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Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords "Sentinel", "deep learning" (DL), and "LSTM" (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images' accuracy. [ABSTRACT FROM AUTHOR]

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures. [ABSTRACT FROM AUTHOR]

With China's rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China's carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are "Low–Low" at the provincial level, and "Low–Low" and "High–High" at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China's carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

We characterize an affordable method of producing stencils for submillimeter physical vapor deposition (PVD) by using paper and a benchtop laser cutter. Patterning electrodes or similar features on top of organic or biological substrates is generally not possible using standard photolithography. Shadow masks, traditionally made of silicon-based membranes, circumvent the need for aggressive solvents but suffer from high costs. Here, we evaluate shadow masks fabricated by CO2 laser processing from quantitative filter papers. Such papers are stiff and dimensionally stable, resilient in handling, and cut without melting or redeposition. Using two exemplary interdigitated electrode designs, we quantify the line resolution achievable with both high-quality and standard lenses, as well as the positional accuracy across multiple length scales. Additionally, we assess the gap between such laser-cut paper masks and a substrate, and quantify feature reproduction onto polycarbonate membranes. We find that ~100 µm line widths are achievable independent of lens type and that average positional accuracy is better than ±100 µm at 4"-wafer scale. Although this falls well short of the micron-size features achievable with typical shadow masks, resolution in the tenths to tens of millimeters is entirely sufficient for applications from contact pads to electrochemical cells, allowing new functionalities on fragile materials. [ABSTRACT FROM AUTHOR]

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NbS) is an approach to solving climate change and social issues based on nature. Despite NbS being widely studied as an effective method to solve social problems, the trends in NbS research have hardly been analyzed. Therefore, this study examined change patterns in NbS-related research topics over time and analyzed the interactions of NbS research and relevant activities in various fields. After reviewing research papers based on the search term 'nature-based solutions' on Scopus, and collecting 1567 research papers, we conducted dynamic topic modeling (DTM) and network analysis. The papers were classified into 19 topics via DTM. Water, forest, and urban topics made up the greatest portion of NbS research, while NbS topics in the forest sector showed a steady increase over time. This study also found close connections between NbS studies on forests and other sectors and confirmed that the forest sector can become an integrated means of contributing to climate change responses and other resultant social issues. This study demonstrates that DTM and network analysis are useful tools for understanding the trends in NbS research and finding the linkages between various fields. [ABSTRACT FROM AUTHOR]

The potential of citizen science to address complex issues has been recognized since the 1990s. However, the systematic integration of public opinion in research has been developed only recently, thanks to the spread of questionnaire web-based surveys and artificial intelligence techniques for data elaboration. Starting from this point, we decided to investigate the literature published in Scopus during the decade 2013–2023, regarding citizen science applications for environmental purposes. More specifically, the focus of our study was to evaluate citizen science's benefits and limitations for managing odour emissions in national industrial plants, as well as to discuss the potential integration of a participatory approach in such a field. In fact, according to European Directive 2010/75/EU, the integrated environmental permits released to reduce industrial pollution should also encourage strong public participation. In this systematic review we first applied the principles of PRISMA methodology to select the most significant papers. Then, we discuss the results of 14 publications, through bibliometric statistics and meta-analysis. Only three of them were discovered to have a specific focus on odour emissions. Overall, we pinpointed the main advantages and limitations of citizen science applied to odour pollution management, to open the door for further research. [ABSTRACT FROM AUTHOR]

The papers by Yang et al. [[5]] and Wang et al. [[6]] mainly studied the corrosion of CO SB 2 sb on gathering pipelines and the monitoring of the corrosion rate. The paper by Hou et al. [[1]] investigated the microscopic adsorption pattern of CH SB 4 sb and the competitive adsorption behavior of CH SB 4 sb and CO SB 2 sb in sodium-based montmorillonite using molecular simulation methods. The CO SB 2 sb pre-pad energized fracturing technology can not only further increase oil production, but also effectively realize the geological storage and utilization of CO SB 2 sb , thus greatly reducing the greenhouse effect. [Extracted from the article]

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a "Time-Longitude-Latitude" three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities. [ABSTRACT FROM AUTHOR]

