环境•农林最新文献

This study aims to assess groundwater quality and the human health risks associated with nitrate contamination using a human health risk assessment model. 45 groundwater samples were collected in 2016 and 2023 in the Angads aquifer. According to the findings, the groundwater was brackish (TDS > 1000 mg/L), extremely hard (TH > 450 mg/L CaCO₃), and contained ions in the sequence Na⁺  > Ca²⁺  > Mg²⁺  > K⁺  > NH₄⁺ for cations, and Cl⁻ > HCO₃⁻ > SO₄²⁻ > NO₃⁻ for anions. The TH-TDS graph and the groundwater quality index (GWQI) show that the majority of samples are unfit for consumption. Based on NO₃⁻ concentrations most often observed in the south of the research area, where many activities are likely to be sources of nitrates, the human health risk assessment reveals high non-carcinogenic risks for people living in the region, particularly for infants (71.1% on 2016 and 93.3% on 2023). Consumption of groundwater contaminated with nitrates over a long period could present a potential risk to human health.

Sunflower bottom ash (SBA), rich in potassium (K) and low in heavy metals, making it a potential solution for cadmium (Cd)-polluted acidic farmland. Field experiments were conducted to investigate the effects of adding different concentrations of SBA (0%, 0.1%, 0.3%, 0.5%, 1.0%) to Cd-contaminated acidic soil on heavy metal bioavailability, soil fertility, and bacterial community structure. The results showed that the bioavailability of Cd decreased from 0.29 mg/kg in the CK treatment to 0.08 mg/kg in the 1% treatment, and the pH increased from 5.7 to 8.1. Notably, in the 0.3% treatment, catalase, urease, and alkaline phosphatase activities reaching 20, 560, and 4.3 (a.u), respectively, and 16S rRNA sequencing analysis revealed that the Shannon index of the 0.3% treatment was the highest at 5.364, indicating the most favorable soil environment with the greatest microbial diversity. Terrabacter was significantly positively correlated with heavy metal availability, while Dyella exhibited significant positive correlations with the availability of phosphorus (P), K, and soil pH, and Terrabacter and Dyella in the acidic Cd-contaminated soil contributed to reducing heavy metal toxicity and enhancing soil fertility. This study offers a promising approach for resource utilization of SBA, and provides a new technology for improving Cd-polluted acidic farmland.

Soluble electron shuttles have been found to facilitate the biodecolorization of azo dyes, yet their loss due to water flow can escalate costs and risk secondary pollution. This issue can be mitigated by immobilizing the shuttles. In this study, we immobilized phenazine-1-carboxylic acid (PCA) using a chitosan/polyvinyl alcohol gel carrier and investigated its effect on the degradation of disperse red S-R (DR S-R) by Shewanella oneidensis FJAT-2478. Both free and immobilized PCA significantly increased the decolorization rate within a 50–400 mg/L concentration range for DR S-R, without affecting the final efficiency. Immobilized PCA was slightly less effective than free PCA (4.18-fold at 100 mg/L DR S-R), but was 3.63-fold more effective than the control group without PCA. It also demonstrated excellent reusability, retaining 83% of its initial activity after 10 cycles. Unlike free PCA, which reduced flavin secretion of FJAT-2478 by 36.4%, immobilized PCA increased it by 19.5%, indicating potential differences in their electron transfer modes. This study highlights the potential of immobilized phenazine-based electron shuttles in biologically decolorizing disperse dye wastewater.

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This paper is focused on an overview of developments and validation procedures in analytical methods for the detection and quantification of emerging contaminants from multiple groups of environmental contaminants in the environment: polyaromatic hydrocarbons (PAHs), phthalate esters (PEs), alkylphenol ethoxylates (APEOs), alkylphenols (APs), and butylated hydroxytoluene (BHT) in river sediments. Especially, the simultaneous extraction of several analyte groups from sediment samples is currently uncommon, but from the perspective of green analytical chemistry, it is highly desirable. In simultaneous analysis, multiple groups of substances are determined at once instead of through several individual analyses, saving energy, time, and chemicals while significantly increasing laboratory throughput. This review reports different approaches for monitoring the presence of environmental contaminants from four different groups of contaminants from river sediments. There are plenty of sampling techniques, with the core and grab sampling being the most employed at present. Sample preparation is a challenging part of the whole analytical method, providing ample opportunity for optimization in accordance with green analytical chemistry principles. Various extraction schemes, including ultrasound-assisted extraction techniques, accelerated solvent extraction, and microwave-assisted extraction are energy-intensive techniques but may be considered "green" due to their possibility to reduce the consumption of organic solvents, which are usually considered toxic. Quick, Easy, Cheap, Effective, Rugged and Safe extraction (QuEChERS) in the case of satisfactory validation parameters is well-evaluated and utilized. The most recent developments in extraction and clean-up techniques for simultaneous analysis of two, three or four groups of contaminants are showing promising results. Separation and detection techniques are shortly discussed. Further optimizations of analytical methodologies are needed, and future developments in the field of analytical methods for sediment samples are expected for more efficient and faster acquisition of data on the presence of contaminants in real monitoring samples.

