Coal ash has emerged as an important alternative source for rare earth elements (REEs). The enrichment and occurrence form of REEs among coal combustion products are of great significance for both technical design and economic evaluation of recovering REEs from the coal ash. Here, the enrichment and occurrence form of REEs in the ash were investigated. Compared with ashes from muffle furnace, coal fly ash (CFA) from power plants involved higher enrichment ratio of REEs, which was explained by the fractionation of coal ashes to concentrate REEs in finer CFA, higher combustion temperature to vaporize more volatile elements, and longer residence time of fly ash to absorb REEs in the gas. In addition, CFA samples were analyzed by sequence chemical extraction procedure (SCEP) and scanning electron microscope with an energy dispersive spectrometer (SEM–EDX), which revealed the important role of aluminum in the occurrence form of REEs compared with Si in aluminosilicates of CFA. This conclusion was further confirmed by thermodynamic equilibrium calculation, which also agreed qualitatively with the observation that REEs mainly existed in the solid phase. Both experimental and computational results of this work provided insights to understand the distribution of REEs in CFA and optimize their extraction processes.

Heavy metal pollution is a global environmental problem, and the potential risks associated with heavy metals are increasing. The acid mine drainage (AMD) which is generated by mining activities at Dabaoshan Mine, the largest polymetallic mine in southern China, is harmful to local residents. A detailed regional survey of the ecological and human health risks of this polluted area is urgently needed. In this study, eight sediments and farmland samples were collected along the flow direction of tailing wastewater and Fandong Reservoir; the content of multiple heavy metals in these samples was determined by inductively coupled plasma mass spectrometry. The biological toxicity of water-soluble extracts from the samples was further assessed by referring to different endpoints of Caenorhabditis elegans (C. elegans). The relationship between specific heavy metals and biological toxicity was estimated by partial least squares regression. The results indicated that the risk of heavy metals in Dabaoshan mining area was very high (potential ecological risk index = 721.53) and was related to geographical location. In these samples, the carcinogenic risk (the probability that people are induced carcinogenic diseases or injuries when exposed to carcinogenic pollutants) of arsenic (As) for adults exceeded the standard value 1 × 10⁻⁴ and indicated that As presented a high carcinogenic risk to adults, while the high risk of non-carcinogenic effects (the hazard degree of human exposure to non-carcinogenic pollutants) in children was related to lead exposure (hazard index = 1.24). In addition, the heavy metals at high concentration in the water-soluble fraction of sediment and farmland soil extracts, which might easily distribute within the water cycle, inhibited the survival rate and growth of C. elegans. Gene expression and enzymatic activity related to oxidative stress were increased and genes related to apoptosis and metallothionein were also affected. In conclusion, the results of chemical analysis and biological assays provided evidence on the toxicity of soil and sediment extracts in the Dabaoshan mining area and advocated the control and remediation of heavy metal pollution around Dabaoshan Mine.

The occurrence of three anticancer drugs (gemcitabine, letrozole, tamoxifen) was studied in wastewater samples from two local wastewater treatment plants (WWTPs) in Finland. Studied pharmaceuticals were selected, as anticancer drugs are potential to cause adverse effects on organisms even at low concentrations, but they are seldom included in the analysis of emerging contaminants. The concentration of anticancer drugs was determined by liquid chromatography-triple quadrupole mass spectrometer (LC–MS/MS). Tamoxifen and letrozole were detected from influent samples ranging from 0.5 to 5.0 ng/L, respectively. Letrozole was detected from effluent samples at a concentration up to 2.4 ng/L. Letrozole has been detected in wastewater effluent only once before, at a lower concentration of 0.28 ng/L. Gemcitabine was not detected in any of the samples. UV irradiation is used in many wastewater treatment plants to disinfect the effluent. Such tertiary treatment might degrade also these potentially harmful drugs and, therefore, photodegradation of the chosen pharmaceuticals was studied in laboratory-scale experiments. Tamoxifen showed high degradation rates, 94% in spiked wastewater with UV fluence 4830 mJ/cm² and 98% in pure water with UV fluence 2520 mJ/cm², respectively. Letrozole showed the lowest degradation rates of 24% in wastewater and 34% in pure water, respectively. The degradation rate at the fluence level typical for UV disinfection stage of wastewater treatment plants was 37% for tamoxifen but only 5% for letrozole. To the best of the authors’ knowledge, this is the first report to show the effectiveness of UV irradiation to degrade letrozole.