The idling of automobiles at street intersections can lead to pollutant accumulation which impacts the health of residents in street-facing buildings. Previous research focused on pollutant dispersion within street canyons and did not consider the coupling of indoor and outdoor pollutants. This paper employs the computational fluid dynamics (CFD) method to simulate the dispersion characteristics of vehicle emission pollutants in street canyons, primarily investigating the indoor and outdoor pollutant dispersion patterns under various window opening configurations (single-sided ventilation, corner ventilation, and different positions of the glass under corner ventilation). Additionally, the study considers the impacts of the aspect ratio and ambient wind speed. Studies have shown that corner ventilation is effective in reducing indoor pollutant levels. When the two window glass positions are far away from the center of the intersection, the average CO mass fraction in the single-sided ventilation room is reduced by 87.1%. The average indoor CO mass fraction on the leeward side decreases with the increasing wind speed and aspect ratio. At a wind speed of 8 m/s, the average indoor CO mass fraction on the leeward side decreases to 2.45 × 10−8. At an aspect ratio of 2, the indoor CO mass fraction on the leeward side decreases with increasing floors before stabilizing at approximately 4.77 × 10−9. This study suggests optimal window opening strategies to reduce indoor pollutant levels in street-facing buildings at street intersections, offering guidance to indoor residents on window ventilation practices. [ABSTRACT FROM AUTHOR]

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere. [ABSTRACT FROM AUTHOR]

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper. [ABSTRACT FROM AUTHOR]

The PIESAT-01 constellation is the world's first multi-baseline distributed synthetic aperture radar (SAR) constellation with a "Cartwheel" formation. The "Cartwheel" formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the "Cartwheel" configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China's 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data. [ABSTRACT FROM AUTHOR]

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset's data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model's stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data's outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors' emissions, and Earth's crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Excessive particle concentrations during heating periods, which greatly affect people's physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of atmospheric particulate matter before and after adjusting heating energy consumption types. The data discussed and analyzed in this paper were collected by monitoring stations and measured from 2016 to 2018 in Xi'an. The data include fractal dimension and particle size concentration changes in the atmospheric particulate matter before and after adjusting the heating energy consumption types. The results indicate that adjusting the heating energy consumption types has a significant impact on particulate matter. The average concentration of PM2.5 decreased by 26.4 μg/m3. The average concentration of PM10 decreased by 31.8 μg/m3. At the same time, the different particle sizes showed a downward trend. The particles ranging from 0.265 to 0.475 μm demonstrated the maximum decrease, which was 8.80%. The heating period in Xi'an mainly involves particles ranging from 0 to 0.475 μm. The fractal dimensions of the atmospheric particulate matter before and after adjusting the heating energy consumption types were 4.809 and 3.397, respectively. After adjusting the heating energy consumption types, the fractal dimension decreased by 1.412. At that time, the proportions of particle sizes that were less than 1.0 μm, 2.0 μm, and 2.5 μm decreased by 1.467%, 0.604%, and 0.424%, respectively. This paper provides new methods and a reference value for the distribution and effective control of atmospheric particulate matter by adjusting heating energy consumption types. [ABSTRACT FROM AUTHOR]

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. [ABSTRACT FROM AUTHOR]

