Frontiers of Environmental Science & Engineering最新文献

Constructed wetlands (CWs) are widely applied for decentralized wastewater treatment. However, achieving efficient removal of ammonia (({rm{N}}{{rm{H}}_4}^ + - {rm{N}})) has proven challenging due to insufficient oxygen. In this study, natural hematite (Fe₂O₃) was employed as a CW substrate (H-CWs) for the first time to drive anaerobic ammonia oxidation coupled with iron(III) reduction (Feammox). Compared to gravel constructed wetlands (G-CWs), ammonia removal was enhanced by 38.14% to 54.03% and nitrous oxide (N₂O) emissions were reduced by 34.60% in H-CWs. The synergistic removal of ammonia and nitrate by H-CWs also resulted in the absence of ammoxidation by-products. Inhibitor and ¹⁵N isotope tracer incubations showed that Feammox accounting for approximately 40% of all ammonia removal in the H-CWs. The enrichment of iron phosphate (Fe₃Fe₄(PO₄)₆) promoted the accumulation of the Feammox intermediate compound FeOOH. Microbial nanowires were observed on the surface of H-CW substrates as well, suggesting that the observed biological ammoxidation was most likely related to extracellular electron transfer (EET). Microbial and metagenomics analysis revealed that H-CWs elevated the integrity and enhanced the abundance of functional microorganisms and genes associated with nitrogen metabolism. Overall, the efficient ammonia removal in the absence of O₂ together with a reduction in N₂O emissions as described in this study may provide useful guidance for hematite-mediated anaerobic ammonia removal in CWs.

Wastewater treatment plants are the major energy consumers and significant sources of greenhouse gas emissions, among which biological nitrogen removal of wastewater is an important contributor to carbon emissions. However, traditional heterotrophic denitrification still has the problems of excessive residual sludge and the requirement of external carbon sources. Consequently, the development of innovative low-carbon nitrate removal technologies is necessary. This review outlines the key roles of sulfur autotrophic denitrification and hydrogen autotrophic denitrification in low-carbon wastewater treatment. The discovered nitrate/nitrite dependent anaerobic methane oxidation enables sustainable methane emission reduction and nitrogen removal by utilizing available methane in situ. Photosynthetic microorganisms exhibited a promising potential to achieve carbon-negative nitrate removal. Specifically, the algal-bacterial symbiosis system and photogranules offer effective and prospective low-carbon options for nitrogen removal. Then, the emerging nitrate removal technology of photoelectrotrophic denitrification and the underlying photoelectron transfer mechanisms are discussed. Finally, we summarize and prospect these technologies, highlighting that solar-driven biological nitrogen removal technology is a promising area for future sustainable wastewater treatment. This review has important guiding significance for the design of low-carbon wastewater treatment systems.

Abstract

The “dual-carbon” strategy promotes the development of the wastewater treatment sector and is an important tool for leading science and technology innovations. Based on the global climate change and the new policies introduced by China, this paper described the new needs for the development of wastewater treatment science and technology. It offered a retrospective analysis of the historical trajectory of scientific and technological advancements in this field. Utilizing bibliometrics, it delineated the research hotspots within wastewater treatment, notably highlighting materials genomics, artificial intelligence, and synthetic biology. Furthermore, it posited that, in the future, the field of wastewater treatment should follow the paths of technological innovations with multi-dimensional needs, such as carbon reduction, pollution reduction, health, standardisation, and intellectualisation. The purpose of this paper was to provide references and suggestions for scientific and technological innovations in the field of wastewater treatment, and to contribute to the common endeavor of moving toward a Pollution-Free Planet.

Climate change significantly impacts human health, exacerbating existing health inequalities and creating new ones. This study addresses the lack of systematic review in this area by analyzing 2440 publications, focusing on four key terms: health, disparities, environmental factors, and climate change. Strict inclusion criteria limited the selection to English-language, peer-reviewed articles related to climate health hazards, ensuring the relevance and rigor of the synthesized studies. This process synthesized 65 relevant studies. Our investigation revealed that recent research, predominantly from developed countries, has broadened its scope beyond temperature-related impacts to encompass diverse climate hazards, including droughts, extreme weather, floods, mental health issues, and the intersecting effects of Coronavirus Disease 2019. Research has highlighted exposure as the most studied element in the causal chain of climate change-related health inequalities, followed by adaptive capability and inherent sensitivity. The most significant vulnerabilities were observed among populations with low socioeconomic status, ethnic minorities, and women. The study further reveals research biases and methodological limitations, such as the paucity of attention to underdeveloped regions, a narrow focus on non-temperature-related hazards, challenges in attributing climate change effects, and a deficit of large-scale empirical studies. The findings call for more innovative research approaches and a holistic integration of physical, socio-political, and economic dimensions to enrich climate-health discourse and inform equitable policy-making.

