Special Issue "Urban and Regional Nitrogen Cycle and Risk Management"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: 28 April 2023 | Viewed by 948

Special Issue Editors

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Interests: ecosystem health; environmental management; urban and regional sustainability; society and environment; environmental footprint; pollution source apportionment; nitrogen cycling
Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Interests: urban science and sustainability; healthy city and public health; suicide and mental health; climate change and environmental management; quantitative methodology and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Interests: ecosystem service; urban forest; stable isotope; air pollution; big data mining

Special Issue Information

Dear Colleagues,

Atmosphere dedicates this Special Issue to the urban and regional nitrogen cycle with urbanzition, which should be addressed by risk management as anthropogenic interventions have globally alterded the multi-scale distributions of  reactive nitrogen, resulting in the greenhouse effect, acid rain, eurtophication and reductions in biodiversity. Therefore, the ‘nitrogen cascade’ effect induced by nitrogen cycle disruption has been regonized as the third most important global environmental problem after biodiversity loss and global warming. In China, the world's largest anthropogenic reactive nitrogen producer, significant progress has been made in recent decades in nitrogen polltuion alleviation. Despite this, previous studies have revealed that insignificant reductions in national reactive nitrogen releasing, mainly contributed by agricultural production (62–69%), are still observed, and 55–59% reactive nitrogen was emitted to the atmosphere. However, based on most city-scale case studies, residental livelihood is supposed to be the main source of reactive nitrogen releases induced by a disrupted nitrogen cycle.

In agricultural, industrial and residential activites, maintaining well-ordered nitrogen cycles with fewer negative environmental impacts is linked to the correct and efficienct risk-management of reactive nitrogen. Possible actions to reduce reactive nitrogen being released to the environment include proper nitrogen management within the production and consumption cycles of essencial resources (e.g., food, energy, water), which could be supported by anthropogenic approachs (e.g., environmental pollution monitoring, environmentally friendly technology and residents’ behavior) and natural-based approaches including nitrogen retention by greenland, wetland, farmland and bare land. The experimental approaches and modeling techniques can help the research in this respect. Different study methods can be adopted to address this Special Issue, depending on the scale of the urban and regional nitrogen cycles.

Authors are welcome to submit their contributions concerning the analysis of sources, sinks and flows of nitrogen cycles and relevant risk management towards SDGs. Field and modeling studies concerning the nitrogen pollution and driving factors, as well as the relaionships between nitrogen cycle and other cycles of water, carbon, phosphorus, sulphur, etc., are also encouraged.

Dr. Chaofan Xian
Dr. Yu-Sheng Shen
Dr. Cheng Gong
Guest Editors

Manuscript Submission Information

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Keywords

  • reactive nitrogen cycle
  • air pollution/air pollutants
  • environmental monitoring and assessment
  • ecosystem service
  • environmental footprint
  • material flow analysis
  • nitrogen source apportionment
  • nitrogen and carbon coupling
  • food, energy and water nexus
  • urban and regional sustainability

Published Papers (1 paper)

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Research

Article
Regional/Single Station Zenith Tropospheric Delay Combination Prediction Model Based on Radial Basis Function Neural Network and Improved Long Short-Term Memory
Atmosphere 2023, 14(2), 303; https://doi.org/10.3390/atmos14020303 - 03 Feb 2023
Viewed by 479
Abstract
Atmospheric water vapor is an essential source of information that predicts global climate change, rainfall, and disaster-natured weather. It is also a vital source of error for Earth observation systems, such as the global navigation satellite system (GNSS). The Zenith Tropospheric Delay (ZTD) [...] Read more.
Atmospheric water vapor is an essential source of information that predicts global climate change, rainfall, and disaster-natured weather. It is also a vital source of error for Earth observation systems, such as the global navigation satellite system (GNSS). The Zenith Tropospheric Delay (ZTD) plays a crucial role in applications, such as atmospheric water vapor inversion and GNSS precision positioning. ZTD has specific temporal and spatial variation characteristics. Real-time ZTD modeling is widely used in modern society. The conventional back propagation (BP) neural network model has issues, such as local, optimal, and long short-term memory (LSTM) model needs, which help by relying on long historical data. A regional/single station ZTD combination prediction model with high precision, efficiency, and suitability for online modeling was proposed. The model, called K-RBF, is based on the machine learning algorithms of radial basis function (RBF) neural network, assisted by the K-means cluster algorithm (K-RBF) and LSTM of real-time parameter updating (R-LSTM). An online updating mechanism is adopted to improve the modeling efficiency of the traditional LSTM. Taking the ZTD data (5 min sampling interval) of 13 international GNSS service stations in southern California in the United States for 90 consecutive days, K-RBF, R-LSTM, and K-RBF were used for regions, single stations, and a combination of ZTD prediction models regarding research, respectively. Real-time/near real-time prediction results show that the root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and training time consumption (TTC) of the K-RBF model with 13 station data are 8.35 mm, 6.89 mm, 0.61, and 4.78 s, respectively. The accuracy and efficiency of the K-RBF model are improved compared with those of the conventional BP model. The RMSE, MAE, R2, and TTC of the R-LSTM model with WHC1 station data are 6.74 mm, 5.92 mm, 0.98, and 0.18 s, which improved by 67.43%, 66.42%, 63.33%, and 97.70% compared with those of the LSTM model. The comparison experiments of different historical observation data in 24 groups show that the real-time update model has strong applicability and accuracy for the time prediction of small sample data. The RMSE and MAE of K-RBF with 13 station data are 4.37 mm and 3.64 mm, which improved by 47.70% and 47.20% compared to K-RBF and by 28.48% and 31.29% compared to R-LSTM, respectively. The changes in the temporospatial features of ZTD are considered, as well, in the combination model. Full article
(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)
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