Climatic Variations Research Articles

Annual 10Be Record for 1510–1701 CE Obtained From Endogenic Travertine at Baishuitai, China: A New Proxy Record of Annual Solar Activity

AbstractThe 10Be record in laminated travertines is a potential proxy for reconstructing past solar activity down to the annual scale; however, correcting for the potential influence of climatic or environmental variations remains challenging. Here, we present an annually resolved 10Be record using travertines from Baishuitai, China, covering the period from 1510 to 1701 CE, along with environmental proxies, to evaluate climatic influences and implement corrections to accurately reconstruct solar activity. We demonstrate that the 10Be deposition in travertines exhibits two environmental impacts: the transport efficiency of atmospheric 10Be into travertine and the additional 10Be inflow from overland flow associated with rainfall. We show that these impacts can be corrected based on iron and potassium contents. The resulting corrected record agrees with ice‐core and tree‐ring records, demonstrating the feasibility of using such carbonate sediment 10Be records to reconstruct past solar activity.

Predators control pests and increase yield across crop types and climates: a meta-analysis.

Pesticides have well-documented negative consequences to control crop pests, and natural predators are alternatives and can provide an ecosystem service as biological control agents. However, there remains considerable uncertainty regarding whether such biological control can be a widely applicable solution, especially given ongoing climatic variation and climate change. Here, we performed a meta-analysis focused on field studies with natural predators to explore broadly whether and how predators might control pests and in turn increase yield. We also contrasted across studies pest suppression by a single and multiple predators and how climate influence biological control. Predators reduced pest populations by 73% on average, and increased crop yield by 25% on average. Surprisingly, the impact of predators did not depend on whether there were many or a single predator species. Precipitation seasonality was a key climatic influence on biological control: as seasonality increased, the impact of predators on pest populations increased. Taken together, the positive contribution of predators in controlling pests and increasing yield, and the consistency of such responses in the face of precipitation variability, suggest that biocontrol has the potential to be an important part of pest management and increasing food supplies as the planet precipitation patterns become increasingly variable.

Shall we go to the mountains or to the sea for the winter holidays? Occurrence drivers and cultural relevance of the climate-vulnerable Snow Bunting Plectrophenax nivalis in Italy

The non-breeding distribution and ecology of many migratory songbirds are often poorly studied, but very relevant for their conservation in a changing climate. The Snow Bunting is the most northerly breeding passerine worldwide, and winters in temperate/temperate-cold areas. Characterised by specialisation for cold climate and strong migratory connectivity, it underwent a recent range contraction and is declining because of rising winter temperatures. Investigating its migration and wintering distribution and ecology is relevant to understand the possible impacts of climate change. We used citizen science records to describe and model the species distribution during migration and winter in Italy, and evaluated its cultural relevance. Snow Buntings occur in Italy in November-mid April, mostly clustered in northern and, particularly, north-eastern regions (coasts and plateaus in mountain regions); all the observations involving >12 individuals occurred at a single site in the Lessinia plateau. The elevation pattern of observations (both during winter and migration) showed a bimodal distribution (mostly at the sea level and in middle-elevation mountains, peaking around 1500 m asl). Species Distribution Models showed that the most suitable areas largely coincide with the areas from where most observations occurred, and were similar during winter and migration. Such areas are characterised by relatively cold and dry climates, and by flat or gentle sloping terrain largely occupied by open or semi-open habitats (grassland, pastures, sandy habitats). Snow Buntings had a much higher occurrence rates in social media/networks than other bunting species, indicating that nature recreationists have a particular preference for this species, which can be used as a flagship species. The species will likely suffer from ongoing climate variation, and preserving suitable open landscapes, with grassland-dominated habitats, is key to the maintenance of suitable stop-over and wintering sites. At the same time, this would also benefit many declining breeding species.

