Fine particulate matter (PM₂.₅) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM₂.₅ concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM₂.₅ values from 2000 to 2012, calculate the missing PM₂.₅ concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM₂.₅ concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM₂.₅ concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM₂.₅ concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM₂.₅ pollution in Yunnan Province in spring.

The environmental neurotoxin β-methylamino-L-alanine (BMAA) may represent a risk for human health in case of chronic exposure or after short-term exposure during embryo development. BMAA accumulates in freshwater and marine organisms consumed by humans. It is produced by marine and freshwater phytoplankton species, but the range of producers remains unknown. Therefore, analysing the phytoplankton composition is not sufficient to inform about the risk of freshwater contamination by BMAA. Filter-feeders mussels have accumulation capacities and therefore appear to be relevant to monitor various pollutants in aquatic ecosystems. We investigated the suitability of the freshwater mussels Dreissena polymorpha and Anodonta anatina for monitoring BMAA in water. Both species were exposed to 1, 10, and 50 μg of dissolved BMAA/L daily for 21 days, followed by 42 days of depuration in clean water. On days 0, 1, 7, 14, and 21 of exposure and 1, 7, 14, 21 and 42 of depuration, whole D. polymorpha and digestive glands of A. anatina were sampled, and the total BMAA concentration was measured. D. polymorpha accumulated BMAA earlier (from day 1 at all concentrations) and at higher tissue concentrations than A. anatina, which accumulated BMAA from day 14 when exposed to 10 μg BMAA/L and from day 7 when exposed to 50 μg BMAA/L. As BMAA accumulation by D. polymorpha was time and concentration-dependent, with a significant elimination during the depuration period, this species may be able to reflect the levels and dynamics of water contamination by dissolved BMAA. The species A. anatina could be used for monitoring water concentrations above 10 μg BMAA/L.

One emerging problem that recently has become a vastly acknowledged topic of concern is the environmental contamination by pharmaceuticals. Diclofenac (DCF) is one of the most common pharmaceuticals found, due to its high utilization and low removal rate in wastewater treatment processes. In this work, Solanum lycopersicum L. was used as a model to unravel how DCF contamination can affect crops, focusing on the internal mechanisms triggered by this exposure. For this purpose, plants were exposed to two different DCF concentrations (0.5 mg L⁻¹ and 5 mg L⁻¹). Results obtained here point towards a loss of shoot performance when plants were exposed to very high concentrations of DCF, but no delay or loss of yield in the flowering and fruit stages were ascribed to DCF contamination. Our data shows that a state of oxidative stress due to high reactive oxygen species accumulation was associated with this contamination, with very high DCF levels leading to a rise of lipid peroxidation, possibly accentuated by the inhibition of ROS-scavenging enzymes and unable to be counteracted by the visible upregulation of proline and the thiol-based redox network. Overall, these results allow to infer that in the current environmental context, no noticeable negative effects should be associated with the presence of DCF in soils where this crop is cultivated. However, the oxidative stress and lower biomass associated with the highest concentration are alarming, since DCF levels in the environment are continuously increasing and further measures are necessary to assess this problematic.

In the present work, low-cost and efficient iron oxide nanoparticle incorporated on mesoporous biochar was prepared from effluent treatment plant (ETP) sludge collected from the textile industry. This sludge contains a higher amount of Fe due to the use of ferric chloride as a coagulant in the treatment of wastewater generated during the process. The raw sludge and prepared biochar was extensively examined by various sophisticated techniques like XRF, XRD, BET, TGA, XPS, RAMAN, FTIR, FESEM, TEM, and VSM. TEM and XRD analysis confirms the presence of iron oxide nanoparticles on mesoporous biochar. The prepared biochar was found to possess BET surface area of 91 m² g⁻¹. Several parameters like pH, dose, initial concentration, temperature and time were optimized for the adsorptive removal of ofloxacin (OFL) from aqueous solution. Biochar (named as BTSFe) achieved ≈96% removal efficiency of OFL with a maximum adsorption capacity (qₘ) of 19.74 mg g⁻¹ at optimum condition. π-π electron–donor-acceptor and H bonding were the major mechanisms responsible for the OFL adsorption. Kinetic and equilibrium thermodynamic study of showed that the adsorption of OFL was represented by the pseudo-second-order kinetics model, and the process was exothermic and spontaneous. Additionally, Redlich-Peterson and Freundlich isotherms best fitted the experimental data indicating multilayer adsorption phenomenon. Biochar was magnetically separated and thermally regenerated after each cycle for five times with a nominal overall decrease of ≈8% in removal efficiency. Leaching of iron during the adsorption process was also checked and found to be within the permissible limit. This study provides an alternative application of the textile industry sludge as an efficient, low-cost biochar for the removal of emerging pharmaceutical compounds.