Abstract

Oxygenated organic molecules (OOMs) play an important role in the formation of secondary organic aerosols (SOAs), but the mixing states of OOMs are still unclear. This study investigates the mixing states of OOM-containing single particles from the measurements taken using a single particle aerosol mass spectrometer in Guangzhou, China in 2022. Generally, the particle counts of OOM particles and the mass concentration of secondary organic carbon (SOC) exhibited similar temporal trends throughout the entire year. The OOM particles were consistently enriched in secondary ions, including ¹⁶O⁻, ²⁶CN⁻, ⁴⁶NO₂⁻, ⁶²NO₃⁻, and ⁹⁷HSO₄⁻. In contrast, the number fractions and diurnal patterns of OOM particles among the total detected particles showed similar distributions in August and October; however, the SOC ratios in fine particulate matter were quite different, suggesting that there were different mixing states of single-particle oxygenated organics. In addition, further classification results indicated that the OOM particles were more aged in October than August, even though the SOC ratios were higher in August. Furthermore, the distribution of hydrocarbon fragments exhibited a notable decrease from January to October, emphasizing the more aged state of the organics in October. In addition, the sharp increase in elemental carbon (EC)-OOM particles in the afternoon in October suggests the potential role of EC in the aging process of organics. Overall, in contrast to the bulk analysis of SOC mass concentration, the mixing states of the OOM particles provide insights into the formation process of SOAs in field studies.

Copper intercalated birnessite MnO₂ (δ-MnO₂) with weak crystallinity and high specific surface area (421 m²/g) was synthesized by a one-pot redox method and investigated for low-temperature CO oxidation. The molar ratio of Cu/Mn was as high as 0.37, which greatly weakened the Mn-O bond and created a lot of low-temperature active oxygen species. In situ DRIFTS revealed strong bonding of copper ions with CO. As-synthesized MnO₂-150Cu achieved 100% conversion of 250 ppm CO in normal air (3.1 ppm H₂O) even at −10 °C under the weight-hourly space velocity (WHSV) of 150 L/(g·h). In addition, it showed high oxygen storage capacity to oxidize CO in inert atmosphere. Though the concurrent moisture in air significantly inhibited CO adsorption and its conversion at ambient temperature, MnO₂-150Cu could stably convert CO in 1.3% moisture air at 70 °C owing to its great low-temperature activity and reduced competitive adsorption of water with increased temperature. This study discovers the excellent low-temperature activity of weakly crystallized δ-MnO₂ induced by high content intercalated copper ions.

Abstract

The thorough investigation of nanoplastics (NPs) in aqueous environments requires efficient and expeditious quantitative analytical methods that are sensitive to environmentally relevant NP concentrations and convenient to employ. Optical analysis-based quantitative methods have been acknowledged as effective and rapid approaches for quantifying NP concentrations in laboratory-scale studies. Herein, we compared three commonly used optical response indicators, namely fluorescence intensity (FI), ultraviolet absorbance, and turbidity, to assess their performance in quantifying NPs. Furthermore, orthogonal experiments were conducted to evaluate the influence of various water quality parameters on the preferred indicator-based quantification method. The results revealed that FI exhibits the highest correlation coefficient (> 0.99) with NP concentration. Notably, the limit of quantification (LOQ) for various types of NPs is exceptionally low, ranging from 0.0089 to 0.0584 mg/L in ultrapure water, well below environmentally relevant concentrations. Despite variations in water quality parameters such as pH, salinity, suspended solids (SS), and humic acid, a robust relationship between detectable FI and NP concentration was identified. However, an increased matrix, especially SS in water samples, results in an enhanced LOQ for NPs. Nevertheless, the quantitative method remains applicable in real water bodies, especially in drinking water, with NP LOQ as low as 0.0157–0.0711 mg/L. This exceeds the previously reported detectable concentration for 100 nm NPs at 40 µg/mL using surface-enhanced Raman spectroscopy. This study confirms the potential of FI as a reliable indicator for the rapid quantification of NPs in aqueous environments, offering substantial advantages in terms of both convenience and cost-effectiveness.

