Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges

Topic Information

Dear Colleagues,

Climate change has emerged as one of the main challenges of our generation. Tragic events, such as geopolitical conflicts including COVID-19, the Russia–Ukraine war or geopolitical tensions such as those in the Asia-Pacific region, not only pose significant threats to world peace and the global economy, but they also generate considerable uncertainty regarding efforts to combat climate change and ensure effective global environmental management. Soil and groundwater supplies have been contaminated, posing a grave threat to the survival of wild creatures and human health, and the ecological environment's worth has also diminished. We require scientific data to comprehend the effects on the global environmental, energy, and resource management and efforts to combat climate change. This Special Issue is devoted to researching the intriguing subject of statistical methods, econometric methods, qualitative methods, machine learning, artificial intelligence, and blockchain applications for diverse environmental, energy, and resource issues. Here, we investigate the convergence of cutting-edge technology with environmental, energy, and resource issues to address critical problems and produce novel solutions that can be implemented practically in decision making and management. This Special Issue, titled “Management of Environment, Energy and Resources: Methods, Applications and Challenges”, is a timely addition to this area of research.

This Special Issue's objectives include the modelling and management of the environment, energy and resources, as well as solutions available to policymakers for addressing environmental challenges. This SI covers topics including but not limited to the following:

• Environmental monitoring and assessment;
• Air quality and pollution control;
• Climate change modelling and prediction;
• Blockchain and sustainable development;
• Environmental risk assessment and mitigation;
• Urban planning and smart cities;
• Natural resource management;
• Land use and land cover change analysis;
• Environmental impact assessment;
• Environmental policy and decision support systems;
• Social and behavioral aspects of environmental management;
• Carbon emission and energy transition;
• Sustainable development of economies;
• ESG and corporate performance;
• Climate policy and sustainable development;
• Energy market and sovereign debt risk;
• Environmental governance and sovereign debt risk;
• Energy market and financial risks;
• Carbon market risk and energy price shocks;
• Economic policy uncertainty and energy and carbon management.

Dr. Yi-Shuai Ren
Dr. Yong Jiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.5	4.6	2010	15.8 Days	CHF 2400	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Environments environments	3.5	5.7	2014	25.7 Days	CHF 1800	Submit
Land land	3.2	4.9	2012	17.8 Days	CHF 2600	Submit
Economies economies	2.1	4.0	2013	21.7 Days	CHF 1800	Submit

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Published Papers (4 papers)

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All Atmosphere Energies Environments Land Economies

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16 pages, 8302 KiB  

Open AccessArticle

Effects of Soil Nutrient Restoration Aging and Vegetation Recovery in Open Dumps of Cold and Arid Regions in Xinjiang, China

by Zhongming Wu, Weidong Zhu, Haijun Guo, Yong Zhang, Chaoji Shen, Jing Guo, Ming Liu, Tuanwei Zhao, Hu Teng, Wanli Zhu, Yongfu Kang, Gensheng Li and Weiming Guan

Land 2024, 13(10), 1690; https://doi.org/10.3390/land13101690 - 16 Oct 2024

Viewed by 604

Abstract

Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic [...] Read more.

Open-pit coal mining inevitably damages the soil and vegetation in mining areas. Currently, the restoration of cold and arid open-pit mines in Xinjiang, China, is still in the initial exploratory stage, especially the changes in soil nutrients in spoil dumps over time. Dynamic remote sensing monitoring of vegetation in mining areas and their correlation are relatively rare. Using the Heishan Open Pit in Xinjiang, China, as a case, soil samples were collected during different discharge periods to analyze the changes in soil nutrients and uncover the restoration mechanisms. Based on four Landsat images from 2018 to 2023, the remote sensing ecological index (RSEI) and fractional vegetation cover (FVC) were obtained to evaluate the effect of mine restoration. Additionally, the correlation between vegetation changes and soil nutrients was analyzed. The results indicated that (i) the contents of nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the soil increased with the duration of the restoration period. (ii) When the restoration time of the dump exceeds 5 years, N, P, K, and OM content is higher than that of the original surface-covered vegetation area. (iii) Notably, under the same restoration aging, the soil in the artificial mine restoration demonstration base had significantly higher contents of these nutrients compared to the soil naturally restored in the dump. (iv) Over the past five years, the RSEI and FVC in the Heishan Open Pit showed an overall upward trend. The slope remediation and mine restoration project significantly increased the RSEI and FVC values in the mining area. (v) Air humidity and surface temperature were identified as key natural factors affecting the RSEI and FVC in cold and arid open pit. The correlation coefficients between soil nutrient content and vegetation coverage were higher than 0.78, indicating a close and complementary relationship between the two. The above results can clarify the time–effect relationship between natural recovery and artificial restoration of spoil dumps in cold and arid mining areas in Xinjiang, further promoting the research and practice of mine restoration technology in cold and arid open pits. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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27 pages, 3574 KiB  