Based on the detection data obtained by the LMI (Lightning Mapping Imager)—China's first satellite-based lightning observation payload—from 2018–2022, combined with the ERA5 (ECMWF Reanalysis v5) reanalysis data of the same period, the temporal and spatial characteristics of lightning activities over the Tibetan Plateau and its response to the atmospheric circulation background are studied in detail in this paper. Based on the LMI data, we obtained consistent and continuous long-time-series lightning observation data for the whole region of the plateau for the first time, and the results show that the lightning density in the Tibetan Plateau is much smaller than that in the central and eastern land regions of China (CELR) at the same latitude. Lightning activity was unevenly distributed over the plateau and had obvious seasonal variation characteristics. The monthly amount of lightning and its ratio in the total amount of lightning for the whole year show the characteristics of "increasing first and then decreasing". Most lightning occurs in June and July, which is about a month earlier than that in the CELR. The amount of lightning fluctuated in May and decreased rapidly after August, which is consistent with the local convective thunderstorm season. The hourly lightning frequency at different altitudes over the Tibetan Plateau is consistent with local convections and unique topography, and it is closely related to the features of the local night rain. The results also reveal comparative features between lightning and the atmospheric circulation background on the plateau, such as the wind field, CAPE (convective available potential energy), temperature, and humidity at 500 hPa. In the context of global warming, the average temperature in the central and western regions of the plateau increased in the past five years. This shows that the Tibetan Plateau, as a summer heat source, has a gradual warming trend, and the corresponding convections and lightning activities are also increasing gradually. Lightning activities can be used as an indicator of DCSs (deep convective systems). This paper gives a more comprehensive understanding of the characteristics of lightning activities all over the Tibetan Plateau, especially in the western part of the plateau, which lacked ground-based lightning observation data before. In addition, it reveals the comparative features between the lightning activities and the circulation background over the plateau in the past five years, which is helpful for further understanding the contribution of lightning activities to the plateau's climate change. It can provide some reference for monitoring and researching the severe convective weather over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino's effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers' livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. [ABSTRACT FROM AUTHOR]

Climate change is one of the biggest threats to human living and health in the 21st century. Whilst a large number of papers have been published addressing the health impact of climate change, there is a lack of comprehensive bibliometric analysis in the crosscutting field. This study evaluated the global scientific output of research in the field of climate change and health between 1990 and 2020, based on the Web of Science Core Collection database. Research themes were identified using a social network analysis technique based on author keywords. Research trends were assessed by the change in overall publication number and the percentage of publications in each research theme. Articles were further categorized by the availability of funding and author affiliation to compare the difference between developed and developing countries. Results showed that the research output in the field of climate change and health has increased dramatically in the past 30 years, mainly dominated by researchers in developed countries. The percentage of research receiving funding was found to be the lowest in those published by developing countries only and the highest in those published by the collaboration of developed and developing countries. A total of nine major research themes was identified. Research related to 'risk assessment and adaptation', 'sustainable development' and 'infectious diseases' were relatively underfunded. A significant research trend was observed between 2006 and 2020, with increased attention on research themes related to 'risk assessment and adaptation', 'sustainable development', 'extreme events' and 'air pollution', and reduced attention on research themes related to 'ocean', 'infectious disease' and 'phenology'. The shift of the research trend was mainly driven by research in developed countries. Suggestions, recommendations and future priorities identified by experts in the field of atmospheric sciences, epidemiology, public health, climate change, environmental sciences, and policy development are also provided to guide future research. It is important to shift our focus from single health aspects to an integrated system (such as One Health framework, which considers environmental health, animal health and human health as a whole), with future research focusing more on the systemic impact of climate change in order to achieve better, more effective and efficient risk governance. More funding should be mobilized to support the research capacity building in developing countries and to support climate change adaptation strategies for sustainable development. [ABSTRACT FROM AUTHOR]

Jack Herring had three mid-1960s numerical papers on Rayleigh-Bénard thermal convection that might seem primitive by today's standards, but already encapsulated many of the questions that are still being asked. All of them use severely truncated versions of the incompressible Navier–Stokes–Boussinesq equations with only one, or just a few, horizontal Fourier modes. In the first two papers, 1963 and 1964, the presented results used only one Fourier mode  α  and three variables. The single mode's variables are its vertical velocity profile  w α (z , t) , its temperature profile  θ α (z , t)  and the horizontally uniform vertical profile of the background temperature  ψ (z , t) . All of the second- and third-order terms are ignored except the convective heat flux  w θ ¯ . The objective was to find asymptotic steady-state solutions. Each paper found evidence for the one-third Nusselt versus Rayleigh scaling of  N u ∼ R a 1 / 3 , originally derived from Malkus' maximum flux principle. The 1963 paper uses free-slip upper and lower boundaries, with magnitudes of  N u  that are a factor of three larger than the experiments. In the 1964 paper, by introducing no-slip/rigid boundary conditions, the magnitude of  N u  dropped to within 20% of the experimental values. Both  N u (R a)  relations are in good agreement with circa-1990 direct numerical simulations (DNS). This dependence upon the boundary condition at the walls suggests that to obtain physically realistic scaling, no-slip boundary conditions are necessary. The third paper is discussed only in terms of what it might have been aiming to accomplish and its relation to the earlier free-slip results. [ABSTRACT FROM AUTHOR]