Livers of caribou and reindeer (Rangifer tarandus) from Canada (n = 146), Greenland (n = 30), Svalbard (n = 7), and Sweden (n = 60) were analyzed for concentrations of eight perfluoroalkyl carboxylic acids and four perfluoroalkane sulfonic acids. In Canadian caribou, PFNA (range < 0.01–7.4 ng/g wet weight, ww) and PFUnDA (<0.01–5.6 ng/g ww) dominated, whereas PFOS predominated in samples from South Greenland, Svalbard, and Sweden, although the highest concentrations were found in caribou from Southwest Greenland (up to 28 ng/g ww). We found the highest median concentrations of all PFAS except PFHxS in Akia-Maniitsoq caribou (Southwest Greenland, PFOS 7.2–19 ng/g ww, median 15 ng/g ww). The highest concentrations of ΣPFAS were also found in Akia-Maniitoq caribou (101 ng/g ww) followed by the nearby Kangerlussuaq caribou (45 ng/g ww), where the largest airport in Greenland is situated, along with a former military base. Decreasing trends in concentrations were seen for PFOS in the one Canadian and three Swedish populations. Furthermore, PFNA, PFDA, PFUnDA, PFDoDA, and PFTrDA showed decreasing trends in Canada’s Porcupine caribou between 2005 and 2016. In Sweden, PFHxS increased in the reindeer from Norrbotten between 2003 and 2011. The reindeer from Västerbotten had higher concentrations of PFNA and lower concentrations of PFHxS in 2010 compared to 2002. Finally, we observed higher concentrations in 2010 compared to 2002 (albeit statistically insignificant) for PFHxS in Jämtland, while PFNA, PFDA, PFUnDA, PFDoDA, and PFTrDA showed no difference at all.

Renewable energy technology innovation (RETI) is a crucial driver for promoting the manufacturing green transformation. However, few studies have explored the impact of RETI on manufacturing carbon intensity (MCI) from the perspective of spatial spillover and regional boundary. Based on the manufacturing panel data of 30 provinces in China from 2006 to 2020, this study examines the mechanism, spatial spillover effects, regional boundaries, and industry heterogeneity of RETI on MCI using the spatial Durbin model. The results show that (1) RETI significantly inhibits local and neighboring MCI. (2) The spatial spillover effect of RETI on MCI has a significant regional boundary, which is inhibitory in the range of 800 km and shows a significant “half-decay” characteristic at 400 km. However, in the range of 800 to 1400 km, RETI significantly promotes neighboring MCI. (3) The inhibitory effect of RETI on MCI has temporal and regional heterogeneity, which gradually increases over time, and the effect from high to low is central, west, and east. (4) RETI has a significant inhibitory effect on MCI of pollution-intensive, high-income, capital-intensive, and labor-intensive manufacturing in local and neighboring areas, but it has a more negligible effect on non-pollution-intensive, low-income, and technology-intensive MCI. The findings provide empirical evidence for formulating targeted and differentiated policy in manufacturing low-carbon development.

Studies examined the connection between antibiotic exposure in urine and dysglycemia risk (including prediabetes and diabetes) in the elderly were limited. Multiple linear regression, binary logistic regression, restricted cubic splines (RCS), and stratified analysis were applied to analyze the relationship between antibiotic exposure and dysglycemia risk. We observed that sulfaclozine exposure 0.07 (95% confidence interval [CI]: 0.01–0.23) significantly increased fasting blood glucose (FBG) level. By mechanism, usage, and antimicrobial action, sulfonamides 0.08 (95% CI: 0.06–0.36), veterinary antibiotics (VA) 0.07 (95% CI: 0.01–0.30), or bacteriostatic antibiotics 0.07 (95% CI: 0.02–0.29) significantly increased FBG level. Additionally, sulfaclozine exposure 1.54 (95% CI: 1.02–2.33) resulted in a higher dysglycemia risk, while doxycycline exposure 0.53 (95% CI: 0.30–0.95) resulted in a lower dysglycemia risk. By mechanism, usage, and antimicrobial action, sulfonamides 1.44 (95% CI: 1.02–2.04), VA 1.68 (95% CI: 1.21–2.35), or bacteriostatic antibiotics 1.40 (95% CI: 1.02–1.93) exposure had a higher dysglycemia risk. Taken together, exposure to sulfonamides, VA, especially sulfaclozine, was correlated with a higher dysglycemia risk in the elderly. Exposure to bacteriostatic antibiotics was associated with a higher dysglycemia risk in the female.

