Environmental Science and Engineering Department (ESED), Indian Institute of Technology Bombay, Mumbai, 400076, India; Civil and Environmental Engineering Department, Veermata Jijabai Technological Institute (VJTI), Matunga, Mumbai, 400019, India.
Environmental Science and Engineering Department (ESED), Indian Institute of Technology Bombay, Mumbai, 400076, India; Indian Institute of Management Jammu, Jammu, 180016, India.
J Environ Manage. 2021 Jan 1;277:111342. doi: 10.1016/j.jenvman.2020.111342. Epub 2020 Oct 17.
Water quality is continuously changing because of anthropogenic origin of point and diffuses (non-point) pollution sources. Most of the time diffuse sources are not considered for rationalization of sampling sites as their accurate estimation is tedious and data intensive. The estimation of diffuse pollution is conventionally carried out using observed water quality data. These conventional approaches are data intensive and demands detailed information for a considerably long-time horizon and hence becomes challenging to implement in data-scarce regions. Also, diffuse pollution sources are characterized by spatio-temporal heterogeneity as they depend upon seasonal behavior of precipitation. The present study proposes an innovative semi-empirical approach of Seasonal Export Coefficients (SECs) for estimation of diffuse pollution loads, especially for tropical countries like India. This approach takes into account the effect of seasonality on the estimation of diffuse pollution loads, by considering seasonal heterogeneity of terrain and precipitation impact factors and land use applications. This seasonal heterogeneity is then tested for its possible impact on rationalization of water quality monitoring locations for Kali River basin in India. The SECs are estimated for available water quality dataset of 1999-2000 and are further used for simulation of nutrient loading for experimental years 2004-2005, 2009-2010, and 2014-2015. The resulting SECs for Kali river basin are: 2.03 (agricultural), 1.44 (fallow), and 0.92 (settlement) for monsoonal nitrate; while for non-monsoonal nitrate, SECs are 0.51 (agricultural), 0.23 (fallow), and 0.10 (settlement). The monsoonal phosphate SECs for land use classes - agricultural, fallow and settlement are 1.01, 0.68, and 0.25, while non-monsoonal phosphate SECs are 0.27, 0.14 and, 0.03 respectively. The seasonal variation of diffuse pollution sources is effectively captured by SECs. The proposed approach, by considering both point and diffuse pollution, is found efficient in determining optimum locations and number of monitoring sites where seasonal variations are found evident during experimental years.
由于点源和非点源（扩散）污染的人为来源，水质不断变化。大多数时候，由于扩散源的准确估计繁琐且数据密集，因此不考虑将其合理化采样点。扩散污染的估计通常使用观测到的水质数据进行。这些传统方法需要大量数据，并且需要在相当长的时间范围内提供详细信息，因此在数据匮乏的地区实施起来具有挑战性。此外，扩散污染源具有时空异质性，因为它们取决于降水的季节性行为。本研究提出了一种创新的半经验方法，即季节出口系数（SEC），用于估计扩散污染负荷，特别是对于印度等热带国家。该方法通过考虑地形和降水影响因素以及土地利用应用的季节性异质性，考虑到季节性对扩散污染负荷估计的影响。然后，对其对印度卡利河流域水质监测点合理化的可能影响进行了测试。根据 1999-2000 年的可用水质数据集估计了 SEC，并进一步用于模拟 2004-2005 年、2009-2010 年和 2014-2015 年实验年份的养分负荷。卡利河流域的 SEC 估计值为：季风硝酸盐的农业为 2.03、休耕为 1.44、定居点为 0.92；而非季风硝酸盐的农业为 0.51、休耕为 0.23、定居点为 0.10。农业、休耕和定居点的季风磷酸盐 SEC 分别为 1.01、0.68 和 0.25，而非季风磷酸盐 SEC 分别为 0.27、0.14 和 0.03。SEC 有效捕捉了扩散污染源的季节性变化。该方法考虑了点源和扩散源，被发现能够有效地确定最佳位置和监测站点数量，这些位置和监测站点在实验年份期间发现季节性变化明显。
