Particle-bound Metals Research Articles

Seasonal Disparities of Human Health Risk and Particle-Bound Metal Characteristics Associated with Atmospheric Particles in a Fishery Harbor.

The effects of atmospheric pollution from ship emissions have been considered for several harbors worldwide. The health risk assessment and source apportionment of particle-bound metals in a fishery harbor were investigated in this study. The most abundant metal elements in particulate matter (PM) on all sampling days in three seasons were Fe (280.94 ± 136.93 ng/m3), Al (116.40 ± 71.25 ng/m3), and Zn (110.55 ± 26.70 ng/m3). The ratios of V/Ni were 1.44 ± 0.31, 1.48 ± 0.09 and 1.87 ± 0.06 in PM10, PM2.5, and PM1, respectively. Meanwhile, the ratios higher than 1 indicated that fuel oil combustion from ship emission in fishery harbor. The highest deposits of total particle-bound metals in the human respiratory tract were in the head airway (HA), accounting for 76.77 ± 2.29% of the total particle-bound metal concentration, followed by 5.32 ± 0.13% and 2.53 ± 0.15% in the alveolar region (AR) and tracheobronchial (TB) region, respectively. The total cancer risk (CR) of inhalation exposure to local residents exceeded 10-6. Mean total CR values followed the sequence: autumn (1.24 × 10-4) > winter (8.53 × 10-5) > spring (2.77 × 10-6). Source apportionment of related metal emissions was mobile pollution emissions (vehicle/boat) (37.10-48.92%), metal fumes of arc welding exhaust (19.68-34.42%), spray-painting process (12.34-16.24%), combustion emissions (6.32-13.12%), and metal machining processes (9.04-16.31%) in Singda fishing harbor. These results suggest that proper control of heavy metals from each potential source in fishing harbor areas should be carried out to reduce the carcinogenic risk of adverse health effects.

Multi-metal contaminant mobilizations by natural colloids and nanoparticles in paddy soils during reduction and reoxidation

Naturally-occurring colloids and nanoparticles are crucial in transporting heavy metal contaminants in soil-water systems. However, information on particle-bound metals’ size distribution and elemental composition in paddy soils under redox-fluctuation is scarce. Here, we investigated the mobilization of Cu, Cd, and Pb-containing nanoparticles and colloids in four contaminated soils with distinctive geochemical properties during reduction and subsequent re-oxidation. Using AF4-UV-ICP-MS and STEM-EDS, we observed that particle-bound metals were primarily associated with two sizes ranges: 0.3−40 kDa (F1) and 130 kDa−450 nm (F2), which mainly consisted of organic matter (OM), iron hydroxide and clay minerals. Cu and Pb were more likely bound to colloid than Cd. Colloidal Cu, Pb and Cd accounted for averages of 83.2%, 72.4% and 19.8% of their total concentration in solution (<0.45 µm) during soil reduction, and decreased during soil re-oxidation. This proportion was also positively correlated with aqueous pH and DOC but negatively correlated with Eh. Further quantitative analysis demonstrated that Cu/Cd positively correlated with OM at nanometric scale (F1). This study provides quantitative insights into the size, composition and abundance of polymetallic pollutant-carrying particles in paddy soils during redox fluctuation, and highlights the importance of nanometric interactions between OM and toxic cationic metals for their release.

Source specific health risks of size-segregated particulate bound metals in an urban environment over northern India

Particulate-bound metals are reported to be toxic upon exposure through multiple pathways. In this study, both non-carcinogenic and carcinogenic health risks of diverse population age groups from the exposure of particulate-bound metals were systematically investigated considering size-segregated (PM1.1; PM2.1; PM > 2.1) metal concentration measured in between 2015 and 2018. Among the three particulate sizes, metal abundance was high in coarse fraction (PM > 2.1; mean ± SD: 43.1 ± 17.7 μgm−3) compared to fine (PM2.1: 18.5 ± 14.1 μgm−3) and sub-micron particulates (PM1.1: 11.4 ± 9.9 μgm−3). Calcium and sodium were the most abundant species irrespective of particulate sizes, accounting 53–58% of detected metal concentration. Potassium and zinc were the next two dominating species with increased abundance during biomass burning episodes. Potential sources of airborne metals were explored using advanced receptor model. Resuspensions of crustal and road dust were the most dominating sources (30–58% of metal abundance) irrespective of particulate sizes. The other recognized sources were emissions from industrial and vehicular sources, and from biomass/waste burning. Assessment of source-specific health risks indicates shared responsibility, industrial emissions (80%) being the most relevant one for carcinogenic risks. Both biomass and waste burning emissions, and vehicular emissions were found to induce non-carcinogenic risks. The non-carcinogenic health risks were mostly attributed to ingestion of metal contaminated food, especially for children (0.9–2.13 × 10−4). Carcinogenic risks, in contrast, were associated with inhalation of carcinogenic metals e.g., Cr, Pb, Co, Ni and Cd, and were high for adults (3.07–6.04 × 10−5). Health risks associated with exposure to size-segregated airborne metals were within the tolerable level (1 × 10−4) but well exceeding safe level of exposure (1 × 10−6). This emphasizes adapting more strategies and control measures of particulate sources to safeguard the health of resident population.

