Characteristics of the extreme precipitation and its impacts on ecosystem services in the Wuhan Urban Agglomeration

A study on the impact of meteorological and emission factors on PM2.5 concentrations based on machine learning

PM2.5 pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution management. This study applies Positive Matrix Factorization (PMF) to identify primary sources of PM2.5 and uses the Light Gradient Boosting Machine (LightGBM) model, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) to quantitatively assess the impact of meteorological and emission factors on PM2.5 concentrations. SHAP results reveal that meteorological factors contribute 16.6% (5.3 μg/m3) to PM2.5, with humidity being the most influential, while emission sources account for 83.4% (26.8 μg/m3), with secondary particulate matter being the dominant factor. Secondary particulate matter and biomass burning significantly impacted PM2.5 in the first and fourth quarters, while dust sources became more influential in the second quarter, and coal emissions were most prominent in the second and third quarters. Two-dimensional PDP analysis indicated that in the first and fourth quarters, secondary particulate matter concentration increased with air pressure, and the atmospheric oxidation process was more pronounced under high-humidity conditions during the day. Strong transport conditions, with wind direction shifting from north to east, also influenced secondary particulate matter levels. This study demonstrates that the LightGBM model effectively captures the nonlinear relationships between PM2.5 and meteorological and emission factors, providing a reliable approach for analyzing the causes of PM2.5 pollution.

Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century

Future variations of global vegetation are of paramount importance for the socio-ecological systems. However, up to now, it is still difficult to develop an approach to project the global vegetation considering the spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid-by-grid; multi-algorithms; optimal combination) to construct an optimal model using six algorithms (i.e., LR: linear regression; SVR: support vector regression; RF: random forest; CNN: convolutional neural network; and LSTM: long short-term memory; transformer) based on five climatic factors (i.e., Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, and CO2). The optimal model is employed to project the future changes in leaf area index (LAI) for the global and four sub-regions: the high-latitude northern hemisphere (NH), the mid-latitude NH, the tropics, and the mid-latitude southern hemisphere. Our results indicate that global LAI will continue to increase, with the greening rate expanding to 2.25 times in high-latitude NH by 2100 against the 1982-2014 period. Moreover, RF shows strong applicability in the global and NH models. In this study, we introduce an innovative model GGMAOC, which provides a new optimal model scheme for environmental and geoscientific research.

Improving grassland ecosystem services for human wellbeing in the karst desertification control area: Anthropogenic factors become more important

Elucidating the spatial and temporal patterns of grassland ecosystem service value (ESV) changes under different karst geomorphic types (KGTs) is crucial for promoting regional sustainable development and enhancing human well-being. Karst ecosystems are characterized by high spatial heterogeneity. However, analyses of the drivers of spatial and temporal changes in ESV in karst grasslands at multiple scales are lacking. In this study, the South China Karst (SCK) region was selected as the focus area, the gross ecosystem product (GEP) accounting method was used to quantify the grassland ESV from 2000 to 2020, and the GeoDetector model was used to elucidate the spatial and temporal evolution of the GEP, the drivers, and their interactions in different KGTs. The results indicate the following: (1) Over the past 20 years, the grassland GEP of SCK has increased from ¥ 14,844.24 × 108 in 2000 to ¥ 17,174.90 × 108 in 2020. Among the various KGTs, the karst gorge exhibited the fastest GEP increase (24.93 %) and karst hilly depressions the slowest (6.22 %). (2) The karst grassland GEP showed a strong positive spatial correlation with significant clustering characteristics (p < 0.05). (3) There are significant differences in the factors influencing the GEP of grasslands with different KGT values, and although they are generally influenced by factors such as NPP, precipitation, and population density, anthropogenic factors are becoming increasingly important. In addition, the multifactor interaction explained GEP better than the single factor. Based on our findings, we propose targeted grassland ESV restoration approaches and management recommendations for various KGTs dominated by distinct factors. Our results provide a scientific basis for decision-making regarding karst ecosystem service enhancement and value realization.

Assessing the impact of human activities on ecosystem asset dynamics in the Yellow River Basin from 2001 to 2020

The intensification of human activities in the Yellow River Basin has significantly altered its ecosystems, challenging the sustainability of the region's ecosystem assets. This study constructs an ecosystem asset index for the period from 2001 to 2020, integrating it with human footprint maps to analyze the temporal and spatial dynamics of ecosystem assets and human activities within the basin, as well as their interrelationships. Our findings reveal significant improvement of ecosystem assets, mainly attributed to the conversion of farmland back into natural habitats, resulting in a 15,994 km2 increase in ecological land use. Notably, 45.88% of the basin has experienced concurrent growth in both human activities and ecosystem assets, with ecosystem assets expanding at a faster rate (22.61%) than human activities (17.25%). Areas with high-quality ecosystem assets are expanding, in contrast to areas with intense human activities, which are facing increased fragmentation. Despite a global escalation in threats from human activities to ecosystem assets, the local threat level within the Yellow River Basin has slightly diminished, indicating a trend towards stabilization. Results highlight the critical importance of integrating spatial and quality considerations into restoration efforts to enhance the overall condition of ecosystem assets, especially under increasing human pressures. Our work assesses the impact of human activities on the dynamics of ecosystem assets in the Yellow River Basin from 2001 to 2020, offering valuable insights for quality development in the region, may provide a scientific basis for general watershed ecological protection and sustainable management in a region heavily influenced by human activity but on a path to recovery.

The influence of environment and Earnings on Prolonged existence and human fertility: A Deeper Dive into Asia's environmentally vulnerable nations

This study inspects the impact of environmental deterioration and income on longevity and fertility in Asian countries, specifically the nations that are highly vulnerable to extreme weather. The study examines the data, covering two decades from 2000 to 2019. The empirical conclusions of the panel ARDL-PMG and the CS-ARDL econometric models indicate that environmental degradation leads to a decline in birth rate and life expectancy, while a rising income has a significant influence over longevity. However, increasing per capita income alone cannot solve the problem of population crisis in climatically susceptible countries. Therefore, the sample countries must prioritize climate action and formulate climate-resilient policies to add more years to the lives of their citizens. Similarly, for increasing childbirth the sample nations need to make peace with nature. The outcomes of this study are strong enough, as both the models support each other's findings, producing similar significant outcomes.

Linking ecosystem service flow to water-related ecological security pattern: A methodological approach applied to a coastal province of China

Water security is a critical concern due to intensifying anthropogenic activities and climate change. Delineating a water-related ecological security pattern can help to optimize spatial configuration, which in turn can inform sustainable water management. However, the methodology remains unclear. In this study, we developed a framework linking ecosystem service flow to water-related ecological security pattern; hence, we identified the sources, sinks, key corridors, and vulnerable nodes in Fujian Province, China. Our results revealed that the sources were located inland at high altitudes with a decreasing area trend in the south and an increasing area trend in the north, whereas the sinks were spread in coastal areas and exhibited a decreasing trend with relatively stable spatial distribution. The water-related ecological security has degraded as represented by a decreasing ecological supply-demand ratio over the last 30 years. Key corridors were identified in 17.12% of the rivers, and 22.5% of the vulnerable nodes were recognized as early warning nodes. Climate variability affected source distribution, while anthropogenic activities drove sink dynamics. These findings have important implications including landscape pattern planning and sustainable water management in the context of accelerated land use/cover and climate changes.