Animal manure application to soils is considered to be one of the main cause of antibiotic and bacterial pathogen spread in the environment. Pig livestock, which is the source of one of the most used fertilizer for cultivated land, is also a hotspot for antibiotics and antibiotic-resistant bacteria. Besides harsh chemical and physical sanitization treatments for the abatement of antibiotics and bacterial load in livestock waste, more sustainable and environmentally friendly strategies need to be considered. In this context, the use of natural substances which are proved useful for pest and disease control is currently under exploration for their role in the reduction of bacterial pathogen population. Among these, plants and derived products from the Brassicaceae family, characterized by the presence of a defensive glucosinolate-myrosinase enzymatic system, have been successfully exploited for years in agriculture using the so-called biofumigation technique against crop diseases. Although the application of biofumigation to suppress a range of soil borne pests has been well documented, no studies have been examined to reduce bacterial population in animal waste. In the present study, the release and the antibacterial activity of bioactive compounds deriving from different Brassicaceae defatted seed meals against pathogens and bacterial population in pig manure is addressed. Rapistrum rugosum and Brassica nigra defatted seed meals were found to be the most active products against tested pathogens and able to significantly reduce the bacterial load in the manure.

Previous studies have found that particulate matter (PM) pollution is a risk factor for respiratory disease by affecting body’s immunity and carrying microorganisms. This study aimed to explore the association between PM and the incidence of mumps in 31 provinces from China. Monthly mumps cases, air pollution concentration, and meteorological factors in each province were obtained between January 2014 to December 2017. We used a generalized additive model (GAM) to investigate the associations of PM2.5 and PM10 with monthly mumps cases. We also tested the statistical significance of the differences between effect estimates in the warm season (April to September) and cold season (October to March) to explore potential effect modification. We found that a 10-μg/m3 increase (lag0) in PM2.5, and PM10 was associated with a 2.34% (95% CI: 1.32 to 3.36) and 1.90% (95% CI: 1.19 to 2.62) increase in the monthly counts of mumps cases, respectively. We also observed significant positive associations of PM2.5 and PM10 with mumps cases at lag0–1. These results were robust in our sensitivity analyses. No significant differences were found between the season-specific effects. Our results indicate that there is a positive relationship between PM and the incidence of mumps, which provides important implications for the prevention and control of mumps.

Sewage contamination is a principal concern in water quality management as pathogens in sewage can cause diseases and lead to detrimental health effects in humans. This study examines the distribution of seven sterol compounds, namely coprostanol, epi-coprostanol, cholesterol, cholestanol, stigmasterol, campesterol, and β-sitosterol in filtered and particulate phases of sewage treatment plants (STPs), groundwater, and river water. For filtered samples, solid-phase extraction (SPE) was employed while for particulate samples were sonicated. Quantification was done by using gas chromatography-mass spectrometer (GC-MS). Faecal stanols (coprostanol and epi-coprostanol) and β-sitosterol were dominant in most STP samples. Groundwater samples were influenced by natural/biogenic sterol, while river water samples were characterized by a mixture of sources. Factor loadings from principal component analysis (PCA) defined fresh input of biogenic sterol and vascular plants (positive varimax factor (VF)1), aged/treated sewage sources (negative VF1), fresh- and less-treated sewage and domestic sources (positive VF2), biological sewage effluents (negative VF2), and fresh-treated sewage sources (VF3) in the samples. Association of VF loadings and factor score values illustrated the correlation of STP effluents and the input of biogenic and plant sterol sources in river and groundwater samples of Linggi. This study focuses on sterol distribution and its potential sources; these findings will aid in sewage assessment in the aquatic environment.

The high NO₂/NOX ratio in the after-treatment system is beneficial to its performance and achieved by NO catalytic conversion in diesel oxidation catalyst (DOC) located upstream (CRDPF), catalytic DPF (CDPF), or a combination of both (CCDPF). In order to effectively control the emission of particulates and nitrogen oxides, various types of diesel particulate filter models are established to compare NO₂ catalytic formation, consumption, and efflux. The results show that the catalytic performance of NO conversion is limited by mass transfer in DOC catalytic coating, while it is almost non-existent in CDPF. At low temperature, the passive regeneration of CDPF is slower than that of CRDPF, but as the temperature increases, the passive regeneration speed of CDPF will exceed that of CRDPF. CCDPF is the most effective for the NO₂ catalytic formation, consumption, and efflux in the hot-start and high-speed cycle and thereby is conducive to improve the performance of the diesel particulate filter and downstream selective catalytic reduction.

The outbreak of COVID-19 has become a global public health event. Many researchers have proposed many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict “onset cases.” In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is divided into two stages, which takes about 15 days or even longer. Therefore, considering the right truncation of the data, we proposed a “predict-in-advance” method, used the number of “visiting hospital cases” to predict the number of “onset cases.” The results not only show that our prediction shortens the delay of the second stage, but also the predicted value of onset cases is quite close to the real value of onset cases, which can effectively predict the epidemic trend of sudden infectious diseases, and provide an important reference for the government to formulate control measures in advance.

