Application of GIS in the study of mass transport of pollutants by Adyar and Cooum Rivers in Chennai, Tamilnadu

Residential, industrial, commercial, institutional and recreational activities discharge degradable and non-degradable wastes that reach the coastal water through rivers and cause coastal pollution. In the present study, mass transport of pollutants by Adyar and Cooum Rivers to the coastal water as a result of land-based discharges was estimated during low tide. The lowest and the highest flow recorded in Adyar varied from 514.59 to 2,585.08×10⁶ litres/day. Similarly, the flow in Cooum River fluctuated between 266.45 and 709.34×10⁶ litres/day. The present study revealed that the Adyar River transported 53.89–454.11 t/d of suspended solids, 0.06–19.64 t/d of ammonia, 15.95–123.24 t/d of nitrate and 0.4–17.86 t/d of phosphate, 0.004–0.09 kg/d of cadmium, 0.15–1.29 kg/d of lead and 3.03–17.58 kg/d of zinc to the coastal water owing to its high discharge. Similarly, the Cooum River transported 11.87–120.06 t/d of suspended solids, 0.08–58.7 t/d of ammonia, 6.11–29.25 t/d of nitrate and 0.66–10.73 t/d of phosphate, 0.003–0.021 kg/d of cadmium, 0.02–0.44 kg/d of lead and 1.36–3.87 kg/d of zinc. A higher concentration of suspended solids was noticed in post monsoon and summer months. An increase in the mass transport of ammonia, nitrate, phosphate in summer months (April and May) and an increase in the mass transport of cadmium, lead and zinc were observed in monsoon months (October–December) in both the rivers. Thus mass transport of pollutants study reveal that Cooum and Adyar Rivers in Chennai contribute to coastal pollution by transporting inorganic and trace metals significantly through land drainage.     

Magnetic mapping of fly-ash pollution and heavy metals from soil samples around a point source in a dry tropical environment

The Singrauli region in the southeastern part of Uttar Pradesh, India is one of the most polluted industrial sites of Asia. It encompasses 11 open cast coalmines and six thermal power stations that generate about 7,500 MW (about 10% of India’s installed generation capacity) electricity. Thermal power plants represent the main source of pollution in this region, emitting six million tonnes of fly-ash per annum. Fly-ash is deposited on soils over a large area surrounding thermal power plants. Fly-ashes have high surface concentrations of several toxic elements (heavy metals) and high atmospheric mobility. Fly ash is produced through high-temperature combustion of fossil fuel rich in ferromagnetic minerals. These contaminants can be identified using rock-magnetic methods. Magnetic susceptibility is directly linked to the concentration of ferromagnetic minerals, primarily high values of magnetite. In this study, magnetic susceptibility of top soil samples collected from surrounding areas of a bituminous-coal-fired power plant were measured to identify areas of high emission levels and to chart the spatial distribution of airborne solid particles. Sites close to the power plant have shown higher values of susceptibility that decreases with increasing distance from the source. A significant correlation between magnetic susceptibility and heavy metal content in soils is found. A comparison of the spatial distribution of magnetic susceptibility with heavy-metal concentrations in soil samples suggests that magnetic measurements can be used as a rapid and inexpensive method for proxy mapping of air borne pollution due to industrial activity.     

生态土壤渗滤系统启动周期研究

利用室内模拟试验装置,考察了4种生态土壤渗滤系统在0.1 m3/(m2·d)的水力负荷条件下对生活污水中TP、COD和NH3-N的去除效果及启动周期; 同时对整个系统及同类生态工艺启动周期的判断方法做了探讨.研究结果表明,生态土壤渗滤系统对TP、COD和NH3-N的启动周期分别为15～27 d、24～40 d和24～26 d; 土壤渗滤系统对TP的启动周期最短,对COD的启动周期最长; 处理系统启动周期的判断原则是综合考察系统对主要污染物各自的启动周期,以最长的作为系统启动周期.4组试验中,1#和2#系统的启动周期为40 d; 3#和4#的为24 d.     

A Procedure for NEPA Assessment of Selenium Hazards Associated With Mining

This paper gives step-by-step instructions for assessing aquatic selenium hazards associated with mining. The procedure was developed to provide the U.S. Forest Service with a proactive capability for determining the risk of selenium pollution when it reviews mine permit applications in accordance with the National Environmental Policy Act (NEPA). The procedural framework is constructed in a decision-tree format in order to guide users through the various steps, provide a logical sequence for completing individual tasks, and identify key decision points. There are five major components designed to gather information on operational parameters of the proposed mine as well as key aspects of the physical, chemical, and biological environment surrounding it — geological assessment, mine operation assessment, hydrological assessment, biological assessment, and hazard assessment. Validation tests conducted at three mines where selenium pollution has occurred confirmed that the procedure will accurately predict ecological risks. In each case, it correctly identified and quantified selenium hazard, and indicated the steps needed to reduce this hazard to an acceptable level. By utilizing the procedure, NEPA workers can be confident in their ability to understand the risk of aquatic selenium pollution and take appropriate action. Although the procedure was developed for the Forest Service it should also be useful to other federal land management agencies that conduct NEPA assessments, as well as regulatory agencies responsible for issuing coal mining permits under the authority of the Surface Mining Control and Reclamation Act (SMCRA) and associated Section 401 water quality certification under the Clean Water Act. Mining companies will also benefit from the application of this procedure because priority selenium sources can be identified in relation to specific mine operating parameters. The procedure will reveal the point(s) at which there is a need to modify operating conditions to meet environmental quality goals. By recognizing concerns early in the NEPA process, it may be possible for a mining company to match operational parameters with environmental requirements, thereby increasing the likelihood that the permit application will be approved.     