These eight articles are summarized below: Two papers address the regional impacts of climate change in East Asia and the Mediterranean. Author Contributions D.E.R. conceptualized the theme of this Special Issue and A.D.P. prepared the original draft of this editorial. Anthropogenic climate change is responsible for warming global surface temperatures, which has increased the frequency and intensity of weather and climate events [[1]]. [Extracted from the article]

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented. [ABSTRACT FROM AUTHOR]

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

Upper air temperature measurements are critical for understanding weather patterns, boundary-layer processes, climate change, and the validation of space-based observations. However, there have been growing concerns over data discrepancies, the lack of homogeneity, biases, and discontinuities associated with historical climate data records obtained using these technologies. Consequently, this article reviews the progress of utilizing radiosondes and space-based instruments for obtaining upper air temperature records. A systematic review process was performed and focused on papers published between 2000 and 2023. A total of 74,899 publications were retrieved from the Google Scholar, Scopus, and Web of Science databases using a title/abstract/keyword search query. After rigorous screening processes using relevant keywords and the elimination of duplicates, only 599 papers were considered. The papers were subjected to thematic and bibliometric analysis to comprehensively outline the progress, gaps, challenges, and opportunities related to the utilization of radiosonde and space-based instruments for monitoring upper air temperature. The results show that in situ radiosonde measurements and satellite sensors have improved significantly over the past few decades. Recent advances in the bias, uncertainty, and homogeneity correction algorithms (e.g., machine learning approaches) for enhancing upper air temperature observations present great potential in improving numerical weather forecasting, atmospheric boundary studies, satellite data validation, and climate change research. [ABSTRACT FROM AUTHOR]

Fine particulate matter with an aerodynamic diameter less than 2.5 µm ( P M 2.5 ) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the P M 2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly P M 2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of P M 2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly P M 2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination ( R 2 ) of 0.887, a root-mean-square error (RMSE) of 18.31 μ g/ m 3 , and a mean absolute error (MAE) of 11.17 µg/ m 3 , indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy. [ABSTRACT FROM AUTHOR]

In the context of global climate change and rising sea levels, the adverse impacts of storm surges on the environment, economy, and society of affected areas are becoming increasingly significant. However, due to differences in geography, climate, and other conditions among the affected areas, a single method for assessing the risk of storm surge disasters cannot be fully applicable to all regions. To address this issue, an increasing number of new methods and models are being applied in the field of storm surge disaster risk assessment. This paper introduces representative traditional statistical methods, numerical simulation methods, and artificial intelligence-based techniques in this field. It compares these assessment methods in terms of accuracy, interpretability, and implementation difficulty. The paper emphasizes the importance of selecting appropriate assessment methods based on specific conditions and scientifically combining various methods in practice to improve the accuracy and reliability of storm surge disaster risk assessments. [ABSTRACT FROM AUTHOR]

In this paper, the urban signal propagation characteristics based on the location of blind sources are investigated. To address the issue of blind electromagnetic radiation sources in complex urban environments, intelligent methods for propagation channel path classification, and model determination are brought forth based on field test data. The intelligent classification method distinguishes between the Line-of-Sight (LoS) path channel and a direct path, the LoS multipath channel with a direct path and other multiple paths, and the Non-Line-of-Sight (NLoS) multipath channel without a direct path from the source to the test point. The modeling aspect determines the model type to which the received signal belongs based on the statistical model derived from the tested data of a specific source. A validation measurement system was constructed for the FM broadcasting band, and validation campaigns were conducted in the city of Wuhan. The process and analysis of the data using this method demonstrate the accurate distinction of the different propagation path channels and models and involve the construction of a statistical model for the FM band in Wuhan's urban area. [ABSTRACT FROM AUTHOR]

