Microbial source tracking (MST) DNA-based assays have been used to successfully solve fecal pollution problems in many countries, particularly in developed nations. However, their application in developing countries has been limited but continues to increase. In this study, sixteen endpoint and quantitative PCR (qPCR) assays targeting universal and human-, swine-, and cattle-specific Bacteroidales gene markers were modified for endpoint PCR, evaluated for their performance with sewage and fecal samples from the Tha Chin watershed and subsequently validated with samples from the Chao Phraya watershed, Thailand. Sample sizes of 81 composite samples (from over 1620 individual samples) of farm animals of each type as well as 19 human sewage samples from the Tha Chin watershed were calculated using a stratified random sampling design to achieve a 90% confidence interval and an expected prevalence (i.e., desired assay's sensitivity) of 0.80. The best universal and human-, swine-, and cattle-specific fecal markers were BacUni EP, HF183/BFDrev EP, Pig-2-Bac EP, and Bac3 assays, respectively. The detection limits for these assays ranged from 30 to 3000 plasmid copies per PCR. The positive predictive values were high in universal and swine- and cattle-specific markers (85–100%), while the positive predictive value of the human-specific assay was 52.2%. The negative predictive values in all assays were relatively high (90.8–100%). A suite of PCR assays in Thailand was established for potential MST use in environmental waters, which supports the worldwide applicability of Bacteroidales gene markers. This study also emphasizes the importance of using a proper sample size in assessing the performance of MST markers in a new geographic region.

The rapid expansion of construction related to coastal development evokes great concern about environmental risks. Recent attention has been focused mainly on factors related to the effects of waterlogging, but there is urgent need to address the potential hazard caused by artificial topography: derived changes in the elemental composition of the sediments. To reveal possible mechanisms and to assess the environmental risks of artificial topography on transition of elemental composition in the sediment at adjoining zones, a nest-random effects-combined investigation was carried out around a semi-open seawall. The results implied great changes induced by artificial topography. Not only did artificial topography alter the sediment elemental composition at sites under the effect of artificial topography, but also caused a coupling pattern transition of elements S and Cd. The biogeochemical processes associated with S were also important, as suggested by cluster analysis. The geo-accumulation index shows that artificial topography triggered the accumulation of C, N, S, Cu, Fe, Mn, Zn, Ni, Cr, Pb, As and Cd, and increased the pollution risk of C, N, S, Cu, As and Cd. Enrichment factors reveal that artificial topography is a new type of human-activity-derived Cu contamination. The heavy metal Cu was notably promoted on both the geo-accumulation index and the enrichment factor under the influence of artificial topography. Further analysis showed that the Cu content in the sediment could be fitted using equations for Al and organic carbon, which represented clay mineral sedimentation and organic matter accumulation, respectively. Copper could be a reliable indicator of environmental degradation caused by artificial topography.

Breast cancer is the most frequent tumor in women worldwide, although well-established risk factors account for 53%–55% of cases. Therefore, other risk factors, including environmental exposures, may explain the remaining variation. Our objective was to assess the relationship between risk of breast cancer and residential proximity to industries, according to categories of industrial groups and specific pollutants released, in the context of a population-based multicase-control study of incident cancer carried out in Spain (MCC-Spain). Using the current residence of cases and controls, this study was restricted to small administrative divisions, including both breast cancer cases (452) and controls (1511) in the 10 geographical areas recruiting breast cancer cases. Distances were calculated from the respective woman's residences to the 116 industries located in the study area. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (95%CIs) for categories of distance (between 1 km and 3 km) to industrial plants, adjusting for matching variables and other confounders. Excess risk (OR; 95%CI) of breast cancer was found near industries overall (1.30; 1.00–1.69 at 3 km), particularly organic chemical industry (2.12; 1.20–3.76 at 2.5 km), food/beverage sector (1.87; 1.26–2.78 at 3 km), ceramic (4.71; 1.62–13.66 at 1.5 km), surface treatment with organic solvents (2.00; 1.23–3.24 at 3 km), and surface treatment of plastic and metals (1.51; 1.06–2.14 at 3 km). By pollutants, the excess risk (OR; 95%CI) was detected near industries releasing pesticides (2.09; 1.14–3.82 at 2 km), and dichloromethane (2.09; 1.28–3.40 at 3 km). Our results suggest a possible increased risk of breast cancer in women living near specific industrial plants and support the need for more detailed exposure assessment of certain agents released by these plants.

