Solubility is a critical component of physicochemical characterisation of engineered nanomaterials (ENMs) and an important parameter in their risk assessments. Standard testing methodologies are needed to estimate the dissolution behaviour and biodurability (half-life) of ENMs in biological fluids. The effect of pH, particle size and crystal form on dissolution behaviour of zinc metal, ZnO and TiO₂ was investigated using a simple 2 h solubility assay at body temperature (37 °C) and two pH conditions (1.5 and 7) to approximately frame the pH range found in human body fluids. Time series dissolution experiments were then conducted to determine rate constants and half-lives. Dissolution characteristics of investigated ENMs were compared with those of their bulk analogues for both pH conditions. Two crystal forms of TiO₂ were considered: anatase and rutile. For all compounds studied, and at both pH conditions, the short solubility assays and the time series experiments consistently showed that biodurability of the bulk analogues was equal to or greater than biodurability of the corresponding nanomaterials. The results showed that particle size and crystal form of inorganic ENMs were important properties that influenced dissolution behaviour and biodurability. All ENMs and bulk analogues displayed significantly higher solubility at low pH than at neutral pH. In the context of classification and read-across approaches, the pH of the dissolution medium was the key parameter. The main implication is that pH and temperature should be specified in solubility testing when evaluating ENM dissolution in human body fluids, even for preliminary (tier 1) screening.

Agrochemical applications may have side effects on soil biochemical properties related to soil nitrogen (N) mineralization and thus affect N cycling. The present study aimed to evaluate the effects of nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) and fungicide iprodione on soil neutral protease (NPR), alkaline protease (APR), chitinase (CHI), and their functional genes (nprA, aprA, and chiA) related to soil N mineralization. The following four treatments were included: blank control (CK), single DMPP application (DAA), weekly iprodione applications (IPR), and the combined applications of DMPP and iprodione (DI). Compared with the CK treatment, DMPP application significantly inhibited the CHI activity in the first 14 days of incubation, and iprodione applications, particularly when applied alone, decreased the NPR, APR, and CHI activities. Relative to the IPR treatment, extra DMPP application had the potential to alleviate the inhibitory effects of iprodione on the activities of these enzymes. DMPP application significantly increased aprA gene abundances after 14 days of incubation. However, repeated iprodione applications, alone or with the DMPP, decreased nprA and chiA gene abundances. Relative to the CK treatment, DMPP application generated negligible effects on the positive/negative correlations between soil enzyme activities and the corresponding functional gene abundances. However, the positive correlation between the CHI activity and chiA gene abundance was changed to negative correlation by repeated iprodione applications, alone or together with the DMPP. Our results demonstrated that agrochemical applications, particularly repeated fungicide applications, can have inadvertent effects on enzyme activities and functional gene abundances associated with soil N mineralization.

Trace elements in the atmosphere could provide information about regional atmospheric pollution. This study presented a whole year of precipitation observation data regarding the concentrations of trace metals (e.g., Cr, Ni, Cu, Mn, Cd, Mo, Pb, Sb, Ti, and Zn), and a TEM-EDX (transmission electron microscope-energy dispersive X-ray spectrometer) analysis from June 2014 to September 2015 at a remote alpine glacier basin in Northwest China, the Laohugou (LHG) basin (4200 m a.s.l.), to determine the regional scale of atmospheric conditions and chemical processing in the free troposphere in the region. The results of the concentrations of trace metals showed that, although the concentrations generally were lower compared with that of surrounding rural areas (and cities), they showed an obviously higher concentration and higher EFs in winter (DJF) and a relatively lower concentration and lower EFs in summer (JJA) and autumn (SON), implying clearly enhanced winter pollution of the regional atmosphere in Northwest China. The TEM observed residue in precipitation that was mainly composed of types of dust, salt-dust, BC-fly ash-soot, and organic particles in precipitation, which also showed remarked seasonal change, showing an especially high ratio of BC-soot-fly ash particles in winter precipitation compared with that of other seasons (while organic particles were higher in the summer), indicating significant increased anthropogenic particles in the winter atmosphere. The source of increased winter anthropogenic pollutants mainly originated from emissions from coal combustion, e.g., the regional winter heating supply for residents and cement factories in urban and rural regions of Northwest China. Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric optical depth (AOD) also showed a significant influence of regional atmospheric pollutant emissions over the region in winter. In total, this work indicated that the atmospheric environment in western Qilian Mountains also showed enhanced anthropogenic pollution in winter, probably mainly caused by regional fossil fuel combustion.

