For divalent metals, the Biotic Ligand Model (BLM) has been proven to be an effective tool to predict biological effects by taking into account speciation calculations and competitive interactions. Nonetheless, the BLM has only rarely been validated for trivalent metals (e.g. rare earth elements), and the potential competitive effects of protons has been understudied. In this paper, the short-term biouptake of indium (In), a trivalent metal that is a byproduct of zinc extraction and used in numerous applications including the semiconductor industry, was evaluated under controlled conditions. Short-term (i.e. 60 min) indium biouptake by Chlamydomonas reinhardtii was measured as a function of pH in order to verify the validity of the BLM. At a given pH, In biouptake could be well described by the Michaelis-Menten equation with conditional stability constants of KIn,pH=4.0 = 106.7 M-1, KIn,pH=5.0 = 108.6 M-1, KIn,pH=6.0 = 109.3 M-1 and maximum internalization fluxes of Jmax, pH=4.0 = 0.74 × 10−14 mol cm−2 s−1, Jmax, pH=5.0 = 1.60 × 10−14 mol cm−2 s−1, Jmax, pH=6.0 = 2.22 × 10−14 mol cm−2 s−1. Although several potential mechanisms for the role of pH were examined, the results were best explained by a competitive interaction of H+ with the In uptake sites using overall stability constants of logKIn = 9.76 M-1 and logKH = 15.66 M-1. Based on these results, pH will play a critical role in bioavailability measurements of the trivalent cations in natural waters.

Suffered from serious air pollution, Beijing, the capital of China, has implemented multiple measures to reduce the discharge of PM₂.₅ (particulate matter with aerodynamic diameters of less than 2.5 μm). The average annual PM₂.₅ concentration of Beijing has shown a continued decline in recent years. However, the improvement was not obvious during the heating season, which had heavier pollution than the non-heating season. Analyzing the spatial distribution of PM₂.₅ concentrations during heating and non-heating seasons, as well as their spatial differences, is believed to benefit the study of spatial-temporal variation of air pollution and provide scientific reference for the control of air pollution in Beijing. In this study, land use regression (LUR) model was employed to simulate the spatial distribution of PM₂.₅ concentrations in Beijing during heating and non-heating seasons in 2015. The spatial distribution of the concentration difference between heating and non-heating seasons was analyzed, and the influencing factors were also examined. The results showed that: (1) PM₂.₅ concentrations during heating and non-heating seasons, as well as their differences, were clearly at a maximum in the south and east of Beijing and at a minimum in the north and west; (2) the area with the biggest concentration difference was situated in a suburban area to the south and east, as well as in outer suburbs to the southeast and northwest; and (3) wind speed, area of transport land and industrial-mining-warehouse land were the main influence factors for the PM₂.₅ concentration difference in the central, eastern and southern area. Heating activity was not the only cause for the increased PM₂.₅ concentration during the heating season, vehicle emission, industrial discharge and regional transport of pollutants also played varied roles in PM₂.₅ pollution in different area.

Cigarette smoking (CS) and betel quid (BQ) chewing are two known risk factors that have synergistic potential for the enhancing the development of oral squamous cell carcinoma (OSCC) in Taiwan. Most mutagens and carcinogens are metabolically activated by cytochrome P450 (CYP450) to exert their mutagenicity or carcinogenicity. Previous studies have shown that metabolic activation of the tobacco-specific nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), by CYP2A6 activity determines NNK-induced carcinogenesis. In addition, safrole affects cytochrome P450 activity in rodents. However, the effect of BQ safrole on the metabolism of tobacco-specific NNK and its carcinogenicity remains elusive. This study demonstrates that safrole (1 mg/kg/d) induced CYP2A6 activity, reduced urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels, and increased NNK-induced DNA damage, including N7-methylguanine, 8-OH-deoxyguanosine and DNA strand breaks in a Syrian golden hamster model. Furthermore, altered NNK metabolism and increased NNK-induced DNA damage were also observed in healthy subjects with CS and BQ chewing histories compared to healthy subjects with CS histories. In conclusion, BQ containing safrole induced tobacco-specific NNK metabolic activation, resulting in higher NNK-induced genotoxicity. This study provides valuable insight into the synergistic mechanisms of CS- and BQ-induced OSCC.

