Climatic Factors Research Articles

Exploring crop maturity times as a conservation tool for improving resilience to human-elephant conflict: elephant crop-raiding

ABSTRACT The purpose of this study was to alleviate negative impacts of elephant crop-raiding faced by smallholder farmers around the Okavango delta. Although many studies have identified the relationship between crop-raiding and climatic factors, little attention was paid to aligning cropping duration to high rainfall. Therefore, this study aimed to investigate how crop maturity duration influences farmers’ vulnerability to elephant crop raiding and food loss. An experimental plot consisting of early and late maturing varieties of maize, sorghum and cowpea was set up and monitored in farms adjacent to corridors. Early maturing varieties yielded more (mean = 0.35 tha−1) than later maturing varieties (mean = 0.25 tha−1). The prolonged stay of crops in fields exposed them more to elephants (p < 0.001). Unlike late varieties, early varieties served as an evasion strategy by allowing earlier harvesting when there was adequate rainfall and elephants rarely frequented farms. Late maturing varieties were harvested at 16–19 weeks when elephants frequented the fields. This harvest timing significantly influences the vulnerability of crops to debilitating elephant raiding. This outcome suggests that policy reviews should incorporate more climate action into smallholder farming systems to strengthen SDG 1: no poverty and SDG 2: zero hunger, ease hostility toward elephants and improve elephant conservation.

Quantifying soil erosion and soil organic carbon conservation services in indian forests: A RUSLE-SDR and GIS-based assessment

Indian forests, having sixteen types of vegetation, provide various ecosystem services. Forests reduce water erosion of soil, reduce floods and conserve low river flow during dry seasons. Present study estimated annual sedimentation, texture, soil organic carbon control service of forests of India using the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) approach through GIS technique along with identifying important drivers responsible for erosion in forests. The soil erosion control service was based on five factors viz., rainfall erosivity, soil erodibility, slope, crop management, conservation practice factor and SDR across the various forest dominated river basins. Geo-detector modelling was performed to evaluate environmental drivers responsible for soil erosion. Valuation of soil control service of the India’s forests was estimated based on damage and replacement cost method. Annual soil loss rate was maximum in Tropical Thorn Forest and in Ganga River basin. Maximum sediment control and SOC conservation against the soil erosion control service by India’ forests were due to Tropical Dry Deciduous Forest with a total annual sedimentation control and SOC conservation of 33,056,560 tonnes and 3,31,115.06 tonnes, respectively. The conservative estimate of the economic value of soil conservation service by the forests of India was US$ 535.6 M with an average of 4.40 US$ ha−1 and a maximum share of value for Tropical Dry Deciduous Forest i.e. 294.18 million US$ and average maximum value for Littoral and Swamp Forest (28.91 US$ ha−1). The soil erosion in Tropical and Sub-tropical Forest was influenced by the interactive effects between NDVI and Bulk Density. In contrast, soil erosion in the temperate and alpine area was influenced by anthropogenic activity in combination with climatic factors. Present study accounts the role of India’s forests for the soil conservation service and may provide inputs to implement forest and reservoir management strategies besides providing information for Integrated Watershed management and Catchment Treatment Plans.

Production patterns of loblolly pine plantations along a geographic gradient in southern Brazil

Pinus taeda plantations depend directly on the supply of resources (light, water, and nutrients) available to the stand. However, studies quantifying edaphic and climatic factors in P. taeda stands are scarce. This study aimed to identify the main edaphic, climatic and stand factors that influence the productivity and growth pattern of P. taeda plantations in southern Brazil. A total of 24 stands, ranging between 8 and 10 years old were evaluated. Biometric variables such as height, diameter at breast height (DBH), stem analysis, growth rings, specific gravity, and soil chemical and physical variables were evaluated. Site index (SI), stem biomass, mean annual increment without bark (MAI) and correlations between variables were calculated. We observed that by nine years after planting, none of the plots had reached their maximum growth; and MAI, standing stem biomass and SI ranged from 15 to 35 m³ ha−1 year−1, 56–131 Mg ha−1, and 12–19 m, respectively, with higher values in Três Barras site. Stem biomass were positively related with SI (r = 0.89), and stem production was related with higher available water content (r = 0.41), warm temperatures (r = 0.61), and higher relative air humidity (r = 0.58). The study corroborates the role of nutrients, well-drained soils, mild climatic conditions, and low water deficit to explain the high yields of P. taeda in Brazil compared to other locations around the world. Based on these results, studies on fertilization, genetic material, and more frequent use of stem analysis, associated with X-ray densitometry, are recommended for P. taeda.

