Phosphorus-rich sludge is one of most suitable raw materials for phosphorus recovery as slow release fertilizers by struvite crystallization. However, alginic acid as a surrogate for extracellular polymeric substances in the sludge has been proved to adversely inhibit struvite crystallization. To quantitatively evaluate the inhibitory effect, the study aimed to investigate the influence of the concentration of alginic acid (0–250 mg/L), reactant concentration (2.5–3.5 mmol/L), pH (8.0–9.5), and ionic strength (0.01–0.2 mol/L NaCl), on the inhibition of the growth rate of struvite crystals, which was accurately determined by constant composition technique. The results indicated that the growth rate of struvite crystals substantially decreased with increasing the concentration of alginic acid, thereby adversely affecting the quantity and quality of struvite crystals. Moreover, as reactant concentration or pH increased, the growth rate of struvite crystals showed a considerable increase, whereas the weaker inhibitory effect of alginic acid was observed. Conversely, the increase of ionic strength drastically reduced the growth rate of struvite crystals, but moderately enhanced the inhibitory effect. Our study provides an effective theoretical foundation for deriving high-quality struvite crystals as slow release fertilizers from the phosphorus-rich sludge commonly containing a considerable number of organic pollutants.

An acquisition of metal tolerance in cladocerans related to the historical exposure has been well documented for the genera Daphnia and Ceriodaphnia, which are frequently used in ecotoxicological studies. However, small-sized cladocerans are rarely investigated for the inter-clonal variation in metal sensitivity, whereas they often dominate zooplankton community in many lakes and ponds, and even in eutrophicated rivers. We investigated the influence of historical copper exposure on the copper sensitivity of Bosmina longirostris. Copper sensitivity was compared among three clones originating from a site (Lake Yanaka), which located at downstream of historically contaminated river (Watarase River) and clones from five different reservoirs. For reference, the background copper concentration (as Cu²⁺ activity) at each site and its toxicity to Daphnia magna were estimated by metal speciation and the biotic ligand model (BLM), respectively. Less copper-sensitive Bosmina clones were obtained only from Lake Yanaka, although the background copper concentrations were far below the lethal levels. The results suggested the variability in copper-sensitivity in B. longirostris and its association with historical copper contamination.

Numerous uncertainty factors in dispersion models should be taken into account in order to improve the reliability of predictions. The ability of a mesoscale meteorological model to assimilate observational data is an efficient way to improve operational air quality model forecasts. In this study, local weather data assimilation based on a flux-adjusting surface data assimilation system (FASDAS) is introduced to a Gaussian atmospheric dispersion model for a period with reported stable meteorological conditions. After evaluating the vulnerabilities of FASDAS, a combined data assimilation method is proposed to simultaneously improve the model weather prediction and retrieve the representation of accurate concentration distributions for short-range dispersion modeling against a control run. The two main uncertainty parameters considered are the wind speed and direction. A twin experiment demonstrates that the combined technique effectively improves the distribution of simulated concentrations. Comparison between results before and after the implement of data assimilation demonstrates that discrepancies between the reference simulation and the model forecast are mitigated after introducing the combined method, with more than 70 % of the predictions within a factor of two of the measurements. The errors in wind predictions in the FASDAS influenced the dispersion calculations, and the implementation of wind data assimilation in conjunction with the FASDAS has an indirect effect on further alleviating pollutant transport modeling errors.

Mine waste contaminated soils are classified as degraded soils with poor conditions such as low soil pH, low organic matter and high metal concentrations. This study evaluated the potential of fly ash enriched vermicompost in improving poor soil conditions in mine waste affected soils. The soils were amended with the vermicompost to supply 0, 10, 20, 40 and 80 mg of phosphorus per kg and incubated for 8 weeks. The soil pH increased from the original acidic range of 3.7–5.3 to 6.8–7.6. Available P significantly improved (P < 0.001) to yield the target P levels; however, at the end of incubation period, 80 mg-P/kg treatment had lower Olsen P relative to the 40 mg-P/kg treatment. Nitrogen mineralisation was enhanced with addition of the vermicompost as reflected by an average increase of 51% in NO₂/NO₃⁻-N while NH₄⁺-N decreased over time. The Mn, Zn and Pb solubility was reduced with addition of the vermicompost, with 20 mg-P/kg resulting in the most reduced solubility. However, concentrations at 20 mg-P/kg treatment were generally not different to 40 mg-P/kg. Solubility of Cu significantly increased in proportion to increase in amendment rate but did not exceed maximum permissible limits. Solubility of Cd and Cr also increased during the incubation study; however, this could not be attributed to the different vermicompost treatments but the soil properties. Therefore, in conclusion, application of fly ash enriched vermicompost at 40 mg-P/kg was found to be optimum for a balanced supply of essential nutrients and reduced metal solubility.

