Climatic Conditions Research Articles

Application of Extreme Machine Learning for Smart Agricultural Robots to Reduce Manoeuvering Adaptability Errors

The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.

Performance of a hybrid thermosyphon cooling system using airside economizers for data center free cooling under different climate conditions

Thermosyphon cooling system is an efficient data center cooling system for energy saving, but its commonly used free cooling technology (water-side economizer) has multiple heat transfer processes, which limit the utilization rate of natural cold sources. In this study, a hybrid thermosyphon cooling system (HTCS) that uses air economizers to assist the water-cooled row-level thermosyphons (terminal equipment part) for free cooling is proposed and investigated in a case data center. The previous cooling system (PCS) designed for this case data center, which used the same terminal equipment part and water-side economizers, is employed for comparison. To analyze the application potential of HTCS under different climate conditions, the energy models of HTCS and PCS are built for comparative studies on energy efficiency, environmental and economic potential in five typical climate cities. The results show that the HTCS has 575-4589 h longer free cooling periods than that of PCS in five typical climate cities, which leads to the annual energy consumption and Power Usage Efficiency of HTCS in most typical climate cities except Kunming are 13.7-24.7% and 0.014-0.041 lower than those of PCS, respectively. Meanwhile, HTCS's annual CO2 emissions and life cycle costs are 344.2-1248.4 tons and 9.5-25.4% lower than those of PCS in five typical climate cities except Kunming, which illustrates HTCS has good energy-saving and economic performance for data centers.

Identifying climate-ready plant for urban environment: Integrating machine learning with traditional plant selection tools

Climate change has intensified the urban heat island effect and increased extreme weather conditions, posing risks to public health and urban vegetation. To address these challenges, selecting climate-ready urban plant species is crucial. Traditional climate niche-based methods often fall short in urban contexts due to neglecting anthropogenic factors. Our study addresses this research gap by introducing an innovative urban plant selection method that integrates vulnerability metrics, expert consensus, and plant introduction records through machine learning.We identified eight climatic variables essential for the survival of urban plants in Beijing, China, and established safety margins for eight climate variables of 1,070 urban plant species across two periods: the baseline (1981-2010) and the future (2041-2070). Based on existing assessment data, expert consensus and plant introduction records, 247 plant species were classified into three levels of adaptability: backbone (highly adaptable, prevalent in Beijing), general (moderately adaptable, requiring specific care), and maladapted (poorly adaptable). Subsequently, we investigated the dynamic relationship between safety margins for eight climate variables across two time periods and the adaptability levels of plants by constructing an optimal machine learning model to predict urban plant adaptability levels, enhancing its accuracy through model comparisons and hyperparameter tuning.Our findings indicate that nearly half (49.0%) of the plant species in Beijing may face reduced adaptability to future climate conditions. However, a majority (75.9%) perform well under baseline climate conditions and are expected to adapt to future climate conditions. The results reaffirm that the species can grow well out of the niche limit, suggesting that the traditional climate niche-based method may be limited in urban contexts. Our approach overcomes the limitations of binary classification of traditional niche-based methods and the neglect of anthropogenic factors by incorporating an urban plant adaptability classification schema and ground truth derived from expert consensus and records of plant introductions through machine learning methods. This study provides a method for selecting climate-ready plant species for urban environments and supports evidence-based urban forestry management amidst climate change.

Risk assessment and source tracing of heavy metals in major rice-producing provinces of Yangtze River Basin