Open AccessReview

Analytical Review of Wind Assessment Tools for Urban Wind Turbine Applications

by Islam Abohela and Raveendran Sundararajan

Atmosphere 2024, 15(9), 1049; https://doi.org/10.3390/atmos15091049 - 30 Aug 2024

Viewed by 866

Abstract

Due to the complex nature of the built environment, urban wind flow is unpredictable and characterised by high levels of turbulence and low mean wind speed. Yet, there is a potential for harnessing urban wind power by carefully integrating wind turbines within the [...] Read more.

Due to the complex nature of the built environment, urban wind flow is unpredictable and characterised by high levels of turbulence and low mean wind speed. Yet, there is a potential for harnessing urban wind power by carefully integrating wind turbines within the built environment at the optimum locations. This requires a thorough investigation of wind resources to use the suitable wind turbine technology at the correct location—thus, the need for an accurate assessment of wind resources at the proposed site. This paper reviews the commonly used wind assessment tools for the urban wind flow to identify the optimum tool to be used prior to integrating wind turbines in urban areas. In situ measurements, wind tunnel tests, and CFD simulations are analysed and reviewed through their advantages and disadvantages in assessing urban wind flows. The literature shows that CFD simulations are favoured over other most commonly used tools because the tool is relatively easier to use, more efficient in comparing alternative design solutions, and can effectively communicate data visually. The paper concludes with recommendations on best practice guidelines for using CFD simulation in assessing the wind flow within the built environment and emphasises the importance of validating CFD simulation results by other available tools to avoid any associated uncertainties. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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23 pages, 17008 KiB  

Open AccessArticle

Application of the NCAR FastEddy^® Microscale Model to a Lake Breeze Front

by Brittany M. Welch, John D. Horel and Jeremy A. Sauer

Atmosphere 2024, 15(7), 809; https://doi.org/10.3390/atmos15070809 - 6 Jul 2024

Viewed by 829

Abstract

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy^® (FE) model. Motivated by the need for sub-10 m scale decision support tools for [...] Read more.

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy^® (FE) model. Motivated by the need for sub-10 m scale decision support tools for uncrewed aerial systems (UAS), the FE model was used to simulate turbulent flows around urban structures at 5 m horizontal resolution with a 9 km × 9 km domain centered on the Salt Lake City International Airport. FE was one-way nested within a 1 km resolution Weather Research and Forecasting (WRF) domain spanning 400 × 400 km. Focused on the late morning lake breeze on 3 June 2022, an FE simulation was compared with WRF outputs and validated using surface and radar observations. The FE simulation revealed low sensible heat flux and cool near-surface temperatures, attributed to a relatively low specification of thermal roughness suitable for previously tested FE applications. Lake breeze characteristics were minimally affected, as FE effectively resolved interactions between the lake breeze and urban-induced turbulent eddies, providing insights into fine-scale boundary layer processes. FE’s GPU acceleration ensured efficient simulations, underscoring its potential for aiding decision support in UAS operations in complex urban environments. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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17 pages, 4829 KiB  

Open AccessArticle

MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area

by Benedito Chi Man Tam, Su-Kit Tang and Alberto Cardoso

Atmosphere 2024, 15(5), 521; https://doi.org/10.3390/atmos15050521 - 25 Apr 2024

Viewed by 863

Abstract

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better [...] Read more.

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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