In recent years, with the expansion of the Weining county in the northeast of Caohai wetland, the construction of a new port in the north, and the large-scale development of cultivated land in the east, land use patterns in lakeshore areas have changed. These changes have affected the state of lake shores water bodies in complex ways, resulting in varying degrees of local water pollution. To explore the distribution and transformation characteristics of water chemistry and heavy metals in different areas of a water body under the influence of different land uses, especially the interactions between water chemical factors and heavy metals in different areas of a water body, this study used Circos diagrams, originally used in biological genetic analysis, to visualize these interactions. This is the first time that the Circos diagram has been applied to the analysis of environmental interactions. The results showed that there are significant differences in the distribution of water chemical factors and heavy metals in different areas of the Caohai wetland. In particular, Cd is affected by anthropogenic sources. The Cd content is higher in the NCL and UL areas, which are at greater risk from pollution. The factors controlling heavy metal levels in water bodies were different in the different regions. The NCL region was mainly affected by construction excavation ore, UL was mainly affected by man-made industrial inputs, CL was mainly affected by pesticide and fertilizer inputs, and ML and FL were mainly affected by Eh and DO. The PCA results showed that the sources of heavy metals in different types of water bodies in the lakeshore zone were both natural and anthropogenic. Therefore, controlling pollutants, reducing environmental pollution inputs to the lakeshore zone, and strengthening supervision and management near wetlands may be of great significance for handling heavy metal pollution.

Contamination of soil with heavy metals seriously harms the growth of crops. Silicon fertilizer is known to promote growth of crops and alleviate heavy metals stresses in vegetables. However, little is known about the effects of silicon fertilizer on pakchoi vegetable growth and soil microbial community in soil contaminated with multiple heavy metals. In order to elucidate this question, current study was designed to analyze the impact of different silicon fertilizer doses on the growth of pakchoi, heavy metals accumulation in pakchoi, and diversity and composition of bacterial community in heavy metals contaminated soil. Results of the study showed that, silicon fertilizer application significantly improved the yield of pakchoi and reduced the content of heavy metals in pakchoi. Moreover, the silicon fertilizer led to the heterogeneity of bacterial community structure in soil. Linear discriminant analysis (LDA) effect size (LEfSe) test showed the change of soil bacterial community structures under the higher silicon fertilizer doses (0.8–3.2%). Similarly, soil bacteria associated with heavy metal resistance and carbon/nitrogen metabolism showed a more active response to medium fertilizer dose (0.8% w/w). In addition, Mantel test and Redundancy analysis (RDA) showed that both the soil bacterial community structures and pakchoi growth were significantly correlated with soil EC, available K and pH. Study suggested that the application of silicon fertilizer provided richer bacteria associated with heavy metal resistance and plant growth, and more favorable soil physicochemical environment for the growth of pakchoi under multiple heavy metal contamination, and the impact was dependent on fertilizing dose.

