atmospheric deposition has a positive effect on heavy metal enrichment in rice grains. Further Random Forest analysis revealed that the transport of heavy metals from atmospheric deposition to leaves and shells were important influencing factors for As, Cd, Ni and Mg accumulation in rice grain. Therefore, more attention should be paid to the effects of atmospheric deposition on the accumulation of heavy metals in paddy fields in order to maintain the production safety of crops.

Comprehensive analysis of metal(loid)s and associated metal(loid) resistance genes in atmospheric particulate matter

Despite global concerns about metal(loid)s in atmospheric particulate matter (PM), the presence of metal(loid) resistance genes (MRGs) in PM remains unknown. Therefore, we conducted a comprehensive investigation of the metal(loid)s and associated MRGs in PMs in two seasons (summer and winter) in Xiamen, China. According to the geoaccumulation index (Igeo), most metal(loid)s, except for V and Mn, exhibited enrichment in PM, suggesting potential anthropogenic sources. By employing Positive Matrix Factorization (PMF) model, utilizing a dataset encompassing both total and bioaccessible metal(loid)s, along with backward trajectory simulations, traffic emissions were determined to be the primary potential contributor of metal(loid)s in summer, whereas coal combustion was observed to have a dominant contribution in winter. The major contributor to the carcinogenic risk of metal(loid)s in both summer and winter was predominantly attributed to coal combustion, which serves as the main source of bioaccessible Cr. Bacterial communities within PMs showed lower diversity and network complexity in summer than in winter, with Pseudomonadales being the dominant order. Abundant MRGs, including the As(III) S-adenosylmethionine methyltransferase gene (arsM), Cu(I)-translocating P-type ATPase gene (copA), Zn(II)/Cd(II)/Pb(II)-translocating P-type ATPase gene (zntA), and Zn(II)-translocating P-type ATPase gene (ziaA), were detected within the PMs. Seasonal variations were observed for the metal(loid) concentration, bacterial community structure, and MRG abundance. The bacterial community composition and MRG abundance within PMs were primarily influenced by temperature, rather than metal(loid)s. This research offers novel perspectives on the occurrence of metal(loid)s and MRGs in PMs, thereby contributing to the control of air pollution.

Spatial and temporal variations in environmental impacts of heavy metal emissions from China's non-ferrous industry: An enterprise-specific assessment

In China, the non-ferrous metal industry is the sector with the highest emissions of arsenic, cadmium, mercury and lead, causing serious impacts on human health and the ecosystem. However, current heavy metal emission inventories are inadequate for figuring out their exposures and associated environmental impacts due to the lack of detailed data. Here, we constructed a high-resolution, enterprise-specific, and long-term dataset detailing heavy metal emissions from the non-ferrous industry in China from 1981 to 2020, using comprehensive enterprise information. Furthermore, an environmental impact assessment was performed using the characterization factors of the IMPACT World + model. Results show that: (1) from 1981 to 2020, the total heavy metal emissions of China's non-ferrous industry reached 144,697 tons (t), with atmospheric emissions (104,524 t) exceeding aquatic ones (40,173 t). (2) The industry's emissions showed a rising and then declining trend, with significant spatial heterogeneity, where heavy metal emissions concentrated in the central and western parts of Yunnan, the southern part of Hunan, the northern part of Guangxi, Henan along the Yellow River, the intersection of Gansu and Shaanxi, the central and eastern parts of Liaoning, and the eastern part of Inner Mongolia. (3) The environmental impact on human health was 1.19 × 107 DALY, and the value of ecosystem quality was 7.26 × 109 species·yr. The top 10 % of enterprises with the largest environmental impacts contributed over 60 % of human health risks and 62 % of ecosystem quality impacts. Improving the removal efficiency of heavy metals by 10 % within the four major industry classes could lead to a 9.92 % reduction in human health impacts and a 9.77 % reduction in ecosystem quality impacts within the non-ferrous metals industry. The findings of this study can provide insights for pollution control, environmental risk reduction, and sustainable development in the non-ferrous metals industry.

Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates

Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China' second Clean Air Action (2018-2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study

Background

In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs.

Methods

We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship.

Results

In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results.

Conclusion

Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.

