Nitrogen Pollution Research Articles

Multi-evidences investigation into spatiotemporal variety, sources tracing, and health risk assessment of surface water nitrogen contamination in China

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

Changes in Seagrass Community Structure in Response to Sediment Load and Excess Nutrients, and its Implication to Carbon Stocks in the Berau Marine Conservation Area

Changes in Seagrass Community Structure in Response to Sediment Load and Excess Nutrients, and its Implication to Carbon Stocks in the Berau Marine Conservation Area

The future of algal blooms in lakes globally is in our hands

Lakes are fundamental to society and nature, yet they are currently exposed to excessive nutrients and climate change, resulting in algal blooms. In the future, this may change, but how and where still needs more scientific attention. Here, we explore future trends in algal blooms in lakes globally for >3500 ‘representative lakes’ for the year 2050, considering the attribution of both nutrient and climate factors. We soft-coupled a process-based lake ecosystem model (PCLake+) with a watershed nutrient model (MARINA-Multi) to assess trends in algal blooms in terms of the Trophic State Index for chlorophyll-a (TSI-Chla). Globally between 2010 and 2050, we show a rising trend in algal blooms under fossil-fuelled development (TSI-Chla increase in 91 % of lakes) and a declining trend under sustainable development (TSI-Chla decrease in 63 % of lakes). These changes are significantly attributed to nutrients. While not always significant, climate change attributions point to being unfavourable for lakes in 2050, exacerbating lake water quality. Our study stresses prioritising responsible nutrient and climate management on policy agendas. This implies that the future of algal blooms in lakes is in our hands.

Scale Effects of Landscape Patterns on Nitrogen and Phosphorus Pollution in Yanshan River Basin, Guilin, China

Scale Effects of Landscape Patterns on Nitrogen and Phosphorus Pollution in Yanshan River Basin, Guilin, China

Tracing nitrogen and phosphorus pollution in urban runoff: Insights from isotopic tracers and SWMM modeling

Water contamination in certain rivers during rainfall necessitates the evaluation of pollutant levels within pipelines and the identification of their sources to ensure effective pollution control. In this study, we assess pollutant concentrations during runoff from surfaces, pipelines, and channels, identify pollutant sources within pipelines, and analyze sewage leakage contributions to channel pollution in residential areas in Wuxi City, China. The results showed that the event mean concentrations of total nitrogen (TN), total phosphorus (TP), ammonium (NH4+-N), and nitrate (NO3−-N) in the drainage outlet runoff were 3.4, 1.1, 8.8, and 14.9 times higher than in impervious surface runoff. The predominant forms of nitrogen and phosphorus in runoff shift from organic to inorganic as they move from the surface to pipelines. The pipeline load and contribution were further estimated by combining water quantity calculations from the calibrated and verified Storm Water Management Model (SWMM) with measured nitrogen and phosphorus concentrations in the runoff. TN, NH4+-N, NO3−-N, and PO43--P within the pipelines accounted for 59%, 83%, 90%, and 77% of the discharge loads, respectively. The application of the Stable Isotope Analysis in R (SIAR) package (MixSIAR) revealed that the domestic sewage and soil organic N were the predominant sources of pollutants within drainage pipelines, contributing 23.7% and 32.6% of NO3−-N, respectively. Additionally, the sewage leakage accounted for 33.7% and 66.9% of NO3−-N in drainage runoff and channels, respectively. Unlike existing methods, this study quantitatively highlights the significant impact of pipeline pollutants and sewage leakage on urban river water quality, providing essential insights for developing effective strategies to mitigate non-point source water pollution.

