It is acknowledged that organohalide-respiring bacteria (OHRB) can degrade polybrominated diphenyl ethers (PBDEs); however, very little is known about the distribution of OHRB or their response to PBDE contamination in natural sediments. We collected sediments from 28 sampling sites in Taihu Lake, China, and investigated the spatial distribution and diversity of OHRB, and the relationships between the PBDE contamination levels and the PBDE removal potential. The abundances of five typical OHRB genera, namely Dehalobacter, Dehalococcoides, Dehalogenimonas, Desulfitobacterium, and Geobacter, ranged from 0.34 × 10⁴ to 19.4 × 10⁷ gene copies g⁻¹ dry sediment, and varied significantly among different areas of Taihu Lake. OHRB were more abundant in sediments from Meiliang and Zhushan Bay, where the PBDE concentrations were higher, and the phylotype diversity of the OHRB belonging to the family Dehalococcoidaceae was lower, than reported for other areas. While the sulfate concentrations explained much of the spatial distribution of OHRB, PBDE concentrations were also a strong influence on the abundance and diversity of OHRB in the sediments. For Dehalococcoides, Dehalogenimonas and Geobacter, the abundance of each genus was positively related to its own potential to remove PBDEs. The dominant OHRB genus, Dehalogenimonas, may contribute most to in situ bioremediation of PBDEs in Taihu Lake.

In China, the cadmium (Cd) levels in paddy fields have increased, which has led to the excessive uptake of Cd into rice grains. In this study, we determined the physicochemical properties of soil samples, including the pH, soil organic matter (SOM) content, cation exchange capacity (CEC), and total Cd content (Cdsoil) in order to establish a quadratic discriminant analysis (QDA) model for assessing the risk of Cd in rice and to calculate its prior probability. Decision tree and logistic regression models were also established for comparison. The results showed that the accuracy rate was 74% with QDA, which was significantly higher than that obtained using the decision tree (67%) and logistic regression (68%) models. The correlation coefficients between the soil pH and the other three factors (CEC, SOM, and Cdsoil) were higher in the inaccurate set than the accurate set, whereas the correlation coefficients were smaller in the inaccurate set than the accurate set.

Thallium (Tl) and its compounds are non-essential and highly toxic for living organisms, even at low concentrations. In this paper, we analyzed the presence and geochemical distribution of Tl in different mining-metallurgical and sediment samples collected from several mining zones of Mexico. A modified BCR sequential extraction procedure was also applied to the samples to investigate the geochemical behavior and potential environmental risk of Tl according to types of ore deposit and mineral processing method applied. Results revealed the presence of Tl in the majority of the mining-metallurgical samples, with labile concentrations reaching up to values of 184.4 mg kg⁻¹, well above the environmental standards. A comparison of Tl partitioning in different samples showed that Tl was usually found associated with labile fractions instead of entrapped in the environmentally-passive residual fraction. Specifically, high levels of Tl were extracted from the exchangeable/acid-extractable and poorly-crystalline reducible fractions, suggesting its association with both soluble and amorphous Fe-Mn oxyhydroxides, respectively. Besides, Tl was also frequently found associated with the crystalline reducible fraction, presumably bonded to manganese oxides and jarosite-like minerals. Lastly, little amounts of Tl were extracted from the oxidizable fraction. Considering the fractionation of Tl in these mining-metallurgical samples, they may pose a significant environmental hazard. This study provides useful insights into the potential sources of Tl pollution in Mexico and emphasizes the need for further research to determine the extent of its impact and to develop effective remediation protocols to protect the environment from Tl toxicity.

Environmental contamination by siloxanes is a matter of concern due to their widespread consumption in personal care as well as industrial products and potential toxicity. Nevertheless, methods for simultaneous determination of cyclic and linear siloxanes in sediment are lacking. In this study, we developed an optimized analytical method to determine cyclic and linear siloxanes based on gas chromatography coupled to tandem mass spectrometry (GC-MS/MS). This method was applied to determine concentrations of 19 siloxane compounds in surface and core sediments from industrialized bays in Korea to assess contamination status, spatial distribution, and historical trends. Total concentrations of siloxanes ranged from 15.0 to 11730 (mean: 712) ng/g dry weight, which were similar to or higher than those reported in other countries. The highest concentrations of siloxanes were found in rivers/streams that discharge into coastal waters and bays close to industrial complexes, indicating that industrial activities are major sources of siloxane contamination. Cyclic siloxanes such as D5 and D6 were predominant in surface and core sediments. A significant correlation existed between the concentrations of cyclic and linear siloxanes, suggesting similar sources in the marine coastal environment. The historical record of cyclic siloxanes in core sediments revealed a clear increasing trend since the 1970s. This finding is consistent with the history of local industrialization and global production of siloxanes. This is the first study of historical trends in siloxanes in the coastal environment.