Risk assessment and source apportionment of available atmospheric heavy metal in a typical sandy area reservoir in Inner Mongolia, China

This study evaluated dry and wet deposition of atmospheric heavy metals (HMs) in a sandy area of Inner Mongolia, China, with the Dahekou Reservoir, Xilin Gol League, adopted as the study area. Monthly monitoring of atmospheric HM dry and wet deposition was conducted over one year (2021 to 2022) at 12 monitoring points, producing 144 dry and wet deposition samples, respectively. The sample contents of eight HMs (Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd) were determined to estimate the fluxes of available forms of heavy metal (AHM) in dry and wet deposition. The potential ecological index (Eri), risk assessment coding (RAC), and ratio of secondary phase to primary phase (RSP) were used to evaluate the impact of atmospheric HM dry deposition on ecological security. Correlation analysis, principal component analysis, and the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model were used to quantitatively analyze the sources of AHMs in atmospheric dry and wet deposition. The results showed that the study area experienced annual dry and wet deposition fluxes of AHMs of 1712.59 kg and 534.97 kg, respectively. Atmospheric heavy metal dry deposition over the entire year presented a strong ecological risk, with Cd contributing most to this risk. Risk assessment of HM speciation showed that the greatest risks of migration and transformation were for Cd and Pb. The APCS-MLR receptor model identified five and three sources of dry and wet deposition, respectively, in order of proportion of total contribution of: natural wind and sand > road traffic and coal combustion > mineral mining > other human activities > industrial soot.

Characteristics of PM2.5-bound metals in Japan over six years: Spatial distribution, health risk, and source analysis

Air sampling campaigns were conducted at 100 survey sites across Japan from April 2014 to February 2020, and a comprehensive database of atmospheric particles was obtained. In this study, the characteristics of PM2.5 and 26 metals were investigated in depth. Spatially, the concentration of PM2.5 gradually increased from the northeast to the southwest of Japan. The pollution in Kitakyushu City was the most serious, reaching 19.8 μg m-3. As an important particle component, metals did not show obviously spatial variation in Japan, with a sum concentration of 0.4 μg m-3. Anthropogenic metals only accounted for about 8% of the total metals, but they could pose a serious threat to public health. For children, the non-carcinogenic risk and carcinogenic risk due to exposure to anthropogenic metals could not be neglected in Japan; the corresponding HI and CR values at 100 survey sites ranged from 2.7 to 15.0 and 4.1 × 10-5 to 3.4 × 10-4, respectively. Adults faced lower health risks than children, with HI values ranging from 0.2 to 2.0 and CR values ranging from 2.0 × 10-5 to 1.6 × 10-4. The integrated health risk assessment results showed that the coastal region of the Seto Inland Sea and the north Tohoku Region were the most heavily polluted areas of Japan; in this study, 20 survey sites were finally determined to be high-risk sites, among which pollution control for Niihama City, Kitakyushu City, Hachinohe City, and Shimonoseki City were of first priority. With further combination with a positive matrix factorization model, it can be known that these four cities mainly had five to seven metal sources, and their heavy pollution was mainly caused by ship emissions, industrial emissions, biomass burning, and coal combustion. Overall, our study comprehensively revealed the regional patterns of PM2.5-bound metal pollution across Japan, which can help in making cost-effective risk management policies with limited national/local budgets.

Blood multiple heavy metals exposure and lung function in young adults: A prospective Cohort study in China

The content of single heavy metal in blood is associated with lung function decline, but there is little evidence on the joint effect of multiple heavy metals on lung function. To explore whether heavy metal mixture exposure is associated with lung function reduction among young adults. The study based on a cohort of 518 students recruited from a college in Shandong, China. We measured their lung function and blood heavy metal concentrations. The BKMR model was used to analyse the association between blood heavy metals mixture levels and lung function, and to identify the critical single heavy metal which contributes most to joint effects. As the sensitivity analysis, we used quantile g-computation model and GLM to explore the joint effect and independent effects of heavy metals. Our findings revealed a significant reduction of FVC and FEV1 levels after exposure to heavy metals mixture. An IQR increase in Cu was associated with a 0.079 L and 0.083 L decrease in FEV1 and FVC, respectively. And an IQR increase in Fe was associated with 0.036 L higher FEV1 and 0.033 L higher FVC. For adults, reducing blood heavy metals concentration might be an effective intervention to protect lung function.