Physiographic Environment Classification: a Controlling Factor Classification of Landscape Susceptibility to Waterborne Contaminant Loss

Spatial variation in the landscape factors climate, geomorphology, and lithology cause significant differences in water quality issues even when land use pressures are similar. The Physiographic Environment Classification (PEC) classifies landscapes based on their susceptibility to the loss of water quality contaminants. The classification is informed by a conceptual model of the landscape factors that control the hydrochemical maturity of water discharged to streams. In New Zealand, a case study using climatic, topographic, and geological data classified the country into six, 36, and 320 classes at Levels 1 (Climate), 1–2 (Climate + Geomorphology), and 1–3 (Climate + Geomorphology + Lithology), respectively. Variance partitioning analysis applied to New Zealand’s national surface water monitoring network (n = 810 stations) assessed the contributions of PEC classes and land use on the spatial variation of water quality contaminants. Compared to land use, PEC explained 0.6× the variation in Nitrate Nitrite Nitrogen (NNN), 1.0× in Total Kjeldahl Nitrogen (TKN), 1.8× in Dissolved Reactive Phosphorus (DRP), 2.3× in Particulate Phosphorus (PP), 2.6× in E. coli, and 4.3× in Turbidity (TURB). Land use explained more variation in riverine NNN, while landscape factors explained more variation in DRP, PP, E. coli, and TURB. Overall, PEC accounted for 2.1× more variation in riverine contaminant concentrations than land use. The differences in contaminant concentrations between PEC classes (p < 0.05), after adjusting for land use, were consistent with the conceptual model of hydrochemical maturation. PEC elucidates underlying causes of contaminant loss susceptibility and can inform targeted land management across multiple scales.

Leaf stoichiometry of potassium, calcium, and magnesium in tropical plants: Responses to climatic and geographical variations—A case study from Hainan Island

AbstractGrasping how climate change will affect the tropical region's leaf potassium, calcium, and magnesium content is pivotal for enhancing our comprehension of plant stoichiometry. Currently, there is limited research on leaf potassium, calcium, and magnesium in tropical plants, which is particularly evident with the increasing importance of tropical rainforest biodiversity studies. This study investigated the impact of geographical and climatic factors on the leaf potassium, calcium, and magnesium content in 346 plants (including woody plants, herbs, and vines) on Hainan Island. The findings demonstrated that the average leaf potassium, calcium, and magnesium contents were 5.77, 11.86, and 2.92 mg·g−1, respectively, which were less than that in the Chinese flora. Mean annual precipitation (MAP) has a promoting effect on leaf potassium content (p &lt; 0.05), suggesting that artificial potassium fertilization is needed due to the increasing MAP. Leaf potassium and magnesium content were positively correlated with latitude (p &lt; 0.05) and longitude (p &lt; 0.01); and there was a negative correlation between leaf calcium content and latitude (p &lt; 0.05), showing that geographical factors have a significant impact on the potassium, calcium, and magnesium content of plant leaves. Additionally, the variables regulating leaf potassium, calcium, and magnesium varied across woody plants, herbs, and vines. Therefore, it is necessary to make corresponding adjustments according to local conditions and the plant functional types during nutrient management. In conclusion, this study sheds light on their potential responses to global changes and is instrumental in informing the management of these nutrients in tropical rainforest plants.

Climatological reference stations: Definitions and requirements

AbstractGround‐based stations are one of many observing systems contributing to the generation of data to evaluate climate trends and variations locally and globally. Networks of stations are made of various numbers of observing sites, equipped with different typologies and varieties of instruments, differently managed and maintained. Among such networks a limited number of stations are required as a reference, to provide top quality traceable measurements and as the top level of a tiered approach; it is these stations which are here designated as Climatological Reference Stations (CRS). At present, there is no agreed definition of the key instrumental and technical features of a CRS, nor are there defined reference measurement procedures. This leads to the situation of a multitude of approaches among different National Meteorological and Hydrological Services (NMHSs), research institutes and other agencies that reduces the comparability of results in space and in time. The lack of CRSs, moreover, is a major contributor to the huge efforts required to harmonize data and detect biases locally, regionally and globally, as in their absence there is a need to fall back on other stations which are more likely to have such biases. This article reports on the outcomes of the work of a group of experts, nominated by the World Meteorological Organization (WMO) Commission for Climatology (CCI), tasked to promote a standardized and agreed concept about the definitions, specifications and technical characteristics of climate reference data and stations for ground‐based networks, with the addition of a practical example of climatological reference station installed in the framework of an European funded project, with the purpose of comparing and confirming the prescribed characteristics and guidance, with respect to practical field use and available instrumentation. The adoption of a unique definition and prescription on the technical setup, measurement procedures and uncertainty evaluation will substantially progress towards a common approach in detecting climate trends. This will improve data comparability in space and time and allow a more robust understanding of climate evolution locally and globally.

