ABSTRACT
A comprehensive understanding of pollutant delivery processes during storm events is essential for developing strategies to minimize adverse impacts on receiving water bodies. In this paper, hysteresis analysis and principal component analysis were coupled with identified nutrient dynamics to determine different pollutant export forms and transport pathways and analyze the impact of precipitation characteristics and hydrological conditions on pollutant transport processes through continuous sampling between different storm events (4 events) and hydrological years (2018-wet, 2019-dry) in a semi-arid mountainous reservoir watershed. Results showed pollutant dominant forms and primary transport pathways were inconsistent between different storm events and hydrological years. Nitrogen (N) was mainly exported in the form of nitrate-N(NO3-N). Particle phosphorous (PP) was the dominant P form in wet years, but total dissolved P (TDP) in dry year. Ammonia-N (NH4-N), total P (TP), total dissolved P(TDP) and PP had prominent flushing responses to storm events and were delivered mainly from overland sources by surface runoff; while the concentrations of total N(TN) and nitrate-N(NO3-N) were mainly diluted during storm events. Rainfall intensity and amount had significant control over P dynamics and extreme events played a key role in TP exports, accounting for >90 % of the total TP load exports. However, the cumulative rainfall and runoff regime during rainy season exerted significant control over N exports than individual rainfall features. In the dry year, NO3-N and TN were delivered primarily through soil water flow paths during storm events; nevertheless, wet year registered complex control on TN exports via soil water release, followed by surface runoff transport. Relative to dry year, wet year registered higher N concentration and more N load exports. These findings could provide scientific basis for determining effective pollution mitigation strategies in Miyun Reservoir basin and provide important references for other semi-arid mountain watersheds.
ABSTRACT
Artificial intelligence (AI) has been widely used in the eco-environment field, but with shortcomings in revealing the laws of natural science, such as insufficient generalization ability and poor interpretability. In order to overcome these shortages and tap into complementary advantages, coupling AI and eco-environmental models containing physical mechanism has been a new research method with fast development in recent years. We introduced the classifications of AI used in eco-environmental field, outlined its applications, and mainly illustrated the progresses, status and inadequacies for the coupling research. Based on all the summaries, we proposed a new coupling method of physical mechanism and AI for reconstructing mechanism processes, followed by analyses of theoretical significance of partial parameters, feasibility of better generalization and interpretability, as well as prospection of imitating physical mechanism. At the end of the review, we discussed the trend of the coupling method of AI and eco-environment models.
Subject(s)
Artificial Intelligence , Models, TheoreticalABSTRACT
In semi-arid and semi-humid areas, the occurrence of non-point source nutrient pollution is mainly driven by rainfall-runoff events, and nutrient loss under rainfall events determines annual total pollution load. Therefore, research on riverine nutrient dynamics under rainfall-runoff events in flood seasons is critical for simulating and controlling pollution load in semi-arid and semi-humid areas. The Chaohe River watershed, upstream watershed of Miyun Reservoir in Beijing was considered as study area, water quantity and quality of rainfall-runoff process at Gubeikou and Xiahui stations were monitored synchronously in flood seasons in 2018 and 2019. The results indicated the following:â Among the three rainfall events (E1, E2, and E3), E1 had the highest precipitation and rainfall intensity, and the corresponding discharge and pollutant concentrations were the highest. â¡ Under different rainfall events, the pollutant concentrations and their variations were different. The variations of concentrations of total nitrogen (TN), ammonia (NH4+-N), nitrate (NO3--N), total phosphorus (TP), and total suspended solids (TSS) were similar to the discharge process under the heavy rainstorm event (E1) and the rainstorm event (E3). The concentrations of total nitrogen (TN), ammonia (NH4+-N), total phosphorus (TP), and total suspended solids (TSS) were similar to the discharge process under the heavy rain events (E2), but the variations of nitrate (NO3--N) concentrations were opposite to those in the discharge process. ⢠The concentrations and variations of different forms of pollutants were different under different rainfall events. Under the event of strong rainfall erosion (E1 and E2), the concentrations of particulate pollutants varied significantly, being positively correlated with that of total suspended solids (TSS). For the rainfall event that did not cause soil erosion (E3), the forms of nitrogen and phosphorus were dominated by total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP) respectively, whose variations were mainly related to discharge. ⣠The discharge and pollutant concentrations at each station varied under different rainfall events. Heavy rainfall erosion was more obvious at Gubeikou station, causing significant variations in discharge, TP, and TSS. Therefore, these results can be used to determine migration patterns of non-point source pollutants caused by rainfall-runoff events and provide references for water quality prediction and control in flood seasons.