Heavy metal contamination in rice constitutes a global concern, its migration is influenced by environmental factors as well as socioeconomic activities. However, tracing its origins within complex context remains a significant challenge. The concentrations of five heavy metals (HMs) in 1754 samples from major rice-producing provinces were analyzed, and their pollution characteristics, associated health risks and temporal-spatial variations were discussed. Potential sources were classified by positive matrix factorization (PMF) models, considering correlations with human activities, climatic conditions, and interaction within ecosystems. The results showed that cadmium (Cd) and arsenic (As) were the primary contributors to pollution risk, with the borders between Hunan and central Jiangxi, as well as northeast Jiangxi and northwest Anhui, identified as critical areas for risk management. PMF serves as an effective methodology for identifying the sources of HMs in rice. Industrial activities, particularly mining and transportation, represent the predominant sources of Cd and lead (Pb), accounting for 75.6% of the total pollution. Conversely, agricultural practices and natural factors constitute the primary sources of As, contributing to the remaining 24.4%. It is noteworthy that the rapid industrial development has facilitated the expansion of the freight industry, consequently increasing the risk associated with Pb. Furthermore, effective governmental policy management can mitigate the risks related to HMs. Our research highlights the influence of industrial development on HMs risk in various regions and the moderating role of policy formulation. SYNOPSIS: Minimal research exists on the impact of regional economic development on heavy metals in rice. This study reports mining and transportation activities increase carcinogenic risks caused by Cd and Pb in rice during industrialization.

European anchovy's abundance, more affected by climatic conditions than fishing activities in the northwest African waters.

The marine waters off the coast of northwest Africa are known for being highly productive upwelling regions in the Eastern Atlantic Ocean. The present study evaluated the combined effects of climate change and sustainable fishing levels on the long-term sustainability of European anchovy (Engraulis encrasicolus), a target pelagic species found along the West African coastal waters. The present study used survey biomass time series from survey vessels and species catch time series from commercial fisheries operating in the region. Hence, we investigated the relationship between fishing dynamics and environmental factors. The status of anchovy stock was determined by analyzing changes in different initial depletion rates using a state-space Bayesian surplus production model. We used Principal component analysis to identify the essential environmental data set, while Generalized Additive Models (GAM) were employed to determine the influence of the environment on fisheries. The stock assessment model revealed that fishing intensity did not affect the abundance of anchovy in the region even when tested under different biomass depletion scenarios. However, by employing GAMs, the research assessed the impact of environmental variables on fish biomass, indicating that sea surface temperature (SST) significantly influences anchovy abundance in the region. The latitudinal gradient also had a significant influence on this species' abundance. These findings underscore the vulnerability of the small-scale fisheries sector in the tropical upwelling zone to climate change. The study concludes that even in the face of potential negative impacts from environmental factors, optimizing fishing methods can contribute to the preservation of pelagic resources in the region. It emphasizes the importance of managing fishery resources in a sustainable, economically viable, and socially acceptable way.

Declining trends in long-term Pinus pinea L. growth forecasts in Southwestern Spain

Warmer and drier climate is among the main factors of the declining processes reported and expected for the future in the Mediterranean forest ecosystems. Pinus pinea is one the main Mediterranean conifers and its largest populations are in SW Spain, providing multifunctional services. The sensitivity of this species to drought is known, but the potentiality of its productivity to decline in SW Spain has not been yet assessed. We modeled P. pinea growth with climate covariates and a large set of tree ring chronologies from the beginning of the 20th century to the 2010s. Then we forecast annual increments over the period 2030-2100 using regionalized estimates of a global change model in three scenarios of greenhouse gas concentration. The climatic conditions between winter and mid spring were the most significant for the model. The climate predictions indicated an increase of potential water stress, and our forecasts described downturn trends of the annual growth, more accentuated in the scenario with the highest emissions and temperatures. These are the first long-term forecasts of growth of P. pinea in SW Spain. Our model cannot be directly applied at higher latitudes, where previous studies have shown differences in climate-growth relationships, but provides a benchmark for research and forestry of the potential climate-driven decrease of productivity of the P. pinea populations in the Southern Iberian Peninsula.

Survey of IoT and AI applications: future challenges and opportunities in agriculture

The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.