In this study, a multi-component catalyst, β-cyclodextrin (β-CD) and reduced graphene oxide (rGO) co-modified Fe₃O₄, was fabricated via one-pot solvothermal method and used as a synergistic catalyzer for Bisphenol A (BPA) removal. The study found that catalytic reactions of BPA followed the pseudo-first-order kinetics model, and the correlation rate constants (kₒbₛ) were calculated. Compared with Fe₃O₄@β-CD (0.02173 min⁻¹), Fe₃O₄/rGO (0.09735 min⁻¹) and Fe₃O₄ (0.01666 min⁻¹), the composite (0.15733 min⁻¹) exhibited stronger catalytic ability to remove BPA from aqueous solution under the same conditions, which were attributed to the synergistic enhancement effect among the components. The introduction of rGO in the composites was beneficial to the generation of •OH, and the role of β-CD might enhance the utilization of •OH. A possible three-element catalytic schematic diagram was described. The effects of pH, dosage of the catalyst, initial H₂O₂ and NH₂OH concentrations on the removal efficiency were further investigated. The removal of BPA and TOC retained 78.2 ± 2.4% and 52.9 ± 2.5% after five cycles, indicating its excellent stability and reusability. Furthermore, a probable reaction pathway of BPA removal was suggested by analyzing the intermediate products. All results indicated that the composite had high and stable catalytic performance, which made it have potential application on the industrial treatment of wastewater.

An understanding of the dispersion and level of emissions source of atmospheric pollutants; whether point, area or volume sources, is required to inform policies on air pollution and day-to-day predictions of pollution level. Very few studies have carried out simulations of the dispersion pattern and ground-level concentration of pollutants emitted from real-world gas flares. The limited availability of official data on gas flares from the oil and gas industries makes accurate dispersion calculations difficult. Using ADMS 5 and AERMOD, this study assessed the sensitivity of dispersion and ground-level concentration of pollutants from gas flares in the Niger Delta to prevailing meteorological condition; fuel composition; and flare size. Although, during the non-WAM (West African Monsoon) months (November and March), the simulated ground-level concentrations of pollutants from a single flare are lower, the dispersion of pollutants is towards both the inland and coastal communities. In the WAM months, the ground-level concentrations are higher and are dispersed predominantly over the inland communities. Less buoyant plumes from smaller flares (lower volume flow rates) and/or flaring of fuel with lower heat content results in higher ground-level concentrations in areas closer to the flare. Considering the huge number of flares scattered around the region, a mitigation of the acute local pollution level would be to combine short stacks flaring at lower volume flow rates to enhance the volume flow rate of a single exhaust, and hence, the buoyancy of the plume exiting the stack.

Aluminum dialkyl phosphinates (ADPs) are a class of promising phosphorus-containing flame retardants, but their environmental fate is not well understood. Sorption and transport behaviors of ADPs, and their hydrolysates dialkyl phosphinic acids (DPAs) were studied by batch and column experiments. ADPs are less mobile in soil columns with more than half (>52.6%) of ADPs retained in the soil and residues in the topmost 2-cm layer account for more than 57% of total residues. Dissolution and dispersion of fine grain ADPs were responsible for the transport of ADPs. Sorption DPAs (logKₒc) was significantly related to the lipophilicity of DPAs (logD) (p < 0.05). Soil pH and clay content were the dominant factors governing the sorption and transport of DPAs in soils, indicating the importance of electrostatic interactions. The retardation factors (R) of DPAs derived from leaching experiments were pH-dependent with larger R values in the acidic soil (pH = 4.0) where anionic and neutral species of DPAs coexisted. Both physical and chemical non-equilibrium convection-dispersion equations (CDE) yield appropriate modeling for DPAs transport. In most cases, R values estimated from column tests differed from those derived from the batch experiments, which might be attributed to non-equilibrium sorption processes in dynamic conditions.