The present study first describes the variations in concentrations of 12 chemical elements in groundwater relative to salinity levels in Southern Quebec (Canada) groundwater systems, and then uses this data to develop an empirical predictive model for evaluating groundwater chemical composition relative to salinity levels. Data is drawn from a large groundwater chemistry database containing 2608 samples. Eight salinity classes were established from lowest to highest chloride (Cl) concentrations. Graphical analyses were applied to describe variations in major, minor, and trace element concentrations relative to salinity levels. Results show that the major elements were found to be dominant in the lower salinity classes, whereas Cl becomes dominant at the highest salinity classes. For each of the major elements, a transitional state was identified between domination of the major elements and domination of Cl. This transition occurred at a different level of salinity for each of the major elements. Except for Si, the minor elements Ba, B, and Sr generally increase relative to the increase of Cl. The highest Mn concentrations were found to be associated with only the highest levels of Cl, whereas F was observed to be more abundant than Mn. Based on this analysis of the data, a correlation table was established between salinity level and concentrations of the chemical constituents. We thus propose a predictive empirical model, identifying a profile of the chemical composition of groundwater relative to salinity levels, to help homeowners and groundwater managers evaluate groundwater quality before resorting to laborious and costly laboratory analyses.

Several countries across the African continent have been challenged with energy crises for decades. A growing number of studies have identified renewable energy as a sustainable way for Africa to address its persisting energy situation while combating climate change, as the continent has in abundance some of the common renewable energy resources. Little has been reported in the body of literature to quantitatively and qualitatively map the knowledge domain of this growing research field. In the current study, we conduct a bibliometric analysis on research documents extracted from the Web of Science Core Collection to identify trends and characteristics of the knowledge domain related to renewable energy in Africa from 1999 to 2021. Using two different software (VOSviewer and ITgInsight), we report the contribution of journals, countries, institutions, and authors and their collaboration patterns. We also perform co-citation and keyword analysis to identify the intellectual base and central themes of this research field. The results from the study revealed a growing interest in Africa’s renewable energy, with about 90% of the total publication from within the last decade. Renewable & Sustainable Energy Reviews was identified as the most productive as well as the most influential journal in this field. The most contributing countries in this field were South Africa, USA, and Algeria. Centre de Developpement Des Energies Renouvelables, a research institute in Algeria, emerged as the most productive and influential institution. The analysis of research hotspots under different categories revealed that “solar energy,” “CO₂ emissions,” and “rural electrification” are the topics that have gained maximum attention over the years. Keyword evolution analysis also identified “economic growth” and “green hydrogen production” as emerging topics that will play a major role in future studies. We conclude our work by providing specific suggestions and strategies to help bridge the gap which exists in the quantity and quality of renewable energy research between Africa and the rest of the world.

In this research work, the experimental tests were conducted on a single-cylinder, constant speed, variable compression ratio (VCR) engine fuelled with green diesel. Initially, bio-oil was extracted from waste Trichosanthes cucumerina fruit seeds using the Soxhlet apparatus. The acquired bio-oil is used to make green diesel through the trans-esterification process. The fuel blends were prepared with different proportions of Trichosanthes cucumerina biodiesel (TCB) in diesel fuel (30%, 50%, and 70%) for the experimental test, and their thermo-physical properties were evaluated according to ASTM standards. At full load condition, the TCB30 blend with CR 18:1 gives closer engine performance of brake thermal efficiency (33.52%), brake specific fuel consumption (0.27 kg/kWh), and exhaust gas temperature (389.56 °C) and reduced emission levels of unburned hydrocarbon by 13.51%, carbon monoxide by 10.82%, smoke opacity by 16.87%, and the penalty of nitric oxide by 17.56% equated with neat diesel fuel. The engine performance and emission parameters are predicted using multiple regression artificial neural network (ANN) models. A database generated from the experimental results is used to train the ANN model. The average correlation coefficient (R) of the trained ANN model is 0.99673, which is closer to 1. It indicates that the proposed ANN model can generate the exact correlation between input factors and output responses. As a result, the application of ANN is a better forecasting tool for predicting VCR engine performance and emission characteristics.