Comparing root-associated microbial communities in Tamarix ramosissima across three Xinjiang basins, China

Analyzing the root-associated microbial communities in desert plants during different seasons may provide a valuable understanding of how desert plants adapt to climate change in harsh environments. Hence, we selected root-associated microbial communities (bacteria and fungi) of Tamarix ramosissima from three regions (Cele, CL; Turpan, TLF; Mosuowan, MSW), focusing on three compartments: root endosphere (RE), rhizosphere soil (RS), and bulk soil (BS). Results indicated that the relative abundance of Actinobacteriota, Proteobacteria, and Ascomycota of root bacteria and fungi taxa was higher the in three compartments (RE, RS, and BS). In the CL region, there was a decline in the relative abundance of Proteobacteria within the RE followed by RS and BS during the autumn. During both summer and autumn, the relative abundance of Ascomycota declined with increasing sampling distance (from the TLF to the MSW to the CL) in the BS. Furthermore, in spring, the bacterial (BS) and fungal (BS) Chao1 and ACE index decreased in TLF compared to the MSW region. However, MSW had a higher bacterial Chao1 and Shannon index of the RE (in summer) and RS (in autumn) than the CL area. Overall, in spring, summer, and autumn, root-associated bacterial and fungal communities across various regions (CL, TLF, and MSW) were more responsive to climate and soil factors than plant nutrients. This research provides compelling theoretical evidence advocating for the restoration of degraded vegetation within arid environments, underscoring the critical significance of judicious plant management practices in these regions.

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Forests, the ancient wooden giants, are both symbols of natural beauty and reservoirs of carbon stocks. The current climate crisis has created an urgent need for an in-depth study of forest ecosystems and carbon stocks. Based on forest inventory data from field surveys and four bioclimatic zones [Zone 1 (Z1, humid forest), Zone 2 (Z2, semi-humid forest), Zone 3 (Z3, semi-humid to semi-arid forest-grassland), and Zone 4 (Z4, semi-arid typical grassland)], two methods [Method 1 (M1) and Method 2 (M2)] were used to estimate carbon stocks in forest ecosystems in Shaanxi Province, China, and explored the spatial patterns of carbon pools and potential influences. The total forest ecosystem carbon pool amounted to 520.80 Tg C, of which 53.60% was stored aboveground, 17.16% belowground, and 29.24% in soil (depth of 0–10 cm). Spatially, there were marked north-south gradients in both biomass (Z2 > Z3 > Z1 > Z4) and soil organic carbon densities (Z1 > Z2 > Z3 > Z4). The differences between aboveground and belowground biomass carbon density across broadleaf, needle-leaf, and broadleaf and needle-leaf mixed forest were not pronounced, while soil organic carbon density had the order of broadleaf (18.38 Mg C/ha) > needle-leaf (11.29 Mg C/ha) > broadleaf and needle-leaf mixed forest (10.33 Mg C/ha). Under an ideal scenario that excludes external factors, mainly forest growth, the sequestration potential of forest biomass by 2032 was estimated by M1 as 85.43 Tg, and by M2 to be substantially higher at 176.21 Tg. As of 2062, M1 estimated 155.97 Tg of sequestration potential for forest biomass. The spatial patterns of forest biomass and soil carbon density were closely related to climatic factors, and these relationships allowed the spatial division into two distinct climatic regions. Moreover, biomass carbon density was significantly correlated with the normalized difference vegetation index, soil silt, and elevation. This study provides key information for promoting the strategic shift from light-green to deep-green forest systems in Shannxi Province and updates the estimation methods of forest ecosystems’ carbon pools based on field surveys.

Seasonal Scale Climatic Factors on Grassland Phenology in Arid and Semi-Arid Zones

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958–2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000–2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Evaluating net primary productivity dynamics and their response to land-use change in the loess plateau after the 'Grain for Green' program