Concentrations of particulate matter less than 1 μm, 2.5 μm, 10 μm and their contents of heavy metals were investigated in two different stations, urban and roadside at Algiers (Algeria). Sampling was conducted during two years by a high volume samplers (HVS) equipped with a cascade impactor at four levels stage, for one year sampling. The characterization of the heavy metals associated to the particulate matter (PM) was carried out by X-Ray Fluorescence analysis (XRF). The annual average concentration of PM1, PM2.5 and PM10 in both stations were 18.24, 32.23 and 60.01 μg m⁻³ respectively. The PM1, PM2.5 and PM10 concentrations in roadside varied from 13.46 to 25.59 μg m⁻³, 20.82–49.85 μg m⁻³ and 45.90–77.23 μg m⁻³ respectively. However in the urban station, the PM1, PM2.5 and PM10 concentrations varied from 10.45 to 26.24 μg m⁻³, 18.53–47.58 μg m⁻³ and 43.8–91.62 μg m⁻³. The heavy metals associated to the PM were confirmed by Scanning Electron Microscopy-Energy Dispersive X-Ray analyses (SEM-EDX). The different spots of PM2.5 analysis by SEM-EDX shows the presence of nineteen elements with anthropogenic and natural origins, within the heavy metal detected, the lead was found with maximum of 5% (weight percent). In order to determine the source contributions of PM levels at the two sampling sites sampling, principal compound analysis (PCA) was applied to the collected data. Statistical analysis confirmed anthropogenic source with traffic being a significant source and high contribution of natural emissions. At both sites, the PM2.5/PM10 ratio is lower than that usually recorded in developed countries. The study of the back-trajectories of the air masses starting from Sahara shows that desert dust influences the concentration and the composition of the PM measured in Algiers.

In this study, 14 antibiotics and one metabolite were determined in sewages and size-dependent sewer sediments at three sampling sites in the city of Dresden, Germany. Adsorption and desorption experiments were conducted with fractionated sediments. All antibiotics and the metabolite investigated were determined in the sewages; 9 of 14 antibiotics and the metabolite were adsorbed to sewer sediments. The adsorbed antibiotic loads in ng of antibiotic per g of sediment correlated with antibiotic concentrations in ng of antibiotic per litre of sewage. The size fractions <63 μm, 63–100 μm and 100–200 μm had significantly higher loads of adsorbed antibiotics than bigger size fractions. In general, the adsorbed load decreased with an increasing size fraction, but size fractions >200 μm had similar levels of adsorbed antibiotic loads. An antibiotic-specific adsorption coefficient, normalized to organic content, was calculated: four antibiotics exceeded 10.0 L g⁻¹, three antibiotics fell below 1.0 L g⁻¹ and all residual antibiotics and the metabolite were in the range of 1.0–10.0 L g⁻¹. The adsorbed antibiotic load and the organic matter increased with time, generally. The mineral composition had a minor effect on the adsorption coefficients. Desorption dynamics of five antibiotics and the metabolite were quantified. Regardless of the size fraction, the predominant part of the equilibrium antibiotic concentration was desorbed after 10 min. The calculated desorption distribution coefficient indicated adsorption as irreversible at the pH investigated (7.5 ± 0.5).

Strong acid rain was recently observed over Northeastern China, particularly in summer in Liaoning Province where alkaline dust largely neutralized acids in the past. This seems to be related to the regional transboundary pollution and poses new challenges in acid rain control scheme in China. In order to delve into the regional transport impact, and quantify its potential contributions to such an “eruption” of acid rain over Liaoning, this paper employs an online source tagging model in coupling with the Nested Air Quality Prediction Modeling System (NAQPMS). Validation of predictions shows the model capability in reproducing key meteorological and chemical features. Acid concentration over Liaoning is more pronounced in August (average of 0.087 mg/m³) with strong pollutant import from regional sources against significant depletion of basic species. Seasonal mean contributions from regional sources are assessed at both lower and upper boundary layers to elucidate the main pathways of the impact of regional sources on acid concentration over Liaoning. At the upper layer (1.2 km), regional sources contribute to acid concentration over Liaoning by 67%, mainly from Shandong (16%), Hebei (13%), Tianjin (11%) and Korean Peninsula (9%). Identified main city-receptors in Liaoning are Dandong, Dalian, Chaohu, Yingkou, Liaoyang, Jinfu, Shengyang, Panjin, Tieling, Benxi, Anshan and Fushun. At lower layer (120 m) where Liaoning local contribution is dominant (58%), regional sources account for 39% in acid concentration. However, inter-municipal acid exchanges are prominent at this layer and many cities in Liaoning are revealed as important sources of local acid production. Seasonal acid contribution average within 1.2 km-120 m attains 55%, suggesting dominance of vertical pollutant transport from regional sources towards lower boundary layer in Liaoning. As direct environmental implication, this study provides policy makers with a perspective of regulating the regional transboundary environmental impact assessment in China with application to acid rain control.

