Industry is one of the main activities in the city and in many cities of the world, and the dominant industrial zones are the most significant morphological forms of concentration of industrial facilities in the city and are concentrated industrial and business activity. Industrial parks combine activities related to energy and resource consumption, emissions, waste generation, economic benefits, and regional development. The focus of this work is the path of transformation between the present and the vision of a sustainable city in the future. The problem and the subject of research related to two related objects of research: the city and sustainable development. In this paper, the co-author’s industrial symbiosis parks, modern tendencies of the spatial distribution of productive activities, circular economy, to attract leading corporations and open the way for new ventures while preserving the living environment in an urban area.

The present study aims at evaluating the batch scale potential of cotton shell powder (CSP), Moringa oleifera leaves (ML), and magnetite-assisted composites of Moringa oleifera leaves (MLMC) and cotton shell powder (CSPMC) for the removal of brilliant green dye (BG) from synthetic wastewater. This is the first attempt to combine biosorbents with nanoparticles (NPs) for the removal of BG. The surface properties of ML, CSP, and their composites were characterized with Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX). The impact of dosage of the adsorbents (1–4 g/L), initial concentrations of BG (20–320 mg/L), pH (6–12), and contact time (15–180 min) on BG removal was evaluated. The BG removal was in order of CSPMC > MLMC > CSP > ML (98.8–86.6% > 98.2–82.0% > 92.3–70.7% > 89.0–57.4%) at optimum dosage (2 g/L) and pH (8). Moreover, maximum adsorption (252.17 mg/g) was obtained with CSPMC. The experimental results showed better fit with Freundlich adsorption isotherm model and kinetic data revealed that sorption followed pseudo-second-order kinetic model. The values of Gibbs free energy and mean free energy of sorption showed that physical adsorption was involved in the removal of BG. FTIR results confirmed that –O-H, –C-OH, =C-H, –C-H, =–CH₃, HC ≡ CH, C=C, –C=O, –C-N, and –C-O-C– groups were involved in the removal of BG. The results revealed that application of low-cost biosorbents combined with NPs is very effective and promising for the removal of textile dyes from wastewater.

In the present work, the sonocatalytic degradation of butylparaben was investigated using Pd immobilized on carbon black as the sonocatalyst. The presence of 25 mg/L 10Pd/C significantly increased the removal rate of butylparaben and the observed kinetic constant increased from 0.0126 to 0.071 min⁻¹, while the synergy index between sonolysis and adsorption was 70.7%. The BP degradation followed pseudo-first-order kinetics with the apparent kinetic constant decreased from 0.071 to 0.030 min⁻¹ when the initial concentration of butylparaben increased from 0.5 to 2 mg/L. The process was being favored slightly under alkaline conditions. The presence of organic matter (20 mg/L humic acid) reduced the apparent kinetic constant more than two times. The addition of chlorides up to 250 mg/L did not significantly reduce the rate of reaction, while the presence of 250 mg/L bicarbonates reduced the observed kinetic constant from 0.071 to 0.0472 min⁻¹. The prepared catalyst retains the efficiency after five subsequent experiments since the apparent kinetic constant was only slightly decreased from 0.071 to 0.059 min⁻¹.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Tₐ), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Tₐ, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Tₐ is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Ozone (O₃) is an adverse environmental factor posing damage to ornamental plants. Thus, it is important to seek an effective way of enhancing plant tolerance to O₃-induced damage. Methyl jasmonate (MJ) and melatonin (MT) are plant growth regulators (PGRs) involved in plant abiotic stress responses. In this study, compared with the control group of plants without ozone, the influence of exogenous MJ (0, 10, 50, 100, and 150 μM) and MT (0, 0.1, 0.5, 2.5, and 12.5 μM) on the resistance of Malus crabapple ‘Hong Jiu’ was evaluated under O₃ stress (100 ± 10 nL/L for 3 h). Our data revealed that levels of MDA were significantly enhanced following O₃ treatment compared with plants without O₃. O₃ induced the activities of antioxidant enzymes and the accumulation of non-enzymatic antioxidants. While lower malondialdehyde (MDA) content, greater activities of antioxidant enzymes, and higher levels of soluble protein and non-enzymatic antioxidants were observed in PGRs-pretreated plants than in non-PGRs-pretreated plants under O₃ stress. Based on the above results and air pollution tolerance index (APTI), an exogenous supply of MJ and MT to Malus crabapple ‘Hong Jiu’ seedlings was protective for O₃-induced toxicity. The present study provides new insights into the mechanisms of MJ and MT amelioration of O₃-induced oxidative stress damages in Malus crabapple ‘Hong Jiu.’

