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The use of reclaimed water (RW) for irrigation alleviates agricultural water shortages. However, N 2 O emissions and N fertilizer transformations in soils irrigated with RW under different N fertilizer types and soil moisture contents are poorly understood. A 216-h laboratory incubation experiment was conducted to evaluate the effects of irrigation water types (RW and fresh water, FW), N fertilizer types ( 15 N-labeled KNO 3 and (NH 4 ) 2 SO 4 ), and soil moisture contents at 40, 60, and 90 % water-filled pore space (WFPS) on N 2 O emissions and N fertilizer transformations in intact soil cores. The results showed that cumulative N 2 O emissions ranged from 3.78 to 36.30 mg N m −2 , and fertilizer-derived N 2 O losses accounted for 0.14-2.44 % of N fertilizers, while fertilizer-derived N residues (NO 3 − -N + NH 4 + -N) accounted for 10.16-26.95 % of N fertilizers. The N 2 O emissions at 40 % WFPS and fertilizer-derived N residues at 60 % WFPS in soils irrigated with RW were significantly (10.98 and 20.95 %, respectively) higher than those irrigated with FW, while fertilizer-derived N 2 O losses at 60 % WFPS in soils irrigated with RW were 10.26 % higher than those irrigated with FW. The N 2 O emissions and fertilizer-derived N 2 O losses in soils amended with (NH 4 ) 2 SO 4 at 40 and 60 % WFPS were significantly (26.61-178.84 %) larger than those amended with KNO 3 , while fertilizer-derived N residues in soils amended with KNO 3 were significantly (41.47 %) higher than those amended with (NH 4 ) 2 SO 4 . The N 2 O emissions significantly increased with increasing soil moisture content. Our results indicate that N fertilizer types and soil moisture contents are the two important factors regulating N 2 O emissions and N fertilizer transformations. When RW irrigation is used, controlling soil moisture contents within 41 and 60 % WFPS (the optimum is 46 % WFPS) and application of KNO 3 can reduce N 2 O emissions and fertilizer-derived N 2 O losses, and correspondingly increase fertilizer-derived N residues, which can contribute to climate change mitigation.

International audienceIrrigation with treated wastewater which has the characteristics of higher salt content, larger sodium adsorption ratio (SAR), and more organic matter and suspended particles can cause the deterioration of the soil environment. Ordinary water, treated wastewater, and saline-sodic solutions with SAR = 3, 10 and 20 (mmolc·L− 1)0.5, respectively, were used as five irrigation water types, and the changes of soil saturated hydraulic conductivity (Ks), soil pores distribution, and soil pores single fractal dimension (Dm) were studied after simulated irrigation for 1 and 2 years with simulated irrigation systems, which consisted of soil bins and simulated evaporation systems. The results showed that soil Ks in the following descending order: CK > SAR3 > WW > SAR10 > SAR20, and the adverse effects on soil Ks caused by suspended solid particles and dissolved organic matter might play a more significant role than sodium in treated wastewater. The 0-5 cm soils had a smaller single soil pore area but larger pores quantity after simulated irrigation, the distribution of soil pores which was irrigated with treated wastewater had a smaller change compared with saline-sodic solutions treatments, and it showed the soil pores structure binary image was an effective method to analysis soil pores distribution. Soil Dm increased after simulated irrigation, and the smallest was the soil simulated irrigation with treated wastewater for 1 year, because the plugging and filling of suspended particles and dissolved organic matter in treated wastewater made the soil pores well distributed, but the soil Dm did not increase with increasing of SAR levels in irrigation waters. The relative SAR levels irrigation to soils and soil Ks had a good linear correlation relationship, while the relationship between soil Dm and the relative SAR levels irrigation to soils was very complicated. The soil Dm which calculated from soil binary images could not well reflect the hydraulic conductivity of saturated soil. Irrigation with treated wastewater had a greater effect on soil Ks than soil Dm, comparing with saline-sodic s...

<p>Over the past decades, urban non-point source (NPS) pollution has been the most severe threat to the urban water environment. The sharp increase of impervious surface and the high level of particulate matter from massive human activities exacerbated the water quality of surface runoff, leading to the significant urban NPS pollution globally whereby it is of importance to have a deep knowledge on the accumulation and transport of pollutants. A series of traditional physical models have been developed to simulate the runoff generating as well as the NPS pollution. However, a disadvantage of process-based modelling is its great demand for a large amount of field data which may normally be inaccessible, as well as the demand for the expertise in applying appropriate modelling method on specific study area. Empirical models do not characterize complex physical processes of NPS pollution and thus require fewer data and modelling skills. Nevertheless, the limitation is that these modelling approaches are region-sensitive and spatially untransferable. It is challenging to fill the gap between the requirement of urban water environment management and existing modelling performance on NPS pollution, in the absence of a more effective model with high accuracy, easy employment, and spatial transferability.</p> <p>Machine learning approach has been utilized in environmental studies for decades, and was originally believed to be a black box that can barely provide any physical insight into environmental processes. However, an approach named physics-informed neural networks (PINN) was proposed lately and then applicated in dynamical system. This approach embeds differential equations of priori knowledge into neural network to make modelling interpretable and generalizable. In this study, a physical process embedded LSTM network was proposed to formulate the cumulation and transport of urban NPS pollution in rainfall runoff, based on the coupling of LSTM and differential equations of classic exponential build-up/wash-off processes. Water quality data of urban runoff from sampling and continuous real-time monitoring campaigns distributed in China, USA and New Zealand were collected and fed into proposed network to model the primary NPS pollutant TSS. The results revealed that the hybrid PINN model excels the vanilla LSTM approach and auto-calibrated SWMM approach in accuracy and convenience. The interpretable model also enhanced the cross-catchment transferability of model for urban water management in data-poor area. In addition, the trained parameters of network units were found consistent with the prior knowledge of accumulation and transport of NPS pollutants, indicating the deep coupling of neural network and physical process. As a very early case of hybrid AI modelling in urban NPS pollution, this study provided a new perspective on water quality modelling and can help in improving the standards of urban environment governance.</p>