Very little is currently known regarding the effects of estrogenic endocrine disrupting chemicals on embryonic and larval development in molluscs, nor the potential effects of parental (F₀) exposure on resultant F₁ offspring. In this study, we assessed the embryotoxic impacts of exposure to environmentally relevant concentrations of the synthetic estrogen, 17α-ethinylestradiol (EE2), to male and female parents (50 ng/L) and their offspring (5 and 50 ng/L) in the native Australian Sydney rock oyster, Saccostrea glomerata. There were no detectable effects of parental exposure on fertilisation success, proportions of early larval (F₁) morphs and unfertilised eggs. Offspring impacts were evidenced in terms of developmental delays, with decreased percentages of D-veligers retained by 45 μm mesh, along with a reduction of swimming capabilities of larvae at 2 days post-fertilisation (dpf) when both parents had been exposed to 50 ng/L EE2. Although no significant parental effects were found on the survival of F₁ larvae at 9 dpf, retardation of shell growth was observed on F₁ larvae in treatments where both parents had been exposed to 50 ng/L EE2. Subsequent larval exposure from 2 to 9 dpf caused declines in survival and reduction of shell length in F₁ larvae at both 5 and 50 ng/L EE2 across all parental exposure treatments. Collectively, parental EE2 imparts effects on offspring in terms of retardation of larval development, and subsequent offspring exposure to EE2 further exacerbates impacts to development. Future research should aim to understand the potential mechanisms of EE2 induced toxicity and its transmission resulting in altered phenotypes of the F₁ generation.

Low pH and aluminum (Al)-toxicity often coexist in acidic soils. Citrus sinensis seedlings were treated with nutrient solution at a pH of 2.5, 3.0, 3.5 or 4.0 and an Al concentration of 0 or 1 mM for 18 weeks. Thereafter, malate, citrate, isocitrate, acid-metabolizing enzymes, and nonstructural carbohydrates in roots and leaves, and release of malate and citrate from roots were measured. Al concentration in roots and leaves increased under Al-toxicity, but it declined with elevating nutrient solution pH. Al-toxicity increased the levels of glucose, fructose, sucrose and total soluble sugars in leaves and roots at each given pH except for a similar sucrose level at pH 2.5–3.0, but it reduced or did not alter the levels of starch and total nonstructural carbohydrates (TNC) in leaves and roots with the exception that Al improved TNC level in roots at pH 4.0. Levels of nonstructural carbohydrates in roots and leaves rose with reducing pH with a few exceptions with or without Al-toxicity. A potential model for the possible role of root organic acid (OA) metabolism (anions) in C. sinensis Al-tolerance was proposed. With Al-toxicity, the elevated pH upregulated the OA metabolism, and increased the flow of carbon to OA metabolism, and the accumulation of malate and citrate in roots and subsequent release of them, thus reducing root and leaf Al and hence eliminating Al-toxicity. Without Al-toxicity, low pH stimulated the exudation of malate and citrate, an adaptive response of Citrus to low pH. The interactive effects of pH and pH on OA metabolism were different between roots and leaves.

Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO₂), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM₁₀ particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM₁₀ and NO₂, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia.

Environmental complexity leads to differences in the spatial distribution of heavy metal pollution in soil and rice. Such spatial differences will seriously affect the safety of planted rice and can impact regional management and control. How to scientifically reveal these spatial differences is an urgent problem. In this study, the spatial mismatch relationship between Cd pollution in soil and rice grains (brown rice) was first explored by the interpolation method. To further reveal the causes of these, the specific recognition rules of the spatial relationship of Cd pollution were extracted based on a decision tree model, and the results were mapped. The results revealed a spatial mismatch in Cd pollution between the soil and rice grains in the study area, and the main results are as follows: (i) slight soil pollution and safe rice accounted for 68.88% of the area; (ii) slight soil pollution and serious rice pollution accounted for 13.39% of the area and (iii) safe soil and serious rice pollution accounted for 11.63% of the area. In addition, 11 recognition rules of Cd spatial pollution relationship between soil and rice were proposed, and the main environmental factors were determined: SOM (soil organic matter), Dis-residence (distance from residential area), soil pH and LAI (leaf area index). The average accuracy of rule recognition was 75.90%. The study reveals the spatial mismatch of heavy metal pollution in soil and crops, providing decision-making references for the spatial accurate identification and targeted prevention of heavy metal pollution spaces.

Fine particulate matter (PM₂.₅) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM₂.₅ concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM₂.₅ concentrations based on long-term monitoring data. The results illustrated that PM₂.₅ concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM₂.₅ concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM₂.₅ concentrations, while precipitation had essentially no effect. A high positive correlation between PM₂.₅ and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM₂.₅ concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

The hydro-acoustic noise radiating from underwater tunnels during vehicle passage may be harmful to aquatic fauna, and this is a particular concern for endangered species. Therefore, the effects of underwater noise radiation and propagation on aquatic biodiversity must be investigated. In this study, the dynamic response of the sediment and tunnel structure in the Yangtze River in China was explored by conducting a field test, and the associated noise radiation from the tunnel was recorded and investigated. A three-dimensional numerical model was then developed to simulate the vibration of the tunnel-sediment coupling system induced by random traffic-flow models. Next, a modal acoustic transfer vector-based method was used to predict underwater noise radiation by use of a three-dimensional finite-element acoustic model. Finally, the accuracy of the simulated results was verified by comparison with measurements. The results showed that the noise radiation induced by passing vehicles was approximately 14 dB greater than the background noise, with a main frequency range of 12–25 Hz. The random traffic-flow model had obvious influence of the simulated noise level above 20 Hz. Vehicle-induced underwater noise may thus have a direct effect on fish species that can perceive low-frequency sound pressure. The proposed method can be used for further investigation of methods to reduce the effect of underwater noise on aquatic fauna, especially endangered species.