Many portable monitors for quantifying mass concentrations of particulate matter air pollution rely on aerosol light scattering as the measurement method; however, the relationship between scattered light (what is measured) and aerosol mass concentration (the metric of interest) is a complex function of the refractive index, size distribution, and shape of the particles. In this study, we compared 33-h personal PM2.5 concentrations measured simultaneously using nephelometry (personal DataRAM pDR-1200) and gravimetric filter sampling for working adults (44 participants, 249 samples). Nephelometer- and filter-derived 33-h average PM2.5 concentrations were correlated (Spearman's ρ = 0.77); however, the nephelometer-derived concentration was within 20% of the filter-derived concentration for only 13% of samples. The nephelometer/filter ratio, which is used to correct light-scattering measurements to a gravimetric sample, had a median value of 0.52 and varied by over a factor of three (10th percentile = 0.35, 90th percentile = 1.1). When 33-h samples with >50% of 10-s average nephelometer readings below the nephelometer limit of detection were removed from the dataset during sensitivity analyses, the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration increased to 25%. We also evaluated how much the accuracy of nephelometer-derived concentrations improved after applying: (1) a median correction factor derived from a subset of 44 gravimetric samples, (2) participant-specific correction factors derived from one same from each subject, and (3) correction factors predicted using linear models based on other variables recorded during the study. Each approach independently increased the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration to approximately 45%. These results illustrate the challenges with using light scattering (without correction to a concurrent gravimetric sample) to estimate personal exposure to PM2.5 mass among mobile adults exposed to low daily average concentrations (median = 8 μg m−3 in this study).

Fomesafen, a long-lived protoporphyrinogen-oxidase inhibitor, specially developed for post-emergence control of broad-leaf weeds, is used widely in soybean fields in northern China (Dayan and Duke, 2010). The impact of fomesafen on microbial communities in rhizosphere soils, however, is unknown. In this study we examined fomesafen degradation as well as its effects in the rhizosphere of soybean plants grown in a greenhouse. Fomesafen had shorter half-life in rhizosphere soil than previously reported for bulk soil from the same location (87 vs 120 days). The enzyme activity of soil extracts and the microbial community composition of 16S rRNA genes (16S) amplified from soil DNA were also investigated. Although not immediately apparent, both the high (37.5 mg kg⁻¹) and low (18.75 mg kg⁻¹) doses of fomesafen significantly decreased urease and invertase activities in the rhizosphere soil from days 30 and 45 respectively until the end of the experiment (90 days). Analysis of 16S amplicons demonstrated that fomesafen had a dose dependent effect, decreasing alpha diversity and altering beta diversity. Significant phylum level decreases were observed in five of the ten phyla that were most abundant in the control. Proteobacteria was the only phylum whose relative abundance increased in the presence of fomesafen, driven by increases in the genera Methylophilacaea, Dyella, and Sphingomonas. The functional implications of changes in 16S abundance as predicted using PICRUSt suggested that fomesafen enriched for enzymes involved in xenobiotic metabolism and detoxification (cytochrome P450s and glutathione metabolism). Our data suggest that, despite being degraded more rapidly in the rhizosphere than in bulk soil, fomesafen had long-lasting functional impacts on the soil microbial community.

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM2.5 concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM2.5-related factors, a novel multi-factor & multi-scale method is proposed for PM2.5 forecasting. Three major steps are taken: (1) multi-factor analysis, to select predictive factors via statistical tests; (2) multi-scale analysis, to extract scale-aligned components via multivariate empirical mode decomposition; and (3) PM2.5 prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques (without multi-factor and multi-scale analysis), semi-improved variants (with either multi-factor or multi-scale analysis), and similar counterparts (with other multi-scale analyses) in terms of prediction accuracy.

In this study, we investigated the physiological and photochemical influences of nanoTiO2 exposure on tomato plants (Lycopersicum esculentum Mill.). Tomato plants were exposed to 100 mg L−1 of nanoTiO2 for 90 days in a hydroponic system. Light irradiances of 135 and 550 μmolphoton m−2 s−1 were applied as environmental stressors that could affect uptake of nanoTiO2. To quantify nanoTiO2 accumulation in plant bodies and roots, we used transmission electron microscopy, energy-dispersive X-ray spectroscopy, inductively coupled plasma mass spectrometry, and X-ray powder diffraction. Phenotypic and physiological influences such as color change, growth rate, fruit productivity, pigment concentration, and enzyme activity (SOD, CAT, APX) were monitored. We observed numerous effects caused by high irradiance and nanoTiO2 exposure, such as rapid chlorophyll decrease, increased anthocyanin and carotenoid concentrations, high enzymatic activity, and an approximately eight-fold increase in fruit production. Moreover, light absorption in the nanoTiO2-treated tomato plants, as measured by a ultraviolet–visible light spectrometer, increased by a factor of approximately 19, likely due to natural pigments that worked as sensitizers, and this resulted in an ∼120% increase in photochemical activities on A, ФPSII, ФCO2, gsw, and E.