Mosses can be used as biomonitors to monitor radionuclide deposition and heavy metal pollution in cities, forests, and grasslands. The aims of this work were to determine the activity concentrations of natural (²¹⁰Po, ²¹⁰Pb or ²¹⁰Pbₑₓ (excess ²¹⁰Pb is defined as the activity of ²¹⁰Pb minus the activity of ²²⁶Ra), ⁷Be, ⁴⁰K, ²²⁶Ra, ²³⁸U, and ²³²Th) and anthropogenic radionuclides (¹³⁷Cs) in moss body profiles and in situ underlying soils of moss samples and to assess/determine the distribution features and accumulation of these radionuclides. Activity concentrations of radionuclides in the samples were measured using a low-background gamma spectrometer and a low-background alpha spectrometer. Consistent with their source, the studied radionuclides in the moss samples and underlying soils were divided according to the principal component analysis (PCA) results into an airborne group (²¹⁰Po, ²¹⁰Pb (²¹⁰Pbₑₓ), ⁷Be, and ¹³⁷Cs) and a terrestrial group (⁴⁰K, ²³⁸U, ²²⁶Ra, and ²³²Th). The activity concentrations of ²¹⁰Po and ²¹⁰Pbₑₓ in moss body profiles were mainly concentrated in the stems–rhizoid parts, in which we measured some of the highest ²¹⁰Po and ²¹⁰Pbₑₓ levels compared to the results in the literature. ⁷Be mainly accumulated in the leaves–stem parts. Different positive correlations were observed between ²¹⁰Po and ²¹⁰Pb and between ⁷Be and ²¹⁰Pb, which indicated that the uptake mechanisms of ²¹⁰Po, ²¹⁰Pb, and ⁷Be by moss plants were different, to some extent. ¹³⁷Cs was detected only in some moss samples, and the fraction of ¹³⁷Cs in the underlying soils was much lower than that in the moss, suggesting that mosses were protecting the underlying soils from further pollution. Except for ⁴⁰K, the terrestrial radionuclide (²³⁸U, ²²⁶Ra, and ²³²Th) content in mosses was predominantly at low levels, which indicated not only the inability of mosses to use those elements for metabolic purposes but also the rather poor capability of mosses to directly mobilize, absorb, and transport elements (U, Ra, or Th) not dissolved in water.

Toxic metal contamination in food products and the environment is a public health concern. Therefore, understanding human exposure to cadmium (Cd), lead (Pb), cobalt (Co), and copper (Cu) levels in the general population of Taiwan is necessary and urgent. We aimed to establish the human biomonitoring data of urine toxic metals, exposure profile changes, and factors associated with metal levels in the general population of Taiwan. We randomly selected 1601 participants older than 7 years of age (36.9 ± 18.7 years (7–84 years)) from the Nutrition and Health Survey in Taiwan (NAHSIT) conducted during 1993–1996 (93–96) and 2005–2008 (05–08) periods and measured the levels of four metals in the participants’ urine samples using inductively coupled plasma-mass spectrometry. The median (range) levels of urinary Cd, Pb, Co, and Cu in participants from the NAHSIT 93–96 (N = 821)/05–08 (N = 780) were 0.60 (ND–13.90)/0.72 (ND–7.44), 2.28 (ND–63.60)/1.09 (0.04–48.88), 0.91 (0.08–17.30)/1.05 (0.05–22.43), and 16.87 (2.62–158.28)/13.66 (1.67–189.70) μg/L, respectively. We found that the urinary median levels of Pb and Cu in our participants were significantly lower in the NAHSIT 05–08 (Pb 1.09 μg/L, Cu 13.66 μg/L) than in the NAHSIT 93–96 (Pb 2.28 μg/L, Cu 16.87 μg/L; P < 0.01), whereas those of Cd and Co were significantly higher in the NAHSIT 05–08 (Cd 0.72 μg/L, Co 1.05 μg/L; P < 0.01). Youths had higher exposure levels of Pb, Co, and Cu than adults. Participants with alcohol consumption, betel quid chewing, or cigarette smoking had significantly higher median levels of urinary Pb or Cu (P < 0.01) than those without. Principal components and cluster analysis revealed that sex had different exposure profiles of metals. We concluded that levels of urinary Cd, Pb, Co, and Cu exposure in the general Taiwanese varied by age, sex, and lifestyles.