Variations on the maximum density-size lines to climate and site factors for Larix spp. plantations in northeast China

The maximum density-size line (MDSL) is a valuable tool in sustainable forest management, as it shows the relationship between site occupancy measures and mean tree size on a log-log scale. However, the responses of MDSLs to different climate and site variables still need to be clarified. Thus, this study aimed to assess the potential effects of various climate- and site-related factors on the slopes and intercepts of MDSLs for Larix spp. plantations in northeast China. The parameters of MDSLs were estimated using stochastic frontier regression (SFR) with three different error distribution assumptions, namely half-normal distribution (HN), exponential distribution (ED), and truncated-normal distribution (TN). Spatial distributions of maximum stand density index (SDImax) were mapped under different climate scenarios (RCP 8.5, RCP 4.5, and RCP2.6). The results revealed that the slopes on MDSLs without covariates were significantly shallower than Reineke's slope (−1.605), ranging from −1.2485 to −1.2026. Of the 22 covariates considered, 13 variables on SFR-HN and SFR-TN and 16 variables on SFR-ED had significant influences on MDSLs. The optimal MDSL model, including mean annual temperature (MAT) and soil pH as covariates using a HN assumption, decreased the Akaike's Information Criterion (AIC) by approximately 7.76%. The results also indicated that increasing MAT significantly reduced the maximum stand density for stands with a natural logarithm of quadratic mean diameter [ln(QMD)] below 2.6, while consistent increases were observed over the entire ln(QMD) range for soil pH. Moreover, the mean SDImax within the whole region increased significantly from 15.04% under RCP4.5 to 27.78% under RCP8.5. These findings emphasize the significant influences of climate and site conditions on the MDSL, thereby calibrating on traditional density management strategies may contribute significantly on carbon sequestration capacity of forests in the face of climate change.

PV/T water-based cooling in hot climate conditions by using semi-circle baffles

PV/T water-based system using multi-row semi-circle baffles (SCB) is numerically investigated. Experimental validation was carried out in hot climate conditions. Factors associated with heat transfer enhancement, like Nusselt number, pressure drop, friction factor, electrical efficiency, and thermal efficiency, were calculated and discussed to show the impact of SCB on photovoltaic or PV cooling and system performance. Six different magnitudes of Reynolds number (500-4000), and mass flow rate (0.0137-0.0998) L/s at different radiation values were carried out in the simulation. Results indicated that utilizing SCB has an important effect on the PV/T system. In comparison to the smooth channel, the average Nusselt number of the enhanced channel increased by around (31.57-132.18) %, and the friction factor increased by (21.93-95.7) % while the thermal efficiency increased by about (16.34-79.29) %. These results indicate the system’s good performance in terms of photovoltaic panel cooling and power production. PV surface temperature reduced by about (4-20) ℃. The electrical efficiency improved up to 32% and 49% for the smooth and enhanced channels respectively. The cost-benefit ratio (CBR) of the enhanced channel demonstrated that there is no detrimental influence of the pressure drop on the thermal performance.

Impact of climate change on rice growth and yield in China: Analysis based on climate year type

Climate change threatens China’s rice production, making it crucial to assess the impact of climate change and climate year type (CYT) on rice production across regions to safeguard food security. The impact of climate change under nine CYTs with different combinations of temperature and precipitation on two rice cropping systems, including single rice and double rice (early and late rice) was evaluated. The results indicate that: (1) the Northeast region was expected to undergo the greatest warming of 2.03–2.48 °C, and future climate conditions would be dominated by Warm-Humid, Warm-Normal, and Warm-Dry CYTs across all regions. (2) Climate change would significantly shorten anthesis days after sowing and maturity days after sowing of single rice by 6–12 days and 9–24 days, with little change observed for late rice (< 1 day). Late rice yield suffered more from climate change compared to single and early rice yield, declining by 8.8 %–16.13 %. (3) Different CYTs exhibited varying impacts on rice yields. Yields were projected to decrease by approximately 4.765 % to 18.645 % in Warm-Humid, Warm-Normal, and Warm-Dry CYTs. Conversely, the Northeast region was anticipated to experience an increase in yield. (4) Relationships between rice yield and meteorological factors varied by region, variety, and CYT. Among the nine CYTs, high killing degree days, mean daily temperature, mean solar radiation and warm spell duration index were the main factors influencing changes in rice yield, explaining nearly 80 % of yield change. Our results would help to develop adaptation strategies in different regions and rice cropping systems.

