Currently, sustainable utilization, including recycling and valorization, is becoming increasingly popular in waste management. Black soldier fly larvae (BSFL) can convert the carbon (C) and nitrogen (N) from organic waste into biomass and improve properties of the substrate to reduce greenhouse gas and NH₃ emissions. In this study, the recycling of C and N and the emissions of greenhouse gas and NH₃ during BSFL bio-treatment of mixtures of pig manure and corncob were investigated under different C/N ratios. The results indicated that initial C/N ratios of feedstock are a crucial parameter affecting the biomass generation of larvae. The BSFL recycled approximately 4.17–6.61% of C and 17.45–23.73% of N from raw materials under different C/N ratios. Cumulative CO₂, CH₄, NH₃, and N₂O emissions at the different C/N ratios ranging from 15 to 35 were 107.92–151.68, 0.08–0.76, 0.14–1.17, and 0.91–1.18 mg kg⁻¹, respectively. Compared with conventional composting, BSFL treatment could reduce the total greenhouse gas emissions by over 90%. The study showed that bio-treatment of mixtures of pig manure and corncob with a proper C/N ratio by BSFL could become an avenue to achieve higher nutrient recycling, which is an eco-friendly process.

In the present work, a lanthanum hydroxide adsorbent was prepared by a simple precipitation process, and its arsenic removal performances and adsorption mechanisms were investigated by batch experiments and various techniques including field emission scanning electron microscopy with energy-dispersive X-ray spectrophotometry (FESEM–EDX), Brunauer–Emmett–Teller (BET) analysis, powder X-ray diffraction (p-XRD), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The influence of pH on arsenic removal showed that the lanthanum hydroxide adsorbent can effectively remove As(V) from solution, whereas the As(III) removal was very low, indicating that the lanthanum hydroxide adsorbent can selectively remove As(V) but not As(III). The isotherm study showed that the maximum adsorption capacities of As(V) at pH 5.0 and 9.0 were 299.4 and 192.3 mg/g, respectively, much higher than those of the widely used ferrihydrite. Significant interference on As(V) removal was caused by the presence of phosphate and natural organic acids (NOAs), such as citric acid. Powder XRD, FTIR, and XPS analysis showed that the lanthanum hydroxide was almost transformed into lanthanum arsenate after As(V) adsorption at pH 4.0, while a portion of lanthanum hydroxide remained after As(V) adsorption at pH 6.0 and 9.0. Furthermore, ligand exchange between the hydroxyl groups of the adsorbent and As(V) and the formation of inner-sphere surface complexes could play a central role in arsenic removal which needs further investigation.

The multiyear cultivation of Miscanthus × giganteus Greef et Deu (M.×giganteus) at the soils polluted by metal(loid)s were researched. The biomass parameters and concentrations of elements: Ti, Mn, Fe, Cu, Zn, As, Sr, and Mo were determined in the plant’s organs at harvest. The same metal(loid)s were monitored in the plant’s leaves throughout three vegetation seasons. The principal component analysis and general linear model approaches were applied for statistical evaluation followed by Box-Cox transformation. The difference in the distribution of elements in the plant, the content of elements in the soil, various regime of uptake to the plant tissues, and the year of vegetation were analyzed as driving factors of the phytoremediation. The results showed that the leading promoter was the factor of the zone, which was the most essential for Ti, Fe, and Cu and the smallest for Mn. The factor of differences in soil pollution was essential for Zn and Mo, much less for As, Sr, and Mn, limited for Fe, and was not seen for Ti and Cu. The factor of the interrelation effects of the zone and experiment reflected the different regime of uptake for the plant tissues was seen for two elements: more prominent for Cu and smaller for Ti. While analyzing the dynamic of foliar concentrations of the metal(loid)s during 3 years, two groups were defined. Firstly, Fe, Ni, Mn, and Sr showed stable curves with limited distribution of the plant life cycle. Secondly, As, Zn, Cu, and Mo showed different fluctuations in the curves, which can be attributed to essential influence of those elements to the plant life cycle. Further research will be focused on the application of M.×giganteus to the polluted soil in a bigger scale and comparison results of laboratory and field experiments.

