Long-term monitoring is required to assess the current state and trend of water quality in anthropogenic-influenced estuarine systems. In this study, we evaluated 1581 samples from a 27-year time series (1990–2016) of physicochemical parameters, microbiological, and heavy metals in the surface waters of the Santa Cruz estuarine channel, Brazil. The results showed that the salinity in the estuarine channel is classified as euhaline (salinity: 30.7) during the dry period and polyhaline (salinity: 26.5) during the rainy season. Statistical analysis showed a significant negative trend for dissolved oxygen (DO) (Mann–Kendall tau; p < 0.05; α: 0.05) with a decrease in DO of − 0.012 mg O₂ l⁻¹ year⁻¹. Total phosphorus (TP) showed a significant positive trend in the period 2006–2016 (Kendall tau test; p = 0.0007; α = 0.05). In the last decade, TP showed an increased rate of + 0.007 mg P l⁻¹ year⁻¹. Additionally, the bacterial load (most probable number-MPN method) reached an average value of 55,000 MPN 100 ml, which is 22 times the allowable value for brackish water. Heavy metals (cadmium and manganese) showed a significant increase rate, induced by factors such as industrial growth in the adjacent region and changes in TP, pH, and oxygenation in the water, which changed the accumulation of Fe and Mn in the aquatic medium. The trophic state varied between unpolluted and eutrophic. Comparisons with other systems revealed high fluxes of TP (> 4 mol km⁻² year⁻¹). Demographic factors (28% average growth rate) and inadequate public sanitation policies (70% untreated sewage) appear to be the main causes of negative variations in some parameters, such as DO, TP, and pH.

Ethidium monoazide bromide (EMA) and propidium monoazide (PMA), in combination with amplicon-based sequencing (ABS) and quantitative polymerase chain reaction (qPCR) assays, were compared for the detection of viable bacterial species in rainwater. The ABS α- and β-diversity indices indicated that, in comparison to the untreated samples, both EMA and PMA reduced the detection of non-viable bacteria in the rainwater samples. However, while comparable results were obtained for the detection of the most abundant bacterial families and genera in the rainwater samples for both the EMA and PMA pre-treatments; the EMA pre-treatment produced highly significant differences in the relative abundance of the dominant bacterial families and genera, and significantly decreased the detection of “unclassified” bacteria in comparison to the untreated samples (i.e. unclassified genera: untreated 46.7% versus EMA-treated 39.7%). Additionally, the inclusion of EMA and PMA allowed for the increased detection of less abundant pathogenic bacteria in the rainwater samples, such as Clostridium, Listeria and Streptococcus spp. The qPCR assays also indicated that the EMA and PMA pre-treatments were comparable for the detection of intact and potentially viable Acinetobacter, Legionella and Pseudomonas spp. The combination of viability pre-treatments with ABS thus offers a comprehensive monitoring approach to detect less abundant bacterial genera and/or pathogens, which may pose a health risk to the end-users (prioritise target organisms) of unpiped environmental water sources. Moreover, the subsequent combination of the viability pre-treatments with qPCR allows for the specific detection and quantification of these pathogenic genera, which increases analysis sensitivity and allows for the application of risk assessment and water safety strategies.

In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

The paper presents the design, development and investigative studies into the use of a solar hot case/food warmer. The solar hot case can prove to be highly useful to the persons who carry hot food to their workplaces for lunch and eat it 4–5 h later. It can help keep food warm and infection free, by maintaining the food at an elevated temperature. The solar hot case has been developed with a total aperture area of 0.673 m². The energy requirements for food warming have been calculated and found to be around 62 kJ in summer and 90 kJ in winter for an average lunch box of mass 0.25 kg containing 0.4 kg of food. The design of the solar hot case has been prepared on the basis of the food warming calculations to allow around ten lunch boxes to be warmed simultaneously. A computer program based on the solar radiation geometry and an ASHRAE model has been prepared to study the energy available in various months and to estimate the solar energy interception by the solar hot case. The results show that the energy absorbed by the system at an instant varies from around 300 to 1400 kJ/m²h in winter and 1000–2000 kJ/m²h in summer. Experimental in-field observations related to the temperature profiles of the developed system are also reported.

Cesium-137, as the main fission product, is of special interest in the marine environment because of its solubility, which results to very low sinking time. Nevertheless, the conservative form of the main percentage of ¹³⁷Cs introduced in the marine environment (70%) makes ¹³⁷Cs to be included in the salinity of sea water. Based on this property, in this study, we examine potential relations between ¹³⁷Cs activity concentrations and marine parameters issued from Earth Observation (EO) data products in the Southern Aegean Sea, in order to investigate the possibility of ¹³⁷Cs to be recorded by satellite data. In particular, measurements of physical and biological marine parameters issued from the Copernicus Marine Environment Monitoring Service (CMEMS) database and MODIS ocean products are retrieved for the dates of ¹³⁷Cs field measurements. Single and multiple regression analyses are performed between the marine parameters and ¹³⁷Cs activity concentration measurements for three distinctive time periods (total, cold, and warm period). The best results are obtained from multiple regressions, one for each time period (r² > 0.70). The models show that during cold period, ¹³⁷Cs activity concentrations are highly correlated to both chlorophyll and nutrients (phosphates) while during warm and the total period, they seem to be mainly correlated to the photosynthetic available incident solar radiation on the sea surface. For each period, we propose a multiparameter model linear in its parameters. Although the results of this study must be considered preliminary due to the limited size of the datasets, for the first time, we show that estimations of ¹³⁷Cs activity concentrations from EO measurements and CMEMS environmental models are feasible, and they can be used as a marine radiological assessment tool for a closed Mediterranean bay such as Souda Bay in Greece.

