Some studies have shown that maternal perfluoroalkylated substances (PFAS) exposure may be associated with low birth weight (LBW) of offspring. We conducted a meta-analysis to assess the association between maternal PFASs exposure and LBW in offspring. The researchers searched PubMed, Science Direct, Scopus, Google Scholar, Web of Science, and Embase to find all the articles before October 2020. The Newcastle-Ottawa Scale was used to evaluate the quality of the studies. Finally, six articles were included for meta-analysis. Our meta-analysis showed no significant correlation between maternal perfluorooctanoic acid (PFOA) exposure and LBW of offspring: odds ratio (OR) = 0.90, 95% confidence interval (95% CI) = 0.80–1.01, with low heterogeneity (I² = 18.4%, P = 0.289); there was a significant positive correlation between maternal perfluorooctane sulfonate (PFOS) exposure and LBW of offspring (OR = 1.32, 95% CI = 1.09–1.55) with no heterogeneity (I² = 0.00%, P = 0.570). The grouping analysis of PFOS showed was a significant positive correlation between maternal PFOS exposure and LBW of offspring in American (OR = 1.44, 95% CI = 1.15–1.72). This study provided a systematic review and meta-analysis evidence for the relationship between maternal PFASs exposure and LBW of offspring through a small number of studies. Researchers should conduct further studies between different regions.

A study is presented on the enhancement of solar still (SS) performance by using chimney exhaust gases (EGs) passing through chimney channels under the still basin. The impact of the exhaust gas temperature on the SS temperature, productivity, efficiency, and freshwater yield cost is considered. The SS performance with the chimney is compared with that of conventional solar still. The study is performed under the hot and cold climate conditions of Upper Egypt. A complete transient mathematical model of the physical model including the solar still regions temperatures, yield, and heat transfer between the SS and the EGs is constructed. This model is solved by using Runge-Kutta method of fourth-order and programmed inside MATLAB software and validated using an experimental setup. The results show that the SS saline water temperature and freshwater yield rise with rising EGs temperatures. Furthermore, the impact of using EGs on the SS performance in winter is superior to that in summer, and also during the daytime is higher than that of night. Using chimney EGs at 75 °C and 125 °C enhances the daily freshwater yield of the SS by more than three times and about six times in winter, respectively, and about two and half times and more than three times in summer, respectively. Using EGs at 125 °C achieves a maximum solar still efficiency of 29.5% in winter and 49.5% in summer with an increase of 41% and 55.7%, respectively, and reduces its yield cost by 63.6% compared to conventional SS.

We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound’s safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals’ use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver’s operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.

In this study, we studied the impact of pollution, metallic, organic, and environmental parameters, on benthic macroinvertebrates of the Reghaïa wetland and coastal zone, which is a nature reserve located in northern Algeria and is one of the last heritage sites on the central coast of the country. To do this, multivariate methods are used. The results of the multivariate analyses clearly show the impact of metal and organic pollution on the macroinvertebrates of this wetland which is really striking; this pollution has destroyed and upset the biodiversity of the benthic macrofauna and it has led to the disappearance of several taxa. This pollution has drained this wetland of the taxonomic richness of benthic macroinvertebrates, the only taxa that have resisted are the Diptera such as Psychodidae, Ceratopogonidae, and Syrphidae.

Highly time-resolved particle number size distributions (PNSDs) were evaluated during 5 years (2013–2017) at four background stations in the Czech Republic located in different types of environments—urban background (Ústí nad Labem), industrial background (Lom), agricultural background (National Atmospheric Observatory Košetice), and suburban background (Prague-Suchdol). The PNSD data was used for new particle formation event determination as well as growth rate (GR) and condensation sink (CS) calculations. The differences or similarities of these parameters were evaluated from perspectives of the different pollution load, meteorological condition, and regional or long-range transport. The median growth rate (4 nm h⁻¹) is very similar at all stations, and the most frequent length of growth lasted between 2 and 4 h. Condensation sink reflects the pollution load at the individual station and their connection to the environment type. The highest median, CS = 1.34 × 10⁻² s⁻¹, was recorded at the urban station (Ústí nad Labem), and the lowest (CS = 0.85 × 10⁻² s⁻¹) was recorded at the agricultural station (National Atmospheric Observatory Košetice). Conditional probability function polar plots illustrate the influence of source location to GR. These primary potential emission sources involve traffic, operation of a power plant, and domestic heating.

