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Microfibers (MFs) are fibrous micro particles of longitude less then 5 mm, including natural fibers and fibrous microplastics. Microplastic pollution has become a world issue. As the major section of fiber production and processing, textile industry is an important potential source of microfibers, while receiving limited attention. To better understand the source and fate of textile microfibers, in this study, a typical textile industrial park in China is selected as the studying site. Microfibers in textile wastewater from typical textile mills and centralized wastewater treatments plants (WWTPs) of the park, and microfibers in nearby surface water were identified and characterized. The main results showed that the microfiber concentration in textile printing and dyeing wastewater could reach as high as 54,100 MFs/L. Although the removal efficiencies of microfibers by existing wastewater treatment processes can be over 85%, the average microfiber concentration in the effluents from the centralized WWTPs of the industrial park still reached 537.5 MFs/L, releasing 430 billion microfiber items per day. Microfiber release from textile wastewater is considerably higher than that from municipal sewage treatment plants, making it a significant contributor to microfibers in natural water bodies. Small-sized and colored microfibers increased in proportion in the treated effluents. Given the complex textile wastewater constituents, the potential negative environmental impacts of textile microfibers may be intensified by the enhanced adsorption and transfer of textile pollutants through these microfibers.Improving the green total-factor productivity (GTFP) is a key measure to coordinate industrial development and environmental protection in China. This study adopts the biennial Malmquist-Luenberger (BML) productivity index to estimate the GTFP change of China's 34 industrial subsectors covering the period 2005-2015. Subsequently, fixed-effect panel quantile regression is applied to analyze the heterogeneous effects of eight selected influencing factors on China's industrial GTFP change. The results show that China's overall industrial GTFP exhibited an increasing trend during the study period and varied greatly in different sub-sectors. Moreover, technological innovation rather than efficiency promotion was the main contributor to the improvement of industrial GTFP in China. The impact of the scale structure (SS) was significantly positive across the quantiles and maintained a slightly downward trend. Androgen Receptor Antagonist manufacturer The impact of the property rights structure (PTS) was significantly negative and showed an increasing trend across the quantiles. The impact of the energy intensity (EI) slightly increased and was significantly negative at most quantiles. The energy consumption structure (ECS) exhibited an increasing trend and had a significantly negative effect at the middle quantiles. Technological innovation (TI) exerted a significantly positive effect and displayed a downward trend across the quantiles, and it was the most important factor to drive industrial GTFP growth. The "pollution halo" hypothesis and the Porter hypothesis were both verified with a certain range from the analysis of foreign direct investment (FDI) and environmental regulation (ER), as well as the interaction between ER and TI. Our results stress the importance of the heterogeneous effects of these influencing factors on different quantile subsectors when formulating the related measures and policies.Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO2 tropospheric levels using satellite data available at the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO2 levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia-Romagna. We found little association of NO2 levels with SARS-CoV-2 prevalence up to about 130 μmol/m2, while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.The accumulation of anthropogenic chemical substances in aquatic organisms is an immensely important issue from the point of view of environmental protection. In the context of the increasing number and variety of compounds that may potentially enter the environment, there is a need for efficient and reliable solutions to assess the risks. However, the classic approach of testing with fish or other animals is not sufficient. Due to very high costs, significant time and labour intensity, as well as ethical concerns, in vivo methods need to be replaced by new laboratory-based tools. So far, many models have been developed to estimate the bioconcentration potential of chemicals. However, most of them are not sufficiently reliable and their predictions are based on limited input data, often obtained with doubtful quality. The octanol-water partition coefficient is still often used as the main laboratory tool for estimating bioconcentration. However, according to current knowledge, this method can lead to very unreliable results, both for neutral species and, above all, for ionic compounds.