Higher Risks of Copper Toxicity in Turbid Waters: Quantifying the Bioavailability of Particle-Bound Metals to Set Site-Specific Water Quality Criteria

In coastal waters, particulate metals constitute a substantial fraction of the total metals; however, the prevalent water quality criteria are primarily based on dissolved metals, seemingly neglecting the contribution of particulate metals. Here we developed a method to quantify the toxicity risk of particulate metals, and proposed a way to calculate modifying factors (MFs) for setting site-specific criteria in turbid waters. Specifically, we used a side-by-side experimental design to study copper (Cu) bioaccumulation and toxicity in an estuarine clam, Potamocorbula laevis, under the exposure to "dissolved only" and "dissolved + particulate" 65Cu. A toxicokinetic-toxicodynamic model (TK-TD) was used to quantify the processes of Cu uptake, ingestion, assimilation, egestion, and elimination, and to relate mortality risk to tissue Cu. We find that particulate Cu contributes 40-67% of the Cu bioaccumulation when the suspended particulate matter (SPM) ranges from 12 to 229 mg L-1. The Cu-bearing SPM also increases the sensitivity of organisms to internalized Cu by decreasing the internal threshold concentration (CIT) from 141 to 76.8 μg g-1. MFs were derived based on the TK-TD model to consider the contribution of particulate Cu (in the studied SPM range) for increasing Cu bioaccumulation (MF = 1.3-2.2) and toxicity (MF = 2.3-3.9). Water quality criteria derived from dissolved metal exposure need to be lowered by dividing by an MF to provide adequate protection. Overall, the method we developed provides a scientifically sound framework to manage the risks of metals in turbid waters.

Nature-based solutions to manage particle-bound metals in urban stormwater runoff: current design practices and knowledge gaps

Nature-based solutions to manage particle-bound metals in urban stormwater runoff: current design practices and knowledge gaps

Chemical fractionation of particulate-bound metal(loid)s to evaluate their bioavailability, sources and associated cancer risk in India

Eleven potentially toxic metal(loid)s (Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn), proven source markers of mineral based coal-fired industrial emissions and vehicular exhausts, were analysed using the four steps sequential extraction method to evaluate metal(loid)s concentration, in total and fractions of bioavailable and non-bioavailable for fine (PM2.5) and coarse (PM10–2.5) particulate modes. A total of 26-day-wise samples with three replications (total number of samples = 78) were collected in January–December 2019 for each PM10 and PM2.5 at an urban-residential site in India. In both the coarse and fine particulate modes, Pb and Cr have respectively shown the highest and lowest total concentrations of the measured metal(loid)s, indicating the presence of coal-fired power plants and heavy vehicular activities near to study area. In addition, Mn has shown highest bioavailable fraction for both coarse and fine particulate modes. More than 50 % of metal(loid)s concentration, in total to a bioavailable fraction (BAF) were observed in case of As, Cd, Cr, Co, Mn, Ni, and Pb of PM2.5. Mn and Zn have shown similar behaviour in the case of coarse particulate mode. Source apportionment of metal(loid)s bioavailable fractions using positive matrix factorization (PMF 5.0) has found three significant sources: crustal and natural dust (30.04 and 39 %), road traffic (49.57 and 20 %), and industrial emission (20.39 and 41 %) for coarse and fine particulate mode, respectively. Cancer risk through the inhalation pathway was high in total concentration but lower in BAF concentration in both age groups (children and adults).

Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250.

New Insight into the Measurements of Particle-Bound Metals in the Urban and Remote Atmospheres of the Sarajevo Canton and Modeled Impacts of Particulate Air Pollution in Bosnia and Herzegovina.