Integrated Modeling of Flow, Soil Erosion, and Nutrient Dynamics in a Regional Watershed: Assessing Natural and Human‐Induced Impacts

AbstractCurrent integrated modeling frameworks for simulating nutrient sources and dynamics are inadequate for large regional watersheds dominated by groundwater‐surface water interactions due to their simplistic representations of groundwater. In this study, we develop a coupled model that integrates comprehensive surface water, 3‐D groundwater, soil erosion, and nutrient processes. The model is intended to enhance the understanding of nutrient dynamics and sources in the Pearl River Basin (PRB). The model exhibits satisfactory performance in simulating streamflow and sediment transport patterns, capturing essential seasonal variations in water quality indicators. Hydrological budget assessments from 2002 to 2020 in the PRB reveal that 54% of precipitation drains into the South China Sea as surface water, while groundwater discharge as baseflow accounts for 18% of the streamflow. The nutrient budget for the PRB indicates that non‐point sources are the dominant contributors to both nitrogen (N) and phosphorus (P), ranging between 64% and 90%. Improved sewage collection and treatment have reduced point source nutrient contributions over the evaluation period. Groundwater remains a significant and consistent source of N, contributing between 11% and 19%. Natural disturbances and fertilization have led to an upward trend in river N inputs, while afforestation and sewage reduction efforts have resulted in a downward trend in river P inputs. Increased fertilization emerges as a central concern for the PRB, suggesting cost‐effective mitigation of fertilizer usage a pragmatic solution. The coupled simulation model developed in this study offers a novel systems approach for basin‐wide nutrient analysis and pollution control strategies, considering both natural and human‐induced disturbances.

One third of African rivers fail to meet the ’good ambient water quality’ nutrient targets

The ambition of Sustainable Development Goal (SDG) target 6.3 is to improve global water quality by 2030. SDG indicator 6.3.2 monitors progress towards this target by assessing water bodies against ‘good’ ambient water quality criteria, with nutrients (nitrogen and phosphorus) as part of the key metrics. However, large data gaps present a fundamental challenge, especially for Africa on how to assess the progress being made with respect to both the current and desired future situations. Here, a continental water quality model for Africa is presented to simulate river sediment load, Total Nitrogen (TN) and Total Phosphorus (TP) loads and concentrations. Furthermore, critical areas and hotspots of TN and TP pollution are mapped for the period 2017 – 2019, in relation to the United Nations Environment Programme (UNEP) target thresholds used for the assessment of SDG indicator 6.3.2. Utilizing the UNEP’s criteria, which designates a water body as having “good ambient water quality” if 80% or more of its monitored values meet their targets, it is estimated that 44 % and 15 % of African rivers fail to meet the set water quality thresholds for simulated TP and TN, respectively. When synthesizing data for both TP and TN, 34 % of the rivers do not qualify as having “good ambient water quality”. Geographically, the most pronounced nutrient pollution hotspots were in North Africa, Niger River Delta, Nile River basin, Congo River basin and specific zones in Southern Africa. These areas correlate with regions characterized by high inputs of fertilizers, manure and wastewater discharge.

Tracing nitrogen sources and transformation characteristics in a large basin with spatially heterogeneous pollution distribution

This study used dual stable isotopes to examine nitrate sources and geographical distribution in the Liao River Basin (LRB), one of China's seven major river basins. During a normal hydrological season in April 2021, water samples were taken from the main streams of the Liao River (MLR), Shuangtaizi River (STR), Hun River (HR), Taizi River (TZR), and Daliao River (DLR). Monitoring results indicated that 93% of the water samples had a total nitrogen level exceeding the Class IV limit (1.5 mg/L) of the ‘Environmental Quality Standards (EQS) for surface water’, indicating a serious nitrogen pollution status. 71.3% of the total nitrogen on average was in the form of nitrate. The scatterplots of δD-H2O and δ18O-H2O showed that water in TZR and DLR were mainly affected by precipitation, while MLR, STR and HR were additionally impacted by evaporation and groundwater. The overall δ15N and δ18O of NO3− varied from 7.7‰ to 17.9‰ and 0.6‰–11.2‰, respectively. The correlations between δ15N-NO3- and δ18O-NO3-, along with attribution results from the Bayesian isotopic mixing model, indicated a predominant role of manure/sewage (MS) pollution in affecting river nitrate, accounting for 78% of total nitrate in MLR and 72% in DLR. A positive correlation between δ15N-NO3- and δ18O-NO3- in MLR indicated the occurrence of denitrification process. Overall, attribution results showed that the primary nitrate sources varied in different river systems within such a large basin, mainly due to spatially varied land use and human activities. Tailored nitrogen management strategies should be implemented to address the main anthropogenic pressures.

Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil and water, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent applications, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This paper investigates the transformation of traditional agriculture into smart farming through the integration of IoT technology and community partnerships. It presents a case study focused on educating farm owners about advanced technologies to enhance decision-making, improve crop yields, and promote sustainability. Additionally, the paper highlights the role of data analytics in agriculture. Farmers in the southern region of Zagreb, Croatia, were trained on the use of sensors and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial experience. The DMAIC methodology was employed to address these issues and measure relevant parameters. The paper also discusses consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It explains the potential of estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, Principal Component Analysis (PCA) biplot analysis is presented, showing that pH values behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

Mechanism underlying revetment effects on the spatial distribution of nitrogen removal and N2O emissions in riparian zones at summer

Study Region, Shanghai, China, Study FocusUrban riparian zones can mitigate or even control nitrogen pollution in rivers through nitrification and denitrification processes. However, these processes also produce N2O, a potent greenhouse gas. During urbanization, various types of revetments have been constructed in riparian zones. These revetments alter the exchange of materials and energy between the riparian zone and the river, which in turn affects the nitrification and denitrification processes in the riparian zone. However, the mechanisms by which revetments impact nitrogen removal and N2O emissions in riparian zones are not yet clear, and the ways in which revetments balance nitrogen removal and N2O emissions remain unknown. Therefore, in a single river section, natural (NR), permeable (PR), and impervious revetment (IR) types were replicated. Measurements are taken of nitrogen removal, N2O emissions, and related environmental indicators in both the river-riparian interface (RRI) and the riparian zone area outside the RRI (RZ). The study analyzes the differences in environmental factors, nitrification and denitrification potentials, and N2O emission rates among the different revetments to identify the main factors affecting nitrogen removal and N2O emissions in the riparian zone. It also explores the potential mechanisms by which revetments influence the spatial distribution of nitrogen removal and N2O emissions in the summer riparian zone.New Hydrological Insights for the Region: PR exhibit greater denitrification potential and lower N2O emission rates. The denitrification potential is higher in the RRI compared to RZ, while the N2O emission rate is lower than in RZ. nirK is the primary factor influencing denitrification potential, whereas nosZII predominantly affects N2O emissions. The lower soil carbon-to-nitrogen ratio in PR increases the abundance of nirK, thereby enhancing the riparian zone's denitrification potential. Moreover, greater aboveground vegetation biomass increases the abundance of nosZII, reducing the N2O emission rate. The study results could provide guidance for urban ecological restoration and greenhouse gas emission reduction.

Predictors of long-term variability in NE Atlantic plankton communities

Anthropogenic pressures such as climate change and nutrient pollution are causing rapid changes in the marine environment. The relative influence of drivers of change on the plankton community remains uncertain, and this uncertainty is limiting our understanding of sustainable levels of human pressures. Plankton are the primary energy resource in marine food webs and respond rapidly to environmental changes, representing useful indicators of shifts in ecosystem structure and function. Categorising plankton into broad groups with similar characteristics, known as “lifeforms”, can be useful for understanding ecological patterns related to environmental change and for assessing the state of pelagic habitats in accordance with the EU Marine Strategy Framework Directive and the OSPAR Commission, which mandates protection of the North-East Atlantic. We analysed 29 years of Continuous Plankton Recorder data (1993–2021) from the North-East Atlantic to examine how trends in plankton lifeform abundance changed in relation to one another and across gradients of environmental change associated with human pressures. Random forest models predicted between 57 % and 80 % of the variability in lifeform abundance, based on data not used to train the models. Observed variability was mainly explained by trends in other lifeforms, with mainly positively correlated trends, indicating bottom-up control and/or shared responses to environmental variability were prevalent. Longitude, bathymetry, mixed layer depth, the nitrogen-to‑phosphorus ratio, and temperature were also significant predictors. However, contrasting influences of environmental drivers were detected. For example, small copepod abundance increased in warmer conditions whereas meroplankton, large copepods and fish larvae either decreased or were unchanged. Our findings highlight recent changes in stratification, reflected by variation in mixed layer depth, and imbalanced nutrient ratios are affecting multiple lifeforms, impacting the North-East Atlantic plankton community. To achieve environmental improvements in North-East Atlantic pelagic habitats, it is crucial that we continue to address climate change and reduce nutrient pollution.