This paper describes the metrological procedure carried out for the evaluation of the building influence on air temperature measurements. This evaluation aims to produce reliable conclusions, information, and data to contribute to the WMO siting classification schemes for air temperature measurements. For this purpose, a field experiment was designed, deployed, and carried out. As a result, one-year-lasting air temperature measurements were collected and analyzed. In this field experiment, a 200 m wide building is the unique artificial heat source and the unique object projecting shades over a flat surface (no discernible slope) in an open space bigger than 40,000 m2, covered with short grass. Eight calibrated thermometers, equipped with the same model of artificially ventilated radiation shields, were set up at a height of 1.5 m from the ground and at different distances from a 200 m wide building. This configuration provides the observation of the horizontal air temperature radially distributed from the building and, as a conclusion, it enables the quantification of the building influences on air temperature measurements at different distances from the building. This document describes the field experiment, the analysis procedure, the evolution of the building influence on air temperature measurements along the day, and the impact of other meteorological parameters on this building effect. Two different building effects are observed: the positive building effect, where the air temperature decreases with the distance to the building, and the negative building effect, where the air temperature increases with the distance to the building. It is also noticed that the building influence is higher on clear days and the daily maximum building influence values are directly linked with the corresponding maximum solar irradiance. The influence of wind on the building effect is also analyzed, reaching the conclusion that due to characteristic of local winds, in terms of low speed and direction, the wind impact could be considered as negligible. The maximum values of building influence on air temperature measurements, the associated uncertainty analysis, and the conclusions are presented in this paper. All these points have been addressed using metrological principles with the purpose of giving consistency and robustness to the evidence presented here. [ABSTRACT FROM AUTHOR]

The first paper published in this SI, by Mazzeo et al. [[1]], used an air quality model, coupled online with a meteorological model, to simulate the impact of emission reductions on PM2.5 in the West Midlands region of the United Kingdom. Atmospheric dispersion and chemical transport models (CTMs) are a key tool in both atmospheric chemistry and environmental sciences. Liu et al. [[4]] analyzed the relationship between sandstorm periods and the transport and dispersion of particulate emissions from coal bases in northwest China, assisted by a backward trajectory analysis performed with an atmospheric dispersion model. [Extracted from the article]

With the rapid development of the economy and continuous improvement in people's living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection. [ABSTRACT FROM AUTHOR]

Lorenz rediscovered the butterfly effect, which is defined as the sensitive dependence on initial conditions (SDIC), in 1963. In 1972, he used the term "butterfly" as a metaphor to illustrate how a small perturbation can lead to a tornado with a complex structure. The metaphorical butterfly effect, which celebrated its 50th anniversary in 2022, is not precisely the same as the original butterfly effect with SDIC. To commemorate the 50th anniversary, a Special Issue was launched and invited the submission of research and review articles that can help to enhance our understanding of both the original and metaphorical butterfly effects. The Special Issue also sought recent developments in idealized Lorenz models and real-world models that address multistability, multiscale predictability, and sensitivity. The call for papers was opened 15 months prior to the completion of the Special Issue and features nine selected papers. This editorial provides a brief review of Lorenz models, introduces the published papers, and summarizes each one of them. [ABSTRACT FROM AUTHOR]

The iron and steel industry is an important foundation of the national economy. It is the inevitable choice, to achieve high-quality development in the new era of the iron and steel industry, to speed up the green development of the iron and steel industry. This paper studies the effect of steel industry agglomeration on regional economic growth and air pollution. Through the analysis of the characteristics of iron and steel industry agglomeration, and the empirical analysis of the relationship between iron and steel industry agglomeration, regional economic growth, and air pollution, it is found that: (1) Iron and steel industry agglomeration helps to promote economic growth; (2) Iron and steel industry agglomeration has an obvious spatial correlation effect and obviously drives the development of surrounding areas; (3) Iron and steel industry agglomeration will cause air pollution. The marginal effect of air pollution will decline rapidly with the development of iron and steel industry agglomeration. (4) The impact of green process innovation investment on air pollution presents an inverted U-shaped effect, which has a positive effect on air recovery after exceeding the critical point. (5) The air self-purification capacity represented by precipitation, helps to reduce air pollution. Based on the above conclusions, this paper puts forward some policy suggestions, such as making a scientific development plan for the iron and steel industry, accelerating green process innovation, effectively improving regional precipitation and precipitation times, vigorously promoting high-quality development of the regional economy, and comprehensively promoting coordinated development of the iron and steel industry, so as to cope with the dilemma of the coordinated development of the iron and steel industry, regional economic growth, and smog pollution, and strive for international development in the future. In the competition, we should gain the first opportunity and obvious competitive advantage. [ABSTRACT FROM AUTHOR]

