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141.
142.
A review of textile industry: Wet processing,environmental impacts,and effluent treatment methods 下载免费PDF全文
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
143.
Sajjad Haider Rab Nawaz Muzammil Anjum Tahir Haneef Vipin Kumar Oad Salah Uddinkhan Rawaiz Khan Muhammad Aqif 《Frontiers of Environmental Science & Engineering》2023,17(9):111
144.
史璐涵 张颖仪 KUNWAR Bhagawati DESHMUKH Dhananjay Kumar KAWAMURA Kimitak 岳玎利 赵燕 赖森潮 《中国环境监测》2023,39(1):81-91
于2018年冬季在广州城区磨碟沙站点开展细颗粒物(PM2.5)样品采集,并获得PM2.5中水溶性离子、含碳组分、稳定碳氮同位素的组成及时间变化特征,重点讨论了PM2.5浓度升高时段的化学组成特征变化,进而利用稳定碳氮同位素变化特征探究了主要污染来源。结果表明:采样期间,研究站点PM2.5平均质量浓度为22.1μg/m3,共出现两个PM2.5浓度水平升高时段,所对应的平均质量浓度分别达46.0μg/m3和63.0μg/m3。风速降低、温度升高等不利气象条件是导致上述时段PM2.5浓度上升的重要原因。在上述时段,伴随着PM2.5浓度的升高,NO-3和NH+4浓度均出现显著升高,NO-3与SO2-4的摩... 相似文献
145.
Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh 下载免费PDF全文
Mahiuddin Alamgir Shamsuddin Shahid Manzul Kumar Hazarika Syams Nashrrullah Sobri Bin Harun Supiah Shamsudin 《Journal of the American Water Resources Association》2015,51(3):794-806
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
146.
Kaur Gurleenjot Singh Neetu Rajor Anita Arya Raj Kumar 《Environmental science and pollution research international》2023,30(4):8485-8499
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
147.
Kaur Manpreet Mehta Surinder Kumar Kansal Sushil Kumar 《Environmental science and pollution research international》2023,30(4):8464-8484
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
148.
Sehrawat Ravin Sahdev Ravinder Kumar Tiwari Sumit 《Environmental science and pollution research international》2023,30(5):11175-11198
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
149.
Pankaj Kumar Vandana Mishra Saiyami Bhardwaj Shafali Garg Ludovic F. Dumée Radhey Shyam Sharma 《环境质量管理》2023,32(4):9-17
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
150.
Jacob A. Zwart Samantha K. Oliver William David Watkins Jeffrey M. Sadler Alison P. Appling Hayley R. Corson-Dosch Xiaowei Jia Vipin Kumar Jordan S. Read 《Journal of the American Water Resources Association》2023,59(2):317-337
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