Mycotoxin is toxic secondary metabolite formed by certain filamentous fungi. This toxic compound can enter the food chain through contamination of food (e.g., by colonization of toxigenic fungi on food). In light of the growing concerns on the health hazards posed by mycotoxins, it is desirable to develop reliable analytical tools for their detection in food products in both sensitive and efficient manner. For this purpose, the potential utility of molecularly imprinted polymers (MIPs) has been explored due to their meritful properties (e.g., large number of tailor-made binding sites, sensitive template molecules, high recognition specificity, and structure predictability). This review addresses the recent advances in the application of MIPs toward the sensing of various mycotoxins (e.g., aflatoxins and patulin) along with their fabrication strategies. Then, performance evaluation is made for various types of MIP- and non-MIP-based sensing platforms built for the listed target mycotoxins in terms of quality assurance such as limit of detection (LOD). Further, the present challenges in the MIP-based sensing application of mycotoxins are discussed along with the future outlook in this research field.

Hourly PM₂.₅ speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM₂.₅ components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM₂.₅ water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwₕₒₗₑ). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwₕₒₗₑ solution. After removing event data, PMF modeling was conducted for individual months (PMFₘₒₙₜₕ) and the 4-year period (PMF₄₋yₑₐᵣ), respectively. PMFₘₒₙₜₕ solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF₄₋yₑₐᵣ solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

This study investigates the incidence of MPs in surface sediment samples, collected from the Anzali Wetland, Gillan province, North of Iran. This natural habitat receives municipal wastewater effluents and hosts industries and recreational activities that could release plastic to the wetland. There is need for studies to understand MPs pollution in wetlands. A total of 40 superficial sediment samples were taken covering potential pollution hotspots in the wetland. The average level of MPs was 362 ± 327.6 MP/kg: the highest MPs levels were near the outlet of a highly urbanized river (Pirbazar River) (1380 MP/kg), which runs through Rasht city. This was followed by 1255 MP/kg where there was intense fishing, boating and tourism activities in the vicinity of Bandar-e Anzali city. Fibers were the most common type of MPs (80% of the total MPs detected). The MPs polluting the wetland were predominantly white/transparent (42%), and about 40% of them were >1000 μm. Polypropylene (PP) and polyethylene (PE) prevailed in MPs found. MPs were characterized with polarized light microscopy, Raman spectroscopy, Scanning Electron Microscopy coupled with Energy-Dispersive X-ray spectroscopy. Microplastics levels were found to correlate significantly (p > 0.7) with electrical conductivity (EC) and sand-size fraction of the sediments. Coarse-grained sediments presented large capacity to lodge the MPs. This study can be used to establish protection policies in wetlands and newly highlights the opportunity of intercepting MPs in the Anzali Wetland, which are generally >250 μm, before they fragment further.

The present study investigated that the potential of soil or foliar applied 15 mg/L zinc oxide quantum dots (ZnO QD, 11.7 nm) to enhance pumpkin (Cucurbita moschata Duch.) growth and biomass in comparison with the equivalent concentrations of other sizes of ZnO particles, ZnO nanoparticles (ZnO NPs, 43.3 nm) and ZnO bulk particles (ZnO BPs, 496.7 nm). In addition, ZnSO4 was used to set a Zn²⁺ ionic control. For foliar exposure, ZnO QD increased dry mass by 56% relative to the controls and values were 17.3% greater than that of the ZnO NPs particles. The cumulative water loss in the ZnO QD treatment was 10% greater than with ZnO NPs, suggesting that QD could better enhance pumpkin growth. For the root exposure, biomass and accumulative water loss equivalent across all Zn treatments. No adverse effects in terms of pigment (chlorophyll and anthocyanin) contents were evident across all Zn types regardless exposure routes. Foliar exposure to ZnO QD caused 40% increases in shoot Zn content as compared to the control; the highest Zn content was evident in the Zn²⁺ ionic treatment, although this did not lead to growth enhancement. In addition, the shoot and root content of other macro- and micro-nutrients were largely equivalent across all the treatments. The contents of other nutritional compounds, including amino acids, total protein and sugar, were also significantly increased by foliar exposure of ZnO QD. The total protein in the ZnO QD was 53% higher than the ZnO particle treatments in the root exposure group. Taken together, our findings suggest that ZnO QDs have significant potential as a novel and sustainable nano-enabled agrichemical and strategies should be developed to optimize benefit conferred to amended crops.

