● Secondary PE-MPs were simulated via the aging processes and mechanical milling.

It has been confirmed that microplastics (MPs) are present in the environment. This study simulated secondary PE-MPs via aging and mechanical processes to evaluate their effects on Pak choi (Brassica rapa L.) over 21 d. Two common pollutants, dichlorodiphenyltrichloroethane (DDT) and naphthalene, were used in the combined toxicity tests. The results indicated that the growth of Pak choi was significantly inhibited after exposure to secondary PE-MPs, and the combined effects were antagonistic, owing to the adsorption capacity of secondary PE-MPs to DDT and naphthalene. Oxidative stress in Pak choi can be markedly affected, leading to oxidative damage to plant cells. The moisture content, soil bulk density, soil density, cation exchange capacity (CEC), and FDA hydrolase in the planted soils increased in the treated groups, and the TOC content changed significantly. We also found that the microbial composition of the soil in the DDT and naphthalene groups showed more significant alterations than that in the other groups. Alpha diversity analysis showed that species diversity increased in the combined groups but indicated a clear downward trend in the single MPs groups. This study suggests that secondary PE-MPs harm the growth of Pak choi and can change soil properties, revealing the harm to the ecosystem of MPs in the soil.

● Manually adjustment of hyperparameters is highly random and computational expensive.

Neural networks (NNs) have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation. An essential step in developing an NN is the hyperparameter selection. In practice, it is common to manually determine hyperparameters in the studies of NNs in water resources tasks. This may result in considerable randomness and require significant computation time; therefore, hyperparameter optimization (HPO) is essential. This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks, including the grid sampling (GS), random search (RS), genetic algorithm (GA), Bayesian optimization (BO) based on the Gaussian process (GP), and the tree Parzen estimator (TPE). For the evaluation of these techniques, this study proposed a method: first, the optimal hyperparameter value sets achieved by GS were regarded as the benchmark; then, the other HPO techniques were evaluated and compared with the benchmark in convergence, optimization orientation, and consistency of the optimized values. The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence, reasonable optimization orientation, and the highest consistency rates with the benchmark values. The optimization consistency rates via TPE for the hyperparameters hidden layers, hidden dimension, learning rate, and batch size were 86.7%, 73.3%, 73.3%, and 80.0%, respectively. Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test, the proposed benchmark-based HPO evaluation approach is feasible and robust.

● A machine learning path for predicting biochar adsorption efficiency was constructed.

Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.

● Immobilization efficiency of cations (Cu, Zn, Mn) was higher than that of anions (As, Cr).

Tin mine tailings (TMT) and fuming slag (FS) contain many heavy metals (As, Cr, Cu, Zn and Mn) that cause severe pollution to the environment. Herein, geopolymers were prepared using TMT, FS and flue gas desulfurization gypsum (FGDG) to immobilize heavy metals, and their compressive strength and heavy metal leaching toxicity were investigated. It was first determined that T4F5 (TMT:FS = 4:5) sample exhibited the highest compressive strength (7.83 MPa). T4F5 achieved 95% immobilization efficiency for As and Cr, and nearly 100% for Cu, Zn and Mn, showing good immobilization performance. A series of characterization analyses showed that heavy metal cations can balance the charge in the geopolymer and replace Al in the geopolymer structure to form covalent bonds. In addition, about 2%–20% of heavy metal Fe was immobilized in hydration products, heavy metal hydroxides and non-bridging Si–O and Al–O coordination with silica-aluminate matrices. AsO₃^3– was oxidized into AsO₄^3–, which may form Ca–As or Fe–As precipitates. Cr₂O₇^2– was converted to CrO₄^2– under alkaline environment and then combined with OH^– to form Cr(OH)₃ precipitates. Mn²⁺ may react directly with dissolved silicate to form Mn₂SiO₄ and also form Mn(OH)₂ precipitates. The unstable Mn(OH)₂ can be further oxidized to MnO₂. The heavy metal cations were immobilized in the silicoaluminate lattice, while the anions tended to form insoluble precipitates. These results may benefit the industry and government for better handling of TMT, FS and solid wastes containing the abovementioned five heavy metals.

● Clindamycin predominates in the waters, while tetracycline prevails in the sediments.

The pollution of antibiotics in aquatic environments has received extensive attention. Yet, research on antibiotic contamination in river-lake systems, a significant form of modern aquatic environments, still needs to be explored. This study focuses on the Chaohu Basin (China) investigating the occurrence characteristics, influencing factors, and risk assessments of antibiotics in the river-lake system. The total antibiotic concentrations in the water phase and sediment phase were 3.14–1887.49 ng/L and 0.92–1553.75 ng/g, respectively. Clindamycin was the predominant antibiotic in the water phase, whereas tetracycline prevailed in the sediment phase. Notable differences in concentration and structural composition of antibiotics between the tributaries (river system) and Chaohu Lake were observed, indicating the involvement of various geochemical processes in the attenuation of antibiotics during transport to the receiving lake. Spatial analysis suggested that the western river is the primary source of antibiotics in Chaohu Lake. Controlling nutrient influx in heavily polluted areas is crucial to addressing the escalating issue of antibiotic pollution in the river-lake system. The widespread occurrence of clindamycin in the waters is likely due to wastewater treatment plant discharges, and high-intensity human activities continue to exacerbate antibiotic contamination. Risk assessment indicated that sulfamethoxazole, tetracycline, lincomycin, and clindamycin ranked in the top four with the highest risks to the most sensitive aquatic organisms. Nonetheless, the antibiotics presented no risk to consumer health. This study provides valuable insights for controlling antibiotic pollution in river-lake systems.

● ECD decreased cell count by five orders of magnitude after 150 s of disinfection.

Electrochemical disinfection (ECD) is a promising disinfection technique for wastewater reclamation; however, the impacts of ECD on the microbiome in secondary effluent wastewater remain unknown. In this study, Propidium monoazide-qPCR (PMA-qPCR) and the plate count method were used to evaluate the inactivation performance, and the PMA-16S rRNA gene sequences of living cells were targeted to study the microbiome. A discrepancy was found between PMA-qPCR and the plate count method in the evaluation of cell count, with increases of 1.5 to 2.2 orders of magnitude in the disinfection rate after 150 s of disinfection. However, the cell count recovered and occasionally exceeded original levels within 3 d after disinfection. Biodiversity was suppressed after ECD, but the microbiome after 150 s disinfection retained a higher level of evenness and stability in the community with a median Shannon index (> 3.7). Pathogenic bacteria remained high in relative abundance even after 150 s of 25 V disinfection, but the biofilm-forming population was effectively suppressed by ECD. The co-occurrence network revealed a centralized and fragile network as disinfection persisted, demonstrating the destabilizing effects of ECD on the microbiome. Functional pathways for cell membrane synthesis and organic compound degradation were enriched after ECD. The reaction of the microbiome after ECD was similar to other disinfection techniques in terms of community structure.

● Variations of DOM in South-to-North Water Diversion is studied in winter and summer.

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.

● Farmlands impacted by coal mines, contained heavy metals like Pb and Cr.

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.

● Performance of optical analysis was assessed for nanoplastic (NP) quantification.

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.

● Copper intercalated weakly crystallized δ-MnO₂ was synthesized via one-pot process.

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.

● Varied factors lead to uneven climate health outcomes were revealed.

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.

● The OOM particles exhibited characteristics similar to those of the SOC in summer.

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.

● Socioecological inequity must be understood to improve environmental data science.

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.