Hydraulic conditions affect pollutant removal efficiency in distributed ditches and ponds in agricultural landscapes

Pollutant removal in an experimental bioretention cell situated in a northern Chinese sponge city

To assess the viability and effectiveness of bioretention cell in enhancing rainwater resource utilization within sponge cities, this study employs field monitoring, laboratory testing, and statistical analysis to evaluate the water purification capabilities of bioretention cell. Findings indicate a marked purification impact on surface runoff, with removal efficiencies of 59.81% for suspended solids (SS), 39.01% for chemical oxygen demand (COD), 37.53% for ammonia nitrogen (NH3-N), and 30.49% for total phosphorus (TP). The treated water largely complies with rainwater reuse guidelines and tertiary sewage discharge standards. Notably, while previous research in China has emphasized water volume control in sponge city infrastructures, less attention has been given to the qualitative aspects and field-based evaluations. This research not only fills that gap but also offers valuable insights and practical implications for bioretention cell integration into sponge city development. Moreover, the methodology and outcomes of this study serve as a benchmark for future sponge city project assessments, offering guidance to relevant authorities.

Preliminary manifestation of the Yangtze River Protection Strategy in improving the carbon sink function of estuary wetlands

In 2016, the Yangtze River Protection Strategy was proposed and a series of measures were applied to restore the health and function of the Yangtze River ecosystem. However, the impact of these measures on the carbon (C) sink capacity of the Yangtze River estuary wetlands has not been exhaustively studied. In this work, the effects of these measures on the C sink capacity of Yangtze River estuary wetlands were examined through the long-term monitoring of C fluxes, soil respiration, plant growth and water quality. The C flux of the Yangtze River estuary wetlands has become increasingly negative after the implementation of these measures, mainly owing to reduction in soil CO2 emission. The decrease in the chemical fertilizer release and returning farmland to wetland had led to the improvement of water quality in the estuary area, which further reduced soil heterotrophic microbial activity, and ultimately decreasing soil CO2 emissions of estuary wetlands.

Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms

Wet ponds have been extensively used for controlling stormwater pollutants, such as sediment and nutrients, in urban watersheds. The removal of pollutants relies on a combination of physical, chemical, and biological processes. It is crucial to assess the performance of wet ponds in terms of removal efficiency and develop an effective modeling scheme for removal efficiency prediction to optimize water quality management. To achieve this, a two-year field program was conducted at two wet ponds in Calgary, Alberta, Canada to evaluate the wet ponds' performance. Additionally, machine learning (ML) algorithms have been shown to provide promising predictions in datasets with intricate interactions between variables. In this study, the generalized linear model (GLM), partial least squares (PLS) regression, support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) were applied to predict the outflow concentrations of three key pollutants: total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Generally, the concentrations of inflow pollutants in the two study ponds are highly variable, and a wide range of removal efficiencies are observed. The results indicate that the concentrations of TSS, TN, and TP decrease significantly from the inlet to outlet of the ponds. Meanwhile, inflow concentration, rainfall characteristics, and wind are important indicators of pond removal efficiency. In addition, ML algorithms can be an effective approach for predicting outflow water quality: PLS, GLM, and SVM have shown strong potential to capture the dynamic interactions in wet ponds and predict the outflow concentration. This study highlights the complexity of pollutant removal dynamics in wet ponds and demonstrates the potential of data-driven outflow water quality prediction.

Agricultural ditches are hotspots of greenhouse gas emissions controlled by nutrient input

