Abatement vs. treatment for efficient diffuse source water pollution management in terrestrial-marine systems

Treatment of nitrogen and phosphorus from sewage tailwater in paddy rice wetlands: concept and environmental benefits

Domestic sewage tailwater (DSTW) reuse for crop irrigation is considered a promising practice to reduce water demand, mitigate water pollution, and substitute chemical fertilization. The level of the above environmental benefits of this water reuse strategy, especially when applied to paddy wetlands, remains unclear. In this study, soil column experiments were conducted to investigate the nitrogen and phosphorus fate in paddy wetlands subjected to different tailwater irrigation and drainage strategies, specifically, (i) TW1 and TW2 for regular or enhanced irrigation-drainage without N fertilization, (ii) TW3 and TW4 for regular irrigation with base or tillering N fertilizer, (iii) conventional fertilization N210, and (iv) no-fertilization controls N0. The results showed that the total nitrogen (TN), nitrate (NO3-), and total phosphorus (TP) removal rates from the paddies irrigated by DSTW ranged between 51.92 and 59.34%, 68.1 and 83.42%, and 85.69 and 86.98% respectively. Ammonia emissions from the DSTW-irrigated treatments were reduced by 14.6~47.2% compared to those paddies subjected to conventional fertilization (N210), similarly for TN emissions, with the exception of the TW2 treatment. Overall, it is established that the paddy wetland could effectively remove residual N and P from surface water runoffs, while the partial substitution of chemical fertilization by DSTW could be confirmed. The outcome of this study demonstrates that DSTW irrigation is a promising strategy for sustainable rice production with a minimized environmental impact.

Meeting the Growing Need for Land-Water System Modelling to Assess Land Management Actions

Elevated contaminant levels and hydrological alterations resulting from land use are degrading aquatic ecosystems on a global scale. A range of land management actions may be used to reduce or prevent this degradation. To select among alternative management actions, decision makers require predictions of their effectiveness, their economic impacts, estimated uncertainty in the predictions, and estimated time lags between management actions and environmental responses. There are multiple methods for generating these predictions, but the most rigorous and transparent methods involve quantitative modelling. The challenge for modellers is two-fold. First, they must employ models that represent complex land-water systems, including the causal chains linking land use to contaminant loss and water use, catchment processes that alter contaminant loads and flow regimes, and ecological responses in aquatic environments. Second, they must ensure that these models meet the needs of endusers in terms of reliability, usefulness, feasibility and transparency. Integrated modelling using coupled models to represent the land-water system can meet both challenges and has advantages over alternative approaches. The need for integrated land-water system modelling is growing as the extent and intensity of human land use increases, and regulatory agencies seek more effective land management actions to counter the adverse effects. Here we present recommendations for modelling teams, to help them improve current practices and meet the growing need for land-water system models. The recommendations address several aspects of integrated modelling: (1) assembling modelling teams; (2) problem framing and conceptual modelling; (3) developing spatial frameworks; (4) integrating economic and biophysical models; (5) selecting and coupling models.

Study of Non-Point Pollution in the Ashe River Basin Based on SWAT Model with Different Land Use

The Ashe River Basin (ARB), long known as the “Golden Waterway” in Manchu, has become one of China’s most polluted rivers. The basin area of the Ashe River is 3545 km2 and the total length of the river is 257 km. There have not been specific studies on land use change and non-point pollution in the ARB region. This paper uses the ARB watershed as the study area, simulates the watershed using the SWAT (Soil and Water Assessment Tool) model, and analyzes the hydrological processes and the temporal and spatial changes of total nitrogen and total phosphorus in the watershed with hydrology and water quality as the objectives under different periods of land use to reduce pollution in the watershed and protect the environment. The results show that the simulation of runoff, and even the R2 and NS (both the coefficient of determination and the Nash–Sutcliffe efficiency coefficient are simulated by SWAT-CUP, which is generally used to validate the simulation results of the hydrological model, where the closer the result is to 1, the better the effect) of total nitrogen and total phosphorus in the watershed, are also all above 0.75 and have good applicability during regular and validation periods. Since 2000, the simulated monthly average total nitrogen and total phosphorus levels have progressively grown. The most polluted areas are concentrated in the middle and lower reaches of the watershed near the main streams owing to the rise in load per unit area caused by the collection of pollutants from the upper watershed to the watershed outlet, and even an increase in fertilizer application due to the larger area of cultivated land.

Leveraging sustainable development of agriculture with sustainable water management: The empirical investigation of "Five Water Cohabitation" of Zhejiang Province in China

In 2013, the government of Zhejiang Province put forward a strategic project named "Five Water Cohabitation" (FWC) by integrating five water treatments: "sewage treatment," "flood prevention," "drainage system improvement," "water supply guarantee," and "water saving promotion." It has been eight years since the project was proposed and launched. The primary purpose of the present study is to investigate the performance and significant effects of the project on the sustainable development of agriculture. This study investigates the project's implementation from four aspects: environmental sustainability, resource sustainability, social sustainability, and economic sustainability. Furthermore, the difference-in-differences approach is applied to verify the treatment effect. Liaoning Province is chosen as the control group because it is also the traditionally agricultural province, and it has not implemented any large-scale water management projects. This study selects six sustainable variables, i.e., per capita GDP, urban-rural disparity, total water resources, domestic waste clearance, urbanization level, and health security level. The results show that the FWC project positively affects the sustainable development of agriculture for Zhejiang Province in economic sustainability, ecological sustainability, and social sustainability.

Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy

Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management.

Multi-scale analysis of the relationship between landscape patterns and a water quality index (WQI) based on a stepwise linear regression (SLR) and geographically weighted regression (GWR) in the Ebinur Lake oasis

Water quality is highly dependent on landscape characteristics. This study explored the relationships between landscape patterns and water quality in the Ebinur Lake oasis in China. The water quality index (WQI) has been used to identify threats to water quality and contribute to better water resource management. This study established the WQI and analyzed the influence of landscapes on the WQI based on a stepwise linear regression (SLR) model and geographically weighted regression (GWR) models. The results showed that the WQI was between 56.61 and 2886.51. The map of the WQI showed poor water quality. Both positive and negative relationships between certain land use and land cover (LULC) types and the WQI were observed for different buffers. This relationship is most significant for the 400-m buffer. There is a significant relationship between the water quality index and landscape index (i.e., PLAND, DIVISION, aggregation index (AI), COHESION, landscape shape index (LSI), and largest patch index (LPI)), demonstrated by using stepwise multiple linear regressions under the 400-m scale, which resulted in an adjusted R ² between 0.63 and 0.88. The local R ² between the LPI and LSI for forest grasslands and the WQI are high in the Akeqisu River and the Kuitun rivers and low in the Bortala River, with an R ² ranging from 0.57 to 1.86. The local R ² between the LSI for croplands and the WQI is 0.44. The local R ² values between the LPI for saline lands and the WQI are high in the Jing River and low in the Bo River, Akeqisu River, and Kuitun rivers, ranging from 0.57 to 1.86.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R² values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R² ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.