In-depth exploration of nanoparticles for enhanced nutrient use efficiency and abiotic stresses management: Present insights and future horizons

Nanotechnology is an innovative method of elevating agricultural output without sacrificing quality due to nanoparticles (NPs) unique characteristics and numerous potential uses. It is also nature-friendly, advantageous to living organisms, and cost-effective. Sustainable agricultural practices are gaining attention on NPs and nanofertilizers (NFs) as practical substitutes for traditional fertilizers and pesticides that nanotechnology could surpass some of the issues with traditional farming methods. There should be an emphasis on cutting-edge studies of NPs applications in agriculture. This article presents a positive perspective on the mechanisms leading to the formation of NPs and their application as NFs for managing nutrients in agriculture. We also share up-to-date findings on NPs interactions with plants, the fate of NPs and potential risks associated with them in plants. The as well as on the role of NPs nanomaterials in decreasing abiotic and heavy metal toxicity stress. NFs help reduce the environmental damage caused by traditional, inorganic fertilizers. Due to their enhanced responsiveness and ability to pierce the epidermis, NFs can decrease nutrient surplus while increasing nutrient usage efficiency. It was also established that NPs are essential for protecting against abiotic stress. However, some studies have shown that NPs are harmful to higher plants because the NPs they are deposited upon the surface of cells or in the cell organelles, leading to oxidative stress symptoms. In this review article, we explore the utilization of NPs for nutrient and abiotic stress management for crop production and protection during the climate change era.

Enhancing nitrate removal from small wetlands via regulating bacterial-algal symbiosis with macrophyte coverage

With increasing land resource constraints, wetlands, as ecological hotspots, are expected to enhance biogeochemical processes to mitigate nitrogen (N) pollution, particularly nitrate-nitrogen (NO3−-N). However, the interactions among bacteria, algae, and macrophytes in wetlands, which are crucial for N removal, remain largely unknown. This study explored how macrophyte coverage influences bacterial-algal interactions, shifting from mutualism to inhibition, thereby affecting N removal. Moderate coverage enhanced NO3−-N and total nitrogen (TN) removal (P < 0.05), which was correlated with increased microbial abundance (P < 0.05). This may have resulted from moderate algal photosynthesis, reduced physiological stress, and the expansion of ecological niches for microbes. Insufficient coverage promoted algal growth (chlorophyll-a > 31.8 μg·L−1), leading to increased competition for substrates and elevated pH, which further inhibited bacterial activity. Excessive coverage also inhibited bacterial activity by reducing illumination and oxidation-reduction potential. Consequently, insufficient and excessive coverage decreased N removal efficiencies by 2.7–15.7 % (NO3−-N) and 3.7–11.1 % (TN) while increasing methane emission potential by 1.4–6.9 times compared with moderate coverage. These findings offer insights into solving NO3−-N contamination using near-natural methods and balancing the ecological and practical considerations for small wetlands.

