A new comprehensive ecological risk index for risk assessment on Luanhe River,China

With the enhancement of human activities which influence the physical and chemical integrity of ecosystem, it was bound to increase ecological risk to the ecosystem, and the risk assessment of small scale, single pollutant, or only on water quality have been not satisfied the demand of sustainable development of basin water environment. Based on the response relationship between environmental flow requirements guarantee ratio (GEF) and river ecological risk index (ERI), the Sediment Quality Guideline Quotient index (SQG-Q), and the Biotic Index (BI), we construct a new comprehensive ecological risk index (CERI) to evaluate the ecological risk of Luanhe River, China. According to the response relationship between GEF and ERI, upper and lower reaches of Luanhe River (Goutaizi to Hanjiaying) were at moderate risk level (0.41 < ERI < 0.56) in dry season, and all sites were at low risk level (ERI < 0.40) in wet season; considering the contribution of heavy metals contamination in the SQG-Q, the Luanhe River was the most influenced by higher levels of heavy metals in dry season and wet season; when this index was applied to the PAHs levels, only 30 and 20% of the sampling sites appeared to be moderately impacted (0.1 < SQG-Q PAHs < 0.5) by the PAHs in dry season and wet season, respectively. The results of BI showed that half of the sites appeared to be at moderately polluted level (50% of the sites, 0.25 < BI < 0.32) and heavily polluted level (Zhangbaiwan, BI = 0.36) in dry season, and 40% of the sites appeared to be at moderately polluted level (0.26 < BI < 0.29) in wet season. The CERI showed that 70 and 30% of the sites were at moderate risk level in dry season (0.25 < CERI < 0.36) and wet season (0.26 < CERI < 0.29), respectively. The results could give insight into risk assessment of water environment and decision-making for water source security.

     

Continuous electrocoagulation degradation of oily wastewater with Fe78Si9B13 amorphous ribbons

Environmental Science and Pollution Research - The oily wastewater was treated by electrocoagulation with Fe78Si9B13 amorphous ribbons as anode and graphite plates as cathode under such processing...     

环境影响评价工作中的问题及解决途径

随着人们对环境问题的日益关注,环境影响评价工作(环评)的重要性日益凸显。当前,环评工作存在低效推进、从业人员素质偏低、公众片面参与等问题,这在一定程度上会影响环境保护效果。本文以现有问题为导向,提出相应的解决途径,旨在改善环境质量。     

Does new-type urbanization curb haze pollution? A case study from China

Environmental Science and Pollution Research - The rapid urbanization process has led to a high concentration of population and economic activities in urban space, thus leading to severe...     

Ecosystem-inspired model and artificial intelligence predicts pollutant consumption capacity by coagulation in drinking water treatment

Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R² correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.

     

MAX-DOAS observation in the midlatitude marine boundary layer: Influences of typhoon forced air mass

As a passive remote sensing technique, MAX-DOAS method was widely used to investigate the vertical profiles of aerosol and trace gases in the lower troposphere. However, the measurements for midlatitude marine boundary layer are rarely reported, especially during the storm weather system. In this study, the MAX-DOAS was used to retrieve the aerosol, HCHO and NO₂ vertical distribution at Huaniao Island of East China Sea in summer 2018, during which a strong tropical cyclone developed and passed through the measurement site. The observed aerosol optical depth (AOD), HCHO- and NO_2-VCDs (Vertical Column Density) were in the range of 0.19-0.97, (2.57-12.27) × 10¹⁵ molec/cm², (1.24-4.71) × 10¹⁵ molec/cm², which is much higher than remote ocean area due to the short distance to continent. The vertically resolved aerosol extinction coefficient (AEC), HCHO and NO₂ presented the decline trend with the increase of height. After the typhoon passing through, the distribution of high levels of aerosol and HCHO stretched to about 1 km and the abundances of the bottom layer were found as double higher than before, reaching 0.51 km⁻¹ and 2.44 ppbv, while NO₂ was still constrained within about 300 m with 2.59 ppbv in the bottom layer. The impacts of typhoon process forced air mass were also observed at the suburban site in Shanghai in view of both the aerosol extinction and chemical components. The different changes on air quality associated with typhoon and its mechanism in two different environments: coastal island and coastal city are worthy of further investigation as it frequent occurred in East Asia during summer and fall.     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Spatiotemporal distribution of water environmental capacity—a case study on the western areas of Taihu Lake in Jiangsu Province,China

Currently, the poor water quality in Taihu Lake is a major problem in China, so pollution control in the upstream areas has become a government priority. In Jiangsu Province, pollution emissions around the western areas of Taihu Lake, including Changzhou Municipality and Yixing City, need to be highly restricted, and calculating the water environmental capacity is important if pollution is to be reduced. In this study, 19 control units in these areas were established, and a 0-D mathematical model was used to calculate the water environmental capacity. For three important control units with important cross sections, a 1-D model was established to redress the results. Finally, the total maximum monthly loads of each control unit were obtained using temporal allocation principles. The results suggested that (1) the total pollution control of chemical oxygen demand was 58,894.2 tonnes per annum (t a^?1), with ammonia nitrogen, total nitrogen, and total phosphorus amounting to 3,808, 6,054.6, and 386.6 t a^?1, respectively; (2) water environmental capacity per unit water area in the ambient control units was smaller than that in the middle control units; and (3) the largest water environmental capacity was in June, and the smallest capacity was in December. The study provides important information for local governments, which will enable them to implement pollution control strategies that will improve the water quality in Taihu Lake.     

滏阳河河系表层沉积物重金属污染特征及其风险评价

采集滏阳河表层沉积物,分析了6种重金属的污染特征,并采用富集系数法和Hankanson潜在生态危害指数法分析了重金属的来源,进而评价其环境风险.结果表明,沉积物中重金属Cr、Cu、Cd、Pb、Zn、Ni的平均含量分别为340.09、90.28、1.28、65.87、760.71、48.62 mg·kg-1,均在一定程度上超过了河北省土壤元素背景值.重金属富集系数分析结果显示,元素Cd、Zn在整个流域尺度内主要源于人为输入,Cr在邵村排干河段及Cu、Pb、Ni在汪洋沟上游河段受人为排放源影响较大,主要是由于河流沿线城市生产和生活污水的排放造成.单一金属的潜在生态危害指数评价结果显示,沉积物中Cd污染达到极强生态危害,其次为Cu、Pb和Zn,而重金属综合生态危害指数(RI)评价表明,滏阳河沉积物重金属总体处于强生态风险,其中,汶河河段达到很强生态风险.     

便携式 GC-MS在快速查找污染源中的应用

由挥发性有机物（VOCs）造成的“化工异味”污染问题,已成为淄博市临淄区的主要环境问题之一。当城区或某一区域闻到“化工异味”时,能不能利用HAPSITE便携式气相色谱-质谱联用仪（GC-MS）,快速到达信访投诉问题的污染现场,通过及时、准确的取样分析,根据特征污染物及气象条件,帮助判断、查找出污染物来自哪家企业,以此敦促企业强化环境管理,明晰责任,最大限度的防止化工废气非正常排放,减少“化工异味”污染。经近一年的工作实践,当能检出一定浓度的特征污染物时,可有效帮助查找到污染源,减少了工作的盲动性与被动性。