This paper determines the impact of the maturation process of composted sewage sludge on the quality of the final product and assesses the stabilization effect. The samples of composted sewage sludge were taken from a wastewater treatment plant located in Pomerania in northern Poland. The sewage sludge was composted in an open windrow composting plant with the addition of straw and wood chips in the turning windrow. The aeration of the sewage sludge mixture was conducted based on two methods. The first phase (intensive degradation phase of 6 to 8 weeks) was characterized by frequently turning; the second phase for maturation used aeration channels (2 to 3 months). In three sampling campaigns samples were taken from the same windrow after 2 (no. 1), 8 (no. 2), and 12 weeks (no. 3) of maturation. Fresh samples were used for analyzing the stabilization parameter as static respiration activity (AT₄). Furthermore, the values of pH, organic matter (OM), total organic carbon (TOC), elementary composition, nutrients, total content, and mobile forms of heavy metals were analyzed in the compost samples. A significant decrease was found in the stabilization parameter (AT₄) during the maturation of tested materials. In turn, no significant differences were found in the elementary composition. The concentration of most metals increased in the final product. The total content of heavy metals in the final product did not exceed the limit values for the agricultural use of sewage sludge, compost from municipal waste, and for organic fertilizers. There were no significant changes in the percentage of bioavailable and mobile forms of heavy metals during compost maturation. Zinc was characterized by the highest level of mobile and bioavailable forms, which may cause bioaccumulation after the fertilization of soil. The study has shown that the process of maturation of compost from sewage sludge not affects changes in the content of heavy metal forms. The scope of this study has been planned on a wider scale for different variants of sewage sludge composting, in order to evaluate the process.

As a representative index of the algal bloom, the concentration of chlorophyll-a (Chl-a) is a key parameter of concern for environmental managers. The relationships between environmental variables and Chl-a are complex and difficult to establish. Two machine learning methods, including support vector machine for regression (SVR) and random forest (RF), were used in this study to predict Chl-a concentration based on multiple variables. To improve the model accuracy and reduce the input number, two feature selection methods, including minimum redundancy and maximum relevance method (mRMR) and RF, were integrated with regression models. The results showed that the RF model had a higher predictive ability than the SVR model. Furthermore, the less computational time cost and unnecessary prior data transformation also indicated a better applicability of the RF model. The comparison between ensemble models of mRMR-RF and RF-RF showed that the RF-RF yielded a better performance with fewer variables. Seven variables selected from the candidate predictors could interpret most information, and their potential implications to Chl-a were discussed based on the level of importance. Overall, the RF-RF ensemble model can be considered as a useful approach to determine the significant stressors and achieve satisfactory prediction of Chl-a concentration.

In Switzerland, surface waters are protected by the Swiss Water Protection Ordinance (OEaux; OFEV 1998), which stipulates that the water quality shall be such that the water, suspended matter, and sediments contain no persistent synthetic substances to ensure the protection of aquatic life. Local agencies are in charge of water quality monitoring, using a set of validated methods. Several lists of priority substances have been developed for aquatic microcontaminants for surface water monitoring but not for sediments. Some local agencies have established sediment monitoring programs, but to date, there exists no harmonized methodology for sediment quality assessment in Switzerland. Within the main goal of developing and providing methodologies for monitoring sediment quality in Switzerland, a screening was performed to help prioritize sediment-relevant microcontaminants. The screening approach was largely based on the NORMAN (network of reference laboratories, research centers, and related organizations for monitoring emerging environmental substances) system and was carried out in four steps: (1) identification of candidate substances, (2) selection of sediment relevant substances, (3) classification of substances into different categories based on identified data gaps and envisaged actions, and (4) ranking within each action category. This paper describes the methodology used in the prioritization process for sediment-relevant substances and provides recommendations for monitoring strategies in Switzerland.

This study combined spectroscopy techniques to assess the composition of refractory organics and highlighted the potential application of excitation–emission matrix (EEM) fluorescence spectroscopy within future monitoring of coal chemical wastewater treatment by the anammox process. The results showed that the anammox process could effectively degrade refractory organic substances, with fulvic-like, UV-humic acid, and Vis-humic acid component removal efficiencies of 43.61, 53.93, and 100%, respectively. In this study, EEM fluorescence spectroscopy was proven to be an effective method of assessing the removal of dissolved organic nitrogen during anammox treatment of mature coal chemical wastewater. Furthermore, remarkable accumulation (9.3–16.2%) of Ca. Kuenenia occurred in the anammox granules that underwent long-term cultivation in mature coal chemical wastewater, which provided the high nitrogen removal rate. The abundance of Anaerolineaceae and Bacteroides was vital in refractory organic degradation.

