Nanoplastics (NPs) are becoming emerging pollutants of global concern. Understanding the environmental behavior of NPs is crucial for their environmental and human risk assessment. In this study, the aggregation and stability of polystyrene (PS) NPs were investigated under different hydrochemical conditions such as pH, salt type (NaCl, CaCl₂, Na₂SO₄), ionic strength (IS), and natural organic matter (NOM). The critical coagulation concentrations of PS NPs were determined to be 158.7 mM NaCl, 12.2 mM CaCl₂, and 80.0 mM Na₂SO₄. Ca²⁺ was more effective in destabilizing PS NPs, compared to Na⁺, owing to its stronger charge screening effect. In the presence of monovalent ions, NOM reduced aggregation through steric repulsion, whereas in the case of divalent ions, NOM induced aggregation through cation bridging. Initial and long-term stability studies demonstrated that, in waters with high IS and NOM content, NOM was the most significant factor affecting NPs aggregation. PS NPs would be highly suspended in all freshwaters, and even in wastewater, whereas they would aggregate rapidly and deposit in seawater. Finally, a statistical model was established to evaluate the hydrodynamic diameter of NPs in different waters. The results indicated the stability of PS NPs in natural aquatic environments and their potential for long-term transport.

Cadmium (Cd) is a heavy metal toxicant as a common pollutant derived from many agricultural and industrial sources. The absorption of Cd takes place primarily through Cd-contaminated food and water and, to a significant extent, via inhalation of Cd-contaminated air and cigarette smoking. Epidemiological data suggest that occupational or environmental exposure to Cd increases the health risk for osteoporosis and spontaneous fracture such as itai-itai disease. However, the direct effects and underlying mechanism(s) of Cd exposure on bone damage are largely unknown. We used primary bone marrow-derived mesenchymal stromal cells (BMMSCs) and found that Cd significantly induced BMMSC cellular senescence through over-activation of NF-κB signaling pathway. Increased cell senescence was determined by production of senescence-associated secretory phenotype (SASP), cell cycle arrest and upregulation of p21/p53/p16ᴵᴺᴷ⁴ᵃ protein expression. Additionally, Cd impaired osteogenic differentiation and increased adipogenesis of BMMSCs, and significantly induced cellular senescence-associated defects such as mitochondrial dysfunction and DNA damage. Sprague-Dawley (SD) rats were chronically exposed to Cd to verify that Cd significantly increased adipocyte number, and decreased mineralization tissues of bone marrow in vivo. Interestingly, we observed that Cd exposure remarkably retarded bone repair and regeneration after operation of skull defect. Notably, pretreatment of melatonin is able to partially prevent Cd-induced some senescence-associated defects of BMMSCs including mitochondrial dysfunction and DNA damage. Although Cd activated mammalian target of rapamycin (mTOR) pathway, rapamycin only partially ameliorated Cd-induced cell apoptosis rather than cellular senescence phenotypes of BMMSCs. In addition, a selective NF-κB inhibitor moderately alleviated Cd-caused the senescence-related defects of the BMMSCs. The study shed light on the action and mechanism of Cd on osteoporosis and bone ageing, and may provide a novel option to ameliorate the harmful effects of Cd exposure.

Tropospheric ozone is a highly oxidative pollutant with the potential to alter plant metabolism. The direct effects of ozone on plant phenotype may alter interactions with other organisms, such as pollinators, and, consequently, affect plant reproductive success. In a set of greenhouse experiments, we tested whether exposure of plants to a high level of ozone affected their phenological development, their attractiveness to four different pollinators (mason bees, honeybees, hoverflies and bumblebees) and, ultimately, their reproductive success. Exposure of plants to ozone accelerated flowering, particularly on plants that were growing in autumn, when light and temperature cues, that commonly promote flowering, were weaker. Simultaneously, there was a tendency for ozone-exposed plants to disinvest in vegetative growth. Plant exposure to ozone did not substantially affect pollinator preference, but bumblebees had a tendency to visit more flowers on ozone-exposed plants, an effect that was driven by the fact that these plants tended to have more open flowers, meaning a stronger attraction signal. Honeybees spent more time per flower on ozone-exposed plants than on control plants. Acceleration of flower production and the behavioural responses of pollinators to ozone-exposed plants resulted in retained reproductive fitness of plants pollinated by bumblebees, honeybees and mason bees, despite the negative effects of ozone on plant growth. Plants that were pollinated by hoverflies had a reduction in reproductive fitness in response to ozone. In a natural setting, acceleration of flowering by ozone might foster desynchronization between plant and pollinator activities. This can have a strong impact on plants with short flowering periods and on plants that, unlike wild mustard, lack compensatory mechanisms to cope with the absence of pollinator activity in the beginning of flowering.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors’ readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran’s I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Artificial light at night (ALAN) is considered one of the most pervasive forms of environmental pollution. It is an emerging threat to freshwater biodiversity and can influence ecologically important behaviours of fish. The European eel (Anguilla anguilla) is a critically endangered catadromous species that migrates downstream to the ocean to spawn in the Sargasso Sea. Given the pervasive nature of ALAN, many eel will navigate through artificially lit routes during their seaward migration, and although considered negatively phototactic, their response has yet to be quantified. We investigated the response of downstream moving European eel to simulated ALAN using a Light Emitting Diode unit in an experimental flume. We presented two routes of passage under: (1) a dark control (both channels unlit), (2) low ALAN (treatment channel lit to ca. 5 lx), or (3) high ALAN (treatment channel lit to ca. 20 lx). Eel were: (i) more likely to reject an illuminated route when exposed to high levels of ALAN; (ii) less likely to select the illuminated channel when given a choice; and (iii) passed downstream more rapidly when the illuminated route was selected. This study quantified the response of the critically endangered European eel to ALAN under an experimental setting, providing the foundations for future field based research to validate these findings, and offering insight on the ecological impacts of this major environmental pollutant and driver of global change.

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO₂. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O₃ m⁻² s⁻¹ were tested.The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m⁻² s⁻² produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m⁻² s⁻¹ or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0–1 nmol m⁻² s⁻¹). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m⁻² y⁻¹ and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O₃ m⁻² s⁻¹), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.

Cadmium (Cd) is a major pollutant in farmland, which not only greatly restricts crop production, but also brings a serious threat to human health through entering the food chain. Our previous study showed that hemin treatment could reduce the accumulation of Cd in pak choi seedlings. However, the underlying mechanism remains unclear. In this study, we used non-invasive micro-test technology (NMT) to detect the real-time Cd²⁺ flux from pak choi roots and demonstrated that hemin treatment decreased Cd uptake rather than its translocation within plants. Moreover, through comparing the responses of different chemical treatments in pak choi seedlings and Arabidopsis wild-type and heme oxygenase-1 (HO-1) mutant, we provided evidence that hemin-decreased Cd uptake was HO-1 dependent. Furthermore, analyses of hemin degradation products suggested that the hemin-derived suppression of Cd uptake suppression was probably relying on its degradation by-products, ferrous iron (Fe²⁺) and carbon monoxide (CO), via repressing the expression of a Fe²⁺/Cd²⁺ transporter BcIRT1 in pak choi roots.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

Ozone (O₃) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O₃ concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O₃ concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O₃ observations on a site scale with much higher accuracy than existing O₃ models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O₃ reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O₃ events and the 1- and 24-h forecasts of hourly O₃ were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O₃, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME).With SHAP, we showed an increasing effect of temperature on O₃ concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O₃ concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O₃ concentrations during cold and wet periods.While recently developed MLAs for the spatial estimation of hourly O₃ concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m³, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m³. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O₃ concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m³, respectively).