Assessing net primary productivity (NPP) dynamics and the contribution of land-use change (LUC) to NPP can help guide scientific policy to better restore and control the ecological environment. Since 1999, the “Green for Grain” Program (GGP) has strongly affected the spatial and temporal pattern of NPP on the Loess Plateau (LP); however, the multifaceted impact of phased vegetation engineering measures on NPP dynamics remains unclear. In this study, the Carnegie-Ames-Stanford Approach (CASA) model was used to simulate NPP dynamics and quantify the relative contributions of LUC and climate change (CC) to NPP under two different scenarios. The results showed that the average NPP on the LP increased from 240.7 gC·m−2 to 422.5 gC·m−2 from 2001 to 2020, with 67.43% of the areas showing a significant increasing trend. LUC was the main contributor to NPP increases during the study period, and precipitation was the most important climatic factor affecting NPP dynamics. The cumulative amount of NPP change caused by LUC (ΔNPPLUC) showed a fluctuating growth trend (from 46.23 gC·m−2 to 127.25 gC·m−2), with a higher growth rate in period ΙΙ (2010–2020) than in period Ι (2001–2010), which may be related to the accumulation of vegetation biomass and the delayed effect of the GGP on NPP. The contribution rate of LUC to increased NPP in periods Ι and ΙΙ was 101.2% and 51.2%, respectively. Regarding the transformation mode, the transformation of grassland to forest had the greatest influence on ΔNPPLUC. Regarding land-use type, the increased efficiency of NPP was improved in cropland, grassland, and forest. This study provides a scientific basis for the scientific management and development of vegetation engineering measures and regional sustainable development.

Integrated metabolomic and metagenomic strategies shed light on interactions among planting environments, rhizosphere microbiota, and metabolites of tobacco in Yunnan, China

Changes in climatic factors and rhizosphere microbiota led plants to adjust their metabolic strategies for survival under adverse environmental conditions. Changes in plant metabolites can mediate crop growth and development and interact with rhizosphere microbiota of the plant rhizosphere. To understand the interactions among environmental factors, rhizosphere microbiota, and metabolites of tobacco, a study was conducted by using integrated metagenomic and metabolomic strategies at four typical representative tobacco planting sites in Yunnan, China. The results showed that the agronomical and biochemical traits were significantly affected by temperature, precipitation (PREP), soil pH, and altitude. Correlation analyses revealed a significant positive correlation of temperature with length, width, and area of the leaf, while PREP correlated with plant height and effective leaf numbers. Furthermore, total sugar and reducing sugar contents of baked leaves were significantly higher, while the total nitrogen and total alkaloid levels were lower in tobacco leaves at site with low PREP. A total of 770 metabolites were detected with the highest number of different abundant metabolites (DMs) at Chuxiong (CX) with low PREP as compared to the other three sites, in which secondary metabolites were more abundant in both leaves and roots of tobacco. A total of 8,479 species, belonging to 2,094 genera with 420 individual bins (including 13 higher-quality bins) harboring 851,209 CDSs were detected. The phyla levels of microorganisms such as Euryarchaeota, Myxococcota, and Deinococcota were significantly enriched at the CX site, while Pseudomonadota was enriched at the high-temperature site with good PREP. The correlation analyses showed that the metabolic compounds in low-PREP site samples were positively correlated with Diaminobutyricimonas, Nissabacter, Alloactinosynnema, and Catellatospora and negatively correlated with Amniculibacterium, Nordella, Noviherbaspirillum, and Limnobacter, suggesting that the recruitment of Diaminobutyricimonas, Nissabacter, Alloactinosynnema, and Catellatospora in the rhizosphere induces the production and accumulation of secondary metabolites (SMs) (e.g., nitrogen compounds, terpenoids, and phenolics) for increasing drought tolerance with an unknown mechanism. The results of this study may promote the production and application of microbial fertilizers and agents such as Diaminobutyricimonas and Alloactinosynnema to assemble synthetic microbiota community or using their gene resources for better cultivation of tobacco as well as other crops in drought environments.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475–525 nm and 665–690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Tiger prowling: Distribution modelling for northward-expanding Aedes albopictus (Diptera: Culicidae) in Japan.

The Asian tiger mosquito, Aedes albopictus, is a significant public health concern owing to its expanding habitat and vector competence. Disease outbreaks attributed to this species have been reported in areas under its invasion, and its northward expansion in Japan has caused concern because of the potential for dengue virus infection in newly populated areas. Accurate prediction of Ae. albopictus distribution is crucial to prevent the spread of the disease. However, limited studies have focused on the prediction of Ae. albopictus distribution in Japan. Herein, we used the random forest model, a machine learning approach, to predict the current and potential future habitat ranges of Ae. albopictus in Japan. The model revealed that these mosquitoes prefer urban areas over forests in Japan on the current map. Under predictions for the future, the species will expand its range to the surrounding areas and eventually reach many areas of northeastern Kanto, Tohoku District, and Hokkaido, with a few variations in different scenarios. However, the affected human population is predicted to decrease owing to the declining birth rate. Anthropogenic and climatic factors contribute to range expansion, and urban size and population have profound impacts. This prediction map can guide responses to the introduction of this species in new areas, advance the spatial knowledge of diseases vectored by it, and mitigate the possible disease burden. To our knowledge, this is the first distribution-modelling prediction for Ae. albopictus with a focus on Japan.