The Sarajevo Canton Winter Field Campaign 2018 (SAFICA) was a project that took place in winter 2017–2018 with an aim to characterize the chemical composition of aerosol in the Sarajevo Canton, Bosnia and Herzegovina (BiH), which has one of the worst air qualities in Europe. This paper presents the first characterization of the metals in PM10 (particulate matter aerodynamic diameters ≤10 μm) from continuous filter samples collected during an extended two-months winter period at the urban background Sarajevo and remote Ivan Sedlo sites. We report the results of 18 metals detected by inductively coupled plasma mass spectrometry (ICP-MS) and electrothermal atomic absorption spectrometry (ETAAS). The average mass concentrations of metals were higher at the Sarajevo site than at Ivan Sedlo and ranged from 0.050 ng/m3 (Co) to 188 ng/m3 (Fe) and from 0.021 ng/m3 (Co) to 61.8 ng/m3 (Fe), respectively. The BenMAP-CE model was used for estimating the annual BiH health (50% decrease in PM2.5 would save 4760+ lives) and economic benefits (costs of $2.29B) of improving the air quality. Additionally, the integrated energy and health assessment with the ExternE model provided an initial estimate of the additional health cost of BiH’s energy system.

Source apportionment and health effects of particle-bound metals in PM2.5 near a precision metal machining factory

Source apportionment and health effects of particle-bound metals in PM2.5 near a precision metal machining factory

Health risks of adults in Hong Kong related to inhalation of particle-bound heavy metal(loid)s

Heterogeneity between ambient and personal exposure to heavy metals has been documented. However, few studies have investigated potential health risks posed by inhalational exposure to airborne heavy metal(loid)s at the individual level. A total of 404 personal fine particles (PM2.5) samples were collected from 61 adult residents (aged 18–63 years) in Hong Kong during 2014–2015. Heavy metal(loid)s were analyzed using energy dispersive X-ray fluorescence. Among the analyzed heavy metal(loid)s, zinc (Zn) was the most abundant component in personal PM2.5, followed by lead (Pb), copper (Cu), and vanadium (V); cobalt (Co) and cadmium (Cd) were not detectable. Health risks of personal exposure to heavy metal(loid)s via inhalation were assessed for adults, including non-cancer risks that were characterized by hazard quotient (HQ) and hazard index (HI). The results indicated that non-cancer risks of heavy metal(loid)s were attributable to Cu, with a 95th HQ value > 1. Arsenic (As) and hexavalent chromium [Cr (VI)] were also significant contributors to inhalation cancer risks (> 1 × 10−6) for the adult participants. Finally, we employed a Monte Carlo simulation to evaluate the uncertainty associated with health risk assessment. The mean and median upper-bound lifetime cancer risk associated with inhalation exposure to carcinogenic heavy metal(loid)s exceeded the acceptable level (1 × 10−6) for adults. Traffic emission (including non-tailpipe exhaust), shipping emission, and regional pollution were significant sources of heavy metals. These findings suggest that emission controls targeting local vehicles and vessels should be given priority in Hong Kong.

Emerging investigator series: aqueous-phase processing of atmospheric aerosol influences dissolution kinetics of metal ions in an urban background site in the Po Valley.

Metals are an important atmospheric aerosol component; their impacts on health and the environment depend also on their solubility, dissolution kinetics and chemical form in which they are present in the aerosol (e.g., oxidation state, inorganic salt or oxide/hydroxide, organic complex). In this study, we investigated the impact of fog processing on the solubility and dissolution of metals in PM2.5 samples collected in an urban background site in Padova (Italy). For each sample, we determined the solubility and dissolution kinetics of 17 elements in a solution simulating fog water in the winter season in the Po Valley (pH 4.7, T 5 °C, and water content ∼0.5 g m-3). We also determined water-soluble inorganic and organic compounds having ligand properties. We used the model E-AIM IV to calculate the aerosol liquid water (ALW) content and pH, and we used the model Visual MinteQ to determine the speciation picture of the most important elements under conditions of both deliquescent aerosol (ALW and pH calculated using E-AIM IV, ambient temperature) and simulated fog. We found that the dissolution of Al, Cu, and Fe metal ions, predicted to be largely coordinated with organic compounds under fog conditions, was either immediate or considerably faster in samples collected on days with observed fog events compared with those collected on days having drier conditions. For readily soluble elements, such as As, Cd, Cr, Sr, and Zn, such an effect was not observed. Our study highlights the importance of coordination chemistry in atmospheric aerosol and fog in determining the bioavailability of particle-bound metals.

