The metal coating process produces wastes containing heavy metals such as cadmium that are harmful to health. One method to remove cadmium is adsorption. This research developed an adsorber, specifically magnetic activated carbon derived from coal (CMAC). Activated carbon is modified with a Fe3O4 composite to make it magnetic and reusable. The objective of this investigation is to optimise the key parameters of activator concentration and thermal activation conditions for the fabrication of high-quality activated carbon. The investigation will evaluate the characteristics of the resulting carbon and its cadmium removal efficiency. The experimental adsorption data were fitted to both the Freundlich and Langmuir isotherm models and evaluated using kinetic equations. Chemical activators were used in a mixture of phosphoric and hydrochloric acid (1: 1) at concentrations of 0.5 M - 1.25 M. Physical activation was carried out at (873 - 1173) K. The optimal activation temperatures were identified as 1123 K and 1173 K. The CMAC removal percentage for Cd was 98.10 %. The Freundlich isothermal adsorption equation shows n = 0.4530 and K-F = 3.9823 mg/g with R-2 = 0.9691. The adsorption data were well-fitted by the Langmuir model, which yielded a maximum monolayer adsorption capacity q(m) of 238.10 mg/g and an affinity constant K-L of 0.0787 L/mg, supported by a high coefficient of determination (R-2 = 0.9082). Furthermore, the adsorption kinetics model yielded a pseudo-second-order equation. The regeneration test over five cycles showed decreases in Cd removal efficiency of 97.04 %, 81.89 %, 42.75 %, 13.83 %, and 9.71 %, respectively, indicating limited stability of the adsorbent after repeated use.
Heavy metal pollution is an important environmental issue in urban and industrial areas because these elements persist in the environment and may cause ecological and health problems. The aim of this study was to investigate the atmospheric deposition of heavy metals (Cu, Mn, Ni, Pb, Zn, and Cr) in the Prishtina region (Kosovo) using mosses and lichens as bioindicators. A total of 24 moss samples (Hypnum cupressiforme) and 9 lichen samples (Cladonia portentosa) were collected between June and August 2025. The plant samples were digested using aqua regia and analysed by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES). Multivariate statistical methods, including basic statistics, Pearson correlation, and principal component analysis (PCA), together with spatial distribution analysis of heavy metals, were applied to facilitate clearer data interpretation. To evaluate the degree of heavy metal contamination at individual sites and across the entire region, four pollution indices (CF, PLI, Eri, and PERI) were calculated. The results indicate that the study area is contaminated with nickel, chromium, and lead, mainly due to mining activities at the open-pit ferronickel mines in Golesh, emissions from the ferronickel smelter in Drenas, and heavy traffic along the Prishtina-Peja highway. The ecological risk index (Eri) for nickel in moss samples ranged from 6.00 to 336, while in lichen samples it ranged from 6.00 to 42.0. Copper showed Eri values of 6.67-16.67 in mosses and 3.33-5.00 in lichens. For lead, Eri values in moss samples ranged from 3.33 to 53.33, whereas in lichens they were below the detection limit. Based on moss data, 9 locations (39.1 %) were classified as moderately polluted, while 14 locations (60.9 %) fell into the unpolluted to moderately polluted category. In lichen samples, 3 locations (37.5 %) were classified as unpolluted and 5 locations (62.5 %) as unpolluted to moderately polluted. Overall, both the concentrations and pollution indices of heavy metals in mosses were 2-7 times higher than those observed in lichens at the same sampling locations. The smaller number of lichen samples compared with moss samples reflects their limited presence at several sampling sites, which represents a limitation of the study.
