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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]

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]

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]

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]

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]

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]

This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ−3 to λ−9. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this. [ABSTRACT FROM AUTHOR]

This paper proposes a short-term forecasting method for high-frequency broadcast Maximum Usable Frequency (MUF) based on Long Short-Term Memory (LSTM) to meet the demand for refined and precise high-frequency broadcast coverage. Based on the existing infrastructure of broadcast and television stations, we established an experimental verification system to collect data for approximately three years. Two links were selected based on data quality: Urumqi to Lhasa and Lanzhou to Lhasa. A short-term forecast of MUF was conducted using the data from these two links. Comparison and analysis were conducted between the forecasting results of our model and those of the REC533 model. Our proposed method outperforms the REC533 forecasting results overall, with a reduction in root mean square error (RMSE) of 0.66 MHz and an improvement in forecast accuracy of 14.77%. The comparison result demonstrates the feasibility and accuracy of our model. [ABSTRACT FROM AUTHOR]

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. [ABSTRACT FROM AUTHOR]

In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans. [ABSTRACT FROM AUTHOR]

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts. [ABSTRACT FROM AUTHOR]

As the most significant interannual signal in the tropical Pacific, the influence of ENSO on the interannual variability in TC genesis location in the western North Pacific (WNP) has received much attention in previous studies. This paper mainly emphasizes the underlying SST factors independent of the ENSO signal and explores how they modulate interannual tropical cyclone genesis (TCG) latitude variability. Our study finds that the meridional sea temperature gradient (SSTG) between the Kuroshio Extension and the WNP still has a significant effect on the interannual variability in the TCG latitude after removing the effect of ENSO (r = 0.6). The interannual forecasts of the TCG latitude were effectively improved from 0.67 to 0.81 when the ENSO-independent SSTG and ENSO were regressed together in a multi-linear regression. We then propose an ENSO-independent physical mechanism affecting the TCG latitude. The equatorward (poleward) SSTG excited the positive (negative) Pacific–Japan telecorrelation pattern over the WNP, forming Rossby wave trains and propagating northward. A significant cyclonic vortex (anticyclonic vortex) with strong convective development (suppression) developed near 20° N, leading more TCs to the northern (southern) part of the WNP. These findings provide a new perspective for the prediction of the interannual variability in the TCG latitude. [ABSTRACT FROM AUTHOR]

Airborne wind measurement is of great significance for understanding atmospheric motion and meteorological monitoring. In this paper, we present the development and verification of an airborne Doppler wind lidar (ADWL), featuring an approach proposed to integrate a real-time wind retrieval method with an intelligent processing method for automatic adaptive wind detection. Several verification experiments were conducted to evaluate the measurement effectiveness, including comparisons with a calibrated ground-based Doppler wind lidar (GDWL) and a sounding balloon. Compared with the sounding balloon, the ADWL demonstrated mean errors of 0.53 m/s for horizontal wind velocity and 4.60° for wind direction. The correlation coefficients consistently exceeded 0.98 in all linear analyses. Employed in multiple airborne wind detection events in North China at altitudes up to 6600 m, the ADWL provided effective wind field results with a vertical resolution of 50 m and a data rate of 2 Hz. The wind field results obtained during the detection events validate the ADWL's capabilities in diverse environments and underscore its potential for the comprehensive detection of meteorological information. [ABSTRACT FROM AUTHOR]

In this paper, the global distribution of precipitation for 2023, in terms of global totals and regional anomaly patterns, is analyzed using information from the new Global Precipitation Climatology Project (GPCP) V3.2 Monthly product, including how the precipitation amounts and patterns from 2023 fit into the longer record from 1983–2023. The tropical pattern of anomalies for 2023 is dominated by the effect of the El Nino which began during the Northern Hemisphere spring, after three plus years of La Nina conditions. The transition from La Nina conditions through 2022 shows the rapid change in many regional features from positive to negative anomalies or the reverse. Comparison of the observed regional trend maps with climate model results indicates similarity between the observations and the model results forced by observed SSTs, while the "free-running" model ensemble shows only a broad general agreement over large regions. Global total precipitation shows about a 3% range over the span of data, with El Nino and La Nina years prominent as positive and negative features, with 2023 showing a small positive global anomaly. The ITCZ (Inter-Tropical Convergence Zone) latitude band, 0–10° N, sets a record high mean rain rate in 2023 after a steady upward trend over the decades, probably a response related to global warming. [ABSTRACT FROM AUTHOR]

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO's Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone's propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem. [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]

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]

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]

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]

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]

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 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]

