Two lab-scale slow sand filters (SSFs), packed with uncoated fine sand (SSFᵤ) and iron-coated fine sand (SSFcₒ), were operated to study their efficiency in treating municipal wastewater. The effects of sand coating; hydraulic loading rates (HLRs) (0.56, 0.85, and 1.12 cm/h); and filter depths (22, 32, and 42 cm) were evaluated. Sand coating did not have any significant effect on wastewater treatment by the SSF at all depths (p > 0.05). The removals of total suspended solids (TSS), chemical oxygen demand (COD), and phosphate decreased with increase in HLR. On the other hand, media depth had positive effects on the removal of turbidity, TSS, COD, and total coliforms (TC). At HLR of 0.56 cm/h, the average removals of each studied parameter, i.e., turbidity, TSS, and COD, at filter depth d₄₂ in SSFᵤ and SSFcₒ were 94.3, 90.1, and 56% and 92.7, 93, and 30.95%, respectively. Both filters efficiently removed the total coliforms (> 90%) and fecal coliform (up to 99%) but inefficient in nitrate removal. Frequent clogging was observed in SSFᵤ due to the colonization of microorganisms on the sand surface, which was confirmed by SEM images. Biofilm formation or microbial colonization was absent in SSFcₒ, which might be responsible for uninterrupted operation of SSFcₒ. Overall, the sand coating is beneficial for long-term operation of SSF.

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Labels are used for marketing, technical information, local of production and environmental declarations of products. The aim of the study was to evaluate the life cycle of two sets of front and back adhesive labels made with different liners: polyethylene terephthalate (PET) and glassine paper. The study is a pioneering initiative in the labelling industry using life cycle assessment, especially in Brazil. The attributional life cycle assessment method was based on the ISO 14044 standard and covered the entire life cycle, cradle to grave. Primary data for key suppliers, printing facility and label use composed the foreground data, while the ecoinvent database composed the background data. The material efficiency was assessed through mass balance. The impact assessment methods were cumulative energy demand for non-renewable energy and ILCD 2011 midpoint for the environmental categories. The glassine liner is heavier than the PET which resulted in larger environmental indicator values for production, transportation and waste treatment. On the other hand, energy demand of label set with glassine liner was lower than the PET. Furthermore, the hotspot analysis of each impact category was presented per process and substance. The label set made with glassine liner tended to present larger environmental indicator values in most categories, 13 of the 16 assessed, compared to the label set made with PET liner, despite the uncertainties. Contribution analysis identified that the pre-manufacturing step presented larger environmental indicator values than manufacturing, use and post-use steps along the label set life cycle. Therefore, environmental improvement opportunities were evaluated through scenarios of end of life, recycled inputs, domestic suppliers, waste prevention and product redesign. Moreover, the life cycle assessment was useful for diagnosing the energy and environmental profiles of self-adhesive labels and planning cleaner production measures that avoid environmental tradeoffs.

The aim of this study was to investigate how the microbial community structure adapts during the start-up phase and how the ¹³C fractionation of biogas reflects the microbial population dynamics in two parallel swine manure–fed anaerobic digesters. Two swine manure–fed reactors for the start-up of continuously stirred tank reactors at mesophilic condition were evaluated. Changes in community structure were monitored using 16S rRNA high-throughput sequencing to measure the abundance of fermenting bacteria and methanogens. Digesters with relatively stable Methanosarcinaceae started up successfully and contained high gas production and low levels of propionate. In contrast, the digester that experienced a difficult start-up period had reduced Methanosarcinaceae along with accumulated propionate and low gas production. Specific gas production, specific methane production, and ¹³C fractionation of biogas were influenced significantly by Methanosarcinaceae, Methanobacteriaceae, and Clostridiaceae, indicating that the ¹³C fractionation of biogas had significant potential to reflect microbial population changes and digester performance during the start-up period.

Surface waters are used by local populations for different purposes, such as recreational activities, water source for human and animal consumption, and irrigation among others, which lead to the need for management strategies on water health and associated risks. During this study, we investigated physicochemical parameters, fecal coliform bacteria, and infectious human enterovirus detection to determine the water quality in different beaches (categorized as an urban area, non-urban areas, and an intermediate position) from San Roque Dam, in Argentina. Multivariate techniques were applied. Principal component analysis allowed identification of subgroup of variables responsible for the water quality. A cluster analysis and multivariate analysis of variance showed the urban beach as the highest pollution area. The following variables (measured at the urban beach) would be enough to describe the quality of the aquatic body: nitrites, fecal coliforms, total phosphorous, and infectious human enterovirus. The infectious human enterovirus was an independent variable detected in 69.1% of the samples showing a steady frequency of detection during the whole period studied and could identify human fecal contaminations as a source of water pollution. The selected variables would contribute to water quality regarding the risk for human health using San Roque dam waters for recreational propose.

