Emission and optical characteristics of brown carbon in size-segregated particles from three types of Chinese ships

Brown carbon(BrC) is one of the important light absorption substances that have high light absorption ability under short wavelength light. However, limit studies have focused on the BrC emission from ships. In this study, size-segregated particulate matters(PM) were collected from three different types of ships, light absorption characteristics and size distribution of methanol-soluble BrC and water-soluble BrC in PM from ship exhausts were investigated. Results showed that four-stroke low-powe...     

水中天然有机溶解物对速灭菊酯毒性的影响

水中天然有机溶解物(DOM)按分子大小分成4个组分.杀虫剂速灭菊酯对大型溞的毒性因加入10倍天然浓度的DOM的1μ—0.2μ组分而显著增大,速灭菊酯的EC_(50)由0.76μg/L降低到0.29μg/L.DOM的其它组分(0.2μ—30kM.W.,30k—10kM.W.,10k—1kM.W)不影响其毒性或稍有降低但不显著.发现粘附大型溞的程度与速灭菊酯的浓度成比例,但是粘附与所用的DOM的大小组分无关.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

NiB2O4 (B = Mn or Co) catalysts for NH3-SCR of NOx at low-temperature in microwave field

● Microwave-assisted catalytic NH₃-SCR reaction over spinel oxides is carried out. ● SCR reaction temperature is tremendously lowered in microwave field. ● NO conversion of NiMn₂O₄ is highly up to 90.6% at 70°C under microwave heating. Microwave-assisted selective catalytic reduction of nitrogen oxides (NO_x) was investigated over Ni-based metal oxides. The NiMn₂O₄ and NiCo₂O₄ catalysts were synthesized by the co-precipitation method and their activities were evaluated as potential candidate catalysts for low-temperature NH₃-SCR in a microwave field. The physicochemical properties and structures of the catalysts were characterized by X-ray diffraction (XRD), Scanning electron microscope (SEM), N₂-physisorption, NO adsorption-desorption in the microwave field, H₂-temperature programmed reduction (H₂-TPR) and NH₃-temperature programmed desorption (NH₃-TPD). The results verified that microwave radiation reduced the reaction temperature required for NH₃-SCR compared to conventional heating, which needed less energy. For the NiMn₂O₄ catalyst, the catalytic efficiency exceeded 90% at 70 °C and reached 96.8% at 110 °C in the microwave field. Meanwhile, the NiMn₂O₄ also exhibited excellent low-temperature NH₃-SCR reaction performance under conventional heating conditions, which is due to the high BET specific surface area, more suitable redox property, good NO adsorption-desorption in the microwave field and rich acidic sites.     

Influence of hydraulic retention time on behavior of antibiotics and antibiotic resistance genes in aerobic granular reactor treating biogas slurry

Longer HRT can enhance degradation rate of sulfamethoxazole in granular reactor. Longer HRT can reduce accumulated concentrations of TCs and QNs in sludge. Longer HRT may have increased relative abundances of ARGs in aerobic granules. The behavior of antibiotics and the corresponding resistance genes in aerobic granular reactors for treating biogas slurry under different hydraulic retention times (10.7 h, R1; 8 h, R2) was investigated in this study. The results indicated that the hydraulic retention time could affect the effluent concentrations and removal efficiencies of sulfonamides. The average removal rates of tetracyclines, fluoroquinolones, and sulfonamides were 63%, 46%, and 90% in R1, and 62%, 46%, and 86% in R2, respectively. Although the removal efficiencies of tetracyclines and fluoroquinolones were similar in both reactors, the respective accumulated concentrations of tetracyclines and fluoroquinolones in R1 were 7.00 and 11.15 µg/g SS, which were lower than those in R2 (8.92 and 13.37 µg/g SS, respectively). The difference in the relative abundance of target antibiotic resistance genes between both reactors was not significant, yet the average relative abundances of all target resistance genes in R1 were higher than those in R2 after 45 days of operation. The results of this study suggested that a longer hydraulic retention time could enhance the antibiotic removal ability of aerobic granular sludge, yet it may also increase the risk of surplus sludge utilization from a resistance genes point of view.     

Long-term trends of fine particulate matter and chemical composition in the Pearl River Delta Economic Zone (PRDEZ), China

Understanding the trends in PM_2.5 levels is essential for formulating clean air plans. This paper analyzes PM_2.5 data from various published sources for the years 2000 to 2010 in the Pearl River Delta Economic Zone (PRDEZ). The long-term variation in PM_2.5 mass concentration is analyzed. Results show that PM_2.5, organic carbon (OC), elemental carbon (EC), and SO42− show a similar trend, increasing before 2005 and then decreasing slightly. The annual average PM_2.5 concentration ranges from 49.1 μg·m⁻³ in 2000 to 64.3 μg·m⁻³ in 2010, with a peak of 84.1 μg·m⁻³ in 2004. None of these 11 years meets the new National Ambient Air Quality standard (NAAQS) for PM_2.5 (35 μg·m⁻³). Overall average concentrations of OC, EC, and SO42− are 13.0, 6.5, and 11.8 μg·m⁻³, respectively. NO3− and NH4+ respectively have concentrations of 1.5 μg·m⁻³ and 2.9 μg·m⁻³ in 2000 and 6.4 μg·m⁻³ and 5.3 μg·m⁻³ in 2010, with a statistically significant average annual trend of+ 0.2 μg·m⁻³·yr⁻¹ and+ 0.1 μg·m⁻³·yr⁻¹. In certain geographic regions, OC and EC contribute most of the PM_2.5, while in other regions secondary water-soluble ions are more important. In general, OC and SO42− are the dominant components of PM_2.5, contributing 20.6% and 18.6%, respectively. These results provide, for the first time, a better understanding of the long-term PM_2.5 characteristics and trends, on a species-by-species basis, in the PRDEZ. The results indicate that PM_2.5 abatement needs to prioritize secondary species.     