Recently, we published two papers in this journal. One of the papers dealt with the action of the radiation fields generated by a traveling-wave element and the other dealt with the momentum transferred by the same radiation fields and their connection to the time energy uncertainty principle. The traveling-wave element is defined as a conductor through which a current pulse propagates with the speed of light in free space from one end of the conductor to the other without attenuation. The goal of this letter is to combine the information provided in these two papers together and make conclusive statements concerning the connection between the energy dissipated by the radiation fields, the time energy uncertainty principle and the elementary charge. As we will show here, the results presented in these two papers, when combined together, show that the time energy uncertainty principle can be applied to the classical radiation emitted by a traveling-wave element and it results in the prediction that the smallest charge associated with the current that can be detected using radiated energy as a vehicle is on the order of the elementary charge. Based on the results, an expression for the fine structure constant is obtained. This is the first time that an order of magnitude estimation of the elementary charge based on electromagnetic radiation fields is obtained. Even though the results obtained in this paper have to be considered as order of magnitude estimations, a strict interpretation of the derived equations shows that the fine structure constant or the elementary charge may change as the size or the age of the universe increases. [ABSTRACT FROM AUTHOR]

Public participation is playing an increasingly important role in environmental governance. This paper uses panel data from Chinese cities to evaluate the causal relationship between public participation, regional carbon emissions, and regional carbon intensity. We obtain the following conclusions: (1) Public participation significantly reduces regional carbon emissions and regional carbon intensity, which remains robust after a series of robustness and endogeneity discussions. (2) The carbon reduction effect of public participation performs better in eastern regions, regions with higher per capita income, and regions with a concentration of tertiary industries and talents. (3) We divided the public participation into resident participation and environment non-governmental organizations (ENGOs) participation. We found an excellent interactive emission reduction effect with resident participation and a good interaction between resident participation and government and environmental organizations. (4) This paper finds that promoting regional green technology innovation is a significant mechanism for public participation in achieving carbon emission reduction. (5) Finally, this paper found an "inverted U-shaped" non-linear relationship between public participation and regional carbon emissions. The results reveal the importance of public participation in regional carbon emissions and provide an empirical basis for promoting informal environmental regulation. [ABSTRACT FROM AUTHOR]

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China's Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy. [ABSTRACT FROM AUTHOR]

A cohort study spanning ten years was conducted to assess the health effects on toner manufacturing workers. A survey consisting of questions investigating subjective respiratory symptoms, current medical history, disease occurrence, dust exposure concentrations at the workplace, respiratory function tests, biochemical and immunological items in blood and urine, and a chest radiograph or chest computed tomography survey was conducted. The results of these surveys have been published in academic journals, and none of the surveys showed any findings suggesting significant health problems in the toner-worker group compared to the non-toner-worker group. The results suggest that the health risks associated with toner handling are not high when the work environment at the toner handling site is well controlled. [ABSTRACT FROM AUTHOR]

With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on the primary product of the radar. This paper researches the effectiveness of deep convolutional neural networks (DCNN) to identify and suppress electromagnetic interference based on the I/Q data output from the front end of a radar receiver. Firstly, this paper selected UNet, ResNet with UNet structure, and DeepLab V3+ for the semantic segmentation of electromagnetic interference and other signals. After semantic segmentation, this paper used the linear interpolation method to suppress EMI. Finally, this paper selected the prediction precision of the model and compared the quality of primary products before and after EMI suppression to evaluate the effectiveness of DCNN. The results showed that all three models could effectively identify the electromagnetic interference and the quality of the data were improved after suppression. It suggests that the use of DCNN on the I/Q data output from the front end of a radar receiver can play a certain effect on the identification of electromagnetic interference. [ABSTRACT FROM AUTHOR]