The annual growth of cork in the face of climate variations (Northwest of Algeria)

ABSTRACT Cork growth rings from three sites in the North West of Algeria were analysed in relation to monthly and annual precipitation and to climatic indices. Ring width and their growth indices were small, ranging from 2.03 mm(ZA), 2.43 mm(TA) and 2.06 mm (AM), corresponding to a thin cork. Thirteen-year time series were compared to precipitation data from 2009 to 2021. Ring width and growth indices revealed a strong correlation with annual rainfall (December to January) and spring rains (May to June). Phellogen activity also remains markedly influenced by the maximum temperatures of March. In the context of global climate change, the water balance of the study area is inadequate. The severe drought in the region was manifested by positive correlations with the index of De Martonne. This also seems valid for the two drought indices (Gaussen index and xerothermic index). The extension of the dry season beyond 6 months affects cork oak activity.

Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming

Current perspectives on lower frequency variations and secular warming have predominantly been shaped by traditional anomalies that assume an annual cycle (AC) with a time-invariant amplitude. However, this anomaly framework falls short in capturing the complexity of multiple periodic modes with intricate waveforms and time dependent amplitude—the traits, in general, shared by externally forced responses of complex dynamical systems. By allowing interannual amplitude modulation of ACs, we show that the monotonic amplitude increases of the first AC of Sea Surface Temperature (SST) are manifested as the basin-wide secular warming of the ocean surface. Notably, the first SST-AC exhibits significant interannual variances and the largest linear warming rates in the Pacific Warm Pool. While the linear warming pattern mirrors that of a long-term mean SST, it depicts an entirely different warming pattern on the surface of the tropical Pacific Ocean compared to those reported so far. Moreover, all interannual warm (El Niño) and cold (La Niña) events in tropical Pacific regions are abnormal interannual modulations in the third and fourth ACs of SST, respectively. Specifically, a strong El Niño event occurs when a positive amplitude modulation leads to the overlap of two consecutive positive phases of the third AC of SST. Conversely, the absence of such overlaps during negative amplitude modulations significantly contributes to the positive skewness of SST anomalies. No systematic decadal changes in the zonal propagation characteristics of SST in the eastern Pacific (EP) and central Pacific (CP) regions were detected. These findings underscore that the secular warming and low-frequency events in EP and CP are intrinsic to three distinct ACs.

Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods

The aquatic market price is an essential indicator for fishermen in making decisions regarding conveyance, fish-catching, and wholesale strategies. As a guide for the aquaculture industry, understanding market prices, the geographical distribution of fish prices, and the prediction of fish prices are vital. In Taiwanese fisheries, however, predicting aquatic prices is challenging due to their drastic fluctuations caused by tropical and subtropical climate variations, export/import fish quantity changes, and political and economic situation uncertainty. Therefore, instead of using a single machine learning model to address the price fluctuations, we propose a hybrid fish price prediction model integrating multiple machine learning and deep learning models to identify and predict fish price dynamics in various wholesale markets. The extensive experimental results on actual market data from the Taiwan Council of Agriculture (COA) show that the proposed models can achieve >90% accuracy in predicting future aquatic prices. Additionally, we developed a fully automated learning and prediction system architecture on the cloud, allowing us to acquire data automatically and fine-tune the AI models continuously to achieve better performance with long-term operability.

Genomic signatures of climate adaptation in bank voles.

Evidence for divergent selection and adaptive variation across the landscape can provide insight into a species' ability to adapt to different environments. However, despite recent advances in genomics, it remains difficult to detect the footprints of climate-mediated selection in natural populations. Here, we analysed ddRAD sequencing data (21,892 SNPs) in conjunction with geographic climate variation to search for signatures of adaptive differentiation in twelve populations of the bank vole (Clethrionomys glareolus) distributed across Europe. To identify the loci subject to selection associated with climate variation, we applied multiple genotype-environment association methods, two univariate and one multivariate, and controlled for the effect of population structure. In total, we identified 213 candidate loci for adaptation, 74 of which were located within genes. In particular, we identified signatures of selection in candidate genes with functions related to lipid metabolism and the immune system. Using the results of redundancy analysis, we demonstrated that population history and climate have joint effects on the genetic variation in the pan-European metapopulation. Furthermore, by examining only candidate loci, we found that annual mean temperature is an important factor shaping adaptive genetic variation in the bank vole. By combining landscape genomic approaches, our study sheds light on genome-wide adaptive differentiation and the spatial distribution of variants underlying adaptive variation influenced by local climate in bank voles.