Accurate measurements of ground-level PM₂.₅ (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM₂.₅ datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage. The CTM-based approaches, however, often yield results with a coarse spatial resolution (typically at 0.1° of spatial resolution) and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM₂.₅ concentrations. In this study, with a focus on the long-term PM₂.₅ distribution in the contiguous United States, we adopt a random forests-based geostatistical (regression kriging) approach to improve one of the most commonly used satellite-derived, gridded PM₂.₅ datasets with a refined spatial resolution (0.01°) and enhanced accuracy. By combining the random forests machine learning method and the kriging family of methods, the geostatistical approach effectively integrates ground-based PM₂.₅ measurements and related geographic variables while accounting for the non-linear interactions and the complex spatial dependence. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with existing PM₂.₅ datasets. This manuscript also highlights the effectiveness of the geographical variables in long-term PM₂.₅ mapping, including brightness of nighttime lights, normalized difference vegetation index and elevation, and discusses the contribution of each of these variables to the spatial distribution of PM₂.₅ concentrations.

Increased reactive nitrogen (N) loadings to terrestrial ecosystems are believed to have positive effects on ecosystem carbon (C) sequestration. Global “hot spots” of N deposition are often associated with currently or formerly high deposition of sulphur (S); C fluxes in these regions might therefore not be responding solely to N loading, and could be undergoing transient change as S inputs change. In a four-year, two-forest stand (mature Norway spruce and European beech) replicated field experiment involving acidity manipulation (sulphuric acid addition), N addition (NH4NO3) and combined treatments, we tested the extent to which altered soil solution acidity or/and soil N availability affected the concentration of soil dissolved organic carbon (DOC), soil respiration (Rs), microbial community characteristics (respiration, biomass, fungi and bacteria abundances) and enzyme activity. We demonstrated a large and consistent suppression of soil water DOC concentration driven by chemical changes associated with increased hydrogen ion concentrations under acid treatments, independent of forest type. Soil respiration was suppressed by sulphuric acid addition in the spruce forest, accompanied by reduced microbial biomass, increased fungal:bacterial ratios and increased C to N enzyme ratios. We did not observe equivalent effects of sulphuric acid treatments on Rs in the beech forest, where microbial activity appeared to be more tightly linked to N acquisition. The only changes in C cycling following N addition were increased C to N enzyme ratios, with no impact on C fluxes (either Rs or DOC). We conclude that C accumulation previously attributed solely to N deposition could be partly attributable to their simultaneous acidification.

Severe and persistent haze accompanied by high concentrations of fine particulate matter (PM₂.₅) has become a great public health concern in urban China. However, research on the health effects of PM₂.₅ in China has been hindered by the lack of high-quality exposure estimates. In this study, we assessed the excess mortality associated with both short- and long-term exposure to ambient PM₂.₅ simultaneously using satellite-derived exposure data at a high spatiotemporal resolution. Adult registries of non-accidental, respiratory and cardiovascular deaths in urban Beijing in 2013 were collected. Exposure levels were estimated from daily satellite-based PM₂.₅ concentrations at 1 km spatial resolution from 2004 to 2013. Mixed Poisson regression models were fitted to estimate the cause-specific mortality in association with PM₂.₅ exposures. With the mutual adjustment of short- and long-term exposure of PM₂.₅, the percent increases associated with every 10 μg/m³ increase in short-term PM₂.₅ exposure were 0.09% (95% CI: −0.14%, 0.33%; lag 01), 1.02% (95% CI: 0.08%, 1.97%; lag 04) and 0.09% (95% CI: −0.23%, 0.42%; lag 01) for non-accidental, respiratory and cardiovascular mortality, respectively; those attributable to every 10 μg/m³ increase in long-term PM₂.₅ exposure (9-year moving average) were 16.78% (95% CI: 10.58%, 23.33%), 44.14% (95% CI: 20.73%, 72.10%) and 3.72% (95% CI: −3.75%, 11.77%), respectively. Both associations of short- and long-term exposure with the cause-specific mortality decreased after they were mutually adjusted. Associations between short-term exposure to satellite-based PM₂.₅ and cause-specific mortality were larger than those estimated using fixed measurements. Satellite-based PM₂.₅ predictions help to improve the spatiotemporal resolution of exposure assessments and the mutual adjustment model provide better estimation of PM₂.₅ associated health effects. Effects attributable to long-term exposure of PM₂.₅ were larger than those of short-term exposure, which should be more concerned for public health.