In the context of carbon neutralization and carbon peak, carbon reduction in key industries has become a central topic in our country. As an important part of technological progress, it is necessary to study the effect of technology import on carbon emission reduction in key industries. Based on the panel data of 30 provinces. from 2011 to 2020, this paper used the fixed-effect model to analyze the emission reduction effect in key industries on the development status of technology import. The spatial econometric model was used to analyze the spatial characteristics of carbon emissions of technology import and key industries. Then, the mediating effect model was used to bring industrial technological innovations into the research category to analyze the mediating role of technology imports on the carbon emissions of key industries. Finally, a robustness test proved the reliability of the model. The findings were as follows: (1) Technology import significantly promoted carbon emission reduction in key industries; (2) In terms of the spatial relationship, technology import and carbon dioxide emissions had significant spillover effects, and there were trends of high and high aggregation and low and low aggregation, with the impact of technology import on carbon dioxide emissions having a siphon effect; (3) Industrial technological innovation played an intermediary role in this path, but it was a negative role, which was not, in general, conducive to the reduction of carbon emissions of key industries. On this basis, the paper puts forward several policy suggestions. [ABSTRACT FROM AUTHOR]

Wildfire is a growing concern worldwide with significant impacts on human lives and the environment. This study aimed to provide an overview of the current trends and research gaps in wildfire prediction by conducting a bibliometric analysis of papers in the Web of Science and Scopus databases. CiteSpace was employed to analyze the co-occurrence of keywords, identify clusters, and detect emerging trends. The results showed that the most frequently occurring keywords were "wildfire", "prediction", and "model" and the top three clusters were related to "air quality", "history", and "validation". The analysis of emerging trends revealed a focus on vegetation, precipitation, land use, trends, and the random forest algorithm. The study contributes to a better understanding of the research trends and gaps in wildfire prediction and provides recommendations for future research, such as incorporating new data sources and using advanced techniques. [ABSTRACT FROM AUTHOR]

The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control. [ABSTRACT FROM AUTHOR]

Ozone is a very important trace gas in the atmosphere, it is like a "double-edged sword". Because the ozone in the stratosphere can effectively help the earth's organisms to avoid the sun's ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA's Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a "-" distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling. [ABSTRACT FROM AUTHOR]

Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO2-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to the seasonal variations of aerosol size modes. With rising CO2 levels, surface warming, and changes in atmospheric circulations and hydrologic cycles are found during both summer (JJA) and winter (DJF). For anthropogenic particles, changes in fine anthropogenic particulate matter (PM2.5, particles with diameters smaller than 2.5 μm) decrease over high-latitude regions and increase over the tropics in both DJF and JJA. Global mean column concentrations of PM2.5 decrease by approximately 0.19 mg m−2, and concentrations of coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase by 0.005 mg m−2 in JJA. Changes in anthropogenic particles in DJF are similar to those in JJA, but the magnitudes of maximum regional changes are much smaller than those in JJA. The coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase over northern Africa and the Arabian Peninsula during JJA, whereas changes in anthropogenic CPM during DJF are minimal. During both JJA and DJF, changes in anthropogenic CPM are about two orders of magnitude smaller than those of anthropogenic PM2.5. Enhanced wet deposition by large-scale precipitation under rising CO2-induced surface warming is the critical factor affecting changes in anthropogenic particles. For natural particles, the distribution of change in the natural PM2.5 burden is similar to that of natural CPM, but much larger than natural CPM during each season. Both natural PM2.5 and CPM burdens increase over northern Africa and the Arabian Peninsula during JJA, but decrease over most of the continental regions during DJF. Changes in surface wind speed, divergence/convergence of surface wind, and precipitation are primary reasons for the variation of natural particles. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the "super typhoon direct-hit" scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO's PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the charge structure and lightning activity characteristics of the thunderstorm that occurred on the Qinghai–Tibet Plateau on 14 August 2014 were analyzed using data collected from a three-dimensional (3D) lightning very-high-frequency (VHF) radiation source location system and Doppler weather radar. The analysis results showed that the charge structure of the hailstorm was maintained as an inverted dipole throughout the thunderstorm's development process. The negatively charged region height was distributed in the 5–7 km range (above ground level (AGL)), and the positively charged region was distributed from 2 to 5 km (AGL). The lightning flash types included only cloud flashes and negative cloud–to–ground (CG) flashes in the hailstorm, and cloud flashes accounted for 93% of the total lightning flashes. Cloud flashes accounted for a high proportion of the total flashes, which may have been related to the deep lower positively charged region observed throughout the thunderstorm process. In the hailstorm development stage, the electric field was dominated by positive polarity. When the hail fell, the electric field changed negatively. When the hail ended, the electric field was dominated by negative polarity. A hail event occurred only once and lasted for a long time in the development stage, but in the mature stage, hail fell many times and every time for only a short time, and in the dissipating stage, hail events also occurred many times and each time for a long time. By comparing the radar echoes of the hailstorm cells and normal thunderstorm cells, we found that the area of the 50 dBZ echo in the hailstorm was small, the occurrence time was late, and the duration was short. [ABSTRACT FROM AUTHOR]