Mosses and vascular plants show diverging diversity patterns along a latitudinal gradient in boreal bogs and fens

AbstractQuestionsThe latitudinal biodiversity gradient; i.e., the increase in biodiversity towards the equator, is one of the most prominent biodiversity patterns. Nevertheless, many questions remain to be answered about the influence of multiple environmental factors on the latitudinal biodiversity gradient, especially for mosses, and the functional diversity of mosses and vascular plants. This study aims at evaluating the influence of latitude, climate, environmental variables, and habitat types (bog vs fen) on taxonomic and functional diversity (α‐diversity and β‐diversity) and the composition of vascular plant and moss species.Location49° N to 55° N in Northwestern Quebec, Canada.TaxonMosses, vascular plants.MethodsWe used a database containing 376 phytosociological plots (400 m2) sampled in boreal peatlands located along a 600‐km latitudinal gradient. We evaluated changes in α‐diversity and β‐diversity in response to latitude, longitude, climate, and local abiotic variables for both taxonomic groups using linear mixed effect models. We evaluated the effects of these variables on taxonomic and functional composition using variance partitioning by redundancy analysis.ResultsMoss diversity increased with latitude, although the effects were masked by environmental variables, whereas vascular plant diversity decreased with latitude in fens and did not vary with latitude in bogs. We observed a decrease in taxonomic and functional uniqueness with latitude. Moss and vascular plant taxonomic and functional composition were primarily structured by contrasting local conditions in bogs and fens, whereas climatic variations along the latitudinal gradient played a secondary role.ConclusionsOur results highlight the contrasting biodiversity patterns in both peatland types and the importance of local habitat conditions in structuring vascular plant and moss diversity. These patterns varied depending on the diversity indicator used, as α‐ and β‐diversity and functional and taxonomic diversity were often decoupled. Future studies should therefore include more than one diversity indicator and consider the differences between ecosystems and taxon groups.

Spatial variation in iodine content with relation to soil physicochemical properties in lower Himalayan region

Topography of a place has a significant impact on soil characteristics that ultimately influence soil iodine levels. Lower Himalayan region (LHR) in Pakistan has a wide range of climatic and geological variations. Hence, an investigation was conducted to analyze the iodine concentration and other physicochemical properties of soils in two LHR districts, Haripur and Mansehra. Spatial analysis indicated a decrease in iodine levels in the mountainous regions in comparison to the flat portions of LHR. Soil samples obtained from different locations across Haripur had a stronger affinity for iodine due to variations in solubility and adsorption of iodine to soil clay components, which can be attributed to lower pH, higher organic matter, and a higher cation exchange capacity (CEC). In contrast to the plains of Haripur, elevated locations in the Mansehra district had decreased levels of iodine, along with a higher soil pH and reduced soil organic matter. The soil erosion and depletion of soil micronutrients in the hilly region of Mansehra may be attributed to the unfavorable soil conditions and excessive precipitation. Presence of clay, iron (Fe), and aluminum (Al) in the soil led to a rise in iodine levels. Iodine concentrations exhibited an inverse relationship with soil acidity. Study revealed a direct correlation between soil iodine levels and their cation exchange capacity (CEC) and clay content. This study aims to gather fundamental data for the chosen regions of LHR to address illnesses caused by iodine deficiency.