The port regions of the Amazon are subject to environmental impacts high shipping traffic. In October 2015, a cargo ship containing 5000 oxen sank in the Port of Vila do Conde, northern Brazil, releasing large amounts of organic matter and diesel oil into the aquatic environment. We evaluated the consequences of this shipwreck on the zooplankton community. Sampling was carried out using a phytoplankton net (64 μm) at two locations close to the port. We calculated the frequency of occurrence, relative abundance, and trophic state index and performed a canonical redundancy analysis of zooplankton in this area. Total density values ranged from 371 to 8600 organisms/m³, with minimum values being recorded during the period of the shipwreck and maximum values after the shipwreck. The areas categorized as super eutrophic had the lowest density values. The most abundant species/groups were nauplii and copepodites of the orders Cyclopoida and Calanoida. Of the environmental variables, only biochemical oxygen demand, chemical oxygen demand, and total dissolved solids were selected by the redundancy canonical analysis. The environmental conditions of the region and the ongoing environmental impacts might substantially influence the structure of the zooplankton community. The predominance of these organisms, in addition to the high densities of nauplii and copepodites, was likely related to the large amounts of nutrients generated by the shipwreck.

Climate change adversely affects food security all over the world, especially in developing countries where the increasing population is confronting food insecurity and malnutrition. Crop models can assist stakeholders for assessment of climate change in current and future agricultural production systems. The aim of this study was to use of system analysis approach through CSM-CERES-Millet model to quantify climate change and its impact on pearl millet under arid and semi-arid climatic conditions of Punjab, Pakistan. Calibration and evaluation of CERES-Millet were performed with the field observations for pearl millet hybrid 86M86. Mid-century (2040–2069) climate change scenarios for representative concentration pathway (RCP) 4.5 and RCP 8.5 were generated based on an ensemble of selected five general circulation models (GCMs). The model was calibrated with optimum treatment (15-cm plant spacing and 200 kg N ha⁻¹) using field observations on phenology, growth and grain yield. Thereafter, pearl millet cultivar was evaluated with remaining treatments of plant spacing and nitrogen during 2015 and 2016 in Faisalabad and Layyah. The CERES-Millet model was calibrated very well and predicted the grain yield with 1.14% error. Model valuation results showed that there was a close agreement between the observed and simulated values of grain yield with RMSE ranging from 172 to 193 kg ha⁻¹. The results of future climate scenarios revealed that there would be an increase in Tₘᵢₙ (2.8 °C and 2.9 °C, respectively, for the semi-arid and arid environment) and Tₘₐₓ (2.5 °C and 2.7 °C, respectively, for the semi-arid and arid environment) under RCP4.5. For RCP8.5, there would be an increase of 4 °C in Tₘᵢₙ for the semi-arid and arid environment and an increase of 3.7 °C and 3.9 °C in Tₘₐₓ, respectively, for the semi-arid and arid environment. The impacts of climate changes showed that pearl millet yield would be reduced by 7 to 10% under RCPs 4.5 and 8.5 in Faisalabad and 10 to 13% in Layyah under RCP 4.5 and 8.5 for mid-century. So, CSM-CERES-Millet is a useful tool in assessing the climate change impacts.