Nanomaterial applications are a fast-developing field. In spite of their powerful advantages, many open questions regarding how these small-sized chemicals may influence the environment and human health. However, scarce reports are available on the potential hazards of combined nanoparticles, taken into consideration that nickel oxide (NiO) and cobalt (II, III) oxide (Co₃O₄) nanoparticles (NPs) are already used together in many applications. Hence, the present work was designed to study the probable changes in some biological, hematological, and serum biochemical variables throughout 2 weeks following an oral administration of 0.5 g and 1.0 g of NiO-NPs or/and Co₃O₄-NPs per kilogram body weight of rats. As compared with the controls, the exposure to NiO-NPs or Co₃O₄-NPs solely caused significant elevations in the relative weights of brain (RBW), kidney (RKW) and liver (RLW), water consumption (WC), red blood cells (RBCs) count, hemoglobin (Hb) content, packed cell volume (PCV), and serum levels of low-density lipoprotein cholesterol (LDL-C), glucose, creatinine, urea, and uric acid as well as serum activities of aspartate and alanine aminotransferases (ASAT and ALAT). In addition, remarkable declines in the total body weight (TBW), feed consumption (FC), white blood cells (WBCs) count, serum levels of total protein (TP), albumin, albumin/globulin ratio, total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were caused by administration of NiO-NPs or Co₃O₄-NPs, separately. On contrary, the co-administration of NiO-NPs and Co₃O₄-NPs together caused less noticeable changes in most of studied variables as compared with those administered NiO-NPs or Co₃O₄-NPs, individually. In conclusion, the exposure to a combination of NiO-NPs and Co₃O₄-NPs suppressed the adverse effects of the individual NPs on the studied variables.

Estimating the volume of macro-plastics which dot the world’s oceans is one of the most pressing environmental concerns of our time. Prevailing methods for determining the amount of floating plastic debris, usually conducted manually, are time demanding and rather limited in coverage. With the aid of deep learning, herein, we propose a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.

Mass transport and residence time of saline water from road salt applications in soil columns composed of Toyoura sand and weathered granite sand were investigated by simulations and in laboratory experiments. Both are sands found in Japan, especially the weathered granite sand. The Toyoura sand has a fairly uniform particle size of 0.1 to 0.4 mm diameter, and a saturated hydraulic conductivity Kₛ = 0.0296 cm/s, while the weathered granite sand used consisted of 13% fine materials (silt and clay) and 87% coarse materials (sand and gravel) with a saturated hydraulic conductivity Kₛ = 0.00393 cm/s. A model was developed to simulate rinsing of brine from a soil column. Assuming a steady, homogeneous flow induced by rainwater infiltration into the soil column, the model was found to match the experimental results for Toyoura sand very well. The normalized salt concentration in the effluent from the 40 cm tall soil column remained constant until about t = 500 s; the concentration then decreased with time quickly and, finally, approached zero. For the weathered granite sand, however, the salt concentrations in the effluent simulated by the model with assumption of homogeneous flow are inconsistent with the experimental data collected. A substantial delay occurs in mass transport of salt from the column, which is different from the Toyoura sand. The delay is attributed to shifts in “active” and “inactive pores” created in the soil due to fine particles such as silt and clay. The proportion of “active pores” and “inactive pores” is not constant but variable with time due to physical and/or electrochemical processes such as pore-size distributions and salt depletion in the soil. A modified model presented, using a time-variable active pore parameter k(t), can reproduce the experimental results for salt mass left in the soil better.

