The textile industry is an important potential source of environmental pollution due to the use of chemical products. Dyes, hydrolyzed dyes, and surfactants, among others, are chemical compounds present in wastewater of textile plant. Moreover, the anionic surfactants have toxic effects for various aquatic organisms even in low concentrations. The methodologies investigated to quantify surfactants, in general, consume a lot of analysis time and frequently use toxic or environmentally objectionable reagents. For these reasons, the objective of this work was to develop a quick and simple method to quantify surfactants without the use of expensive reagents and equipment, avoiding extraction and preconcentration stages. The proposed method is based on fluorescent spectroscopy measurements for the acquisition of second-order data in excitation-emission matrices and multivariate calibration techniques applied to the data. The unfolded partial least squares combined to residual bilinearization (U-PLS/RBL) algorithm was better than parallel factor analysis (PARAFAC). U-PLS/RBL accurately quantified alkylnonylphenolethoxylated (APEO), dodecylbenzenesulfonic acid (ADBS), and 2-phenoxy-ethoxylated fatty alcohol (AGFE) surfactants. The chemometric model obtained good analytical figures of merit: REP% between 5 and 13 and LOQ between 0.45 and 2.77 μg mL⁻¹. This methodology had no significant difference compared with results obtained by a HPLC-FD reference technique, in addition with a considerable reduction in analysis time, reagent consumption, and therefore lower cost. For environmental applications, APEO, ADBS, and AGFE were quantify in textile wastewater treatment and in the receiving water body. The concentrations varied from 8.73 to 73.94 μg mL⁻¹ in the textile wastewater and were not detected in the receiving water body.

In this work, tea waste biochar was prepared and used to activate peroxodisulfate (PDS) for the removal of tetracycline (TC) efficiently. And SEM, XRD, Raman, and FTIR were used to characterize the biochar. The effects of reaction conditions including initial pH, biochar dosage, and PDS concentration on the removal of TC were explored, and the result showed that compared with the biochar prepared at 400 °C and 500 °C, the biochar pyrolyzed at 600 °C (TBC600) had the highest TC removal performance due to its higher sp² hybrid carbon content, richer defective structure, and stronger electron deliverability. Under the optimal dosage of PDS (4 mM) and TBC600 (0.8 g L⁻¹), the removal efficiency of TC (10 mg L⁻¹) reached 81.65%. After four cycles of TBC600, the removal rate could still reach 75.51%, indicating that TBC600 has excellent stability. In addition, quenching experiments and electron paramagnetic resonance (EPR) verified that the active oxygen including SO₄·⁻, ·OH, O₂·⁻, and singlet oxygen (¹O₂) was involved, among which ¹O₂ and OH were the main active substance in the TC removal. Therefore, this work provided a green and efficient persulfate activator and a method for recycling tea waste.

Over the years, biodegradation has been an effective technique for waste water treatment; however, it has its own limitations. In order to achieve a higher degradation efficacy, integrated processes are being focus in this area. Therefore, the present study is targeted towards the coupling of biodegradation and photocatalytic degradation of p-cresol. The biodegradation of p-cresol was performed via lab isolate Serratia marcescens ABHI001. The obtained results confirmed that ~85% degradation of p-cresol was accomplished using Serratia marcescens ABHI001 strain in 18 h. Consequently, degradation of remaining residue (remaining p-cresol concentration initially used) was also examined in a batch reactor using activated carbon−TiO₂ nanocomposite (AC/TiO₂-NC) as a catalyst under the exposure of UV radiation. The AC/TiO₂-NC was processed via sol-gel technique and characterized by various techniques, namely Brunauer-Emmett-Teller (BET), scanning electron microscope (SEM), X-ray diffraction (XRD), and Fourier transformed infrared spectroscopy (FT-IR). The investigation allowed p-cresol degradation further augment up to ~96% with the help of spectrophotometer trailed by high performance liquid chromatography (HPLC). This study demonstrates that integrated process (biodegradation-photodegradation) is the cost-effective bioremediation process to overcome such kinds of pollutant issues.