Insights into the source-specific health risk of ambient particle-bound metals in the Pearl River Delta region, China

Quantification of source-specific health risks of PM2.5 plays an essential role in health-oriented air pollution control. However, there is limited evidence supporting the source-based risk apportionment of particle-bound metals. In this study, source-specific cancer and non-cancer risk characterization of 12 particle-bound metals was performed in the Pearl River Delta (PRD) region, China. A combination of health risk assessment model and receptor-based source apportionment modeling with positive matrix factorization (PMF) was applied for characterizing the spatial-temporal patterns for inhalation health risks of particle-bound metals in three main city clusters, inland area and coastal area in the region from December 2014 through July 2016. Results showed that the carcinogenic risk of particle-bound metals for adults (4.13 × 10-5) was higher than that for children (9.53 × 10-6) in the PRD region. The highest and significant non-carcinogenic risk was found in the northwest city cluster. Industrial emission (63.3%) were the dominant contributors to the cancer risk, while the main contributors to the non-cancer risk were the vehicle emission source (33.2%) in the dry season and industrial emission (30.8%) in the wet season. Our results provide important evidence for spatial source-specific health risks with temporal characteristics of particle-bound metals in most densely populated areas in the southern China, and suggest that reduction of industrial and vehicle emissions could facilitate more cost-effective PM2.5 control measures to improve human health.

Granulometric distribution of metals in road-deposited sediments by using different sieving methods

Road-deposited sediments (RDS) contain metals that can pollute receiving waters during precipitation events. The concentration of particle-bound metals tends to increase with decreasing particle size. In terms of metal load, the highest loads were often located in the medium range of particle sizes. Many studies have used varying or undocumented sieving techniques without showing the quality of the separation process. This is important as different particle size classes are documented with different pollution contents and loads. This knowledge builds the foundation for the selection of best management practices to improve receiving water quality. Based on the knowledge that the separation of particle sizes by the commonly applied dry sieving can be methodologically biased, this study compares particle size classes obtained by dry and wet sieving methods. The aim of the study was to show the methodological influence of the sieving process on the particle-size distribution (PSD) with focus on the metal distribution within RDS. Therefore, concentrations of Cu, Zn and Pb were determined and loads were calculated per sieve fraction after wet and dry sieving. Wet sieving causes a shift of the PSD to finer particles and both metal concentrations and loads are significantly higher especially in the smallest sieve fractions. Fine, highly polluted particles remained in coarser sieve fractions after dry sieving. This leads to diverging metal contents and especially loads in the fractions > 250 µm and thus lead to varying conclusions from the results. The results of the comparison of sieving methods however confirmed the relevance of the fine particle fractions in terms of best management practices. The fraction < 40 µm showed highest concentration and load in RDS after wet sieving. The results also showed the considerable influence of the sieving method used and the need to document laboratory methods as part of good laboratory practice.

Insights into Particle-Bound Metal(loid)s in Winter Snow Cover: Geochemical Monitoring of the Korkinsky Coal Mine Area, South Ural Region, Russia

Snow plays an important role in air quality and winter geochemical monitoring in the South Ural region. This study deals with the air pollution monitoring of particle-bound metal(loid) concentrations using snow cover around the deepest coal mine in Eurasia, the Korkinsky coal mine. We studied the concentrations and ratios of suspended and dissolved forms of metal(loid)s (Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, Sr, and Zn) in snow samples. We examined 56 snow cover samples, collected at 12 sites located north, south, east and west of the Korkinsky coal mine. All snow samples were taken in January 2020. The spectral reflectance curves, cluster analysis, and spatial distribution maps were used to evaluate the potential sources of PM-bound metal(loid)s and the potential relationship among them. The highest concentrations (μg/L) were reported for Fe, Al, and Zn. In addition to the mine influence, burning coal for residential heating was identified as the major anthropogenic metal(loid) source. It was shown that elevated concentrations of some trace metals in snow samples were associated with southerly winds and the location of spoil heaps.