Epidemiological Analysis of Purple Blotch Disease on Onion under the Climatic Conditions of El-Minya Governorate, Egypt

Epidemiological Analysis of Purple Blotch Disease on Onion under the Climatic Conditions of El-Minya Governorate, Egypt

Microbial communities of urban and industrial polluted soils in the Russian Arctic

The Russian Arctic presents a unique environment for studying the effects of anthropogenic pressure on soil microbial communities under severe climatic conditions. This study investigated the impact of chemical pollution on soil microbial properties by comparing urban and industrially polluted soils in Murmansk region with natural Podzols. Urban soils exhibited significant alterations, including shifts in pH and increased carbon and nutrient contents compared to natural soils. Industrially polluted soils near the copper‑nickel smelter were characterized by elevated heavy metal concentration, while those near the aluminum smelter showed high fluorine and aluminum content. In both cases, carbon content and pH remained similar to natural soils. Industrial emissions significantly changed the soil microbiome, with effects varying depending on the pollution source and chemical composition of the emissions. Soils near the copper‑nickel smelter showed a decline in bacterial gene copies and actinomycete mycelium length, with a predominance of Chloroflexii and Ascomycota. Conversely, soils near the aluminum smelter exhibited less pronounced changes, with Proteobacteria and Basidiomycota being prevalent. Despite these differences, both industrially impacted sites displayed reduced microbial diversity, regardless of the composition of the emissions. In contrast, urban soils demonstrated increased microbial diversity, likely attributed to the emergence of new, favorable ecological niches. Microbial communities in both cities were similar, dominated by Proteobacteria and Ascomycota, and displayed an increase in bacterial gene copies compared to natural soils. These findings highlight the contrasting influences of urban and industrial development on soil microbial communities. While industrial activities suppress microbial life, urbanization fosters the creation of new niches, promoting microbial diversity. This underscores the potential of urban soils to support diverse microbial communities, which is crucial for sustainable development and ecological strategies in Arctic cities.

The East Asian monsoon variability in the Nihewan Basin, northern China, during the Early Pleistocene: A grain size end-member modelling analysis

The Pleistocene sediments of the Nihewan Basin in northern China preserve a detailed terrestrial sediment archive for reconstructing the palaeoenvironment and palaeoclimate changes during early times of hominin occupation in E Asia, following the earliest locations outside of Africa. In this study, we investigate the composite 86.2-m long NH-T section of Dachangliang which was formed with an astronomically tuned age between ca. 1.66 and 0.78 Ma. Parameterized grain size end-member modelling analysis is applied for the first time in the Nihewan Basin to unravel the characteristics of sediment sources, transportation dynamics, and climatic signals in the region. The grain-size distributions of the NH-T sediment samples are attributed to a mixture of four distinct end members (EMs 1-4). EM 1 represents a global atmospheric fine silt component (mode at 7.9 μm) which probably resulted from high-altitude westerly transport from distal sources. EM 2, a medium silt component (mode at 27.6 μm) was probably transported by the low-level westerly winds during the spring from a relatively proximal source in comparison to EM 1. EM 3, a coarse silt component (mode at 59.9 μm), represents short distance suspended materials that were blown out by N or NW winds of the East Asian winter monsoon (EAWM). EM 4 is a fine sand component (mode at 221.1 μm), probably representing fluvial deposits carried by overland flow. Therefore, the temporal fluctuations in the abundances of EMs 1-3 are used to infer the history of the East Asian summer monsoon (EASM) (EM 1), of dust-storm outbreaks during springtime (EM 2), and of the EAWM (EM 3). The NH-T climate record shows an overall increase in EM 3 peaks, accompanied by a decrease in EM 1 minima from ca. 1.45 to 0.82 Ma, indicating a long-term aridification and cooling trend in the Nihewan Basin. At ca. 1.25 Ma, the EM patterns transition from short-frequency to longer fluctuations, apparently coinciding with the onset of the Mid-Pleistocene transition (MPT). Periods of stronger EASM with more frequently warm and wet conditions probably occurred in the basin between ca. 1.66-1.62, 1.52-1.25 and after 0.82 Ma. Intensified EAWM conditions probably prevailed in the basin during the intervening periods from ca. 1.62-1.52 and the MPT 1.25-0.82 Ma. The inferred warmer and wetter conditions likely supported hominin activities in the Nihewan Basin, in contrast to mostly colder and drier conditions. Nevertheless, the higher number of discovered Palaeolithic sites and recorded lithic artifacts from the first half of the MPT apparently reflects the successful adaptability of hominins to the prevailing cold climate. The relatively consistent patterns observed between the variations of a median grain-size stack of the Chinese Loess Plateau (CLP MD) and EM 3, and of the magnetic susceptibility stack of the CLP (CLP MS) and EM 1, indicate the climatic sensitivity of these EMs in response to the long-term glacial/interglacial oscillations previously inferred from the CLP. However, significant differences in the CLP MD and CLP MS on the one hand side and in the trends of the EMs 3 and 1 on the other suggest that regional climate conditions varied between the CLP and the Nihewan Basin. Further research is required to explore such regional climate differences and their possibly underlying factors in the Early Pleistocene.

