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

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 4999

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Special Issue Editors

Dr. Chaofan Xian

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Guest Editor

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

Dr. Yu-Sheng Shen

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Guest Editor

1. Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
2. Xiamen Key Laboratory of Urban Metabolism, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
3. University of Chinese Academy of Sciences, Beijing 101408, 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

Dr. Cheng Gong

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Guest Editor

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 (5 papers)

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Research

16 pages, 6605 KiB

Open AccessArticle

Insight into Municipal Reactive Nitrogen Emissions and Their Influencing Factors: A Case Study of Xiamen City, China

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Atmosphere 2023, 14(10), 1549; https://doi.org/10.3390/atmos14101549 - 11 Oct 2023

Viewed by 627

Abstract

Reactive nitrogen (Nr) has been confirmed as an indispensable nutrient for the city ecosystem, but high-intensity human activities have led to nitrogen pollution in cities, especially in coastal cities, jeopardizing ecosystem services and human health. Despite this, the characteristics and influencing factors of Nr remain unclear in coastal cities, particularly in the context of rapid urbanization. This study used the material flow analysis method to estimate Nr emissions in Xiamen from 1995 to 2018 and evaluated the characteristics of excessive Nr emissions. The STIRPAT model was used to identify and explore factors contributing to observed Nr levels in coastal cities. As indicated by the results, (1) the quantity of Nr generated by human activities increased 3.5 times from 1995 to 2018. Specifically, the total Nr entering the water environment showed a general increase with fluctuations, exhibiting an average annual growth rate of 3.1%, increasing from 17.2 Gg to 35.1 Gg. (2) Nr loads in the nearby sea increased notably from 8.1 Gg in 1995 to 25.4 Gg in 2018, suggesting a threefold augmentation compared with surface waters and groundwater. (3) NO_x was the gaseous Nr with the greatest effect on the atmosphere in Xiamen, which was primarily due to fossil fuel consumption. (4) Population and per capita GDP were major factors contributing to Nr load in the water environment, while Nr emission to the atmosphere was influenced by population and energy consumption. These findings provide valuable insights for tailored approaches to sustainable nitrogen management in coastal cities. Full article

(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)

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29 pages, 9555 KiB

Open AccessArticle

Comparative Study on the Influencing Factors of the Greenhouse Gas Budget in Typical Cities: Case Studies of Beijing and Shenzhen

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Atmosphere 2023, 14(7), 1158; https://doi.org/10.3390/atmos14071158 - 17 Jul 2023

Viewed by 607

Abstract

Clarifying the pattern of the urban greenhouse gas (GHG) budget and its influencing factors is the basis of promoting urban low-carbon development. This paper takes Beijing and Shenzhen—the capital city and the most rapidly developing city in China, respectively—as case studies, comprehensively accounts their GHG budgets from 2005 to 2020, and investigates and compares the factors affecting their GHG budgets. The total GHG emissions in Beijing were lowest in 2005 (160.3 TgCO₂ equivalents) and peaked at 227.7 TgCO₂ equivalents in 2019, and then decreased to 209.1 TgCO₂ equivalents in 2020. Meanwhile, the total GHG emissions in Shenzhen gradually increased from 36.0 TgCO₂ equivalents in 2005 to 121.4 TgCO₂ equivalents in 2019, and then decreased to 119.1 TgCO₂ equivalents in 2020. The energy activity sector was the greatest contributor to GHG emissions in this period, accounting for 82.5% and 76.0% of the total GHG emissions in Beijing and Shenzhen, respectively. The carbon sink of the ecosystems of these two cities could absorb only small parts of their emissions, and the neutralization rates of sinks ranged from 1.7% to 2.3% in Beijing and from 0.3% to 1.5% in Shenzhen. The enhancement of population, economic product, and consumption increased the greenhouse gas emissions in both cities. A 1% increase in population size, per capita GD (gross domestic product), and residential consumption level would increase total GHG emissions by 0.181%, 0.019%, and 0.030% in Beijing, respectively. The corresponding increases in Shenzhen would be 0.180%, 0.243%, and 0.172%, respectively. The household size had opposite effects on the two cities, i.e., a 1% increase in household size would increase GHG emissions by 0.487% in Shenzhen but reduce them by 2.083% in Beijing. Each 1% increase in secondary industry and energy intensity would reduce GHG emissions by 0.553% and 0.110% in Shenzhen, respectively, which are more significant reductions than those in Beijing. Full article

