Environmental Modelling of Surface Water Quality of Banas River using AQUATOX Simulation Model: A Case Study of Pollution River Stretches (PRS), North Gujarat, India

There are 14 major rivers, 44 medium rivers and 53 small rivers in India, the seat for setup of big cities and their educational, political and regional developments. Gujarat State is profusely endowed with number of perennial rivers such as Narmada, Tapi, Mahi and Sabarmati, used by people as a source of water to dispose sewage and industrial effluents, altering quality and ecology of water bodies, brings new challenges to both water resource managers and aquatic ecologists. Gujarat is one of the leading largest industrial states of the country, known for textile industries and engineering units, which produce chemicals, fertilizers, dyes, fibres, polymers, drugs, catalysts etc. These industries are major contributors to air, water and soil pollution at a great extent. Urban cities located along river banks cause significant negative impacts on water quality owing to unrestrained garbage dumping and discharge of untreated or partially treated effluents from adjacent industries. As a consequence, proximate rivers are getting deteriorated day by day. Looking into an urgent need of an hour, the present study was aimed to assess surface water quality from March 2014 to September 2014 with a view to predict hydro-chemical parameters for subsequent five years (2015-2019) using AQUATOX Simulation Model (EPA, Release 3.1) at Banas River, North Gujarat. For analysis of hydro-chemical parameters, surface water samples were collected from five permanent sampling stations (Upper Stream: US: Dantiwada Dam; Non-Control Point: NCP: Deesa; Middle Steam: MS: Deliyathara; Control Point: CP: Mota Jampur; Down Stream: DS: Totana) during Pre-Monsoon (March & April) and Post-Monsoon (August & September). Physico-chemical parameters such as NH3-NH4+, NO3, Total Soluble P (TSP), Oxygen, Total Suspended Solids (TSS), Temperature & pH, were analyzed using APHA (2012). Based on Biological Monitoring Working Party (BMWP) Score, pollution gradient was NCP>DS>MS>US>CP under ‘Moderately Polluted or Impacted’ River category. In case of NH3-NH4+, mean concentration (0.125026536), mean difference (0.625132675), and mean % increase (99.9999998) was observed from 2015 to 2019, that of NO3, mean concentration (2.612430993), mean difference (10.0590781), and mean % increase (94.36554743), that of TSP, mean concentration (1.687903404), mean difference (-0.007325245), and mean % decrease (-0.435496), that of Oxygen, mean concentration (7.798760987), mean difference (-0.011005071), and mean % decrease (-0.141272556), that of TSS, mean concentration (373.9252877), mean difference (0.032911562), and mean % increase (0.0088001022), that of Temperature, mean degree of centigrade (26.25641984), mean difference (0.000519778), and mean % increase (0.001979592), and that of pH, mean content (7.789118651), mean difference (8.49373), and mean % increase (0.001090452) was observed. Further research is warranted is in line for aquatic pollution abatement for environmental sustenance therein.印度境内共有14条大型河流、44条中型河流及53条小型河流，这些水系是大型城市的重要选址依托，同时支撑了区域教育、政治与社会发展进程。古吉拉特邦拥有纳尔默达河（Narmada）、塔皮河（Tapi）、马希河（Mahi）与萨巴尔马蒂河（Sabarmati）等众多常年性河流，当地民众常以这些河流作为排放生活污水与工业废水的受纳水体，此举导致水体水质与生态系统发生改变，为水资源管理者与水生生态学家带来了全新挑战。古吉拉特邦是印度顶尖的工业大邦之一，以纺织工业与工程制造产业闻名，其生产范畴涵盖化学品、化肥、染料、纤维、聚合物、药品、催化剂等各类产品。这些产业在很大程度上成为大气、水体与土壤污染的主要来源。沿河分布的城市因随意倾倒垃圾、以及周边工业排放未经处理或仅部分处理的废水，对水体水质造成了显著负面影响。由此，邻近河流水质正逐日恶化。鉴于当下的迫切需求，本研究于2014年3月至9月期间开展地表水水质评估工作，旨在借助AQUATOX模拟模型（美国环境保护署，Release 3.1），对古吉拉特邦北部巴纳斯河2015至2019年的水化学参数进行预测。为开展水化学参数分析，本研究分别在季风前期（3月、4月）与季风后期（8月、9月），从5个固定采样站点采集地表水样本：上游（Upper Stream, US）：丹蒂瓦达大坝（Dantiwada Dam）；非控制点（Non-Control Point, NCP）：迪萨（Deesa）；中游（Middle Stream, MS）：德利亚塔拉（Deliyathara）；控制点（Control Point, CP）：莫塔詹布尔（Mota Jampur）；下游（Down Stream, DS）：托塔纳（Totana）。本研究采用美国公共卫生协会（APHA, 2012）制定的标准方法，对氨氮（NH3-NH4+）、硝酸盐（NO3）、总可溶性磷（Total Soluble P, TSP）、溶解氧、总悬浮固体（Total Suspended Solids, TSS）、温度及pH值等理化参数进行检测分析。基于生物监测工作组（Biological Monitoring Working Party, BMWP）评分体系，该流域在“中度污染或受干扰”河流类别下的污染梯度为：NCP>DS>MS>US>CP。针对2015至2019年的监测数据，氨氮的平均浓度为0.125026536，平均变化量为0.625132675，平均增幅达99.9999998%；硝酸盐的平均浓度为2.612430993，平均变化量为10.0590781，平均增幅达94.36554743%；总可溶性磷的平均浓度为1.687903404，平均变化量为-0.007325245，平均降幅为0.435496%；溶解氧的平均浓度为7.798760987，平均变化量为-0.011005071，平均降幅为0.141272556%；总悬浮固体的平均浓度为373.9252877，平均变化量为0.032911562，平均增幅达0.0088001022%；水温的平均摄氏温度为26.25641984℃，平均变化量为0.000519778，平均增幅达0.001979592%；pH值的平均含量为7.789118651，平均变化量为8.49373，平均增幅达0.001090452%。后续仍需开展相关研究，以推动水生污染治理，保障区域环境可持续发展。