Variability of Silver Fir Needle (Abies alba Mill.) Anatomical Features in the Southeast Europe Natural Populations

The survival of marginal/peripheral silver fir (Abies alba Mill.) populations in the broader region of Southeast Europe is endangered due to climate change and population decline. This study aimed to determine the level and pattern of variability for the anatomical traits of needles and the possibility of linking the pattern of phenotypic variability with environmental factors. In most of the analyzed needle traits, the statistically significant variability between populations was determined. According to the results of the multivariate principal component analysis, it is evident that the populations are distinct from each other, in three groups. The climatic factors Hargreaves reference evaporation, mean annual temperature, and growing degree-days, were statistically significantly correlated. The altitude and heating degree-days are statistically significantly correlated with the following three environmental factors: Hargreaves reference evaporation, mean annual temperature and growing degree-days, but negatively with others. The paper’s findings indicate significant moderate and high correlations between the anatomical traits of the needles’ central bundle diameter with the resin duct diameter, the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, the resin duct diameter with the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, as well as the distance between the vascular bundle and the resin duct with the hypodermis height and the epidermis thickness with cuticle. The results of agglomerative hierarchical clustering analysis, performed for anatomical and climatic traits, confirmed the existence of three groups of tested populations according to the altitude gradient. Research results provide knowledge on the diversity and structure of Abies alba populations of Southeast Europe, important for further research and guidelines for the species’ conservation and genetic variability preservation in the southern marginal distribution area and keeping in line with climate change projections.

Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom

Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes.

Energy Compensation for Crop Growth under Plastic Mulching: Theories, Models, and Limitations

Plastic film mulching (PM) is a useful agronomic means to adapt to the environmental conditions of dryland agriculture and improve crop production. To improve the theoretical framework of PM technology, this study focuses on the interaction between the soil temperature change caused by PM and crop growth. The definition, action mechanism, and simulation of the compensatory effect of PM on growing degree days are introduced to reveal the effect of soil temperature under PM on crop development and growth. Our summary shows that the strength of the warming effect changes with the growth and development of crops, strengthening during the early stage of crop growth and gradually weakening as a crop canopy develops. Generally, the warming effect has a good promotion effect on crop growth, but the crop growth is hampered even with a yield reduction when the increased soil temperature caused by PM exceeds the tolerant temperature for plant growth. Moreover, the compensatory effect of PM could be used to quantify the growth and development of crops under PM and has been widely applied to cotton, corn, winter wheat, and rice. The compensation coefficient is larger in the early stage of crop growth than in the later stage. The compensation coefficient has certain differences for the same crop because of the influence of climate factors, soil moisture content, and soil microtopography. In future research, the theoretical integration of the safety period of PM and the time threshold of the compensatory effect could be theoretically interpreted, and the construction of the compensatory effect module in the crop models will also be an important issue.

Probabilistic scenario-based assessment of national food security risks with application to Egypt and Ethiopia

Abstract This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socio-economic Pathways and Representative Concentration Pathways. This innovative method allows each scenario, encompassing socio-economic, and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.

Climate determines diversity but not abundance of nitrogen-cycling microorganisms in rivers across a latitudinal gradient

A central goal in microecology is to understand the ecological factors shaping spatial patterns of microbial communities and the underlying mechanisms. However, the biogeographical patterns of microbial functional groups involved in nitrogen cycling (e.g., nitrifiers and denitrifiers) and their drivers at a large scale remain poorly understood. In this study, we evaluated the geographical distributions of nitrifiers and denitrifiers in riparian rhizosphere soils, riparian bulk soils, and channel sediments of 30 river regions along a latitudinal gradient from 21.8°N to 45.1°N across eastern China. We found that the diversity of nitrifiers and denitrifiers exhibited a gradual latitudinal gradient, in direct contrast to no significant changes observed for their abundance along the same latitudinal gradient. Statistical analyses suggested that climatic factors (mean annual temperature and mean annual precipitation) were the major drivers for the diversity of nitrifiers and denitrifiers, whereas soil properties were the key environmental factors shaping the abundance of nitrifiers and denitrifiers. Overall, these results provide new insights into the biogeographical distributions and ecological drivers for nitrogen-cycling microorganisms in river ecosystems at a large scale and can help us to improve the simulation models of the nitrogen cycle for accurately predicting the diversity of nitrogen-cycling microbes under ongoing climate changes.