Central nervous system is sensitive and vulnerable to microwave radiation. Numerous studies have reported that microwave could damage cognitive functions, such as impairment of learning and memory ability. However, the biological effects and mechanisms of accumulative microwave radiation on cognitive functions were remained unexplored. In this study, we analyzed differential expressed proteins in rat models of microwave-induced cognitive impairment by iTRAQ high-resolution proteomic method. Rats were exposed to 2.856 GHz microwave (S band), followed by 1.5 GHz microwave exposure (L band) both at an average power density of 10 mW/cm² (SL10 group). Sham-exposed (control group), 2.856 GHz microwave-exposed (S10 group), or 1.5 GHz microwave-exposed (L10 group) rats were used as controls. Hippocampus was isolated, and total proteins were extracted at 7 days after exposure, for screening differential expressed proteins. We found that accumulative microwave exposure induced 391 differential expressed proteins, including 9 downregulated and 382 upregulated proteins. The results of GO analysis suggested that the biological processes of these proteins were related to the adhesion, translation, brain development, learning and memory, neurogenesis, and so on. The cellular components mainly focused on the extracellular exosome, membrane, and mitochondria. The molecular function contained the protein complex binding, protein binding, and ubiquitin-protein transferase activity. And, the KEGG pathways mainly included the synaptic vesicle cycle, long-term potentiation, long-term depression, glutamatergic synapse, and calcium signaling pathways. Importantly, accumulative exposure (SL10 group) caused more differential expressed proteins than single exposure (S10 group or L10 group). In conclusion, 10 mW/cm² S or L band microwave induced numerous differential expressed proteins in the hippocampus, while accumulative exposure evoked strongest responses. These proteins were closely associated with cognitive functions and were sensitive to microwave.

Algal blooms are one of the greatest aquatic environmental concerns, and the control of algal blooms has become a great challenge in recent years. In this study, we evaluated the effects of Bidens pilosa plant extracts in comparison to those of several widespread plants, including rice (Oryza sativa), Pistia stratiotes, Eichhornia crassipes, and Pteris vittata, on the growth of the bloom-forming blue-green alga Microcystis aeruginosa. Both ethanolic and methanolic extracts of B. pilosa, in contrast to the other plant extracts, exhibited high inhibitory effects on M. aeruginosa growth at a concentration of 500 mg/L (dry weight equivalent, DWE). The inhibition efficiency in terms of the cell density and chlorophyll a concentration significantly reached 84–88% (p < 0.05). In these treatments, a change in algal culture color (from green to brown) and cell death were obviously observed. When we determined the effective concentrations, the B. pilosa extract at concentrations of 250 and 500 mg/L DWE showed significant inhibitory effects on M. aeruginosa growth (p < 0.05), whereas lower concentrations (50–125 mg/L DWE) showed slight or no effects. These data indicate that B. pilosa plant extracts could be used to control M. aeruginosa algal blooms.

This study investigated a small waterway that had been impacted by upwelling groundwater due to recent geological strata fracturing caused by subsidence activity from longwall coal mining. Documents from the coal mine report that subsidence has undermined and fractured the stream channel for more than 10 years prior to this study. Mine documents also report many years of variably degraded water quality (salinity, elevated metals) in the reaches affected by fracturing. In this study, water quality of the stream was monitored over an 11-month period with water flow dominated by ground water upwelling through fractures in the creek channel. The upwelling water caused extensive modifications to the creek’s surface water quality relative to unmined reference sites. The mean electrical conductivity increased by seven times from 230 μS/cm at reference sites to 1833 μS/cm below the upwelling. Dissolved oxygen in the upwelling groundwater was extremely low (2.7% saturation) and was mildly acidic (5.8 pH). Alterations to the ionic composition included sevenfold increases in magnesium, sodium, and chloride concentrations. Heavy metals iron and manganese increased by more than ten times, with nickel by more than 60 times compared to the reference sites. The alteration to ionic composition was inferred to be saline groundwater intrusion. The ecological impacts of such large modifications to surface stream water quality would be hazardous for integrity of downstream aquatic ecosystems.