Cadmium (Cd) is an element injurious for human health and is possibly toxic to organisms at minor concentrations. While some of other trace metallic elements have antagonistic features to it. One of them is the interaction between selenium (Se) and Cd in plant different organs. Literature review disclosed that the intake of Se to some extent can reduce the accumulation of Cd in plants, while the research on of trace metallic elements (Cd) and Se-enriched food (rice) in the living body has rarely been reported. This study intended to explore whether there was a mitigating effect of Se-enriched rice on mice poisoned with Cd. A mouse model of low-dose and high-dose Cd poisoning was established (supplemented with cadmium chloride(CdCl₂·2½H₂0)), followed by feeding two groups (1) Se-enriched rice and (2) setting an equal amount of inorganic Se group. After that, the impact of Se-enriched rice on the antioxidant activity was evaluated. The Se-enriched diet enhanced the total antioxidant capacity (T-AOC), superoxide dismutase (SOD), and enzyme activities of GSH peroxidase (GSH-Px) in mice livers and kidney whereas significantly decreased the malondialdehyde (MDA) contents. Moreover, the degree of physiological damage in mice with low cadmium poisoning was significantly alleviated, and the expression of antioxidant genes (Nrf-2, GPX1, TrxR2, TNF-2) was increased. In conclusion, the Se-enriched diet has a positive effect on the biological effects in mice, and it can be used as a daily diet to resist damage to the body’s low Cd state and support enzymatic antioxidant systems by eliminating oxidative injury.

Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper aims to identify the structural and technological determinants of provincial carbon intensity in the electricity generation sector (CIE) using the multiplicative LMDI-II method. Results demonstrate that (1) China’s overall CIE decreases by 7.3% in 2001–2015, and the research period can be divided into four stages according to CIE changes (i.e., rapid growth, rapid decline, slow growth, and transition). The CIE in the 12th FYP estimated in this paper, 24.9% lower than that using the emission factors from IPCC, is closer to China’s actual situation. (2) There exists huge heterogeneity in the determinants of provincial CIE changes in four stages. CIE growth in the Northwest and Northeast is caused by the coal-dominated energy structure. CIE growth in the Southwest is attributed to the electricity structure effect, while that of the Coast region is caused by the geographic distribution effect. The electricity efficiency effect is attributed to the CIE growth for these regions and the Southwest should also place focus on the electricity trade effect. The impact of electricity trade-related factors depends on the region being a net exporter or importer of electricity. (3) To achieve carbon intensity reduction targets, 30 provinces are categorized into four types based on various combinations of structural and technological determinants. The findings provide insights into capturing future emission-mitigating focus as well as defining the emission-mitigating responsibilities between electricity exporters and importers in China.

This study is the first to investigate the hepato- and nephron-preventive effect of levan from Bacillus mojavensis (BM-levan) against toxicity induced by carbon tetrachloride (CCl₄) and cisplatin. Thirty-six male albino rats weighing between 230 and 250 g were used for this experiment. The groups received multiples doses of BM-levan and were compared to the untreated group. The in vitro and in vivo biological potentials of BM-levan were evaluated by measuring its antioxidant capacity as well as its hepato- and nephron-protective activities in rat models. The investigations highlighted a significant in vitro antioxidant activity indicated by the radical-scavenging capacity, the reducing power, and the total antioxidant activity measurement. In addition, results demonstrate that BM-levan supplementation during 8 weeks (100 mg/kg body weight) significantly (p < 0.05) decreased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities and remarkably (p < 0.05) attenuated the altered lipid profile by decreasing the levels of triglycerides (TG) and total cholesterol (TC), LDL cholesterol (LDL-C) and by enhancing the HDL cholesterol (HDL-C) content, when compared with the CCl₄ group. BM-levan also reduced the content of plasma renal biomarkers (urea, creatinine, and uric acid) in the cisplatin-treated group. Moreover, BM-levan inhibited hepatic and renal oxidative stress generated by CCl₄ and cisplatin administration, through the enhancement of the antioxidant catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) and the diminishment of lipid peroxidation. The harmful effects of CCl₄ or cisplatin on hepatic and renal histology were found to be decreased by the addition of BM-levan. Therefore, BM-levan has proved promising for biomedical applications thanks to its in vitro and in vivo antioxidant properties.

Deterioration of air quality through the combustion of hydrocarbon fuels has been one of the global transboundary problems put before the research community since last five decades. According to the updated statistics, 79% of energy needs in India are met by fossil fuel combustion which results in the emission of toxic pollutants like carbon monoxide, oxides of nitrogen, and unburned hydrocarbons. Air quality has seriously been affected in many parts of India, and statistically, 13 out of 15 most polluted cities in the world lie in India. Magnetic field-assisted combustion has been proven as a reliable technology in internal combustion engines for enhancing the combustion of fuels and reduction of harmful emissions that are the byproducts of incomplete combustion of fuels. In the present work, the magnetic field-assisted combustion of a liquid-phase and a gas-phase fuel (gasoline and LPG) has been studied in a multicylinder automobile engine replicating on road driving conditions in a laboratory focusing on the levels of emissions in comparison with normal combustion of both the fuels. The experimental study concludes that the applied magnetic field positively influences combustion, resulting in reduced level of emission of toxic components irrespective of the phase of hydrocarbon fuels. It is also observed that the percentage reduction in emissions increases with increase in intensity of magnetization. The maximum reduction obtained for CO and UBHC emissions through this technique is 20.58% and 14.47%, respectively. The effectiveness of MFAC in countering air pollution from vehicular exhaust is also studied with respect to fuel phase and mode of operation. The effectiveness of MFAC is observed to be more in high-speed operation of the engine and decreases in the order CO > UBHC > NO. The obtained emission results have a cumulative significance as 45% of total air pollution in India is caused by combustion of hydrocarbons in automotive engines.

The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson’s correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson’s correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.