Optimization and analysis of methanol production from CO2 and solar-driven hydrogen production: A Danish case study

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems.

The Impact of Climate Change on Tourism Demand in China

This study quantified the impact of climate on the international tourism demand in China. Due to rising awareness and urgency to combat climate change, there is a growing need to better grasp the possible implications and the risk of climate change to the tourism industry. The tourists’ travel decision can be influenced by both climatic and non-climatic factors, and thus this study has integrated both factors into the models. The climatic variable is represented by Tourism Climate Index (TCI) and Holiday Climate Index (HCI) while the selected non-climatic determinants are tourists’ income, tourism price, exchange rate and transportation cost. The empirical findings showed that climate conditions significantly and adversely affect China’s tourism demand. Tourists’ income level, as expected, positively affects their decision to travel to China. Meanwhile, the tourism price, exchange rate, and transportation cost negatively impact China’s tourism industry. Although some valuable results have been found, more works are required to understand its long-haul significance to the tourism industry, especially during extreme events such as extreme weathers, pandemics, financial crises and etc.

Predicting the geographical distribution and niche characteristics of Cotoneaster multiflorus based on future climate change

IntroductionArid and semi-arid regions are climate-sensitive areas, which account for about 40% of the world’s land surface area. Future environment change will impact the environment of these area, resulting in a sharp expansion of arid and semi-arid regions. Cotoneaster multiflorus is a multi-functional tree species with extreme cold, drought and barren resistance, as well as ornamental and medicinal functions. It was found to be one of the most important tree species for ecological restoration in arid and semi-arid areas. However, bioclimatic factors play an important role in the growth, development and distribution of plants. Therefore, exploring the response pattern and ecological adaptability of C. multiflorus to future climate change is important for the long-term ecological restoration of C. multiflorus in arid and semi-arid areas.MethodsIn this study, we predicted the potential distribution of C. multiflorus in China under different climate scenarios based on the MaxEnt 2.0 model, and discussed its adaptability and the major factors affecting its geographical distribution.ResultsThe major factors that explained the geographical distribution of C. multiflorus were Annual precipitation (Bio12), Min air temperature of the coldest month (Bio6), and Mean air temperature of the coldest quarter (Bio11). However, C. multiflorus could thrive in environments where Annual precipitation (Bio12) &amp;gt;150 mm, Min air temperature of the coldest month (Bio6) &amp;gt; -42.5°C, and Mean air temperature of the coldest quarter (Bio11) &amp;gt; -20°C, showcasing its characteristics of cold and drought tolerance. Under different future climate scenarios, the total suitable area for C. multiflorus ranged from 411.199×104 km² to 470.191×104 km², which was 0.8~6.14 percentage points higher than the current total suitable area. Additionally, it would further shift towards higher latitude.DiscussionThe MaxEnt 2.0 model predicted the potential distribution pattern of C. multiflorus in the context of future climate change, and identified its ecological adaptability and the main climatic factors affecting its distribution. This study provides an important theoretical basis for natural vegetation restoration in arid and semi-arid areas.

Ozone Production over Arid Regions: Insights into Meteorological and Chemical Drivers

Abstract Arid urban areas are pivotal in the global landscape, and their air quality issues are highlighted by the complexities of tropospheric ozone production. Here, we use recent satellite observations from TROPOMI and a longer record of data from OMI to investigate the levels of ozone precursors (NO2 and CH2O) in 12 major cities in arid regions. Using a space-based CH2O/NO2 indicator, we identified the dominant chemical regime influencing ozone formation, revealing a clear temporal trend that aligns with previously reported economic trajectories as well as variation in emission control strategies implemented in these cities. Our results show that, NO2 concentrations decreased in cities with proactive regulatory policies, such as Madrid and Los Angeles in semi-arid and arid regions. A contrasting increase was observed in rapidly developing cities within arid and hyper-arid regions, such as Tehran and Cairo, where emission controls are less strict. An increase in CH2O levels was also apparent, requiring more attention to VOCs control. Furthermore, our analysis clearly shows that the interactions between ozone production and climatic factors such as temperature exhibit a nonlinear relationship, especially in arid climates. These findings highlight the importance of emission reduction strategies that consider the meteorological and chemical drivers of dry regions, particularly in light of the rising global aridity.