Rich north, poor south - Regionalization of European water retention: The landscape hydric potential concept

Natural water retention varies greatly across Europe. Understanding the key ecosystem factors that affect the quantity, quality, availability, and spatiotemporal distribution of water resources is essential for effective landscape management. This study presents the Landscape Hydric Potential (LHP) index of Europe, which serves as an indicator of the natural environment's ability to infiltrate and retain water. The LHP index was aggregated into 12 relatively homogeneous regions, each representing similar values of natural water retention. The main factors influencing the hydric potential, and thus the water resources of Europe, include climatic conditions, geomorphology, soil properties, and land cover and use, especially the proportion of urbanized areas. Northern Europe, in comparison to southern Europe, exhibits higher LHP index values, indicating greater retention capacities. The climatic water balance is the main factor determine poorer retention capacities in the southern part of Europe. In northern Europe positive values of climatic water balance and slope inclination promote to higher LHP value and thus higher retention capacity. The results also demonstrate that there are sufficient, freely available, and precise data to assess water retention potential - landscape hydric potential - even at a continental scale.

Appropriate water and nitrogen supply regulates the dynamics of nitrogen translocation and thereby enhancing the accumulation of nitrogen in maize grains

To improve nitrogen uptake and grain quality in maize, this study explores the dynamic processes of nitrogen accumulation, distribution, and translocation under varying water and nitrogen supplies, aiming to optimize water-nitrogen management practices. Field trials were conducted in Karamay, Xinjiang, in 2022 and 2023, with different irrigation levels (75% ETc, 100% ETc, 125% ETc) and nitrogen application rates (0, 93, 186, 279kgN·hm−2). The effects of water and nitrogen supply on nitrogen accumulation and distribution in aboveground maize organs were analyzed, and the dynamic characteristics of maize nitrogen accumulation were examined using the characteristic parameters of the Richards nitrogen accumulation equation. The results showed that beyond the W2N2 treatment (irrigation at 100% ETc and nitrogen application of 186kgN hm−2), increases in irrigation and nitrogen did not significantly enhance nitrogen accumulation per plant. Under W2N2, high levels of nitrogen were accumulated in maize leaf, stem, bract, cob, and grain. The nitrogen transfer among different organs and their contribution to grain nitrogen showed the following hierarchy: leaf > stem > cob > bract, with the contribution rates to grain nitrogen ranging from 26.16% to 56.23% over the two years. The Richards model accurately quantified the dynamic relationship between water-nitrogen supply and crop nitrogen accumulation, with the coefficient of determination (R²) ranging from 0.9864 to 0.9999 and the normalized root mean square error (NRMSE) from 0.70% to 6.51%. Optimal water-nitrogen supply significantly reduced the accumulated temperature required for maize to enter the rapid nitrogen accumulation phase and achieve maximum growth rates, while extending the duration of the rapid growth phase and increasing both the maximum growth rate and the average growth rate during this period. Grain nitrogen accumulation was positively correlated with nitrogen accumulation rates, as well as nitrogen accumulation and translocation in various organs. Under suitable irrigation and nitrogen application, the interactive effects of water and nitrogen (W × N) significantly increased both nitrogen accumulation and nitrogen accumulation rates, laying a foundation for nitrogen translocation to grains in the late growth stages and enhancing grain nitrogen accumulation. Thus, appropriate water and nitrogen supply can significantly influence nitrogen accumulation, distribution, and translocation processes in maize, regulating grain nitrogen accumulation. This study provides valuable information for nitrogen accumulation regulation and grain quality improvement in maize in Xinjiang and other regions with similar climatic conditions.