Agricultural ditches are pervasive in agricultural areas and are potential greenhouse gas (GHG) hotspots, since they directly receive abundant nutrients from neighboring farmlands. However, few studies measure GHG concentrations or fluxes in this particular water course, likely resulting in underestimations of GHG emissions from agricultural regions. Here we conducted a one-year field study to investigate the GHG concentrations and fluxes from typical agricultural ditch systems, which included four different types of ditches in an irrigation district located in the North China Plain. The results showed that almost all the ditches were large GHG sources. The mean fluxes were 333 μmol m-2 h-1 for CH4, 7.1 mmol m-2 h-1 for CO2, and 2.4 μmol m-2 h-1 for N2O, which were approximately 12, 5, and 2 times higher, respectively, than that in the river connecting to the ditch systems. Nutrient input was the primary driver stimulating GHG production and emissions, resulting in GHG concentrations and fluxes increasing from the river to ditches adjacent to farmlands, which potentially received more nutrients. Nevertheless, the ditches directly connected to farmlands showed lower GHG concentrations and fluxes compared to the ditches adjacent to farmlands, possibly due to seasonal dryness and occasional drainage. All the ditches covered approximately 3.3% of the 312 km2 farmland area in the study district, and the total GHG emission from the ditches in this area was estimated to be 26.6 Gg CO2-eq yr-1, with 17.5 Gg CO2, 0.27 Gg CH4, and 0.006 Gg N2O emitted annually. Overall, this study demonstrated that agricultural ditches were hotspots of GHG emissions, and future GHG estimations should incorporate this ubiquitous but underrepresented water course.

Variability in Nutrient Dissipation in a Wastewater Treatment Plant in Patagonia: A Two-Year Overview

Constructed wetlands are environmental solutions that mitigate the impacts of urban effluents. It is unclear how the performance of these wastewater treatment plants (WWTP) is affected by climatic conditions. The dissipation of nutrients, suspended solids, and changes in dissolved oxygen were investigated on a monthly basis over two years (2018/2019) at six sampling points across a WWTP located in Esquel, Patagonia. It was predicted that climatic variables (rain pattern and air temperature) would affect the functioning and efficiency of the WWTP (i.e., via nutrient load mitigation and sediment retention). Rainfall and temporal patterns differed markedly between and throughout the two years, leading to a clear seasonality in the transformation of pollutants. Nitrate loads were significantly higher in 2018 than in 2019 suggesting some degree of operational failure, whereas ammonia levels in treated effluents were extremely high during both years, with marked peaks occurring during autumn 2018 and summer 2019. The WWTP was moderately successful (~36%) in reducing TSS contents during 2018 but was inefficient in 2019. Ammonia levels in receiving waters underwent dilution due to rains rather than due to adequate WWTP nutrient retention. In terms of nutrients, effluent values exceeded those established by governmental regulation during most months, but worsened during summer coinciding with low flows. This lack of predictability for the values of the treated effluent strongly jeopardizes the ecological integrity and biodiversity of the receiving stream.

Improved export coefficient model for identification of watershed environmental risk areas

As a complex system under the joint action of man and nature, land use/cover directly or indirectly affects the environmental quality of the freshwater ecosystem. Studying the response of water environment quality to land use/cover change was significant to accurately simulate lake water quality and effectively enhance the management level. As an empirical model, the classical export coefficient model has been widely used and developed in agricultural non-point source pollution research because of its simple structure and convenient application. However, it assumes that the export coefficient of a particular type of land use/cover was constant, ignoring the influence of surface runoff and interception on the output intensity of pollutants in pollutant migration. This study improved the classical export coefficient model by adding factors such as precipitation, surface cover, and topography, evaluated the contribution of land use/cover to total nitrogen load into the lake in Dianchi Lake Basin, and applied the pollution assessment results to the identification of watershed environmental risk areas. The results showed that the improved export coefficient model could better simulate the relationship between land use/cover and total nitrogen load into Dianchi Lake from the basin. At the same time, spatial characteristics of the total nitrogen load contribution of the terrestrial could be represented. The high-risk areas in the basin were mainly cultivated land and construction areas with low vegetation coverage around lakes or downstream. The contribution per unit area to the TN load into the lake from areas with a high risk was 14.28 t/km², which was 3.47 times that of medium-high-risk areas and 52.28 times that of the medium-risk area. Land use control measures in high-risk areas in the basin should be further strengthened, especially in the lakeside zone.