Climate change driven by greenhouse gas emissions is a global priority. Deep large reservoirs represent significant potential emission sources, yet their greenhouse gas source-sink dynamics and driving mechanisms remain inadequately understood. This study targeted three representative deep reservoirs of the upper Yellow River (Longyangxia (LYX), Lijiaxia (LJX), and Liujiaxia (LJXX)). Comparative analysis of water column conditions during the stratification period (summer) and mixing period (winter) was conducted. We quantified the spatial distribution of dissolved partial pressure of CO2 (pCO2) and the water-air interface exchange flux (FCO2), elucidating their spatiotemporal patterns and carbon source-sink attributes. Key findings include: (1) Concentration and Flux Characteristics: Mean pCO2 was 765.00 mu atm in summer and 739.00 mu atm in winter. Corresponding mean FCO2 was 12.273 mmol/(m2 & centerdot;d) and 9.111 mmol/(m2& centerdot;d). (2) Regulatory Mechanisms: Thermal stratification, climatic fluctuations, and reservoir-specific properties influenced pCO2 by modulating carbon cycling pathways and carbonate equilibria. (3) Source-Sink Nature: All three reservoirs exhibited significant spatial gradients and seasonal variations in pCO2 and FCO2, collectively functioning as sources of the CO2. This study provides critical theoretical support for quantifying the carbon footprint of hydropower projects.
As wind and photovoltaic deployment in industrial parks approaches practical upper bounds, the central planning issue shifts from simply expanding renewable-electricity capacity to identifying where the remaining decarbonisation leverage exists. This study addresses this later-stage planning problem by developing a representative-day bi-objective planning model for an industrial park electricity-heat system integrating wind power, photovoltaic generation, geothermal heat utilisation, thermal energy storage (TES), battery storage, and bidirectional grid interaction, and solves it using multi-objective particle swarm optimisation. The results show that wind and photovoltaic capacities remain close to their feasible upper bounds across representative Pareto solutions, whereas the main structural variation occurs in geothermal deployment and thermal storage. From the carbon-oriented end to the economy-oriented end of the frontier, annualised capital expenditure (CAPEX) decreases from (2691.10 to 977.75) & centerdot; 104 RMB, while carbon reduction decreases from (24.808 to 21.294) & centerdot; 104 tCO(2) (t = tonne = 106 g = Mg = 103 kg) and the heat deficit ratio increases from 8.50 % to 36.46 %. The representative middle-frontier solution achieves a more balanced outcome, with an economic-efficiency index of 15.212, carbon reduction of 23.090 & centerdot; 104 tCO(2), and a heat deficit ratio of 6.40 %. Winter representative-day operation shows that TES provides 48.8 % of heat demand, whereas medium- and high-temperature geothermal heat contribute 33.9 % and 10.7 % respectively; by contrast, geothermal electricity remains negligible. Additional heat-adequacy screening and TES cost-sensitivity analyses support the same interpretation. These results indicate that, once electricity-side renewable deployment is high, the main planning lever for balancing investment burden, decarbonisation performance, and heat-service adequacy shifts toward coordinated heat-side design in industrial parks.
Excessive pesticide residues seriously threaten the safety of agricultural products and the health of consumers. As a widely consumed beverage, the monitoring of pesticide residues in tea is of vital importance. To improve the sensitivity and accuracy of organophosphorus pesticide residue detection in tea, an integrated method combining solid-phase microextraction pretreatment and high-performance liquid chromatography-tandem mass spectrometry analysis was researched and developed. The research optimised the sample pretreatment process. Tea powder was extracted by vortex using a mixed solvent of ethyl acetate and n-hexane with a volume ratio of 9 : 1. The isotope internal standard method was used for quantification. By optimising the chromatographic conditions and mass spectrometry parameters, the detection methods for five target pesticides were established, and the linear range, quantification limit, accuracy and precision of the methods were systematically evaluated. The results show that the linear relationship of the five organophosphorus pesticides is good within the range of (5.0 - 500.0) ng/mL, and the quantitative limit of the method is extremely low, ranging from 0.0011 mg/kg to 0.0026 mg/kg. The correlation coefficients of the standard curves were all greater than 0.99, and the average recovery rates ranged from 72 % to 109 %. The results show that the pretreatment of the research method is simple, rapid and has low solvent consumption, effectively improving the detection efficiency and accuracy. The research provides efficient and reliable analytical means for ensuring the quality and safety of tea, safeguarding consumers' health and promoting the sustainable development of the tea industry.