New Insights into Health Risk Assessments for Inhalational Exposure to Metal(loid)s: The Application of Aqueous Chemistry Modelling in Understanding Bioaccessibility from Airborne Particulate Matter

Aqueous modelling of chemical speciation in simulated lung fluid (SLF) enables a better understanding of the underlying chemical factors that influence metal(loid) inhalation bioaccessibility from airborne particulate matter. Such an approach can be used to supplement experimental techniques that are integral to the health risk assessment of metal(loid) exposure by inhalational routes. In this paper, we modelled the aqueous chemistry of airborne particulate-bound metal(loid)s (As, Cu, Mn, Pb and Zn) in a SLF based on Gamble’s solution (neutral pH). The modelling was performed using two software packages (Geochemist’s Workbench 14 and OLI Studio 9.5) and a total of five thermochemical databases (GWB Thermo, MINTEQ, PHREEQC, WATEQ4F and the default database for OLI Studio). Modelled results were compared with experimentally determined bioaccessibilities for the NIST 2710a standard reference material (SRM) and with literature-reported bioaccessibilities for NIST 1648a and BCR 038 SRMs. Whilst the models correctly describe the observed increase in bioaccessibility for more dilute solid/liquid extraction ratios, the performance of the models against the fractional bias of the mean (FBmean) and the normalised mean square error (NMSE) statistical metrics was generally outside the acceptance criteria. Findings from an analysis of the main aqueous chemical species predicted to be present in SLF indicate that carbonate and chloride complexes of Cu, Mn, Pb and Zn predominate, whilst free cations (for Cu, Mn and Zn) and hydroxides (for Cu) also play a role in solubilisation. Arsenic is not predicted to form significant complexes with the SLF components and is present in solution mainly as the HAsO42− ion and its conjugate acid, H2AsO4−. For modelled runs where glycine and citrate were present, significant increases in the bioavailability of Cu and Zn were predicted as a result of complexation with these ligands. An additional finding from our experimental bioaccessibility results for NIST 2710a was that the inclusion of the lung fluid surfactant dipalmitoylphosphatidylcholine (DPPC) in the SLF did not significantly affect the bioaccessibility. Our study provides useful insights into the likely aqueous- and solid-phase speciation of metal(loid)s in SLF and highlights that future developments in this area should consider the role of mineralogy and surface interactions.

Human health-risk assessment based on chronic exposure to the carbonyl compounds and metals emitted by burning incense at temples.

Health effects resulting from the smoke of carbonyl compounds (aldehydes and ketones) and metal-containing incense particles at temples during incense burning periods were evaluated at temple A (without incense reduction activities) and B (with incense reduction activities), Nantou County, in 2018. The predominant size fractions of particles were PM1, PM1-2.5, and PM2.5-10 at both temples. The total particle mass at temple A was approximately 1.1 times that of temple B due to incense reduction at temple B. The most abundant metal elements in all particle size fractions at both temples were Fe, Al, and Zn. Metal species of incense smoke are divided into three groups by hierarchical cluster analysis and heatmaps, showing higher metal contents in groups PM1, PM18-10, and PM18-2.5 at temple A. In contrast, higher metal contents were observed in PM18-10 and PM2.5-1 at temple B. Most of the carbonyl species were formaldehyde and acetaldehyde, released during incense burning periods, with concentrations ranging from 6.20 to 13.05μg/m3 at both temples. The total deposited fluxes of particle-bound metals at temples A and B were determined to be 83.00% and 84.82% using the International Commission on Radiological Protection (ICRP) model. Health-risk assessments revealed that the risk values of metals and carbonyls were above recommended guidelines (10-6) at temple A. Since worshippers and staff are exposed to incense burning environments with poor ventilation over a long period, these toxic organic compounds and metals increase health risks in the respiratory tract. Therefore, incense reduction is important to achieve healthy temple environments.

Resonance-enhanced detection of metals in aerosols using single-particle mass spectrometry