Application of organics in soil can reduce uptake of arsenic (As) in food crops grown on soil spiked with As. However, the results may be different if the crop is grown with As-contaminated irrigation water. In this study, a modest initiative has been undertaken to assess the organo-arsenic equilibria using the fulvic and humic extracted from paddy husk (PH), vermicompost (VC), and soil. The fulvic and humic acids were found to be polyprotic in nature. Fulvic acid extracted from paddy husk have the highest mole ratio or combining ability of 1.88 ± 0.59 and stability constant (log K) of 8.96 ± 0.21. Based on the above findings, a greenhouse experiment with wheat crop was conducted using different concentrations of As in irrigation water (1, 2, 4, and 8 mg L⁻¹), irrigated 5 times throughout the crop growth period and 3 levels of amendments (No amendment, VC and PH @ 10 t ha⁻¹ each). Paddy husk and also vermicompost can be effectively used as an amendment to trim down the bioavailability of arsenic in the wheat grain provided the As content in irrigation water is < 2 mg L⁻¹. Risk assessment of As spiked soils was carried out and expressed in hazard quotient as per USEPA guideline. Solubility free ion activity model was also used to forecast the transfer of As from As spiked soil to wheat grain.

Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have gained increasing concern due to their persistent characteristics, wide distribution, biotoxicity, and bioaccumulative properties. The current remediation technologies for PFOA and PFOS are primarily focused on physical and chemical techniques. Phytoremediation has provided promising alternatives to traditional cleanup technologies due to their low operational costs, low maintenance requirements, end-use value, and aesthetic nature. In this review, uptake, translocation, and toxic effects of PFOS and PFOA are summarized and discussed. Several potential hyperaccumulators of PFOS and PFOA are provided according to the existing data. Biomass, chlorophyll, soluble protein, enzyme activities, oxidative stress, and other variables are assessed for potential indicator of PFOS/PFOA biotoxicity. The various studies on multiple scales are compared for identifying the threshold values. Several important implications and recommendations for future research are proposed at the end. This review provides an overview of current studies on plant uptake of PFOS and PFOA from the perspective of phytoremediation.

Landfill leachate is commonly heavily contaminated and consists of high amount of organic compounds, inorganic salts, toxic gases, halogenated hydrocarbons, and heavy metals that exerts a serious threat to public health and the environment. Thus, it requires treatments before direct release into receiving waters. Selecting the efficient method for leachate treatment is still a major challenge. While physicochemical treatment methods such as coagulation-flocculation, adsorption, membrane filtration, ozonation, air stripping, and advanced oxidation processes (AOP) are appropriate for mature leachate, young leachate requires biological treatments including membrane bioreactor (MBR), activated sludge (AS), upflow anaerobic sludge blanket (UASB), and rotational biological contactor (RBC). Recently, the integration of biological processes and physicochemical methods has been demonstrated to be very efficient. It is found that combined coagulation-flocculation/nanofiltration and activated sludge/reverse osmosis are more efficacious than other integrated physicochemical methods and combined physicochemical/biological methods, respectively.

The reinvasion and recruitment of overwintering cyanobacteria in sediments in spring have an important impact on cyanobacterial blooms in summer and autumn, but until now, this process has not been observed in natural water bodies. In this study, wireless sensors and automatic water sample collection systems were used to carry out continuous high-frequency monitoring of cyanobacteria and related environmental indicators in Meiliang Bay, Lake Taihu, during a northwest wind event in spring. The results showed that there were many dormant cyanobacteria seeds in the sediments of Lake Taihu. These cyanobacterial seeds were easily resuspended from sediments, allowing them to reinvade and remain in the overlying water. Simultaneously, the water temperature exceeded the recovery temperature after the northwest wind event and the available light was sufficient to allow the reinvading cyanobacteria to recruit. The circadian photosynthetic rhythm of these recruited cyanobacteria eventually led to a rapid increase in dissolved oxygen in the water body during the south wind phase to a supersaturated state, and periodic diurnal fluctuations of the water body dissolved oxygen saturation curve. This study provided direct evidence for fully understanding the annual cycle of cyanobacterial blooms.

Ionizing radiation (IR) is a form of high energy. It poses a serious threat to organisms, but radiotherapy is a key therapeutic strategy for various cancers. It is significant to reduce radiation injury but maximize the effect of radiotherapy. MicroRNAs (miRNAs) are posttranscriptionally regulatory factors involved in cellular radioresponse. In this review, we show how miRNAs regulate important genes on cellular response to IR-induced damage and how miRNAs participate in IR-induced carcinogenesis. Additionally, we summarize the experimental and clinical evidence for miRNA involvement in radiotherapy and discuss their potential for improvement of radiotherapy. Finally, we highlight the role that miRNAs play in accident exposure to IR or radiotherapy as predictive biomarker. miRNA therapeutics have shown great perspective in radiobiology; miRNA may become a novel strategy for damage and protection against IR.