The temporal and spatial trends in sediment of 22 poly- and perfluorinated (PFAS) compounds were investigated in the southern Great Lakes Erie and Ontario as well as Lake St. Clair. Surface concentrations measured by Ponar grab samples indicated a trend for greater concentrations near to urban sites. Mean concentrations ∑22PFAS were 15.6, 18.2 and 19 ng g−1 dm for Lakes St. Clair, Erie and Ontario, respectively. Perfluoro-n-butanoic acid (PFBA) and Perfluoro-n-hexanoic acid (PFHxA) were frequently determined in surface sediment and upper core samples indicating a shift in use patterns. Where PFBA was identified it was at relatively great concentrations typically >10 ng g−1 dm. However as PFBA and PFHxA are less likely to bind to sediment they may be indicative of pore water concentrations Sedimentation rates between Lake Erie and Lake Ontario differ greatly with greater rates observed in Lake Erie. In Lake Ontario, in general concentrations of PFAS observed in core samples closely follow the increase in use along with an observable change due to regulation implementation in the 1970s for water protection. However some of the more water soluble PFAS were observed in deeper core layers than the time of production could account for, indicating potential diffusion within the sediment. Given the greater sedimentation rates in Lake Erie, it was hoped to observe in greater resolution changes since the mid-1990s. However, though some decrease was observed at some locations the results are not clear. Many cores in Lake Erie had clearly observable gas voids, indicative of gas ebullition activity due to biogenic production, there were also observable mussel beds that could indicate mixing by bioturbation of core layers.

Fungi are well associated with the degradation of hydrocarbons by the production of different enzymes, among which catalases (CBH), laccases (LCC) and peroxidases (LiP and MnP) are of immense importance. In this study, crude oil tolerance and enzyme secretions were demonstrated by rhizospheric fungal strains. Four most abundant strains were isolated from the rhizosphere of grasses growing in aged oil spill sites and identified through morphological characterization and molecular PCR-amplification of 5.8–28S ribosomal rRNA using ITS1 and ITS4 primers. These strains were subjected to crude oil tolerance test at 0–20% concentrations. Presence and transcriptase responses of putative genes lig (1–6), mnp, cbh (1.1, 1.1 and 11), and lcc encoding lignin peroxidase, manganese peroxidase, catalase, and laccase enzymes respectively were also studied in these strains using RT-PCR. In addition, activities of secreted enzymes by each strain were studied in aliquots. The strains were identified as Aspergillus niger asemoA (KY473958), Talaromyces purpurogenus asemoF (KY488463), Trichoderma harzianum asemoJ (KY488466), and Aspergillus flavus asemoM (KY488467) through sequencing and comparing the sequences’ data at NCBI BLAST search software. All the isolated strains showed tolerance to crude oil at 20% concentration, but the growth rate reduced with increasing in oil concentrations. All the isolated strains possess the tested genes and lig 1–6 gene was overexpressed in A. niger and T. harzianum while lcc and mnp genes were moderately expressed in all the four strains. Almost 145 U.mL⁻¹ of lignin and manganese peroxidase, 87 U.mL⁻¹ of catalase, and 180 U.mL⁻¹ of laccase enzymes were produced by these strains and it was also observed that these strain mostly produced studied enzymes in response to increasing crude oil concentrations. Considering the robust nature and diverse production of these catalytic enzymes by these strains, they can be exploited for various bioremediation technologies as well as other biotechnological applications.

Recently it has been shown that there are two types of magnetite particles in the human brain, some, which occur naturally and are jagged in appearance, and others that arise from industrial sources, such as coal fired power plants, and are spherical. In order to confirm the latter, the magnetic component of coal ash is first purified and characterized by XRD, showing that it is magnetite with an average particle size of 211 nm. Studies confirm the coal ash magnetic behavior, and that the magnetite is not bound to the other components of coal ash but exist as an isolatable component. SEM studies confirm that in the process of burning coal at very high temperatures for industrial uses, the magnetite formed is spherically shaped, as recent studies of brain tissues of highly exposed urban residents have found. As such, the use of coal for industrial applications such as coking in the production of steel and in power plants is indicated to be a major source of the spherical magnetic combustion-associated magnetite fine particle and nanoparticle environmental pollution. The capacity of these magnetic particles to penetrate and damage the blood-brain-barrier and the early development of Alzheimer's disease hallmarks in exposed populations calls for detail analysis of magnetic fine and nanoparticle distribution across the world.Summation: Industrial coal usage produces spherical magnetic particles and nanoparticles, identical to those associated with neurological disorders.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