Predicting air quality is essential for environmental monitoring and public health. In this work, we suggest a novel method for time series forecasting that uses Long Short-Term Memory (LSTM) networks and the Model-Agnostic Meta-Learning (MAML) algorithm to explicitly target air quality factors. The dataset employed includes features such as carbon monoxide concentration, sensor responses, and meteorological variables. Through extensive experimentation, our MAML-enhanced LSTM model demonstrates improved adaptability to new air quality forecasting tasks, particularly when data is limited. We present comprehensive results, including comparisons with traditional LSTM models, highlighting the efficacy of the proposed approach. This research contributes to the advancement of meta-learning techniques in the domain of environmental monitoring and offers insights into the potential of MAML for enhancing time series forecasting models.

This study investigates the potential of neem seed waste as an adsorbent for reducing total dissolved solids (TDS), hardness, calcium, and magnesium concentration in groundwater. Groundwater samples were collected from various locations within Quaid-e-Awam University of Engineering, Science and Technology (QUEST) and Nawabshah city. The objective of this research is to optimize the use of neem seed powder for removal of TDS, hardness, calcium, and magnesium concentration and physicochemical parameters from groundwater samples. Different dosages (0.5, 1.0 1.5, 2.0, 2.5, 5.0, and 10.0 g) and retention speed (0, 50, 100, 125, 150, 175, 200) were tested to optimize the treatment process. At a neem seed powder dosage of 2.0 g, a notable reduction in TDS was observed, with values of 48% for S1, 45% for S2, 52% for S3, and 58% for S4, respectively. Additionally, under a retention speed of 150 rpm, a significant decrease in TDS concentrations was recorded, with reductions of 72%, 65%, 79%, and 62% for S1, S2, S3, and S4, respectively. These results underscore the adsorbent's efficiency. Characterization techniques such as FESEM and FTIR were employed to understand the adsorption mechanism. The neem seed powder exhibited a considerable surface area of 55.30 m²/g according to BET analysis. Kinetic adsorption analysis showed a good fit with the pseudo-second-order (PSO) model with R² values 0.9978, 0.9946, 0.9967, and 0.9954 for TDS, Hardness, Calcium, and Magnesium. A higher R-squared value indicates that the PSO model aligns more closely with the data compared to the pseudo-first-order (PFO) model. The adsorbent molecules undergo a chemical reaction between surface molecules and adsorbate. This indicates chemisorption of adsorbs molecule. The study concludes that neem seed powder is a viable option for TDS removal due to its cost-effectiveness and availability compared to other materials like sodium zeolite and kaolin. Future research could explore the applicability of neem seed powder for removing other contaminants in groundwater or provides valuable insights into utilizing agricultural waste for groundwater treatment, offering a sustainable solution to water quality challenges.

Pesticide pollution of surface water is a serious global problem. This research was focused on the monitoring of pesticides in surface waters and their subsequent removal using adsorption on activated carbon (AC). Based on the monitoring, four pesticides—acetamiprid, diethyltoluamide, thiacloprid and thiamethoxam—occurred in higher concentrations in all sampling points. Invasive plants occurring near monitored water bodies, Reynoutria japonica (RJ) and Impatiens glandulifera (IG) were used for the preparation of activated carbon with an activating agent (H₃PO₄ and NaOH) using microwave pyrolysis. The prepared AC was subsequently used for adsorption of the above-mentioned pesticides. The prepared AC was characterized by Brunauer–Emmett–Teller analysis, elemental analysis, Fourier transform infrared spectroscopy and scanning electron microscopy. Individual AC types showed different effects for different pesticides. The maximum adsorption capacity calculated from the Langmuir model was 18.30 mg g⁻¹ for thiacloprid on H₃PO₄-activated AC from I. glandulifera.