乡镇工业污染防治战略初探

本文通过对乡镇工业经济发展现状及其环境问题的评价分析,提出乡镇工业环境管理应按本地自然条件、经济发展水平和乡镇企业的特点,实施分区域、按行业、有重点地进行管理的防治战略。并建议我国环境管理在以城市和大工业为重点的同时,及早将乡镇工业污染防治工作放在战略高度加以重视,切实加强乡镇工业环境管理。     

Estimation of Land Use Specific Runoff and Pollutant Concentration for Tapi River Basin in India

Non-point source (NPS) pollution is the result of various land use practices such as agriculture, sites of construction and waste disposal, urban development and so on. The control of NPS pollution is possible by regular monitoring and assessment on watershed basis to educate people for implementing well-known structural and non-structural measures. Recent trend is to use GIS based modelling tool for assessment of rainfall-runoff and non-point loading. The approach requires generation and analysis of basin wide data on various features of land and estimates of Event Mean Concentrations (EMCs) of pollutants in the runoff. In the present paper, basin wide data in different districts of Tapi basin has been analysed for land use distribution; fertilizer application; low, medium and high-density habitation; and annual rainfall. Coefficients of runoff have been estimated considering pervious and impervious area for different land use types, and compared with the reported values for Indian conditions. The estimated mean annual runoff flow indicated that two districts Jalgaon and Dhule contribute maximum runoff to the Tapi River. Estimates of EMCs for BOD and nutrients (N and P) in the runoff from various districts are useful in GIS-based modelling study for NPS pollution assessment.     

A Marine Pollution Study of Northeast Coastal Water Off Taiwan Island

The coastal water of northeast Taiwan island, called 'Yin-Yang Hai' for its distinct yellow colour compared with blue offshore water, was investigated from 1989 to 1990 by the authors. Biological study showed the dominant species of plankton to be Copepoda, Cladocera, planktonic eggs and Diatoma. Dominant species of benthos were young crabs, Amphipoda and Annelida, with Amphipoda usually occurring in heavily polluted areas. Heavy metal data showed that the concentration of copper was high. the copper and iron concentration in algae of the intertidal zone was also high. the concentrations of iron and copper in inshore water were also higher than in offshore water. By comparison of the pH and salinity distribution of this area, we conclude that this coastal water has been polluted by acid waste water from coastal industry. the suspended solids concentration in sea water is high. Flocculation occurring at the boundary of fresh and saline water might be a reason for the distinct yellow colour of the water of this area. Further study is required.     

降水空间异质性对非点源关键源区识别面积变化的影响

针对地形起伏和降水空间差异较大的农业区非点源污染问题,基于SWAT模型评估了阿什河流域在异质性降水和均匀降水两种情景下总氮、总磷关键源区空间变化规律,统计了两种情景下识别的关键源区面积变化,并分析其与降水特征参数的关系.结果表明,降水量一定时,两种情景下识别的总氮、总磷关键源区面积变化趋势大致相同,且总磷关键源区面积不易受降水空间异质性的影响,但总氮关键源区面积却明显受到其影响.对各年份总氮和总磷关键源区面积与降水特征参数的相关分析表明,总磷关键源区面积与当年降水量呈显著正相关,而总氮关键源区面积却与前一年降水量呈显著正相关.研究结果对进一步探讨降水这一重要驱动因子的不确定性对非点源污染关键源区的影响,以及农业非点源污染的治理具有重要意义.     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

长江经济带突发水污染风险分区研究

长江经济带突发水污染事件频发,对区域人群健康和生态安全造成严峻挑战.环境风险分区是环境风险管理的基础和有效工具.本研究以2015年为基准年,基于环境统计数据、DEM数据、水质监测断面数据和基础地理数据,综合考虑了水系流向、水系级别及水质等因素,以1 km×1 km的网格为基本单元,对长江经济带开展突发水污染风险分区.结果表明:①高风险区面积为3348.9 km~2,占评估区总面积的0.16%;较高风险区面积为26030.7 km~2,占比1.27%;中风险区面积为97971.1 km~2,占比4.79%;低风险区面积为1916838.7 km~2,占比93.77%;②从沿长江干流两岸分布来看,高风险区面积沿长江上游至下游呈逐渐增加趋势,主要集中分布在重庆市中部、湖北省东部、安徽省东部、江苏省中西部、浙江省北部、上海市西部等地;③从沿长江主要支流两岸分布来看,高风险区主要分布在嘉陵江南段、乌江南段、汉水东段、湘江北段、赣江北段等.研究结果可为长江经济带生态环境管理提供科学依据.