Based on the panel data of 46 countries in “Belt and Road” (B&R) from 2004 to 2016, this paper studies the impact of innovation investment and institutional quality on green total factor productivity (GTFP). Firstly, the ICRG database, World Bank WDI database, Traditional Foundation database, and Wind database are matched to obtain the balanced panel data of 46 countries along the B&R from 2004 to 2016. Secondly, the Malmquist-Luenberger index, which can be included in the unexpected output, is used to calculate the GTFP of countries along B&R. Thirdly, the evaluation system of national institutional quality of B&R is constructed from three dimensions (political institutional quality, economic institutional quality, and legal institutional quality), and the overall system quality of different countries is measured by entropy method. Finally, an empirical study is made on the relationship among innovation investment, institutional quality, and green total factor productivity. The results show that innovation investment has significantly promoted the GTFP of the B&R countries. It is worth noting that there is a non-linear relationship between innovation investment and GTFP in the B&R countries. With the improvement of overall system quality, political system quality, economic system quality, and legal system quality, the promotion effect of innovation investment on GTFP is further enhanced. In addition, the heterogeneity regression results show that the impact of innovation investment on GTFP is significantly heterogeneous in different regions of the B&R countries. Specifically, innovation investment has the greatest impact on GTFP in South Asia, followed by East Asia and Pacific, Europe and Central Asia, Middle East, and North Africa.

Governments actively encourage renewable energy use to deal with climate change and achieve carbon emission reduction targets. It is crucial to find out the driving factors that affect the utilization of renewable energy. Therefore, based on China’s 2010–2016 input–output table, this paper uses the input–output model and structural decomposition analysis (SDA) to analyze the driving factors of renewable energy changes in the production end, household end, and the aggregate economy. The results show that the changes in the consumption structure (F) is the most crucial factor for renewable energy use, followed by technology progress (T) and final demand per capita (V). Sector SEHW (supply of electric power, heat power, and water) and MCRP (manufacture of coke and refined petroleum products) are the two vital sectors to achieve China’s energy transition of the production level. However, as for households, the proportion of renewable energy has been declining. Hence, the government should promote renewable energy use and achieve the green transition in production and household levels.

Electrocoagulation (EC) is an excellent and promising technology in wastewater treatment, as it combines the benefits of coagulation, flotation, and electrochemistry. During the last decade, extensive researches have focused on removal of emerging contaminants by using electrocoagualtion, due to its several advantages like compactness, cost-effectiveness, efficiency, low sludge production, and eco-friendness. Emerging contaminants (ECs) are micropollutants found in trace amounts that discharging into conventional wastewater treatment (WWT) plants entering surface waters and imposing a high threat to human and aquatic life. Various studies reveal that about 90% of emerging contaminants are disposed unscientifically into water bodies, creating problems to public health and environment. The studies on removal of emerging contaminants from wastewater are by global researchers are critically reviewed. The core findings proved that still more research required into optimization of parameters, system design, and economic feasibility to explore the potential of EC combined systems. This review has introduced an innovative collection of current knowledge on electro-coagulation for the removal of emerging contaminants.

This research article aims to establish an easy and well-defined analytical method for detection and quantification of multiclass pesticides in Gomti river water samples because the increased agricultural activities, industrialization, and urbanization had increased the presence of pesticides in the ecosystem which causes the depletion of water quality making it a global concern. The analytical method, vortex-assisted ultrasonication-based dispersive liquid–liquid microextraction-solidification of floating organic droplets (VAUS-DLLME-SFO) was optimized using one parameter at a time approach which gave the recovery between 69.45 and 114.15%, limit of detection (LOD), and limit of quantification (LOQ) 0.0011–0.0111 µg/L and 0.0033–0.0368 µg/L, respectively, and RSD in the range of 0.75–1.29 which shows sensitivity and accuracy better than earlier reported methods. The data obtained were subjected to measurement uncertainty, risk assessment, and multivariate statistical analysis to establish the robustness of the developed analytical method. The measurement uncertainty found was concluded to be in the acceptable range for analytical results. Furthermore, the real samples were analyzed and the associated value of the risk quotient was found to be less than 1, except for aquatic invertebrates, establishing the fact that the current concentration of pesticides has no such negative threat to flora and fauna. The possible source of pesticides in the Gomti river system was established by multivariate analysis. It was thus concluded that anthropogenic activity is responsible for the variable concentration of pesticides found in the sample.

Antifungal azoles are the most frequently used fungicides worldwide and occur as active ingredients in many antifungal pharmaceuticals, biocides, and pesticides. Azole fungicides are frequent environmental contaminants and can affect the quality of surface waters, groundwater, and drinking water. This study examined the potential of combined vacuum UV (185 nm) and UVC (254 nm) irradiation (VUV/UVC) of the azole fungicide tebuconazole and the transformation product 1,2,4-trizole on degradation and changes in ecotoxicity. In vivo ecotoxicity was examined before and after UV treatment using bioassays with test organisms from different trophic levels to integrate changes in biological effect of the parent compound and the degradation products. The test battery included the luminescent bacterium Aliivibrio fischeri, the Gram-positive bacterium Bacillus subtilis, the fungus Fusarium graminearum, the green microalga Raphidocelis subcapitata, and the crustacean Daphnia magna. The combined VUV/UVC treatment of tebuconazole in drinking water efficiently degraded the parent compound at the µg/L-mg/L level and resulted in transformation products with lower toxicity than the parent compound. A direct positive correlation was observed between the applied UV dose (fluence, J/cm²), the disappearance of tebuconazole, and the decrease in ecotoxicity. The combined VUV/UVC process does not require addition of supplementary oxidants or catalysts and our study suggests that VUV/UVC-mediated photolysis of azole fungicides in water can decrease the overall toxicity and represent a potentially environmentally friendly treatment method.