Vegetation is a natural tie that connects the atmosphere, hydrosphere, biosphere, and pedosphere. Quantitatively evaluating the variability of vegetation coverage and exploring its associated influence factors are essential for ecological security and sustainable economic development. In this paper, the spatiotemporal variation of vegetation coverage and its response to climatic factors and land use change were investigated in the Yangtze River Delta (YRD) from 2001 to 2015, based on normalized difference vegetation index (NDVI) data, vegetation type data, climate data, and land use/cover change (LUCC) data. The results indicated that the annual mean vegetation coverage revealed a nonsignificant decreasing trend over the whole YRD. Areas characterized by significant decreasing (P < 0.05) trends were mainly concentrated on the central and northern part of the YRD, and significant increasing (P < 0.05) trends were mainly located in the southern part of the study area. Except for grassland and cultivated crops, vegetation coverage of the other types of vegetation was all exhibiting increasing trends. Temperature has a more pronounced impact on vegetation growth than precipitation at both the annual and monthly scales. Furthermore, vegetation growth exhibited a time lag effect for 1~2 months in response to precipitation, while there was no such phenomenon with temperature. Land use change caused by urbanization is an important driving factor for the decrease of vegetation coverage in the YRD, and the effect of land use change on the vegetation dynamic should not be overlook.

Rainwater harvesting brings various desired environmental and social benefits in urban development. Tanks in rainwater harvesting systems need low-maintenance and low-cost approaches to manage water quality, especially for scattered small rainwater tanks associated with complex terrains in mountainous cities. Four rain barrels were set up to store roof runoff at the campus of Chongqing University, Chongqing, China. Barrel 1 (B1) and barrel 2 (B2) stored the first-flush water and the roof runoff with first-flush water diverted, respectively, while barrel 3 (B3) was loaded with a biological ceramsite and barrel 4 (B4) used biological ceramsite as a substrate media and planted with Cyperus alternifolius (C. alternifolius) to treat the first-flush water. The performances of the rain barrels were evaluated as well as the variations in water quality parameters were examined. The removal efficiency of B3 was 48.2%, 76.0%, 44.3%, and 24.6% for COD, NH₄⁺–N, TN, and TP, respectively, while B4 had removal efficiencies of 93.4%, 71.0%, 75.0%, and 76.5% for COD, NH₄⁺–N, TN, and TP, respectively. B4 had BOD, NH₄⁺–N, TN, and TP concentrations within the class III Chinese Standard requirement after a storage period of about 240 days. Furthermore, the turbidity in B4 kept dropping. Thus, B4 is a more promising alternative for water quality management in mountainous cities of China.

Nanomaterials (NMs) commercially used for various activities mostly end up in landfills. Reduced biogas productions reported in landfill reactors create a need for more comprehensive research on these greatly-diverse microbial pools. In order to evaluate the impact of one of the most widely-used NMs, namely nano-zinc oxide (nano-ZnO), simulated bioreactor and conventional landfills were operated using real municipal solid waste (MSW) for 300 days with addition nano-ZnO. Leachate samples were taken at different phases and analyzed by 16S rRNA gene amplicon sequencing. The bacterial communities were distinctly characterized by Cloacamonaceae (phylum WWE1), Rhodocyclaceae (phylum Proteobacteria), Porphyromonadaceae (phylum Bacteroidetes), and Synergistaceae (phylum Synergistetes). The bacterial community in the bioreactors shifted at the end of the operation and was dominated by Rhodocyclaceae. There was not a major change in the bacterial community in the conventional reactors. The methanogenic archaeal diversity highly differed between the bioreactors and conventional reactors. The dominance of Methanomicrobiaceae was observed in the bioreactors during the peak methane-production period; however, their prominence shifted to WSA2 in the nano-ZnO-added bioreactor and to Methanocorpusculaceae in the control bioreactor towards the end. Methanocorpusculaceae was the most abundant family in both conventional control and nano-ZnO-containing reactors.

The Batchwa vacha, Eutropiichthys vacha is commercially important, supporting a viable small- and large-scale fishery throughout the Ganges River, NW Bangladesh. This study provides detail information on reproduction of E. vacha including size at sexual maturity, spawning and peak spawning season, and fecundity based on 734 female specimens through regular monthly sampling using cast net, gill net, and square lift net in the Ganges River during January to December 2016. Also, our study estimated the effects of climate change including temperature and rainfall on reproduction of E. vacha in the Ganges River. For each individual, lengths (total length, TL; standard length, SL) and body weight (BW) were measured with slide caliper and digital balance, respectively. Gonads (ovaries) were collected carefully by ventral dissection of each female specimen and weighed to the nearest 0.01 g accuracy. The gonadosomatic index (GSI % = (GW/BW) ×100), modified gonadosomatic index (MGSI % = (GW/BW − GW) × 100), and Dobriyal index (DI = [Formula: see text]) were calculated to estimate the size at sexual maturity (L₅₀) and spawning season. Based on GSI, MGSI, and DI, the L₅₀ was calculated as 12.5 cm TL for female. The TL₅₀, the TL at which 50% of individuals become mature, was calculated by logistic equation as 12.7 cm. Also, on the basis of higher values of GSI, MGSI, and DI, spawning season was ranged from April to August, with the peak in June–July, signifying the peak spawning season for E. vacha in the Ganges River. The total fecundity (FT) ranged from 4800 to 77,976 (mean ± SD, 31384 ± 23,747) and was highly correlated with TL and BW. Water temperature during the spawning period ranged from 28 to 34 °C, with an average of 31 °C and there was significant correlation between temperature and GSI. Also, the spawning season coincides with the peak rainfall and there was significant correlation between rainfall and GSI. Additionally, analysis of long data series indicated that annual average air temperature is increasing by 0.0258 °C/year, while the annual average rainfall is decreasing by 3.107 mm/year. Finally, the findings of this study would be very effective to impose specific management for E. vacha in the Ganges River and surrounding ecosystems.