Spatial variation of volatile organic compounds in a "Hot Spot" for air pollution

The spatial variations of volatile organic compounds (VOCs) were characterized in the Village of Waterfront South neighborhood (WFS), a "hot spot" for air toxics in Camden, NJ. This was accomplished by conducting "spatial saturation sampling" for 11 VOCs using 3500 OVM passive samplers at 22 sites in WFS and 16 sites in Copewood/Davis Streets (CDS) neighborhood, an urban reference area located ～1000 m east of the WFS. Sampling durations were 24 and 48 h. For all 3 sampling campaigns (2 in summer and 1 in winter), the spatial variations and median concentrations of toluene, ethylbenzene, and xylenes (TEX) were found significantly higher (p < 0.05) in WFS than in CDS, where the spatial distributions of these compounds were relatively uniform. The highest concentrations of methyl tert-butyl ether (MTBE) (maximum of 159 μg m(-3)) were always found at one site close to a car scrapping facility in WFS during each sampling campaign. The spatial variation of benzene in WFS was found to be marginally higher (p = 0.057) than in CDS during one sampling campaign, but similar in the other two sampling periods. The results obtained from the analyses of correlation among all species and the proximity of sampling site to source indicated that local stationary sources in WFS have significant impact on MTBE and BTEX air pollution in WFS, and both mobile sources and some of the stationary sources in WFS contributed to the ambient levels of these species measured in CDS. The homogenous spatial distributions (%RSD < 24%) and low concentrations of chloroform (0.02-0.23 μg m(-3)) and carbon tetrachloride (0.45-0.51 μg m(-3)) indicated no significant local sources in the study areas. Further, results showed that the sampling at the fixed monitoring site may under- or over-estimate air pollutant levels in a "hot spot" area, suggesting that the "spatial saturation sampling" is necessary for conducting accurate assessment of air pollution and personal exposure in a community with a high density of sources.     

Prediction of individual Freundlich isotherms from binary and ternary phenolic compounds mixtures

Method for simultaneous determination of individual component's adsorption equilibrium parameters for binary and ternary phenolic compounds mixtures was investigated in this research. The Freundlich equilibrium parameters of binary and ternary component of phenolic compounds were determined from one fixed composition of phenolic wastewater. The simulation results obtained from both binary and ternary systems on the basis of ideal adsorbed solution theory were consistent with single component equilibrium data.     

Antibiotic effect of exogenously applied salicylic acid on in vitro soilborne pathogen, Fusarium oxysporum f.sp.niveum

Salicylic acid, which is biosynthesized inside plant and is often found and accumulated in soil due to plant debris decaying, is considered as a signaling substance during plant-microbe interactions. It is involved in the cycling of biogeochemistry and related to plant resistance to biotic and abiotic stress. The antibiotic effect of salicylic acid on Fusarium oxysporum f.sp.niveum (FON) was studied to investigate the relationships between the salicylic acid and the fungus in the ecological interaction of plant-microbe. Results showed that the biomass, colony diameter, number of conidium germination and conidium production of FON were decreased by 52.0%, 25.7%, 100% and 100% at concentrations of 800 mg L(-1). However, mycotoxin yield was increased by 233%, pectinase activity raised by 168.0% and cellulase activity increased by 1325% compared to control at higher concentrations. It was concluded that salicylic acid as an allelochemical greatly inhibited FON growth and conidia formation and germination, though stimulated mycotoxin production and activities of hydrolytic enzymes by FON.     

Long-term joint e ect of nutrients and temperature increase on algal growth in Lake Taihu, China

To study how global warming and eutrophication a ect water ecosystems, a multiplicative growth Monod model, modified by incorporating the Arrhenius equation, was applied to Lake Taihu to quantitatively study the relationships between algal biomass and both nutrients and temperature using long-term data. To qualitatively assess which factor was a limitation of the improved model, temperature variables were calculated using annual mean air temperature (AT), water temperature (WT), and their average temperature (ST), while substrate variables were calculated using annual mean total nitrogen (TN), total phosphorus (TP), and their weighted aggregate (R), respectively. The nine fitted curves showed that TN and AT were two important factors influencing algal growth; AT limited growth as algal photosynthesis is mainly carried out near the water surface; N leakage of phytoplankton and internal phosphorus load from sediment explains why TN was the best predictor of peak biomass using the Monod model. The fitted results suggest that annual mean algal biomass increased by 0.145 times when annual mean AT increased by 1.0°C. Results also showed that the more eutrophic the lake, the greater the e ect AT had on algal growth. Subsequently, the long-term joint e ect of annual temperature increase and eutrophication to water ecosystems can be quantitatively assessed and predicted.