Chemometrics in assessment of seasonal variation of water quality in fresh water systems

The spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) was investigated in Gomti River, a major tributary of the Ganga river (India). A total of 96 samples (water and sediments) were collected from eight different sites over a period of 2 years and analysed for 16 PAHs. The total concentrations of 16 PAHs in water and bed sediments ranged between 0.06 and 84.21 ??g/L (average (n?=?48), 10.33 ± 19.94 ??g/L) and 5.24?C3,722.87 ng/g dw [average (n?=?48): 697.25 ± 1,005.23 ng/g dw], respectively. In water, two- and three-ring PAHs and, in sediments, the three- and four-ring PAHs were the dominant species. The ratios of anthracene (An)/An + phenenthrene and fluoranthene (Fla)/Fla + pyrene were calculated to evaluate the possible sources of PAHs. These ratios reflected a pattern of pyrolytic input as a major source of PAHs in the river. Principal component analysis, further, separated the PAHs sources in the river sediments, suggesting that both the pyrolytic and petrogenic sources are contributing to the PAHs burden. The threat to biota of the river due to PAHs contamination was assessed using effect range low and effect range median values, and the results suggested that sediment at some occasions may pose biological impairment.     

Predicting the contents of BTEX and MTBE for the three types of tollbooth at a highway toll station via the direct and indirect approaches

This study was set out to assess the contents of five volatile organic compounds (VOCs), including BTEX (the acronym for benzene, toluene, ethylbenzene, and xylene) and methyl tertiary-butyl ether (MTBE), in three types of tollbooth (including the car lane/ticket-collecting, car lane/cash-collecting, and bus/truck lane tollbooths) at a highway toll station via the direct and indirect approaches. For the direct approach, VOC samples were collected from the breathing zone of booth attendants at all selected tollbooths during the three workshifts. For samples collected during the dayshift, we found VOC contents of BTEX and MTBE in both the car lane/ticket-collecting (=6.23, 21.93, 3.24, 8.56, and 5.63 ppb, respectively) and car lane/cash-collecting tollbooths (=5.98, 21.71, 3.25, 8.59, and 6.04 ppb, respectively) were quite comparable, but both were significantly higher than that in the bus/truck lane tollbooth (=3.13, 13.91, 2.05, 4.52, and 2.70 ppb, respectively). The same pattern can also be found for the other two workshifts. For the indirect approach, we conducted multivariate regression analyses to predict VOC contents for any given type of tollbooth by using the four independent variables of the vehicle flowrate, wind speed, relative humidity, and air temperature. We found that, except the vehicle flowrate, the other three factors did not have a significant effect on VOC contents in the three types of tollbooth. In addition, the magnitudes of the effect of the vehicle flowrate on VOC contents for the three types of tollbooth were: car lane/cash-collecting>bus/truck lane>car lane/ticket-collecting. All regression results yielded R²-values in the range of 0.41−0.74 indicating that the developed indirect approach was able to predict VOC contents for three types of tollbooth.     

Kinetics and mechanism of TNT degradation in TiO2 photocatalysis

The photocatalytic degradation of TNT in a circular photocatalytic reactor, using a UV lamp as a light source and TiO(2) as a photocatalyst, was investigated. The effects of various parameters such as the initial TNT concentration, and the initial pH on the TNT degradation rate of TiO(2) photocatalysis were examined. In the presence of both UV light illumination and TiO(2) catalyst, TNT was more effectively degraded than with either UV or TiO(2) alone. The reaction rate was found to obey pseudo first-order kinetics represented by the Langmuir-Hinshelwood model. In the mineralization study, TNT (30 mg/l) photocatalytic degradation resulted in an approximately 80% TOC decrease after 150 min, and 10% of acetate and 57% of formate were produced as the organic intermediates, and were further degraded. NO(-)(3) NO(-)(2), and NH(+)(4) were detected as the nitrogen byproducts from photocatalysis and photolysis, and more than 50% of the total nitrogen was converted mainly to NO(-)(3)in the photocatalysis. However, NO(-)(3) did not adsorbed on the TiO(2) surface. TNT showed higher photocatalytic degradation efficiency at neutral and basic pH.     

Process Optimization and Adsorptive Mechanism for Reactive Blue 19 Dye by Magnetic Crosslinked Chitosan/MgO/Fe3O4 Biocomposite

Journal of Polymers and the Environment - A new biocomposite cross-linked glutaraldehyde-chitosan/MgO/Fe3O4 (CTS-GL/MgO/Fe3O4) adsorbent with magneto-responsiveness was prepared and applied for the...     

次区域城市群协同发展时空演变及动态模拟研究——以宜荆荆恩城市群为例

建立"经济水平—交流服务—产业发展—生态文明—公共服务—科技创新"框架,以宜荆荆恩城市群为例,并与相似区域城市群对比,综合运用耦合协调度、BP神经网络模型、ArcGIS空间分析工具研究次区域城市群协同发展时空演变规律及发展趋势。结果表明:(1)宜荆荆恩城市群综合发展能力指标总体协调发展不均衡。(2)与苏锡常都市圈相比,宜荆荆恩城市群的综合协同发展水平较低,且处于波动状态。(3)在动态模拟方面,宜荆荆恩城市群2020—2025年协同水平差距将持续增大,其协同发展水平在南北方向呈倒"U"型,东西方向呈"两边低,中间高"的趋势。     

ModelOps for enhanced decision-making and governance in emergency control rooms

Environment Systems and Decisions - For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation as a disaster unfolds is critical...     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion

Environmental Science and Pollution Research - With the continuous improvement of urbanization level and the continuous expansion of city scale, there are some unreasonable land development and...     

Inactivation of E. coli and Streptococcus agalactiae by UV/persulfate during marine aquaculture disinfection

Environmental Science and Pollution Research - Sulfate radical (?SO4?)–based advanced oxidation processes have attracted a great deal of attention for use in water disinfection...     

Coexistence of Cr and Ni in anthropogenic soils and their chemistry: implication to proper management and remediation

Environmental Science and Pollution Research - In anthropogenic soils, there have been relatively limited studies focusing on Cr and Ni contaminants because they exhibit less toxic effects to...