Research progress of different components of PM2.5 and ischemic stroke

PM2.5 is a nonhomogeneous mixture of complex components produced from multiple sources, and different components of this mixture have different chemical and biological toxicities, which results in the fact that the toxicity and hazards of PM2.5 may vary even for the same mass of PM2.5. Previous studies on PM2.5 and ischemic stroke have reached different or even opposing conclusions, and considering the heterogeneity of PM2.5 has led researchers to focus on the health effects of specific PM2.5 components. However, due to the complexity of PM2.5 constituents, assessing the association between exposure to specific PM2.5 constituents and ischemic stroke presents significant challenges. Therefore, this paper reviews and analyzes studies related to PM2.5 and its different components and ischemic stroke, aiming to understand the composition of PM2.5 and identify its harmful components, elucidate their relationship with ischemic stroke, and thus provide some insights and considerations for studying the biological mechanisms by which they affect ischemic stroke and for the prevention and treatment of ischemic stroke associated with different components of PM2.5.

Distribution of Minor and Major Metallic Elements in Residential Indoor Dust: A Case Study in Latvia

The coronavirus disease 2019 (COVID-19) pandemic has not only brought considerable and permanent changes to economies and healthcare systems, but it has also greatly changed the habits of almost the entire society. During the lockdowns, people were forced to stay in their dwellings, which served as a catalyst for the initiation of a survey on the estimation of the metallic element content in residential indoor dust in different parts of Latvia. This article presents the study results obtained through the analysis of collected dust samples from 46 dwellings, both in the capital of Latvia, Riga, and in smaller cities. Two methods were employed for indoor dust collection: vacuum sampling and manual sampling with a brush and plastic spatula. After microwave-assisted acid extraction, the samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) in terms of the major (Na, K, Ca, Mg, Al and Fe) and minor (Mn, Ni, Co, Pb, Cr, As, Ba, Li, Be, B, V, Cu, Zn, Se, Rb, Sr, Cd, La, Ce and Bi) elements. For the data analysis, principal component analysis was performed. Among the measured metals, the highest values were determined for the macro and most abundant elements (Na > K > Ca > Fe > Mg > Al). The concentration ranges of the persistently detected elements were as follows: Pb, 0.27-1200 mg kg-1; Cd, 0.01-6.37 mg kg-1; Ni, 0.07-513 mg kg-1; As, 0.01-69.2 mg kg-1; Cu, 5.71-1900 mg kg-1; Zn, 53.6-21,100 mg kg-1; and Cr, 4.93-412 mg kg-1. The critical limit values of metallic elements in soil defined by the legislation of the Republic of Latvia (indicating the level at or above which the functional characteristics of soil are disrupted, or pollution poses a direct threat to human health or the environment) were exceeded in the following numbers of dwellings: Pb = 4, Ni = 2, As = 1, Cu = 16, Cr = 1 and Zn = 28.

Contamination and source apportionment of metals in urban road dust (Jinan, China) integrating the enrichment factor, receptor models (FA-NNC and PMF), local Moran's index, Pb isotopes and source-oriented health risk

Contamination and source identifications of metals in urban road dust are critical for remediation and health protection. Receptor models are commonly used for metal source identification, whereas the results are usually subjective and not verified by other indicators. Here we present and discuss a comprehensive approach to study metal contamination and sources in urban road dust (Jinan) in spring and winter by integrating the enrichment factor (EF), receptor models (positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC)), local Moran's index, traffic factors and Pb isotopes. Cadmium, Cr, Cu, Pb, Sb, Sn and Zn were the main contaminants, with mean EFs of 2.0-7.1. The EFs were 1.0-1.6 times higher in winter than in spring but exhibited similar spatial trends. Chromium contamination hotspots occurred in the northern area, with other metal contamination hotspots in the central, southeastern and eastern areas. The FA-NNC results indicated Cr contamination primarily resulting from industrial sources and other metal contamination primarily originating from traffic emissions during the two seasons. Coal burning emissions also contributed to Cd, Pb and Zn contamination in winter. FA-NNC model-identified metal sources were verified via traffic factors, atmospheric monitoring and Pb isotopes. The PMF model failed to differentiate Cr contamination from other detrital metals and the above anthropogenic sources, largely due to the model grouping metals by emphasizing hotspots. Considering the FA-NNC results, industrial and traffic sources accounted for 28.5 % (23.3 %) and 44.7 % (28.4 %), respectively, of the metal concentrations in spring (winter), and coal burning emissions contributed 34.3 % in winter. Industrial emissions primarily contributed to the health risks of metals due to the high Cr loading factor, but traffic emissions dominated metal contamination. Through Monte Carlo simulations, Cr had 4.8 % and 0.4 % possibilities posing noncarcinogenic and 18.8 % and 8.2 % possibilities posing carcinogenic risks for children in spring and winter, respectively.