Paddy soil Cd contamination and the related accumulation risk in rice grains have attracted global attention. The application of selenium and humic substances is considered to be a cost-effective Cd mitigation measure. However, the effect of a combined application of the two materials remains unclear. Therefore, a 2-season pot experiment was conducted, wherein sodium selenite (Se) and biochemical fulvic acid (BFA) were applied alone and together. Paddy soils with two levels of Cd contamination were used. The results indicate that Se application alone considerably decreased the rice grain Cd content by 36.1–48.7% compared to the control rice grain Cd concentration, which was above the food safety limit (0.2 mg kg⁻¹). Although the application of BFA alone decreased the soil pH, it also increased the soil CaCl₂ extractable Cd content by 0.2 to 19.3% and had a limited effect on Cd in the rice grains. The combined application of Se and BFA did not affect the soil pH or the CaCl₂ extractable Cd, and more effectively reduced the Cd contents of the rice grains by 50.2 to 57.1%, except for the control rice grain Cd content, which was below the limit. The combined application of Se and BFA also inhibited Se accumulation in rice grains, maintaining the Se content at a safe level (0.33–0.58 mg kg⁻¹) compared to Se application alone. The effects of reducing the Cd content of rice grains while safely increasing their Se contents could persist for at least two seasons. Therefore, the combined application of Se and BFA should be recommended to mitigate Cd contamination risks in Cd-contaminated paddy soil.

Air pollution and its negative effects on health of people have been a global concern. Many studies had found a strong association between air pollutants and risk of death, but few had focused on the effects of six pollutants and rural areas. Our study aimed to investigate the effects of six air pollutants (CO, NO₂, O₃, PM₂.₅, PM₁₀, and SO₂) on non-accidental and respiratory deaths in rural areas of Anhui Province by adjusting for confounding factors, and to further clarify which populations were susceptible to death associated with air pollution. In the first phase of the analysis, the generalized additive models were combined with the distributed lag non-linear models to evaluate the individual effects of air pollution on death in each area. In the second stage, random-effects models were used to aggregate the associations between air pollutants and mortality risk in nine areas. Overall, six pollutants had the strongest effects on the risk of death on the lag 07 days. The associations between PM₂.₅ and NO₂ and daily non-accidental deaths were strongest, with maximum RR (lag 07): 1.63 (1.37–1.88) and 1.67 (1.37–1.96). The maximum pooled effects of association between six air pollutants and RD were PM₂.₅, with RR (lag 07): 1.89 (1.45–2.34). PM₂.₅ and PM₁₀ had significant differences between the elderly and the non-elderly with respectively, RRR: 1.22 (1.04–1.41) and 1.26 (1.11–1.42). In general, we found that six air pollutants were the important risk factors for deaths (deaths from respiratory disease and non-accidental) in rural areas of Anhui Province. PM₁₀ and PM₂.₅ had a considerable impact on the elderly.

Good surface water quality is critical to human health and ecology. Land use determines the surface water heat and material balance, which cause climate change and affect water quality. There are many factors affecting water quality degradation, and the process of influence is complex. As rivers, lakes, and other water bodies are used as environmental receiving carriers, evaluating and quantifying how impacts occur between land use types and surface water quality is extremely important. Based on the summary of published studies, we can see that (1) land use for agricultural and construction has a negative impact on surface water quality, while woodland use has a certain degree of improvement on surface water quality; (2) statistical methods used in relevant research mainly include correlation analysis, regression analysis, redundancy analysis, etc. Different methods have their own advantages and limitations; (3) in recent years, remote sensing monitoring technology has developed rapidly, and has developed into an effective tool for comprehensive water quality assessment and management. However, the increase in spatial resolution of remote sensing data has been accompanied by a surge in data volume, which has caused difficulties in information interpretation and other aspects.

Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.

The contradiction between China’s economic development and the reduction of carbon emission is increasingly deepening along with the complex carbon emission transfer. Optimizing provincial-level carbon emission transfer in China is important for facilitating economic development and carbon emission reduction. Under these dual constraints, this study uses the slacks-based measure, marginal abatement cost, and geographically and temporally weighted regression models to measure the economic and carbon emission reduction effects and the carbon emission reduction baseline. Then, the optimization strategy and path of provincial carbon emission transfer network structure are proposed to provide policy support for achieving the dual goals of economic development and carbon emission reduction in China. This article draws the following important research conclusions. First, under the three economic development scenarios, provinces in the eastern coastal developed regions are capable of completing the expected carbon emission reduction, whereas the underdeveloped provinces in the central and northern regions are not. Second, from the perspective of the economic effect of carbon emission transfer, carbon emission transfer from most provinces promotes economic development, whereas carbon emission transfer from a few economically underdeveloped provinces hinders economic development. Third, from the perspective of the carbon emission reduction impact of carbon emission transfer, carbon emission transfer in the northeast region has a negative impact on carbon emission reduction, and carbon emission transfer in developed regions also has a negative impact on carbon emission reduction. Fourth, the optimization of the carbon emission transfer can be divided into four categories.

Microorganisms play a major role in the natural attenuation of pollutants in groundwater, such as petroleum hydrocarbons. However, there are few studies on the structure and diversity of archaeal bacterial communities in extreme environments formed by petroleum hydrocarbon-contaminated groundwater. In this study, archaeal community composition of five petroleum hydrocarbon-contaminated groundwater wells located at an abandoned chemical factory representing low (0.00 ± 0.01 mg/L), mild (0.59 ± 0.02 mg/L), and high (2.05 ± 0.72 mg/L) concentration of total petroleum hydrocarbons (TPH) was investigated by sequencing microbial 16S rRNA gene amplicons. The results showed that Euryarchaeota and Thaumarchaeota were the main archaeal groups in TPH-contaminated groundwater, which composed 99% of the community abundance. TPH was found to be the dominant factor affecting the diversity and abundance of archaeal communities in high-contaminated wells. Total phosphorus, NO₃⁻-N, and pH greatly influenced the archaeal communities in mild-contaminated wells, while low-contaminated wells were mainly influenced by temperature. Our results provided insights into the archaeal community composition of petroleum hydrocarbon-contaminated groundwater.

The release of toxic metals in the water creates an adverse condition for the living organisms (e.g., fish). The aim of this research was to learn more about the spatiotemporal variations and toxicity of heavy metals (As, Cr, Cd, and Pb) among fish species that are economically important (Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Mystus vittatus, Glossogobius giuris, Harpadon nehereus, Pseudapocryptes elongatus, Polynemus paradiseus, and Sillaginopsis panijus) collected from Pasur River. Heavy metal (HMs) concentrations were evaluated using the atomic absorption spectrometry (AAS) technique. Most of the metals showed no significant variation spatiotemporally (p ˃ 0.05) except As and Cr showed substantial variation in terms of seasons (p ˂ 0.05). All fish species’ Cr and Pb concentrations, as well as As and Cd values, were estimated to be greater than FAO/WHO tolerable concentrations, implying that these metals pose danger to humans. HM has a total hazard quotient (THQ) value in individual fish species reported to be greater than 1, whereas an individual metal, arsenic, exceeds the standard value (THQ > 1), causing a significant noncarcinogenic issue in the study region. The target hazard (TR) value for As and Pb exceeds the USEPA norm (10⁻⁴) suggesting that long-term consumption of fish poses a chronic cancer risk to the people in the study field. According to the findings, the fish in the Pasur River are unfit for human consumption. The correlation matrix (CM) indicates that sources of metals are similar (e.g., industries, ships, agricultural inputs, etc.).