In this study, samples were taken from three locations, upstream to downstream, along the central route project of the China South to North Water Diversion (SNWD) scheme in summer and winter. These were used to reveal the variations of dissolved organic matter (DOM) during the water transfer process, and the effects of these variations on drinking water treatment and disinfection by-products formation potential (DBPs-FP). The results showed that polysaccharides accumulate in summer and reduce in winter with flow distance, which has an important effect on the overall properties of DOM, as well as on the performance of coagulation, ultrafiltration, and the formation of DBPs. Humic substances, and their hydrophilic content, also increased in summer and decreased in winter with flow distance. In contrast, the concentration of small organic substances (MW ⩽ 1000 Da) increased in both summer and winter with flow distance, which affected both nanofiltration (NF) membrane fouling and DBPs-FP. The results provide a useful case study of spatial and temporal changes in raw water DOM during long distance water transfer and their impact on the treatment and quality of drinking water from the SNWD.

Abstract

The present study assesses the concentration, probabilistic risk, source classification, and dietary risk arising from heavy metal (HMs) pollution in agricultural soils affected by coal mining in eastern part of India. Analyses of soil and rice plant indicated significantly elevated levels of HMs beyond the permissible limit in the contaminated zones (zone 1: Pb_Soil: 108.24 ± 72.97, Cu_Soil: 57.26 ± 23.91, Cd_Soil: 8.44 ± 2.76, Cr_Soil: 180.05 ± 46.90, Ni_Soil: 70.79 ± 25.06 mg/kg; Pb_Grain: 0.96 ± 0.8, Cu_Grain: 8.6 ± 5.1, Cd_Grain: 0.65 ± 0.42, Cr_Grain: 4.78 ± 1.89, Ni_Grain: 11.74 ± 4.38 mg/kg. zone 2: Pb_Soil: 139.56 ± 69.46, Cu_Soil: 69.89 ± 19.86, Cd_Soil: 8.95 ± 2.57, Cr_Soil: 245.46 ± 70.66, Ni_Soil: 95.46 ± 22.89 mg/kg; Pb_Grain: 1.27 ± 0.84, Cu_Grain: 7.9 ± 4.57, Cd_Grain: 0.76 ± 0.43, Cr_Grain: 8.6 ± 1.58, Ni_Grain: 11.50 ± 2.46 mg/kg) compared to the uncontaminated zone (zone 3). Carcinogenic and non-carcinogenic health risks were computed based on the HMs concentration in the soil and rice grain, with Pb, Cr, and Ni identified as posing a high risk to human health. Monte Carlo simulation, the solubility-free ion activity model (FIAM), and severity adjusted margin of exposure (SAMOE) were employed to predict health risk. FIAM hazard quotient (HQ) values for Ni, Cr, Cd, and Pb were > 1, indicating a significant non-carcinogenic risk. SAMOE (risk thermometer) results for contaminated zones ranged from low to moderate risk (Cr_SAMOE: 0.05, and Ni_SAMOE: 0.03). Fuzzy-TOPSIS and variable importance plots (from random forest) showed that Ni and Cr were mostly responsible for the toxicity in the rice plant, respectively. A self-organizing map for source classification revealed common origin for the studied HMs with zone 2 exhibiting the highest contamination. The positive matrix factorization model for the source apportionment identified coal mining and transportation as the predominant sources of HMs. Spatial distribution analysis indicated higher contamination near mining sites as compared to distant sampling sites. Consequently, this study will aid environmental scientists and policymakers controlling HM pollution in agricultural soils near coal mines.

Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning (ML)—are current issues subject to debate and evolution. There is growing consensus around embedding equity throughout all research and design domains—from inception to administration, while also addressing procedural, distributive, and recognitional factors. Yet, practically doing so may seem onerous or daunting to some. The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity, using the Systemic Equity Framework, and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings. Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint. To this end, this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol, employing fairness metrics, and systematically addressing irreproducibility; emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research; and establishes a ten-step path forward. Afterall, the work that environmental scientists and engineers do ultimately affect the well-being of us all.