Removal performance of Nitrogen, sulfur and chlorine pollutants during chemical looping combustion of textile dyeing sludge using red mud as an oxygen carrier

Textile dyeing sludge (TDS) is a solid waste produced by the textile industry, which contains a large number of N, S and Cl components. Direct heat treatment of TDS will cause a large number of N, S, Cl pollutants to be released. This study verified the possibility of chemical looping combustion (CLC) of TDS using alkaline WS900 red mud (RM) oxygen carrier, which is rich in Na/Ca, to remove N, S and Cl pollutants at the same time. Raising the reaction temperature in the range of 750–900 °C, prolonging the residence time and increasing the oxygen excess coefficient can promote the oxidation performance of WS900 to NOx precursor, while CO2 atmosphere inhibits the oxidation ability. After 10 cycles of CLC, the Cl fixation rate of WS900 is still close to 100% and its S fixation rate is more than 97%. It can maintain more than 90% of carbon conversion and 86% of CO2 selectivity. Compared with the pyrolysis condition, the removal rate of N pollutants is about 67%. Alkaline RM is also neutralized after absorbing acid gases during CLC, which is convenient for its further treatment and utilization.

Calculation process and spatial heterogeneity characteristic of non-point source pollution caused by rainfall-runoff in Taizhou City

ABSTRACT With improved point source pollution management, the impacts of non-point source pollution on the ecosystem are increasingly evident. However, conventional techniques like output coefficient approaches fail to adequately capture its geographical and temporal variability. This study proposed a grid-based calculation technique to quantify non-point source pollution from rainfall-runoff in the Jinqing Watershed, Taizhou City, Zhejiang Province, based on the principles of the Soil Conservation Service-Curve Number (SCS-CN) model. The modified SCS-CN model effectively represented both temporal and geographical variations in rainfall-runoff pollution. Precipitation emerged as the primary determinant of non-point source pollutant load over time due to its strong positive association with rainfall. From January to April, there was a constant pollutant load; besides irregular rainfall patterns, land use types also influenced pollutant loads at a geographical scale. Building and cultivated areas exhibited positive correlations with nitrogen (r = 0.44, P = 0.05) and phosphorus (r = 0.54, P = 0.05), while forested and grassland areas showed negative correlations with nitrogen (r = −0.44, P = 0.05) and phosphorus pollutants (r = −044, P = 0.05). Pollution loads were also influenced by landscape patterns; concentrated dominant land use types resulted in higher contamination levels compared with scattered dominant land use types.

Recovery of plant nutrients from human excreta and domestic wastewater for reuse in agriculture: a systematic map and evidence platform

BackgroundAchieving a more circular and efficient use of nutrients found in human excreta and domestic (municipal) wastewater is an integral part of mitigating aquatic nutrient pollution and nutrient insecurity. A synthesis of research trends readily available to various stakeholders is much needed. This systematic map collates and summarizes scientific research on technologies that facilitate the recovery and reuse of plant nutrients and organic matter found in human excreta and domestic wastewater. We present evidence in a way that can be navigated easily. We hope this work will help with the uptake and upscaling of new and innovative circular solutions for the recovery and reuse of nutrients.MethodsThe systematic map consists of an extension of two previous related syntheses. Searches were performed in Scopus and Web of Science in English. Records were screened on title and abstract, including consistency checking. Coding and meta-data extraction included bibliographic information, as well as recovery pathways. The evidence from the systematic map is embedded in an online evidence platform that, in an interactive manner, allows stakeholders to visualize and explore the systematic map findings, including knowledge gaps and clusters.ResultsThe evidence base includes a total of 10 950 articles describing 11 489 recovery pathways. Most of the evidence base is about recovery technologies (41.9%) and the reuse of recovered products in agriculture (53.4%). A small proportion of the evidence base focuses on the characteristics of recovered products (4.0%) and user acceptance and perceptions of nutrient recovery and reuse (0.7%).ConclusionsMost studies we mapped focused on nutrient recovery from ‘conventional’ systems, that is, from centralized sewer and wastewater treatment systems that produce biosolids and a treated effluent. While we also found substantial research on nutrient recovery from source-separated urine, and to some extent also on nutrient recovery from source-separated excreta (notably blackwater), the body of research on nutrient recovery from source-separated feces was relatively small. Another knowledge gap is the relative lack of research on the recovery of potassium. More research is also needed on user acceptance of different recovery technologies and recovered products.