Abstract. We describe resonance effects in laser desorption–ionization (LDI) of particles that substantially increase the sensitivity and selectivity to metals in single-particle mass spectrometry (SPMS). Within the proposed scenario, resonant light absorption by ablated metal atoms increases their ionization rate within a single laser pulse. By choosing the appropriate laser wavelength, the key micronutrients Fe, Zn and Mn can be detected on individual aerosol particles with considerably improved efficiency. These ionization enhancements for metals apply to natural dust and anthropogenic aerosols, both important sources of bioavailable metals to marine environments. Transferring the results into applications, we show that the spectrum of our KrF-excimer laser is in resonance with a major absorption line of iron atoms. To estimate the impact of resonant LDI on the metal detection efficiency in SPMS applications, we performed a field experiment on ambient air with two alternately firing excimer lasers of different wavelengths. Herein, resonant LDI with the KrF-excimer laser (248.3 nm) revealed iron signatures for many more particles of the same aerosol ensemble compared to the more common ArF-excimer laser line of 193.3 nm (nonresonant LDI of iron). Many of the particles that showed iron contents upon resonant LDI were mixtures of sea salt and organic carbon. For nonresonant ionization, iron was exclusively detected in particles with a soot contribution. This suggests that resonant LDI allows a more universal and secure metal detection in SPMS. Moreover, our field study indicates relevant atmospheric iron transport by mixed organic particles, a pathway that might be underestimated in SPMS measurements based on nonresonant LDI. Our findings show a way to improve the detection and source attribution capabilities of SPMS for particle-bound metals, a health-relevant aerosol component and an important source of micronutrients to the surface oceans affecting marine primary productivity.

Effects of ambient PM2.5 and particle-bound metals on the healthy residents living near an electric arc furnace: A community- based study

Fine particulate matter (PM2.5) emitted from electric arc furnaces (EAFs) poses health concerns. However, little research has been done on the impact of EAF on the health of community residents. This cross-sectional study conducted a PM2.5 exposure assessment and health examination of community residents living near an EAF. A total of 965 residents aged 40–90 years were recruited. The residents' exposure to PM2.5 was categorized according to the distance of their residence from the EAFs (<500, 500–1000, 1000–1500, 1500–2000, and > 2000 m). Average ambient PM2.5 concentrations were estimated using a hybrid kriging/land-use regression (LUR) model. In addition, we selected two air-sampling sites to monitor the 2-year levels of PM2.5 and particle-bound metals. A spot urine sample and blood samples were collected and ten heavy metal concentrations in the blood were analyzed. Inflammation- and oxidative stress-related biomarkers were measured. The associations between environmental factors and a biochemical examination were estimated using a generalized linear model. Active air sampling and hybrid kriging/LUR model simulation indicated increased levels of PM2.5 near the EAF. The metal concentrations in PM2.5 included Fe, Pb, Mn, Ni, As, Cu, Ni, Zn, and Al, which also significantly increased near the EAF. PM2.5 levels were significantly associated with an increased total cholesterol–high-density lipoprotein (TC/HDL) ratio. High levels of PM2.5 and malondialdehyde were associated with a 1.72-fold increased risk of TC/HDL ratio ≥ 4 (95% CI: 1.12–2.65) after adjusting for potential confounding factors. Blood Pb levels were significantly associated with increased systolic and diastolic blood pressure and decreased estimated glomerular filtration rate but negatively associated with distance from the EAF. The results show that people living near EAFs should pay more attention to adverse health problems, including atherogenic dyslipidemia, hypertension, and chronic kidney disease associated with exposure to PM2.5 and particle-bound metals.

Coal mine-affected water releases, turbidity and metal concentrations in the Fitzroy River Basin, Queensland, Australia

Historical water quality datasets from the Fitzroy Basin, a large river system that receives mine-affected water from more than 40 mines, and drains into the Great Barrier Reef Lagoon, were analysed. There is great public and environmental interest in these waters that are used for agriculture, recreation and raw drinking water supplies. Aside from one AMD-impacted area, waters were generally alkaline. Turbidity data regularly exceeded the regional water quality objective (< 50 NTU), indicating the presence of particulates that may accumulate or transport metal contaminants within the waters. Analyses of data for 14 metals showed dissolved Cu and Al, followed by Zn, were the parameters that were most often greater than Australian toxicant guideline values. Analysis of coal mine-affected water samples and the receiving creek data, showed that median turbidity was greater downstream than upstream of the mine water release points, suggesting release-water may contain particulate-bound metals. Median dissolved Al and Cu concentrations were lower downstream than upstream and were above guidelines. Preliminary modelling using WHAM7 suggested downstream dissolved Cu (2–5 µg/L) was bound to fulvic acid and was likely not bioavailable. Dissolved Al (170–550 µg/L) may have existed as Al(OH)4−. Overall, suspended particulate matter appeared to be a water quality parameter of interest in this mine-affected river basin. Further targeted sampling, direct measurement of particulate-bound metals and multivariate statistical analysis are needed to help better understand metal partitioning in these turbid, alkaline waters.

The association of household particulate-bound metal concentrations and tuberculosis in women and children in Pune, India

The association of household particulate-bound metal concentrations and tuberculosis in women and children in Pune, India