In parallel to technological advances and ever-increasing use of nanoparticles in industry, agriculture and consumer products, the potential ecotoxicity of nanoparticles and their potential accumulation in ecosystems is of increasing concern. Because scientific reports raise a concern regarding nanoparticle toxicity to plants, understanding of their bioaccumulation has become critical and demands more research. Here, the synthesis of isotopically-labeled nanoparticles of silver, copper and zinc oxide is reported; it is demonstrated that while maintaining the basic properties of the same unlabeled (“regular”) nanoparticles, labeled nanoparticles enable more sensitive tracing of nanoparticles within plants that have background elemental levels. This technique is particularly useful for working with elements that are present in high abundance in natural environments. As a benchmark, labeled and unlabeled metal nanoparticles (Ag-NP, Cu-NP, ZnO-NP) were synthesized and compared, and then exposed in a series of growth experiments to Arabidopsis thaliana; the NPs were traced in different parts of the plant. All of the synthesized nanoparticles were characterized by TEM, EDS, DLS, ζ-potential and single particle ICP-MS, which provided essential information regarding size, composition, morphology and surface charge of nanoparticles, as well as their stability in suspensions. Tracing studies with A. thaliana showed uptake/retention of nanoparticles that is more significant in roots than in shoots. Single particle ICP-MS, and scanning electron micrographs and EDS of plant roots showed presence of Ag-NPs in particular, localized areas, whereas copper and zinc were found to be distributed over the root tissues, but not as nanoparticles. Thus, nanoparticles in any natural matrix can be replaced easily by their labeled counterparts to trace the accumulation or retention of NPs. Isotopically-labeled nanoparticles enable acquisition of specific results, even if there are some concentrations of the same elements that originate from other (natural or anthropogenic) sources.

Even though the Amazon region is widely studied, there is still a gap regarding Cr exposure and its risk to human health. The objectives of this study were to 1) determine Cr concentrations in seven chemical fractions and 6 particle sizes in Amazon soils, 2) quantify hexavalent Cr (CrVI) concentrations using an alkaline extraction, 3) determine the oral and lung bioaccessible Cr, and 4) assess Cr exposure risks based on total and bioaccessible Cr in soils. The total Cr in both A (0–20 cm) and B (80–100 cm) horizons was high at 2346 and 1864 mg kg⁻¹. However, sequential extraction indicated that available Cr fraction was low compared to total Cr, with Cr in the residual fraction being the highest (74–76%). There was little difference in total Cr concentrations among particle sizes. Hexavalent Cr concentration was also low, averaging 0.72 and 2.05 mg kg⁻¹ in A and B horizon. In addition, both gastrointestinal (21–22 mg kg⁻¹) and lung (0.95–1.25 mg kg⁻¹) bioaccessible Cr were low (<1.2%). The low bioavailability of soil Cr and its uniform distribution in different particle sizes indicated that Cr was probably of geogenic origin. Exposure based on total Cr resulted in daily intake > the oral reference dose for children, but not when using CrVI or bioaccessible Cr. The data indicated that it is important to consider both Cr speciation and bioaccessibility when evaluating risk from Cr in Amazon soils.

Although organic species are transported and efficiently transformed in clouds, more than 60% of this organic matter remains unspeciated. Using GCxGC-HRMS technique we were able to detect and identify over 100 semi-volatile compounds in 3 cloud samples collected at the PUY station (puy de Dôme mountain, France) while they were present at low concentrations in a very small sample volume (<25 mL of cloud water). The vast majority (∼90%) of the detected compounds was oxygenated, while the absence of halogenated organic compounds should be specially mentioned. This could reflect both the oxidation processes in the atmosphere (gas and water phase) but also the need of the compounds to be soluble enough to be transferred and dissolved in the cloud droplets. Furans, esters, ketones, amides and pyridines represent the major classes of compounds demonstrating a large variety of potential pollutants. Beside these compounds, priority pollutants from the US EPA list were identified and quantified. We found phenols (phenol, benzyl alcohol, p-cresole, 4-ethylphenol, 3,4-dimethylphenol, 4-nitrophenol) and dialkylphthalates (dimethylphthalate, diethylphthalate, di-n-butylphthalate, bis-(2-ethylhexyl)-phthalate, butylbenzylphthalate, di-n-octyl phthalate). In general, the concentrations of phthalates (from 0.09 to 52 μg L−1) were much higher than those of phenols (from 0.03 to 0.74 μg L−1). To our knowledge phthalates in clouds are described here for the first time. We investigated the variability of phenols and phthalates concentrations with cloud air mass origins (marine vs continental) and seasons (winter vs summer). Although both factors seem to have an influence, it is difficult to deduce general trends; further work should be conducted on large series of cloud samples collected in different geographic areas and at different seasons.