Evaluation of the DRAINMOD Model’s Performance Using Different Time Steps in Evapotranspiration Computations

The DRAINMOD model is a superior tool used to predict the changes in farmland water balance under different agricultural drainage layouts, fields, weather conditions, and management practices. In the present study, we assessed the sensitivity of the DRAINMOD predictions in farmland water balance to the time step (hourly or daily) in daily evapotranspiration (ET₀) computations for 12-hectares of farmland located at the lower reaches of the Yangtze River basin. The model was calibrated and validated and then was applied twice under two sets of daily ET₀ values, computed using the standardized ASCE Penman–Monteith model (one using the hourly time step (HTS) and the other using the daily time step (DTS)). Regarding daily computed ET₀ values, results show that abrupt diurnal changes in the weather always result in significant differences between daily ET₀ values when computed based on DTS and HTS. DRAINMOD simulations show that such differences between daily computed ET₀ values affected the model’s predictions of the “water fate” in the study area; e.g., adopting HTS rather than DTS resulted in a 4.8% increase, and a 3.1% and 1% decrease in the models’ cumulative predictions of runoff, drainage, and infiltration, respectively. Therefore, for a particular study area, it is critical to pay attention when deciding the best time step in ET₀ computations to ensure accurate DRAINMOD simulations, thereby ensuring better utilization of agricultural water alongside high agricultural productivity.

Farmers’ Awareness in the Context of Climate Change: An Underutilized Way for Ensuring Sustainable Farmland Adaptation and Surface Water Quality

Simulations using the Crop Water and Irrigation Requirements model (CROPWAT), show that the projected climatic changes over the period from 2026 to 2050 in the Yanyun irrigation district, Yangzhou, China, will cause the paddy lands there to lose about 12.4% to 37.4%, and 1.6% to 45.6%, of their future seasonal rainwater in runoff under the Representative Concentration Pathways (RCP45 and RCP85), respectively. This may increase future irrigation requirements (IRs), alongside threatening the quality of adjacent water bodies. The CROPWAT simulations were re-run after increasing the Surface Storage Capacity (SSC) of the land by 50% and 100% of its baseline value. The results state that future rainwater runoff will be reduced by up to 76% and 100%, and 53% and 100% when the SSC is increased by 50% and 100%, under RCP45 and RCP85, respectively. This mitigates the future increase in IRs (e.g., under RCP45, up to about 11% and 16% of future IRs will be saved when increasing the SSC by 50% and 100%, respectively), thus saving the adjacent water bodies from the contaminated runoff from these lands. Adjusting the SSC of farmlands is an easy physical approach that can be practiced by farmers, and therefore educating them on how to follow up the rainfall forecast and then adjust the level of their farmlands’ boundaries according to these forecasts may help in the self-adaptation of vast areas of farmlands to climate change. These findings will help water users conserve agricultural water resources (by mitigating the future increase in IRs) alongside ensuring better quality for adjacent water bodies (by decreasing future runoff from these farmlands). Increasing farmers’ awareness, an underutilized approach, is a potential tool for ensuring improved agricultural circumstances amid projected climate changes and preserving the available water resources.

Effects of land use change, wetland fragmentation, and best management practices on total suspended sediment concentrations in an urbanizing Oregon watershed, USA

While many different watershed management strategies have been implemented to improve water quality, relatively few studies empirically tested the combined effects of different strategies on water quality in relation to land cover changes using long-term empirical data at the sub-basin scale. Using 10 years of total suspended solids (TSS) data, we examined how the conversion of wetland, wetland fragmentation, beaver dams, and Best Management Practices (BMPs) affect wet season TSS concentrations for the 25 monitoring stations in the Tualatin River basin, USA. Geographic information systems, FRAGSTATS, and correlation analysis were used to identify the direction of land cover change, degree of wetland fragmentation, and the strength of the relationship between TSS change and explanatory variables. Improvement in TSS concentrations was tightly coupled with the aggregation of wetlands, presence of beaver dams, particularly during the mid-wet season when flows were highest. Other BMPs effectively reduced TSS concentrations for the early and late-wet seasons when flows were not as high as in the middle wet-season. Aggregated wetlands were more effective for improving water quality than smaller disaggregated wetlands of similar total area when combined with the presence of beaver dams and BMPs. These findings offer important scientific and practical implications for management of urbanizing watersheds that seek to achieve the dual goals of improving environmental quality and land development.