In this study, the role of organic matter structure and microporosity in the adsorption and degradation of radioactive nonylphenol in sediments treated with potassium ferrate solutions was investigated. The demineralized fractions and acid non-hydrolyzable fractions were isolated and characterized via advanced solid-state ¹³C nuclear magnetic resonance and CO₂ gas adsorption technology, respectively. Radioactive nonylphenol in the sediments was also fractionated into ¹⁴CO₂, water-soluble residues, extractable residues, and strongly bound residues after treatment with potassium ferrate. A first-order, two-compartment kinetic model well described the mineralization and degradation kinetics of radioactive nonylphenol in the sediment (R² > 0.99). The degradation percentages of spiked nonylphenol were highly negatively correlated with aromatic carbon, aliphatic carbon, and microporosity estimated from acid-non-hydrolyzable fractions in the bulk sediments (R² > 0.82, p < 0.01). The percentages of adsorbed parent nonylphenol residues were highly positively correlated with aromatic carbon, aliphatic carbon, and microporosity estimated from acid-non-hydrolyzable fractions in the bulk sediments (R² > 0.90, p < 0.01). The parent nonylphenol compound desorbed into the aqueous phase and was completely degraded. This study is the first to demonstrate the important role of aromatic carbon, aliphatic carbon, and microporosity in acid non-hydrolyzable fractions on the degradation of nonylphenol during the potassium ferrate oxidation treatment process.

Acid mine drainage (AMD) due to the mining of sulfide deposits is one of the most important causes of water pollution worldwide. Remediation measures, especially in historical abandoned mines, require a deep knowledge of the geochemical characteristics of AMD effluents and metal fluxes, considering their high spatial and temporal evolution, and the existence of point and diffuse sources with a different response to rainfall events. This study investigates the temporal variations and hydrogeochemical processes affecting the composition of main AMD sources from the Tharsis mines (SW Spain), one of most important historical metal mining districts in the world. To address this, a fortnightly-monthly sampling was performed during two years in the main AMD sources and streams within the mine site covering different hydrological conditions. A seasonal pattern was observed linked to hydrological variations; higher pollutant concentrations were observed during the dry season (maximum values of 4,6 g/L of Al, 11,8 g/L of Fe, and 67 g/L of sulfate) and lower ones were observed during the rainy periods. Stream samples exhibited a negative correlation between electrical conductivity (EC) and flow, while positive values were observed in AMD sources, where groundwater fluxes were predominant. High flow also seems to be the main driver of Pb fluxes from AMD sources, as the concentration of Pb in waters increased notably during these events. The precipitation of secondary Fe minerals may limit the mobility of As and V, being retained in the proximity of mine sites. The concentration of Zn in waters seems to be controlled by the original grade in the metal deposit from which the waste is generated, together with the age of these wastes. The pollutant load delivered by the Tharsis mines to the surrounding water courses is very high; e.g., mean of 733 ton/yr of Al or 2757 ton/yr of Fe, deteriorating the streams and reservoirs downstream.

Estimations of greenhouse gas (GHG) emissions from municipal wastewater treatment plants (MWTPs) remain significant uncertainties in China owing to a lack of reliable emission factors (EFs). This study developed a framework to obtain multi-level (technology, province, and nation) GHG EFs of MWTPs using a database containing 3107 MWTPs in China and published site-specific monitoring data. Results show that GHG EFs of different technologies range widely from 180.0 to 615.7 g CO₂-eq/t wastewater, and significant differences are also observed among different provinces in China (190.5–600.3 g CO₂-eq/t wastewater), which are generally lower than the previous estimates. It confirms the importance of more detailed technology classification and considering the technological disparity of different provinces in refining GHG estimations of MWTPs. To test the feasibility of the developed EFs, we compared GHG emissions from MWTPs based on multi-level EFs at different spatial and temporal scales. Similar estimation results imply that selecting corresponding EF depending on the availability of activity data would simplify GHG estimations of MWTPs without sacrificing much accuracy. This study contributes a set of well-developed EFs to improve the estimates of GHG emissions from MWTPs, and also offers a method to develop GHG EFs for other sectors.