Multidimensional aspects of riverine biodiversity can vary in response to nutrient pollution and environmental dynamics across climatic watersheds

The concurrent impact of anthropogenic and bioclimatic factors on biodiversity is a key focus in macroecological and biogeographical considerations in conservation programs within riverine ecosystems. However, there is still a lack of understanding about how multidimensional alpha and beta diversity measures respond to anthropogenic and bioclimatic drivers. Here, we assess the variations in taxonomic, phylogenetic, and functional alpha and beta diversity of riverine macroinvertebrate communities across different watersheds in China. Our results show significant declines in most facets of alpha diversity across watersheds with low environmental heterogeneity, reflecting the loss of species with unique traits and evolutionary legacies. Both functional and phylogenetic beta-diversity values reveal a decreasing pattern along low heterogeneity environments, whereas taxonomic beta-diversity shows a contrasting pattern, which highlights the influence of microhabitat variation. Moreover, our findings identify nutrient levels, organic matter, water quality indicators, climatic variation, and geographic and habitat characteristics as key determinants of diversity patterns that are indicative of broader water pollution challenges. These factors jointly influence functional and phylogenetic alpha diversity and contribute to spatial homogenization, which is reflected in decreased functional and phylogenetic beta diversity. These trends highlight the complex interactions of chemical and physical factors in shaping biodiversity characteristics across watersheds. Based on the null model, macroinvertebrate communities primarily show random patterns, whereas clustering and overdispersion were sporadically observed in specific communities. We propose that conservation and restoration efforts should be aimed at enhancing aquatic biomes by managing extreme environmental conditions and amplifying spatial spillover, thereby supporting the intrinsic dynamics within natural metasystems and thus preserving the multidimensional aspects of biodiversity.

Ameliorating the response of slow-releasing nitrogen fertilizer on sustainable maize growth

The extensive utilization of nitrogen (N) fertilizers within maize cultivation systems has resulted in diminished nitrogen use efficiency (NUE) and contributed to nitrogen pollution on a global scale. To assess the ecological repercussions of excessive fertilization, it is imperative to elucidate both the nitrogen use efficiency and the fate of nitrogenous fertilizers upon application. The current research evaluated the potential of a newly developed slow-release nitrogen fertilizer on maize growth and its behavior in soil under controlled conditions. Six different levels of urea fertilizer (UF) and slow-release nitrogen fertilizer (SRNF) were administered within the field, representing 100%, 85%, and 70% of the recommended application rates. The slow-release nitrogen fertilizer (SRNF) exhibited superior performance regarding growth, yield and nutrient retention in comparison to urea fertilizer (UF). Moreover, minimal ammonia emissions were detected with the employment of the slow-release nitrogen fertilizer (SRNF), while other urea-based fertilizers proved inefficient in mitigating ammonia emissions, despite enhancing various growth and yield parameters. The efficiency in nutrient recovery followed a distinct pattern, with polymer-coated fertilizers demonstrating superiority. The plots treated with SRNF displayed significantly higher growth and yield characteristics compared to those treated with urea fertilizer. In terms of NH3 volatilization, the urea fertilizer (UF) treatment at 100% application rate showed higher emissions (1.99 mg g-1) after a 27-day incubation period, as opposed to the slow-release nitrogen fertilizer (SRNF) treatment (1.68 mg g-1). Leaching data indicated that urea fertilizer treatments led to greater losses of NO3-N (2.01 mg L-1) compared to SRNF treatments (0.88 mg L-1).

Uncovering the Synergistic Role of Endogenous Minerals and Microwaves in Limiting the Formation of Gaseous Nitrogen Pollutants during Sludge Pyrolysis

Uncovering the Synergistic Role of Endogenous Minerals and Microwaves in Limiting the Formation of Gaseous Nitrogen Pollutants during Sludge Pyrolysis

Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.