The globally changing environmental climate, ocean acidification, and heavy metal pollution are of increasing concern. However, studies investigating the combined effects of ocean acidification and zinc (Zn) exposure on macroalgae are very scarce. In this study, the photosynthetic performance of the red alga Pyropia yezoensis was examined under three different concentrations of Zn (control, 25 (medium), and 100 (high) μg L⁻¹) and pCO₂ (400 (ambient) and 1000 (high) μatm). The results showed that higher Zn concentrations resulted in increased toxicity for P. yezoensis, while ocean acidification alleviated this negative effect. Ocean acidification increased the relative growth rate of thalli under both medium and high Zn concentrations. The net photosynthetic rate and respiratory rate of thalli also significantly increased in response under ocean acidification, when thalli were cultured under both medium and high Zn concentrations. Malondialdehyde levels decreased under ocean acidification, compared to ambient CO₂ conditions and either medium or high Zn concentrations. The activity of superoxide dismutase increased in response to high Zn concentrations, which was particularly apparent at high Zn concentration and ocean acidification. Immunoblotting tests showed that ocean acidification increased D1 removal, with increasing expression levels of the PSII reaction center proteins D2, CP47, and RbcL. These results suggested that ocean acidification could alleviate the damage caused by Zn exposure, thus providing a theoretical basis for a better prediction of the impact of global climate change and heavy metal contamination on marine primary productivity in the form of seaweeds.

Unintentional injuries pose a great risk for human health in China. Few studies have focused on unintentional injuries at national level from urbanization perspective. The panel data of mortality rate of transportation accidents (TA), fall and drowning and sinking (DS) is investigated, and urbanization development index is collected. Global Moran’s I and linear regression of panel data are applied to determine the spatial distribution and spatial influencing factors of unintentional injuries. The results are the following: (1) The unintentional injury such as TA, fall, and DS shows clear non-uniformity of spatial distribution and relative immobility through time. (2) A 10,000 tons increase in SO₂ emission amount (SO₂ EA) and emission of smoke and dust (ESD) can result in 15.7 and 12.5 increases in TA death in eastern region, respectively. Meanwhile, A 10,000 tons increase in NOₓ emission amount can cause 15.1 increase in TA death in western region. For every 100 billion yuan increase in GDP, the fall death can reduce by 8.4 in central region. One bed increase in number of hospital beds per 10,000 population (NHBP) is favorable for decreasing in fall death by 16.7 in eastern region. However, increase in number of workers enjoying industrial injury (NEWII) does not reduce the fall death in eastern region. (3) For every 1 ten thousand people increase in number of students in ordinary high schools (NSOHS) is conductive to reducing DS death by 7.8 in the western region. Our findings show that there exist spatial differences for urbanization influencing TA, fall, and DS death in eastern, western, and central regions. This study is expected to provide a reference for unintentional injuries control in those three regions.

The present work represented first study of the spatio-seasonal distribution of the multi-elements in the suspended particulate matter (SPM) of the tropical Hooghly river estuary (HRE), eastern part of India. The high load of SPM (20–3460 mg/l) might have induced negative impact on the phytoplankton density. The relative abundance of the studied elements exhibited the following decreasing trend (concentration in μg/g and %): Si(26.44 ± 3.75%) > Al(7.94 ± 1.52%) > Fe(6.17 ± 1.9%) > K(3.05 ± 1.5%) > Ca(1.97 ± 1.11%) > Mg(1.57 ± 1.71%) > Na(1.45 ± 8.40%) > Mn(1273 ± 2003) > Zn(178.43 ± 130.95) > V(151.54 ± 27.13) > Cr(147.08 ± 32.21) > Cu(62.06 ± 14.03) > Ni(49.64 ± 12.09) > Pb(21.5 ± 10.45). The accumulation of Ni, Cr, Pb, and Cu is mainly controlled by the formation of Fe hydroxides along with particulate organic carbon (POC) and salinity. The average geo-accumulation index (Igₑₒ) and enrichment factor (EF) endorsed the substantial input of Cr (Igₑₒ = 0.037; EF = 1.61) and Zn (Igₑₒ = 0.123; EF = 2.07) from diffused pollution sources. From ecotoxicological point of view, the quality guidelines (QGs) suggested that Cr and Ni might possess frequent adverse biological effects. However, the mean probable effect level (PEL) quotient values revealed 49% probability of toxicity to the aquatic biota for five toxic elements (Cr, Ni, Cu, Zn, and Pb). The geochemical approaches, pollution indices, and statistical evaluation together revealed low to moderate contamination in the estuary. This baseline data would be beneficial in adopting proper management strategies for sustainable utilization and restoration of the water resources. The authors strongly recommend continuous systematic monitoring and installation of treatment plants for management of this stressed estuary.