Leaf carbohydrate metabolic enzyme activities are associated with salt tolerance and yield stability in the climate-resilient crop Camelina sativa

Soil salinity is an increasingly severe problem affecting plant growth and development thus posing a threat to agricultural production worldwide. Many crops currently grown are susceptible to even moderate salt stress, and crop diversification is sought to cope with increasingly challenging environmental conditions. Camelina sativa is a versatile, underutilized, low-input Brassicaceae oilseed crop valued for its high-quality seeds and its resilience to a wide range of climate conditions. In this study, the effects of salt stress on the growth and productivity of two camelina cultivars and six landraces from different geographic regions were examined. The performance of these lines was related to adjustments in their carbohydrate metabolic enzyme activity profiles in leaves as a central physiological hub. Profiling enzyme activities and their regulation in response to salt stress revealed significant genotype × treatment (G × T) interactions and allowed the identification of specific activity signatures associated with differences in yield stability in the tested lines. Yield-stable landraces showed distinct regulation patterns contrasting those of less yield-stable lines. In particular, upregulation of specific enzyme activities was associated with yield stability under salt stress. Camelina landraces may be promising resources to improve tolerance to salinity, with plasticity in carbohydrate metabolism as a contributing mechanism. Overall, these results provide a valuable basis for enzyme activity signatures as new physiological markers for supporting breeding programmes.

Global Climate Change Impacts on the Potential Distribution of Typical Trachinotus Fishes and Early Warning Assessment of Invasions

Marine habitats and ecosystems are increasingly being impacted by global climate change and the global spread of captive breeding. In this study, we focused on five typical Trachinotus species (Trachinotus anak, Trachinotus blochii, Trachinotus mookalee, Trachinotus goreenisi, Trachinotus ovatus) as research subjects. We utilized species distribution models and ecological niche models to predict the present and future potential distribution of these species, as well as to assess ecological niche overlap and evaluate the early warning of invasion by Trachinotus species. T. ovatus stands out with its broad distribution range and high adaptability to different environments. It occupies 1.114% of medium-high suitable areas, spanning 100,147 km2. Our predictions also suggest that T. ovatus would undergo a significant expansion (approximately 55% of the total area) under both past and future environmental scenarios, demonstrating a higher tolerance and adaptability to changes in ambient temperatures. It can be discerned that T. ovatus exhibits strong environmental adaptability, which may potentially lead to biological invasion along the southeastern coast of China. The T. anak, on the other hand, showed a higher expansion trend under high carbon dioxide concentrations (RCP8.5), indicating a certain convergence with carbon dioxide concentration. Our models showed that under future climatic conditions, T. ovatus would become the dominant species, with increased competition with T. mookalee and decreased competition with T. goreenisi, T. mookalee, and T. anak. Based on our findings and the net-pen culture mode of T. ovatus, we identified the hotspot habitat of T. ovatus to be located in the Indo-Pacific convergence zone. However, there is a possibility of an expansion trend towards the southeast coast of China in the future. Therefore, it is crucial to provide an early warning for the potential biological invasion of T. ovatus.