It is believed that nanoemulsions were emerged as a promising candidate to improve the qualities of natural essential oil towards antimicrobial and insecticidal applications. In the present study, we have focused on the encapsulation of Vitex negundo L. leaf essential oil using Polysorbate80 for its different biological activities including antioxidant, bactericidal and larvicidal activity against dengue fever vector Aedes aegypti L. Initially, the nanoemulsion was prepared by low energy method and droplet size of the formulated nanoemulsion was characterized by using Dynamic Light Scattering analysis. The freshly prepared V. negundo essential nanoemulsion was observed with the mean droplet size of below 200 nm indicating its excellent stability. Further, the larvicidal activity of essential oil and nanoemulsion with various concentrations (25, 50, 100, 200 and 400 ppm). The larvicidal activities were tested 2nd and 3rd instar larval mortality rate that was observed against the 12 and 24 h exposure period. After a 12 h exposure period, the larvicidal activities of 2nd instar larva were observed as essential oil (73.33 ± 1.88), nanoemulsion (81.00 ± 0.88) and the larvicidal activities of 3rd instar larva were displayed essential oil (70.33 ± 2.60) and nanoemulsion (79.00 ± 3.70). Likewise, after a 24 h exposure period, the larvicidal activities of 2nd instar larva were observed as essential oil (90.30 ± 2.15), nanoemulsion (94.33 ± 1.20) and the larvicidal activities of 3rd instar larva were essential oil (80.66 ± 0.66) and nanoemulsion (93.00 ± 1.25) respectively. We finally concluded that the developed plant-based emulsion essential oil systems were thermodynamically stable. Owing to its improved bioavailability and biocompatibility, formulated nanoemulsion can be used in various biomedical applications including drug delivery as well as disease transmitting mosquito vector control. Graphical abstract ᅟ

Agricultural non-point source pollution is a major factor in surface water and groundwater pollution, especially for nitrogen (N) pollution. In this paper, an experiment was conducted in a direct-seeded paddy field under traditional continuously flooded irrigation (CFI). The water movement and N transport and transformation were simulated via the Hydrus-1D model, and the model was calibrated using field measurements. The model had a total water balance error of 0.236 cm and a relative error (error/input total water) of 0.23%. For the solute transport model, the N balance error and relative error (error/input total N) were 0.36 kg ha⁻¹ and 0.40%, respectively. The study results indicate that the plow pan plays a crucial role in vertical water movement in paddy fields. Water flow was mainly lost through surface runoff and underground drainage, with proportions to total input water of 32.33 and 42.58%, respectively. The water productivity in the study was 0.36 kg m⁻³. The simulated N concentration results revealed that ammonia was the main form in rice uptake (95% of total N uptake), and its concentration was much larger than for nitrate under CFI. Denitrification and volatilization were the main losses, with proportions to total consumption of 23.18 and 14.49%, respectively. Leaching (10.28%) and surface runoff loss (2.05%) were the main losses of N pushed out of the system by water. Hydrus-1D simulation was an effective method to predict water flow and N concentrations in the three different forms. The study provides results that could be used to guide water and fertilization management and field results for numerical studies of water flow and N transport and transformation in the future.

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.

Bioretention, also known as rain garden, allows stormwater to soak into the ground through a soil-based medium, leading to removal of particulate and dissolved pollutants and reduced peak flows. Although soil organic matter (SOM) is efficient at sorbing many pollutants, amending the bioretention medium with highly effective adsorbents has been proposed to optimize pollutant removal and extend bioretention lifetime. The aim of this research was to investigate whether soil amended with activated carbon produced from sewage sludge increases the efficiency to remove hydrophobic organic compounds frequently detected in stormwater, compared to non-amended soil. Three lab-scale columns (520 cm³) were packed with soil (bulk density 1.22 g/cm³); activated carbon (0.5% w/w) was added to two of the columns. During 28 days, synthetic stormwater—ultrapure water spiked with seven hydrophobic organic pollutants and dissolved organic matter in the form of humic acids—was passed through the column beds using upward flow (45 mm/h). Pollutant concentrations in effluent water (collected every 12 h) and polluted soils, as well as desorbed amounts of pollutants from soils were determined using GC-MS. Compared to SOM, the activated carbon exhibited a significantly higher adsorption capacity for tested pollutants. The amended soil was most efficient for removing moderately hydrophobic compounds (log K ₒw 4.0–4.4): as little as 0.5% (w/w), carbon addition may extend bioretention medium lifetime by approximately 10–20 years before saturation of these pollutants occurs. The column tests also indicated that released SOM sorb onto activated carbon, which may lead to early saturation of sorption sites on the carbon surface. The desorption test revealed that the pollutants are generally strongly sorbed to the soil particles, indicating low bioavailability and limited biodegradation.