The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: "Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.". This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms. [ABSTRACT FROM AUTHOR]

Space weather science has been a growing field in Africa since 2007. This growth in infrastructure and human capital development has been accompanied by the deployment of ground-based observing infrastructure, most of which was donated by foreign institutions or installed and operated by foreign establishments. However, some of this equipment is no longer operational due to several factors, which are examined in this paper. It was observed that there are considerable gaps in ground-based space-weather-observing infrastructure in many African countries, a situation that hampers the data acquisition necessary for space weather research, hence limiting possible development of space weather products and services that could help address socio-economic challenges. This paper presents the current status of space weather science in Africa from the point of view of some key leaders in this field, focusing on infrastructure, situation, human capital development, and the research landscape. [ABSTRACT FROM AUTHOR]

Situated at middle-to-high latitudes with limited thermal resources, Northeast China is the primary maize-producing region in China. It is also one of the regions most significantly impacted by climate change. Given the persistent impact of climate change, it is crucial to elucidate the effects of the varying thermal conditions and low temperatures for different sowing dates on the growth, development, and grain maturity of spring maize. To ensure secure maize production and disaster prevention, choosing the optimal sowing time for spring maize holds significant implications for the judicious utilization of climatic resources, risk mitigation, and the provision of meteorological guidance. Moreover, it can serve as a technical reference for relevant departments to conduct climate evaluation, disaster monitoring, prediction, and assessment, as well as impact analysis of corn production safety. Additionally, it can provide meteorological evidence to ensure food security and promote the sustainable development of modern agriculture. An interval sowing experiment of spring maize was conducted in Harbin in the north of Northeast China. Two varieties were used in the experiment. Four sowing dates were set, and the interval between adjacent sowing dates was 10 days. The local perennial sowing time, 5 May, was set as the second sowing date, with one date set later and two dates set earlier. During the experiment, the growth process, grain dry matter, seed moisture content, yield components, and temperature of spring maize were observed. The impact of temperature conditions on maize growth and yield formation was analyzed in this paper through mathematical statistics, which further led to the establishment of a monitoring and evaluation model for assessing the effect of thermal conditions and temperature on maize. The results showed that the growth rate of spring maize was closely related to temperature. When the average temperature, minimum temperature, and maximum temperature increased by 1 °C, the average emergence rate increased by 1.05%, 0.99%, and 1.07%, respectively, and the average vegetative growth rate increased by 0.16%, 0.16%, and 0.09%, respectively. The change rate of ≥10 °C active accumulated temperature was significantly correlated with the change rate of the dry weight of the grain kernel, which conformed to the quadratic equation of one variable. The temperature influence coefficients of different sowing dates varied from 1.0% to 1.7%. The relationship between the accumulated values of 10 ℃ active accumulated temperature and the grain moisture content of spring maize was a logarithmic function. From 10 to 50 days after anthesis, the effect of temperature can explain about 95% of the change in grain moisture content. After physiological maturity, the effect of thermal conditions can only explain 56–83%. The temperature influence coefficient ranges from 1.3% to 13.8%. Comparatively speaking, the second sowing date is the most suitable sowing date. Early sowing is prone to encounter low temperatures, resulting in underutilization of the early heat, while late sowing is prone to less heat. Both conditions are not conducive to better improve the yield of spring maize. [ABSTRACT FROM AUTHOR]