Slow nutrient turnover and destructed soil function were the main factors causing low efficiency in phytoremediation of heavy metal (HM)-contaminated soil. Soil ecoenzymatic stoichiometry can reflect the ability of soil microorganisms to acquire energy and nutrients, and drive nutrient cycling and carbon (C) decomposition in HM-contaminated soil. Therefore, for the first time, we used the enzymatic stoichiometry modeling to examine the microbial nutrient limitation in rhizospheric and bulk soil of different plants (Medicago sativa, Halogeton arachnoideus and Agropyron cristatum) near the Baiyin Copper Mine. Results showed that the main pollutants in this area were Cu, Zn, Cd, and Pb, while Cd and Zn have the greatest contribution according to the analysis of pollution load index (PLI). The activities of soil C-, nitrogen (N)-, and phosphorus (P)-acquiring enzymes in the rhizosphere of plants were significantly greater than that in bulk soil. Moreover, microbial C and P limitations were observed in all plant treatments, while the lower limitation was generally in the rhizosphere compared to bulk soil. The HM stress significantly increased microbial C limitation and decreased microbial P limitation, especially in the rhizospheric soil. The partial least squares path modeling (PLS-PM) further indicated that HM concentration has the greatest effects on microbial P limitation (−0.64). In addition, the highest enzyme activities and the lowest P limitation were observed in the rhizospheric and bulk soil of M. sativa, thereby implying that soil microbial communities under the remediation of M. sativa were steadier and more efficient in terms of their metabolism. These findings are important for the elucidation of the nutrient cycling and microbial metabolism of rhizosphere under phytoremediation, and provide guidance for the restoration of HM-contaminated soil.

Summertime ozone (O₃) pollution has frequently occurred in the Beijing–Tianjin–Hebei (BTH) region, China, since 2013, resulting in detrimental impacts on human health and ecosystems. The contribution of weather shifts to O₃ concentration variability owing to climate change remains elusive. By combining regional air chemistry model simulations with near-surface observations, we found that anthropogenic emission changes contributed to approximately 23% of the increase in maximum daily 8-h average O₃ concentrations in the BTH region in June–July–August (JJA) 2017 (compared with that in 2013). With respect to the weather shift influence, the frequencies, durations, and magnitudes of O₃ exceedance were consistent with those of the heat wave events in the BTH region during JJA in 2013–2017. Intensified heat waves are a significant driver for worsening O₃ pollution. In particular, the prolonged duration of heat waves creates consecutive adverse weather conditions that cause O₃ accumulation and severe O₃ pollution. Our results suggest that the variability in extreme summer heat is closely related to the occurrence of high O₃ concentrations, which is a significant driver of deteriorating O₃ pollution.

Accurate quantification of nanoplastics (NPs) in complex matrices remains a challenge, especially for biological samples containing high content of organic matters. Herein, a new method extracting and quantifying polystyrene (PS) NPs from biological samples was developed. The extraction included alkaline digestion, centrifugation, and cloud point extraction (CPE), and the quantification included gold nanoparticles formation and labeling on surfaces of the extracted NPs and thereafter measurement with single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). Results show that 25% tetramethylammonium hydroxide solution was an effective alkaline digestion solution for biological matrices, and CPE after centrifugation (3000 rpm, 10 min) was applicable to purify and enrich PS NPs with different sizes (100 and 500 nm) and surface functionalities (-COOH and –NH₂ modifications) from the digestion solution. The efficiency of Au labeling on PS NPs surface was improved by about 70% in the presence of 100 μM cetyltrimethylammonium bromide. The performance of the quantification method was examined by extraction and measurement of PS NPs spiked in four representative organism samples including bacteria, algae, nematode, and earthworm, and was further validated by analyzing the accumulated PS NPs in exposed nematodes. Good recovery rates (65 ± 10%–122 ± 22%) were achieved for spiking levels of 5–50 μg g⁻¹; the limit of detection was 3.7 × 10⁷ particles g⁻¹, corresponding to the mass concentration of about 0.02 and 2.5 μg g⁻¹ for the 100 nm and 500 nm PS NPs, respectively. The established extraction and quantification methods are efficient and sensitive, providing a useful approach for further exploring the environmental behavior and toxicity of NPs.