Impacts of Precipitation and Temperature on Changes in the Terrestrial Ecosystem Pattern in the Yangtze River Economic Belt, China

Impacts of climate change on the fate of contaminants through extreme weather events

The direct impacts of climate change involve a multitude of phenomena, including rising sea levels, intensified severe weather events such as droughts and flooding, increased temperatures leading to wildfires, and unpredictable fluctuations in rainfall. This comprehensive review intends to examine firstly the probable consequences of climate change on extreme weather events such as drought, flood and wildfire. This review subsequently examines the release and transformation of contaminants in terrestrial, aquatic, and atmospheric environments in response to extreme weather events driven by climate change. While drought and flood influence the dynamics of inorganic and organic contaminants in terrestrial and aquatic environments, thereby influencing their mobility and transport, wildfire results in the release and spread of organic contaminants in the atmosphere. There is a nascent awareness of climate change's influence of climate change-induced extreme weather events on the dynamics of environmental contaminants in the scientific community and decision-making processes. The remediation industry, in particular, lags behind in adopting adaptive measures for managing contaminated environments affected by climate change-induced extreme weather events. However, recognizing the need for assessment measures represents a pivotal first step towards fostering more adaptive practices in the management of contaminated environments. We highlight the urgency of collaboration between environmental chemists and climate change experts, emphasizing the importance of jointly assessing the fate of contaminants and rigorous action to augment risk assessment and remediation strategies to safeguard the health of our environment.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.

The Impacts of Calamity Logging on the Sustainable Development of Spruce Fuel Biomass Prices and Spruce Pulp Prices in the Czech Republic

Currently, due to the calamity of unplanned harvesting, the amount of biomass from wood products has increased. Forests occupy 33.7% of the total area of the Czech Republic; therefore, wood and non-wood forest products are important renewables for the country. Wood biomass consists mainly of branches and bark that are not used in the wood or furniture industry. However, it can be used in bioenergy, including wood processing for fuel. As spruce production in the Czech Republic increased from the planned 15.5 million to almost 36.8 million trees in 2020, the price of wood biomass can be expected to be affected. This study aims to develop a predictive model for estimating the decline in the price of wood biomass for wood processors, such as firewood or sawdust producers, as well as for the paper industry. Wood biomass prices are falling with each additional million m3 of spruce wood harvested, as is the decline in wood pulp, which is intended for the paper and packaging industries. The proposed predictive model based on linear regressions should determine how the price of wood biomass will decrease with each additional million harvested spruce trees in the Czech Republic. This tool will be used for practical use in the forestry and wood industry. The linear regression model is suitable for practical forestry use due to its simplicity and high informative value. The aim of the research is to model the dependence of the prices of firewood in the form of wood briquettes and pellets for domestic and industrial processing, as well as the prices of wood pulp on the volume of unplanned logging. It is a guide for the practice of how to use excess spruce wood from unplanned mining in the field of alternative processing with a sustainable aspect for households or heat production for households. The intention is to carry out modelling in such a way that it does not include prices of higher quality wood assortments, which are intended for the woodworking industry.

Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China

Ecosystem services (ESs) are highly vulnerable to human activities. Understanding the relationships among multiple ESs and driving mechanisms are crucial for multi-objective management in complex social-ecological systems. The goals of this study are to quantitatively evaluate and identify ESs hotspots, explore the relationships among ESs and elucidate the driving mechanisms. Taking central urban area Chongqing municipality as the study area, biodiversity (BI), carbon fixation (CF), soil conservation (SC) and water conservation (WC) were evaluated based on the InVEST model and ESs hotspots were identified. The complex interactions among multiple ESs were determined by utilizing multiple methods: spearman correlation analysis, bivariate local spatial autocorrelation and K-means clustering. The linear or nonlinear relationships between ESs and drivers were discussed by generalized additive models (GAMs). The results showed that during 2000–2018, except for CF that exhibited no obvious change, all other ESs showed a decrease tendency. High ESs were clustered in mountains, while ESs in urban areas were lowest. At administrative districts scale, ESs were relatively higher in Beibei, Banan and Yubei, and drastically decreased in Jiangbei. Multiple ES hotspots demonstrated clear spatial heterogeneity, which were mainly composed of forestland and distributed in mountainous areas with high altitude and steep slope. The relationships between ES pairs were synergistic at the entire scale. However, at grid scale, the synergies were mainly concentrated in the high-high and low-low clusters, i.e., mountainous areas and urban central areas. Five ESs bundles presented the interactions among multiple ESs, which showed well correspondence with social-ecological conditions. GAMs indicated that forestland and grassland had positive impact on BI and CF. Additionally, SC was mainly determined by geomorphological factors, while WC were mainly influenced by precipitation. Furthermore, policy factors were confirmed to have a certain positive effect on ESs. This study provides credible references for ecosystem management and urban planning.