The aim of this study was to analyze the occurrence of sulfonamide resistance genes (sul1–3) and other genetic elements as antiseptic resistance gene (qacEΔ1) and class 1 and class 2 integrons (int1–2) in the upper layer of substrate and in the effluent of microcosm constructed wetlands (CWs) treating artificial wastewater containing diclofenac and sulfamethoxazole (SMX), which is a sulfonamide antibiotic. The bacteria in the substrate and in the effluents were equipped with the sul1–2, int1, and qacEΔ1 resistance determinants, which were introduced into the CW system during inoculation with activated sludge and with the soil attached to the rhizosphere of potted seedlings of Phalaris arundinacea ‘Picta’ roots (int1). By comparing the occurrence of the resistance determinants in the upper substrate layer and the effluent, it can be stated that they neither were lost nor emerged along the flow path. The implications of the presence of antibiotic resistance genes in the effluent may entail a risk of antibiotic resistance being spread in the receiving environment. Additionally, transformation products of SMX were determined. According to the obtained results, four (potential) SMX transformation products were identified. Two major metabolites of SMX, 2,3,5-trihydroxy-SMX and 3,5-dihydroxy-SMX, indicated that SMX may be partly oxidized during the treatment. The remaining two SMX transformation products (hydroxy-glutathionyl-SMX and glutathionyl-SMX) are conjugates with glutathione, which suggests the ability of CW bacterial community to degrade SMX and resist antimicrobial stress.

The detailed analysis of columnar optical and microphysical properties of aerosols obtained from the AErosol RObotic NETwork (AERONET) Cimel sun photometer operated at Skukuza (24.98° S, 31.60° E, 150 m above sea level), South Africa was carried out using the level 2.0 direct sun and inversion products measured during 1999–2010. The observed aerosol optical depth (AOD) was generally low over the region, with high values noted in late winter (August) and mid-spring (September and October) seasons. The major aerosol types found during the study period were made of 3.74, 69.63, 9.34, 8.83, and 8.41% for polluted dust (PD), polluted continental (PC), non-absorbing (NA), slightly absorbing (SA), and moderately absorbing (MA) aerosols, respectively. Much attention was given to the aerosol fine- and coarse-modes deduced from the particle volume concentration, effective radius, and fine-mode volume fraction. The aerosol volume size distribution pattern was found to be bimodal with the fine-mode showing predominance relative to coarse-mode during the winter and spring seasons, owing to the onset of the biomass burning season. The mean values of total, fine-, and coarse-mode volume particle concentrations were 0.07 ± 0.04, 0.03 ± 0.03, and 0.04 ± 0.02 μm³ μm⁻², respectively, whereas the mean respective effective radii observed at Skukuza for the abovementioned modes were 0.35 ± 0.17, 0.14 ± 0.02, and 2.08 ± 0.02 μm. The averaged shortwave direct aerosol radiative forcing (ARF) observed within the atmosphere was found to be positive (absorption or heating effect), whereas the negative forcing in the surface and TOA depicted significant cooling effect due to more scattering type particles.

This paper reanalyzes the determinants of the CO₂ emissions in France. For this purpose, it considers the unit root test with two structural breaks and a dynamic ordinary least squares estimation. The paper also considers the effects of the energy consumption and the economic complexity on CO₂ emissions. First, it is observed that the EKC hypothesis is valid in France. Second, the positive effect of the energy consumption on CO₂ emissions is obtained. Third, it is observed that a higher economic complexity suppresses the level of CO₂ emissions in the long run. The findings imply noteworthy environmental policy implications to decrease the level of CO₂ emissions in France.