The purpose of this study was to determine the contents of 12 metals in obtainable chalk sticks and assess their associated health risk. Chalk stick samples from 16 factories were analyzed by inductively coupled plasma-atomic emission spectrometry (ICP-AES). The results showed that 12 metals were detectable in white and colored chalks. The contents of Al, Fe, and Mg were in the range of 646.2–3909 μg/g, 408.8–2075.1 μg/g, and 125–6825.7 μg/g, respectively. Additionally, the levels of Cu, Pb, Mn, and Cr were ranked in the order of Cu>Cr>Pb>Mn, while the maximum levels of As, Ni, Cd, and Sn in all samples (9.90, 10.14, 7.27, and 6.08 μg/g, respectively) were relatively lower than those of other metals. Furthermore, the cumulative hazard index (HI) values of all metals and carcinogenic risk (CR) of As (1.12E-4), Ni (1.39E-4), and Cr (1.15E-4) for children were also higher than the threshold value (1.0E-6 to 1.0E-4), suggesting that chalk dust particles may exert adverse effects on children.

As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studies that exploited various machine learning techniques to build predictive model for pollutant concentration or air quality prediction. However, enhancing the prediction performance always is the common problem of existing studies. Traditional templates based on machine learning and deep learning methods, such as GBTR (gradient boosted tree regression), SVR (support vector machine-based regression), and LSTM (long short-term memory), are most promising approaches to address these problems. Some previous researches showed that ensemble learning technology can improve predictive performance of other domains. In order to improve the accuracy of forecasting, in this paper, we propose a hybrid model and framework to improve the forecasting accuracy of air pollution. We not only exploit stacking-based ensemble learning scheme with Pearson correlation coefficient to calculate the correlation between different machine learning models to integrate various forecasting models together, but also construct a framework based on Spark+Hadoop machine learning and TensorFlow deep learning framework to physically integrate these models to demonstrate the next 1 to 8 h’ air pollution forecasting. We also conduct experiments and compare the result with GBTR, SVR, LSTM, and LSTM2 (version 2) models to demonstrate the proposed hybrid model’s predictive performance. The experimental results show that the hybrid model is superior to the existing models used for predicting air pollution.

In this study, we have examined how the activation of hydrothermally carbonized sewage sludge and horse manure influences the inorganic component of these materials and surface chemistry. This was examined through statistical correlations between kinetic tests using trimethoprim, fluconazole, perfluorooctanoic acid, and copper, zinc, and arsenic and physicochemical properties. Yield and inorganic content varied considerably, with potassium hydroxide–activated materials producing lower yields with higher inorganic content. Phosphoric acid activation incorporated inorganically bound phosphorus into the material, although this showed no statistically relevant benefit. A maximum surface area of 1363 m²g⁻¹ and 343 m²g⁻¹ was achieved for the horse manure and sewage sludge. Statistical analysis found positive correlations between carbon-oxygen functionalities and trimethoprim, fluconazole, perfluorooctanoic acid, and copper removal, while inorganic content was negatively correlated. Conversely, arsenic removal was positively correlated with inorganic content. This research provides insight into the interactions with the organic/inorganic fraction of activated waste materials for water treatment.

Cadmium and lead-based nanotechnologies are increasingly used in agricultural, industrial, and biological processes; however, potential adverse effects of nanomaterials on honey bees had not been assessed. In this study, effects of exposures to sublethal concentrations of PbO and CdO nanoparticles (NPs), either separately or in combination on honey bee (Apis mellifera) workers, were assessed. Honey bee workers were orally exposed for 9 days under laboratory conditions to sublethal concentrations (20% of LC₅₀) of CdO (0.01 mg/ml⁻) and PbO (0.65 mg/ml⁻) NPs either separately or combined. Effects on survival, feeding rate, activity of acetylcholinesterase (AChE), and expression of selected stress-related detoxifying enzymes were quantified. Survival and feeding rates decreased particularly in bees fed sugar syrup containing CdO NPs or binary mixtures of NPs of both metal oxides. Expressions of genes involved in detoxification of xenobiotics were affected by various combinations. Expression of catalase was 13.6-fold greater in bees consumed sugar syrup diet containing binary mixture of sublethal concentrations of both CdO and PbO NPs than it was in unexposed, control bees. AChE activity in heads of honey bees was inhibited by 3.8-, 3.0-, and 2.8-fold relative to control, respectively, in response to exposure to Cd or/and Pb oxide NPs. This result indicates potential neurotoxic effects of these NPs to honey bees. CdO NPs exhibited greater potency to honey bees. Overall, sublethal concentrations of CdO or/and PbO NPs resulted in detrimental effects on honeybee workers.