Impacts of Climate Change and Human Activity on Lakes around the Depression of Great Lakes in Mongolia

The western region of Mongolia is characterized by an arid climate and a fragile ecological environment. It is a sensitive zone in response to global climate change and one of the major sources of dust globally. This region is home to numerous lakes, and their dynamic changes not only reflect global climate variations but also have implications for the global ecological environment quality. In this study, Landsat images were used as the data source, and Google Earth Engine (GEE) was employed to extract lakes with an area larger than 1 km2 from 1992 to 2021. The spatiotemporal characteristics of lake water area (LWA) changes were analyzed, and a structural equation model was applied to attribute the lake changes. The results indicate an overall trend of increasing lake area followed by a decrease in the study area. Specifically, lakes in the provinces of Khovd and Gobi-Altai exhibited a decreasing trend followed by an increasing trend, while lakes in the provinces of Uvs and Zavkhan showed an increasing trend followed by a decreasing trend. Three typical types of lakes, namely, alpine lakes, throughflow lakes, and terminal lakes, all exhibited a trend of increasing area followed by a decrease. The analysis of driving forces behind lake area changes reveals that climate change and human activities primarily exert indirect influences on the lake area changes in each province. Specifically, climate change and human activities lead to changes in soil moisture, which have a significant explanatory power for lake area changes. Regarding the typical types of lakes, climate change serves as the primary driving force for alpine lakes, while human activities are the main driving forces for throughflow lakes and terminal lakes.

The Geologic History of Plants and Climate in India

India's diverse vegetation and landscapes provide an opportunity to understand the responses of vegetation to climate change. By examining pollen and fossil records along with carbon isotopes of organic matter and leaf wax, this review uncovers the rich vegetational history of India. Notably, during the late Miocene (8 to 6 Ma), the transition from C3 to C4 plants in lowland regions was a pivotal ecological shift, with fluctuations in their abundance during the late Quaternary (100 ka to the present). In India, the global phenomenon of C4 expansion was driven by the combined feedback of climate variations, changes in substrate conditions, and habitat disturbances. The Himalayan region has experienced profound transformations, including tree-line migrations, shifts in flowering and fruiting times, species loss, and shifts in plant communities due to changing monsoons and westerlies. Coastal areas, characterized by mangroves, have been dynamically influenced by changing sea extents driven by climate changes. In arid desert regions, the interplay between summer and westerlies rainfall has shaped vegetation composition. This review explores vegetation and climate history since 14 Ma and emphasizes the need for more isotope data from contemporary plants, precise sediment dating, and a better understanding of fire's role in shaping vegetation. ▪This review highlights diverse vegetation and landscapes of India as a valuable source for understanding the vegetation-climate link during the last 14 Myr.▪A significant ecological shift occurred during 8 to 6 Ma in India, marked by the transition from C3 to C4 plants in the lowland regions.▪The abundance of C3 and C4 plants varied in India during the late Quaternary (100 ka to present).▪This review emphasizes the importance of more isotope data, precise sediment dating, and a better understanding of fire's role in shaping vegetation.

Bioenergy and sustainable agriculture: A review of synergies and potential conflicts

The intersection of bioenergy and sustainable agriculture presents a dynamic landscape with both promising synergies and potential conflicts that demand careful consideration. This comprehensive review explores the intricate relationship between bioenergy production and sustainable agricultural practices, aiming to elucidate the opportunities and challenges inherent in their integration. Synergies between bioenergy and sustainable agriculture are evident in their shared goal of mitigating climate change. Bioenergy crops, such as perennial grasses and woody biomass, can sequester carbon and contribute to greenhouse gas reduction, aligning with the principles of sustainable agriculture that emphasize environmental stewardship. Additionally, the integration of bioenergy systems with agricultural practices can foster resource efficiency, as crop residues and organic waste become valuable feedstocks for biofuel production. However, potential conflicts arise when land-use competition intensifies. The expansion of bioenergy crops may encroach upon land designated for food production, leading to concerns about food security and biodiversity loss. Striking a balance requires careful planning, considering regional variations in land availability, climate, and socio-economic factors. Furthermore, the review delves into the technological advancements driving the coexistence of bioenergy and sustainable agriculture. Precision farming techniques, agroforestry, and innovative cropping systems emerge as tools to optimize land use, enhance resource efficiency, and minimize environmental impacts. In conclusion, the intertwining realms of bioenergy and sustainable agriculture offer a complex tapestry of synergies and potential conflicts. A nuanced and context-specific approach is essential for maximizing the benefits while minimizing adverse impacts. This review provides insights into the current state of knowledge, highlighting the need for interdisciplinary collaboration, policy frameworks, and technological innovations to ensure a harmonious coexistence between bioenergy production and sustainable agriculture.