Microplastics (MPs), as a new type of environmental pollutant, pose a serious threat to soil ecosystems. The activities of soil extracellular enzymes produced by microorganisms are the potential sensitive indicators of soil quality. However, little is known about the response mechanism of enzyme activities toward MPs on a long-term scale. Moreover, information on differences in enzyme activities across different soil aggregates is lacking. In this study, 150 days of incubation experiments and soil aggregate fractionation were combined to investigate the influence of MPs on extracellular enzyme activities in soil. 28% concentration of polyethylene with size 100 μm was adopted in the treatments added with MPs. The results show that MPs inhibited enzyme activities through changing soil nutritional substrates and physicochemical properties or through adsorption. Moreover, MPs competed with soil microorganisms for physicochemical niches to reduce microbial activity and eventually, extracellular enzyme activity. Enzyme activities in different aggregate-size fractions responded differently to the MPs exposure. The catalase in the coarse particulate fraction and phenol oxidase and β-glucosidase in the micro-aggregate fraction exerted the greatest response. With comparison, urease, manganese peroxidase, and laccase activities showed the greatest responses in the non-aggregated silt and clay fraction. These observations are believed to stem from differences in the key factors determining the enzyme activities in different aggregate-size fractions.The inhibitory pathway of microplastics on activities of extracellular enzymes in soil varies significantly across different aggregate fractions.

Estimates of the brown carbon (BrC) absorption and their contribution to light absorption in ambient aerosols are poorly understood. The existing approaches to apportion light absorption into black carbon (BC) and BrC mainly use the assumption of fixed angstrom absorption exponent (AAE) for BC (1.0), which is not always true for ambient aerosols. Besides, these estimates are seldom validated, leaving significant uncertainty with derived values. Also, BrC absorption studies are largely focused on aqueous extracts, which truly do not represent the aerosolized form, hence the relationship between aqueous extracts and aerosolized form is a subject of research. With this in mind, we collected ambient PM₂.₅ filter samples at Lumbini, Nepal, at the northern edge of the Indo-Gangetic Plains (IGP) during winter 2017-18. These samples were analyzed for different compositions of carbonaceous aerosol and optical properties. BC and BrC absorptions were derived using a preexisting simplified two-component model but with “improved conditions”. Although BC dominated spectral absorption, BrC contribution for the carbonaceous aerosol absorption increased substantially at ultraviolet wavelengths (example 14.8–53.6% at 365 nm). Further water-soluble BrC absorption value in aerosol was found to be higher by 1.8 times to that obtained in aqueous extracts. Water-soluble OC contributed ∼65% to OC loading and 50% to BrC absorption at 365 nm, indicated the equally important role of water-insoluble organics. Mass absorption efficiency (MAE) of water-soluble BrC in aerosol was found to be 1.7 m²/g, lower to water-insoluble by 2.2 times. High BC MAE was observed which showed positive dependence on secondary coating. Sample collected during events with fog droplets showed a reduction in carbonaceous components loading and light absorption but enhancement in MAE for BrC and BC, signifying that aqueous processing can significantly modify the aerosol optical properties.

We report on commuters’ exposure to black carbon (BC), PM₂.₅ and particle number (PN, with aerodynamic diameter, dₐ, in the range 0.01 <dₐ< 1.0 μm) collected on-board diesel- and biodiesel-fuelled buses of the Bus Rapid Transit (BRT) system of the city of Curitiba, Brazil. Particulate concentrations measured at high sampling rates allowed the capture of fine gradients along the route and the comparison of in-cabin air pollution on buses of different technologies.Of all metrics, BC showed the largest discrepancies, with mean concentrations of 20.1 ± 20.0 μg m⁻³ and 3.9 ± 26.0 μg m⁻³ on diesel- and biodiesel-fuelled buses, respectively. Mean PM₂.₅ concentrations were similar (31.6 ± 28.5 μg m⁻³ and 29.0 ± 17.8 μg m⁻³), whilst mean PN concentrations were larger on the biodiesel buses (56,697 ± 26,800 # cm⁻³vs. 43,322 ± 32,243 # cm⁻³). The results are in line with studies on biodiesel emission factors that reported lower BC mass but more particles with smaller diameters. Our hypothesis is that different emission factors of diesel and biodiesel engines reflected in differences of in-cabin particulate concentrations. We found that the passenger exposure during the bus commutes was affected not only by the fuel used but also by the street geometry along the route, with segments with canyon configurations resulting in peak exposure to particulates. The results suggest that i) switching from diesel to biodiesel may help abate commuters’ exposure to BC particles on-board buses of the BRT system, whilst it would need to be complemented with after-treatment technologies to reduce emissions; ii) further reductions in exposure (to peaks in particular) could be achieved by changing bus routes to ones that avoid passing through narrow urban street canyons.

The emergence of organophosphorus flame retardants and the efficient removal from aquatic environments have aroused increasing concerns. The Urea functionalized Fe₃O₄@LDH (Urea-Fe₃O₄@LDH) was prepared and used to adsorb triphenyl phosphate (tphp) for the first time. The tphp adsorption capacity was up to 589 mg g⁻¹, and the adsorption rate reached 49.9 mg g⁻¹ min⁻¹. Moreover, the influences of various environmental factors (pH, ionic strength and organic matter) on the tphp adsorption on the Urea-Fe₃O₄@LDH were investigated. The initial pH of the solution significantly affected the tphp adsorption, whereas the ionic strength and HA slightly affected the adsorption. The main adsorption mechanism was attributed to electrostatic interaction and π-π interaction. We believe that urea is one of excellent functional groups for the tphp adsorption removal and the materials with urea groups as the adsorbents exhibit good prospects in the future.