The word “textile” means to weave and was taken from the Latin word “texere.” Nowadays, textiles not only fulfill humankind's basic necessity for clothing, they also allow individuals to make fashion statements. As one of the oldest industries, the textile industry occupies a unique place in India. It is responsible for 14% of the total industrial manufacture in India. However, the textile industry is also considered to be one of the biggest threats to the environment. Pretreatment, dyeing, printing, and finishing operations are among the various stages of the industrial textile manufacturing process. These fabrication operations not only utilize huge quantities of power and water, they also generate considerable amounts of waste. The textile industry utilizes a number of dyes, chemicals, and other materials to impart the required qualities to the fabrics. These operations produce a significant amount of effluents. The quality of effluents is such that they cannot be put to other uses, and they can create environmental problems if they are disposed of without appropriate treatment. This review discusses different textile processing stages, pollution problems associated with these stages, and their eco‐friendly alternatives. Textile wet processing is described in detail, as it is the key process in the industry and it also generates the greatest amount of pollutants in textile processing. The environmental impact of textile effluents is discussed, as textile effluents not only impose negative effects on the quality of water and soil, they also imperil plant and animal health. In this paper, various methods for treating textile effluents are described. Discussion of physical, chemical, biological, and advanced treatment technologies of effluent treatment are included in this paper. 相似文献
Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro‐based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre‐Kharif (March‐May), Kharif (May‐October), and Rabi (December‐February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961‐2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north and northwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh. 相似文献
Environmental Science and Pollution Research - The huge demand and consumption of DOX, its incomplete metabolism, and complex behavior in atmosphere are causing a great ecological issue, which... 相似文献
Environmental Science and Pollution Research - Existence of pharmaceutical residues in water has endangered environmental pollution worldwide, which makes it ineludible to develop prospective... 相似文献
Environmental Science and Pollution Research - Solar energy is a vast renewable energy source, but uncertainty in the demand and supply of energy due to various geographical regions raises a... 相似文献
Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans. 相似文献
Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making. 相似文献
The present investigation is the first in situ comparative study for the identification of Ni and Cu accumulation strategies involved in Odontarrhena obovata (syn. Alyssum obovatum (C.A. Mey.) Turcz.) growing in Cu-rich smelter-influenced (CSI) and non-Cu-influenced (NCI) sites. The total and Na2EDTA (disodium ethylenediaminetetraacetic acid)-extractable metal concentration in soils and plant tissues (roots, stem, leaves and flowers) were determined for CSI and NCI sites. High concentrations of total Ni, Cr, Co and Mg in the soil suggest serpentine nature of both the sites. In spite of high total and extractable Cu concentrations in CSI soil, majority of its accumulation was restricted to O. obovata roots showing its excluder response. Since the translocation and bioconcentration factors of Ni?>?1 and the foliar Ni concentration?>?1000 μg g?1, it can be assumed that O. obovata has Ni hyperaccumulation potential for both the sites. No significant differences in chlorophyll content in O. obovata leaves were observed between studied sites, suggesting higher tolerance of this species under prolonged heavy metal stress. Furthermore, this species from CSI site demonstrated rather high viability under extreme technogenic conditions due to active formation of antioxidants such as ascorbate, free proline and protein thiols. The presence of Cu in higher concentration in serpentine soil does not exert detrimental effect on O. obovata and its Ni hyperaccumulation ability. Thus, O. obovata could act as a putative plant species for the remediation of Cu-rich/influenced serpentine soils without compromising its Ni content and vitality.