In the present study, long-term environmental impacts of compact and ground cement composites, in which 30 wt.% of cement was replaced by washed municipal solid wastes incineration (MSWI) fly ash, were investigated for use in building industry. Consecutive leaching tests over a time span of 180 days were performed in acid water, deionized water, and saline water, respectively, with the accumulative concentration of different elements determined in the leachate. Different leaching behaviors are observed among different potential toxic elements (PTEs). For instance, higher concentrations of V in the leachate were observed from the compact cement composites than those from the ground ones. The concentration of Ba in the leachate increased with the decrease of particle size of the cement composites, and an initial increase in the leaching efficiency of Sn was followed by a clear decline with the leaching time. In addition, kinetic study revealed that the leaching behaviors of potential toxic elements follow a second-order model. The results demonstrated that the addition of washed MSWI fly ash into cement can contribute to the attrition resistance, indicating that the washed MSWI fly ash could be a promising alternative for cement as supplementary building materials.

Earthworms are commonly referred as environmental engineers and their guts are often compared with chemical reactors. However, modeling experiments to substantiate it are lacking. The aim of this study was to use established reactor models, particularly PFR, on the gut of the vermicomposting earthworm Eudrilus eugeniae to understand more on its digestion. To achieve the objective, a mathematical model based on first-order kinetics was framed and used to determine the pattern of digestion rates of nutrient indicators, namely total carbon (%), total nitrogen (%), C/N ratio, ¹³C (‰), and ¹⁵N (‰) at five intersections (pre-intestine, foregut, midgut A, midgut B, and hindgut) along the gut of E. eugeniae. The experimental results revealed that the concentrations of TC, TN, ¹³C, and ¹⁵N decreased during gut transit, whereas C/N ratio increased. The first-order model demonstrated that all the nutrients exhibit a linear pattern of digestion during gut transit, which supports the PFR model. On this basis, the present study concludes that the gut of E. eugeniae functions as PFR.

Determining the quantity of sewage sludge is a major component of designing sludge treatment units and their handling and disposal facilities including its fluctuation over a wide range. In the present study, the capabilities of the hybrid wavelet-gene expression programming (WGEP), wavelet-model tree (WMT), and wavelet-evolutionary polynomial regression (WEPR) models have been investigated to predict the quantity of daily sewage sludge. In the first step, the single gene expression programming (GEP), model tree (MT), and evolutionary polynomial regression (EPR) models were employed to predict the amounts of sewage sludge based on the input vector content produced by the sewage sludge data series, which ranged from lagged-1 day to lagged-4 days. In this study, the WGEP, WMT, and WEPR models were obtained through the combination of two methods: discrete wavelet transforms (DWT) and simple GEP, MT, and EPR models. Incidentally, the models were implemented by transforming the input datasets using the Meyer wavelet function in order to reveal the temporal and spectral information contained within the data, and subsequently, this transformed data was used as the input vectors for the simple GEP, MT, and EPR models. In addition, the results of the wavelet conjunction model were compared with those obtained using the simple GEP, MT, and EPR models. The study indicated that the performance of the wavelet coupled-models was better than the simple models. The quantitative comparisons demonstrated that the WMT, with root mean square error (RMSE) of 8.15 and R = 0.98, performed better than the WGEP (RMSE = 15.26 and R = 0.92) and WEPR (RMSE = 18.20 and R = 0.89) models. Overall, the use of wavelet conjunction models provided an acceptable performance in order to improve precision in one of the most effective parameters involved in the design of a wastewater treatment plant (WWTP).

This study is conducted to examine the removal of phenol using spent black tea leaves (SBTL) by the process of ultrasound-assisted adsorption. The effect of different treatment processes, i.e., sonolysis, adsorption, and ultrasound-assisted adsorption, was investigated. The morphology of SBTL was studied using a scanning electron microscope (SEM), and the porous structure of the SBTL was identified before phenol was adsorbed onto the adsorbent. FTIR analysis of SBTL after adsorption showed the presence of an aliphatic band of carboxylic acids which depict degradation of the phenol molecule due to ultrasound-assisted adsorption. The experimental results showed that the hybrid process was found more effective for phenol removal (85%) as determined by a spectrophotometer. The optimum conditions of the reaction parameters were found as: phenol conc. = 25 mg L⁻¹, pH = 3.5, time = 60 min, adsorbent dosage = 800 mg L⁻¹, ultrasound power = 80 W, and operating temperature = 30 ± 2 °C. Chemical oxygen demand (COD) and total organic carbon (TOC) were found to be 78 and 39%, respectively. HPLC studies suggest nonselective oxidation of phenol resulting in by-products such as catechol and hydroquinone and finally carboxylic acids and CO₂. In order to find reaction kinetics, different kinetic models, viz. pseudo-first- and pseudo-second-order models, were studied. The best fit to the isotherm models, i.e., Langmuir and Freundlich, was determined. It is concluded that phenol removal by the hybrid process follows the pseudo-second-order reaction kinetics and Langmuir isotherm model. In addition, thermodynamic studies revealed the nonspontaneous and exothermic nature of the phenol adsorption process.

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.