(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)

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14 pages, 6097 KiB

Open AccessArticle

The Bibliometric Analysis of Low-Carbon Transition and Public Awareness

Xialu Wu

and

Yu-Sheng Shen

Atmosphere 2023, 14(6), 970; https://doi.org/10.3390/atmos14060970 - 01 Jun 2023

Cited by 2 | Viewed by 850

Abstract

After the agreements of the Conference of the Parties, more and more countries announced plans to achieve net zero emissions over the coming decades and published new policies in response to the agreements. Public awareness is a crucial factor in achieving the goals of the agreements. Therefore, the study of public awareness/behavior toward the low-carbon transition is important. However, this topic lacks a comprehensive and systematic review. Thus, this study used bibliometric analysis, including performance analysis and scientific mapping analysis, to reveal research trends and clarify the status of studies in low-carbon transition and public awareness. We found that 95% of the literature on this topic was published from 2011 to 2022. Judging from keywords, the hotspots of this topic are “Sustainability”, “Energy Transition”, “Low-carbon Economy”, and “Carbon Emission Reduction”. Regarding the research field transition for this topic, environmental sciences have always been a core subject. Furthermore, economics, management, political science, and sociology have focused on this topic in recent years. Additionally, there are gaps between low-carbon policy and public awareness/behavior. Therefore, the frontier directions of low-carbon transition and public awareness include “low-carbon education”, “policies with specific guidelines”, and “worldwide collaboration”. Full article

(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)

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19 pages, 7612 KiB

Open AccessArticle

Driving Forces on the Distribution of Urban Ecosystem’s Non-Point Pollution Reduction Service

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Atmosphere 2023, 14(5), 873; https://doi.org/10.3390/atmos14050873 - 16 May 2023

Viewed by 870

Abstract

In the context of increasing urbanization and worsening environmental pollution, nonpoint source pollution during high-frequency rainfall has become a major ecological problem that endangers residents in cities. This study takes Shenzhen as an example. On the basis of a large number of soil sample test data, and combined with relevant environmental variables, it has drawn the high-resolution, high-precision spatial distribution maps of soil attributes within the city. In addition, this paper combines the revised universal soil loss equation and the GeoDetector model to evaluate the supply capacity of nonpoint source reduction services in the city’s ecological space and the main driving factors of spatial distribution characteristics for different types of land. The study found that increasing soil point density and combining environmental variables can help improve the accuracy of spatial mapping for soil attributes. The ME, MSE, ASE, RMSE, and RMSSE of spatial mapping all meet the accuracy evaluation criteria and are better than many existing studies; the spatial distribution characteristics of soil attributes and nonpoint source reduction services show significant differences among the whole city, secondary administrative regions, and different types of land; the GeoDetector results show that among the three main types of land use (forested land, industrial land, and street town residential land), topographic factors, habitat-quality factors, and ecosystem types have the greatest impact on the spatial differentiation characteristics of nonpoint source reduction services. Among climate factors, only precipitation factors have the greatest impact on the spatial differentiation characteristics of services. Facing the above factors, the q-values calculated by the GeoDetector are all higher than 10%. The results of this study can provide information for making better decisions on regional ecological system management and soil protection and on restoration work aimed at improving nonpoint source reduction services. Full article

(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)

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22 pages, 5481 KiB

Open AccessArticle

Regional/Single Station Zenith Tropospheric Delay Combination Prediction Model Based on Radial Basis Function Neural Network and Improved Long Short-Term Memory

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Atmosphere 2023, 14(2), 303; https://doi.org/10.3390/atmos14020303 - 03 Feb 2023

Cited by 1 | Viewed by 963

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) 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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