ABSTRACT
Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N=431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE=7.36;MAE=6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as “autochthonous sources” of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.
ABSTRACT
The sudden lockdown recovers the health of the total environment particularly air and water while the country's economic growth and socio-cultural tempo of people have been completely hampered due to the COVID-19 pandemic. Most of the industries within the catchment area of river Damodar have been closed; as a result, significant changes have been reflected throughout the stretch of river Damodar. The main objective of the study is to analyze the impact of lockdown on the water quality of river Damodar. A total of 55 samples was collected from eleven different confluence sites of nallas with the main river channel during and pre-lockdown period. The relevant methods like WQI, TSI, Pearson's correlation coefficient, and "t" test have been applied to evaluate the physical, chemical, and biological status of river water. The result of "t" test indicated that there are significant differences (α = 0.05) of each parameter between pre and during lockdown. Water quality index (WQI) is used for analysis of drinking water quality suitability followed by BIS. The values of WQI showed "very poor" (S1, S2, S3, S6, S7, and S11) to "unfit for drinking" (S4, S5, S8, S9, and S10) of river water during pre-monsoon season. The nutrient enrichment status of the river was analyzed by Trophic State Index (TSI) method and it shows the "High" eutrophic condition with a heavy concentration of algal blooms in almost an entire stretch. During lockdown, nutrient supplies like TN and TP have been reduced and is designated as "Low" (S1, S2) to "Moderate" (S3 to S11) eutrophic condition of middle stretch of Damodar. This research output of river Damodar will definitely assist to policy makers for sustainable environmental management despite the dilemma between development and conservation.