Plant species occurrence in Europe is affected by changes in nitrogen deposition and climate. Insight into potential future effects of those changes can be derived by a model approach based on field-based empirical evidence on a continental scale. In this paper, we present a newly developed empirical model PROPS, predicting the occurrence probabilities of plant species in response to a combination of climatic factors, nitrogen deposition and soil properties. Parameters included were temperature, precipitation, nitrogen deposition, soil pH and soil C/N ratio. The PROPS model was fitted to plant species occurrence data of about 800,000 European relevés with estimated values for pH and soil C/N ratio and interpolated climate and modelled N deposition data obtained from the Ensemble meteo data set and EMEP model results, respectively. The model was validated on an independent data set. The test of ten species against field data gave an average Pearson’s r-value of 0.79.PROPS was applied to a grassland and a heathland site to evaluate the effect of scenarios for nitrogen deposition and climate change on the Habitat Suitability Index (HSI), being the average of the relative probabilities, compared to the maximum probability, of all target species in a habitat. Results for the period 1930–2050 showed that an initial increase and later decrease in nitrogen deposition led to a pronounced decrease in HSI, and with dropping nitrogen deposition to an increase of the HSI. The effect of climate change appeared to be limited, resulting in a slight increase in HSI.

Jarosite and birnessite secondary minerals play a pivotal role in the mobility, transport and fate of trace elements in the environment, although geochemical interactions of these compounds with extremely toxic thallium (Tl) remain poorly known. In this study, we investigated the sorption behavior of Tl(I) onto synthetic jarosite and birnessite, two minerals commonly found in soils and sediments as well as in mining-impacted areas where harsh conditions are involved. To achieve this, sorption and desorption experiments were carried out under two different acidic conditions and various Tl(I) concentrations to mimic natural scenarios. In addition, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and inductively coupled plasma (ICP) analyses were conducted to determine the performance of both minerals for Tl(I) sequestration. Our results indicate that both phases can effectively remove aqueous Tl by different sorption mechanisms. Jarosite preferentially incorporates Tl(I) into the structure to form Tl(I)-jarosite and eventually the mineral dorallcharite (TlFe₃(SO₄)₂(OH)₆) as increasing amounts of Tl are employed. Birnessite, however, favorably uptakes Tl(I) through an irreversible surface adsorption mechanism, underlining the affinity of Tl for this mineral in the entire concentration range studied (0.5–5 mmol L⁻¹). Lastly, the presence of Tl(I) in conditions where aqueous molar ratio Tl/Mn is ∼0.25 inhibits the formation of birnessite since oxidation of Tl(I) to Tl(III) followed by precipitation of avicennite (Tl₂O₃) take place. Thus, the present research may provide useful insights on the role of both jarosite and birnessite minerals in Tl environmental cycles.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Microplastics are known to be associated with co-contaminants, but little is understood about the mechanisms by which these chemicals are transferred from ingested plastic to organisms. This study simulates marine avian gastric conditions in vitro to examine the bioaccessibility of authigenic metals (Fe, Mn) and trace metals (Co, Pb) that have been acquired by polyethylene microplastic pellets from their environment. Specifically, different categories of pellet were collected from beaches in Cornwall, southwest England, and exposed to an acidified saline solution of pepsin (pH ∼ 2.5) at 40 °C over a period of 168 h with extracted metal and residual metal (available to dilute aqua regia) analysed by ICP-MS. For Fe, Mn and Co, kinetic profiles consisted of a relatively rapid initial period of mobilisation followed by a more gradual approach to quasi-equilibrium, with data defined by a diffusion model and median rate constants ranging from about 0.0002 (μg L⁻¹)⁻¹ h⁻¹ for Fe to about 7 (μg L⁻¹)⁻¹ h⁻¹ for Co. Mobilisation of Pb was more complex, with evidence of secondary maxima and re-adsorption of the metal to the progressively modified pellet surface. At the end of the time-courses, maximum total concentrations were 38.9, 0.81, 0.014 and 0.10 μg g⁻¹ for Fe, Mn, Co and Pb, respectively, with maximum respective percentage bioaccessibilities of around 60, 80, 50 and 80. When compared with toxicity reference values for seabirds, the significance of metals acquired by microplastics from the environment and exposed to avian digestive conditions is deemed to be low, but studies of a wider range of plastics and metal associations (e.g. as additives) are required for a more comprehensive risk assessment.