Geospatial modeling of climate change indices at Mexico City using machine learning regression

PurposeClimate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.Design/methodology/approachIn this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.FindingsThis paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R2 score of 0.99, in contrast to Support Vector Regression, which yielded an R2 score of 0.74.Originality/valueThe full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Influence of climatic variation on microbial communities during organic Pinot noir wine production.

To assess the possible impact of climatic variation on microbial community composition in organic winemaking, we employed a metabarcoding approach to scrutinize the microbiome in a commercial, organic, Pinot noir wine production system that utilizes autochthonous fermentation. We assessed microbial composition across two vintages (2018 and 2021) using biological replicates co-located at the same winery. Microbial dynamics were monitored over four important fermentation time points and correlated with contemporaneous climate data. Bacterial (RANOSIM = 0.4743, p = 0.0001) and fungal (RANOSIM = 0.4738, p = 0.0001) compositions were different in both vintages. For bacteria, Lactococcus dominated the diversity associated with the 2018 vintage, while Tatumella dominated the 2021 vintage. For fungal populations, while Saccharomyces were abundant in both vintages, key differences included Starmerella, copious in the 2018 vintage; and Metschnikowia, substantive in the 2021 vintage. Ordination plots correlated the climatic variables with microbial population differences, indicating temperature as a particularly important influence; humidity values also differed significantly between these vintages. Our data illustrates how climatic conditions may influence microbial diversity during winemaking, and further highlights the effect climate change could have on wine production.

Can “climate upheaval” be a more informative term than “climate change”?

Terminology frames reality and affects how people perceive and respond to it. What makes the global climate situation concerning is not simply a change in climate, but the anthropogenic change, in addition to natural climate variations, occurring at a rapid rate relative to the pre-industrial era. However, in general language, “climate change” does not differentiate itself from natural climate variations nor reflect the rate of change. Based on theoretical reasoning, this paper argues that “climate upheaval” frames and communicates the global climate situation more informatively. This is because “climate upheaval” highlights the additional change at a rapid rate, which is crucial for (1) describing and informing the current climate status, (2) understanding the impacts of climate on natural, socioeconomic, and cultural aspects, and (3) taking climate actions. This argument can serve as a hypothesis for future exploration. Specific questions for empirical tests may include: How do people interpret “climate upheaval” and “climate change”? Do people prefer “climate upheaval”, and why? Is the intended message of “climate upheaval” different from people’s perceptions? As certain terms may offer more utility and information than others, it is important to weigh the advantages and drawbacks of different terms. Strategies aimed at reducing potential confusion and misunderstanding associated with "climate upheaval" may include providing scientific clarification, establishing communication channels, and conducting public education on this term. Terminology does not solely represent scientific labels but also carries significant social and cultural implications, encompassing public perceptions, social mobilization, policymaking, governance, negotiations, and international initiatives.

Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Early Cretaceous climatic influences on organic matter enrichment in lacustrine shales: Insights from the Liupanshan Basin of northwestern China

Organic-rich petroleum source rocks are widespread in Early Cretaceous strata, but the related paleoclimatic conditions are still uncertain. Aptian-Albian lacustrine sediments from the Liwaxia and Madongshan formations in the Liupanshan Basin of northwestern China provide vital material to reveal correlations between the Early Cretaceous paleoclimate and the formation of organic-rich lacustrine shales. Outcrop samples were analyzed for total organic carbon (TOC), C/N ratio, CaCO3 content, lipid biomarkers, and compound-specific n-alkane carbon isotopes. Results show that organic matter (OM) of the Liwaxia Formation was mainly sourced from lacustrine plankton and deposited in a weakly stratified and suboxic water column under a hot and arid climate. Organic matter in the lower part of the Madongshan Formation was derived from a mixture of aquatic organisms and higher plants under a warm and humid climate; plant carbon sources were widely available, and the water column was strongly saline, stratified, and anoxic. Elevated temperatures and a less humid climate accompanied the deposition of the upper part of the Madongshan Formation. Higher plants were the primary OM source, and the carbon source became limited. The water column was hypersaline and stratified, and the bottom water was less anoxic. Abrupt changes in the OM inputs and depositional environments documented in the Madongshan Formation were ascribed to global climatic variations. A warm and humid climate during the Aptian-Albian transition enhances continental nutrient inputs and promotes the formation of reduced water conditions, which directly controls the formation of the coeval organic-rich shales in lacustrine basins in China.