Variation pattern, influential factors, and prediction models of PM2.5 concentrations in typical urban functional zones of northeast China

This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.

Understanding Decadal-Scale Dynamics in the Bering Sea: Investigating Trends and Variability in Sea Ice, Winds, and Waves

Abstract Despite the societal and economic importance of the Bering Sea, recent rapid changes in surface ocean conditions are not well documented. We address this gap by characterizing the Bering Sea winter ice–wind–wave climate using satellite observations and reanalysis data. Nine of the last 10 years (2014–23) have experienced anomalously low sea ice extent relative to the 1980–2023 mean. Between 2003 and 2023, gale force wind speeds have become more common and satellite altimeter observations show significant wave height (SWH) has increased ≥6 m, and has increased by 1 m between 2003 and 2023, while 20 days yr−1 exhibit SWH &gt; 9 m. Winters between 2018 and 2023 were the six stormiest since 2003, and the sea ice season decreases by 1–4 days yr−1. Should ice loss in the Bering Sea continue to decline, we hypothesize that the frequency of extreme waves over the Bering Sea shelf would increase, severely impacting coastal communities. In September 2022, extratropical cyclone Merbok caused unprecedented SWH (&gt;15 m) over the shelf which were uncharacteristic for the season, and caused widespread coastal inundation and property damage. Such anomalous wave conditions exemplify the emerging threat storms pose in a future where sea ice is absent more frequently in winter, when severe storms are most common. Satellite altimeters have improved the observation of surface ocean conditions in the Bering Sea during the most intense storms, and we demonstrate that a suite of five or more altimeters is required to capture regional events adequately. Significance Statement Although the Bering Sea region has recently endured rapid sea ice loss and the decline of several species important for subsistence and commercial fishing, it has relatively few resources to monitor and mitigate changing climatic conditions. In this article, we show that extreme wind and wave conditions in the Bering Sea have strongly increased in recent years, as sea ice has declined. This points to a need for reliable and continued monitoring, and with only two wave buoys providing year-round observations, an adequate constellation of ocean remote sensing instruments is required to capture extreme ocean–ice–atmosphere events in an isolated and challenging environment.

Emerging contaminants as indicators of short-term environmental changes in an eutrophicated coastal lagoon

This research aimed to assess the potential of emerging contaminants as environmental quality indicators for short-term monitoring programs, in contrast with traditional contaminants. Thirteen sediment samples from from Piratininga, a coastal lagoon subjected to anthropogenic impact, were analyzed for the following compounds: napropamide, diclofenac, naproxen, triclosan, ibuprofen, 17β-estradiol, bisphenol A, nadolol, ethofibrate and carbamazepine. The developed method, which included solid phase extraction, derivatization, and analysis by gas chromatography–mass spectrometry, demonstrated robustness and suitability with quantification limits between 0.32 and 1.49 ng g−1. The concentrations of etofibrate, nadolol, napropamide and diclofenac were below the quantification limits for all the analyzed samples. The highest concentration was reported for bisphenol A (4.76 ± 0.44 ng g−1), followed by 17β-estradiol (2.88 ± 0.65 ng g−1), ibuprofen (2.70 ± 1.67 ng g−1) and triclosan (1.5 ± 0.43 ng g−1). The detected concentrations, spatial distribution, and local climatic conditions with distinct dry and wet seasons indicate the feasibility of biannual environmental quality monitoring. By evaluating the concentrations and statistical treatment, the use of contaminants of emerging concern as short-term indicators proved promising, leading to an optimized monitoring program from nine to three compounds (bisphenol A, ibuprofen and triclosan) and a sampling grid reduced from 13 to 4 points.