In the land use planning framework in the neighbourhood of industrial facilities, the current approach to predicting the consequences of massive toxic gas releases is generally based on Gaussian or integral models. For many years, CFD models have been more and more used in this context, in accordance with the development of high-performance computing (HPC). The present paper focuses on harmonising input data for atmospheric transport and dispersion (AT&D) modelling between the widely used approaches. First, a synthesis of the practice's harmonisation for atmospheric dispersion modelling within the framework of risk assessment is presented. Then, these practices are applied to a large-scale INERIS ammonia experimental release. For illustration purposes, the impact of the proposed harmonisation will be evaluated using different approaches: the SLAB model, the FDS model, and the Code_Saturne model. The two main focuses of this paper are the adaptation of the source term dealing with a massive release and the wind flow modelling performance using an experimental signal for CFD model inflow. Finally, comparisons between the modelling and experimental results enable checking the consistency of these approaches and reinforce the importance of the input data harmonisation for each AT&D modelling approach. [ABSTRACT FROM AUTHOR]

The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus "gray-zone" resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications. [ABSTRACT FROM AUTHOR]

In engineering pulse-jet dust collector applications, the filtration area and injection pressure are chosen mostly based on experience. The peak pressure is tested under different injection pressures and filtration areas controlled by an electromagnetic valve, and then comprehensively analyzes the effects of dust intensity, uniformity, and air consumption on dust cleaning to obtain a better filtration area controlled by an electromagnetic valve and injection pressure. The results show that considering the uniformity and intensity of dust cleaning, the filtration area of 33 m2 under the injection pressure of 0.4 MPa should be preferentially selected, with a standard deviation of 0.246 and a variance of 0.061. The filtration area of 27 m2 under the injection pressure of 0.3 MPa should be preferentially selected considering the unit air consumption, uniformity, and intensity of dust cleaning, the standard deviation of 0.252, and the variance of 0.064. The paper presents a theoretical foundation for selecting the optimal injection pressure and filter area regulated by an electromagnetic valve in pulse-jet dust collector systems. [ABSTRACT FROM AUTHOR]

The technology of CO2 geological storage and CH4 intensive mining (CO2-ECBM) in coal seams integrates greenhouse gas emission reduction and new fossil energy development and has great development prospects. The CO2 injection, CO2 sequestration mechanism and storage capacity, and CH4 stimulation effect constitute the core content of the effectiveness of CO2-ECBM, among which CO2 injection is the most critical. Traditional seepage analysis methods often struggle to tackle flow-related issues influenced by microscale effects and intricate channels. This paper highlights the advantages of employing lattice Boltzmann (LBM) numerical simulations to study CO2 seepage behaviors when teaching a Rock Mass Seepage Mechanics Course. This course primarily covers topics such as the pore structure of rock, unstable liquid seepage, gas seepage theory and related subjects. Its goal is to provide students with a solid theoretical foundation to address the complexities of fluid seepage in pours media encountered in practical scenarios. A novel LBM-based methodology was employed to estimate the CO2 seepage capacity by incorporating the effects of different concentrations of [Bmin]Cl solution (0 wt%, 1 wt%, 3 wt%, and 5 wt%). The CO2 velocity distribution cloud map of each coal sample was simulated; the average velocity distribution curve of each coal sample was obtained; and the velocity profile of the seepage channel of each coal sample was described. This study can provide theoretical guidance for the technology of CO2 geological storage and CH4 intensive mining in coal seams. [ABSTRACT FROM AUTHOR]

Morphological features of the Mediterranean Sea basin have recently been precursors to a significant increase in the formation of extreme events, in relation to climate change effects. It happens very frequently that rotating air masses and the formation of mesoscale vortices can evolve into events with characteristics similar to large-scale tropical cyclones. Generally, they are less intense, with smaller size and duration; thus, they are called Medicanes, a short name for Mediterranean hurricanes, or tropical-like cyclones (TLCs). In this paper, we propose a new perspective for the study and analysis of cyclonic events, starting with data and images acquired from satellites and focusing on the diagnostics of the evolution of atmospheric parameters for these events. More precisely, satellite remote sensing techniques are employed to elaborate on different high spatial-resolution satellite images of the events at a given sensing time. Two case studies are examined, taking into account their development into Medicane stages: Ianos, which intensified in the Ionian Sea and reached the coast of Greece between 14 and 21 September 2020, and Apollo, which impacted Mediterranean latitudes with a long tracking from 24 October to 2 November 2021. For these events, 20 images were acquired from two different satellite sensors, onboard two low-Earth orbit (LEO) platforms, by deeply exploiting their thermal infrared (TIR) spectral channels. A useful extraction of significant physical information was carried out from every image, highlighting several atmospheric quantities, including temperature and altitude layers from the top of the cloud, vertical temperature gradient, atmospheric pressure field, and deep convection cloud. The diagnostics of the two events were investigated through the spatial scale capabilities of the instruments and the spatiotemporal evolution of the cyclones, including the comparison between satellite data and recording data from the BOLAM forecasting model. In addition, 384 images were extracted from the geostationary (GEO) satellite platform for the investigation of the events' one-day structure intensification, by implementing time as the third dimension. [ABSTRACT FROM AUTHOR]

This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002–2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of ~20 K observed at 10° S in August, followed by WN2 with ~13.9 K at 10° S in February, and the weakest is WN3 with ~12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at −30° modip latitude in October, followed by WN2 with 5.4 TECU at 30 modip in March, and the last is WN4 with 3.7 TECU at −30 modip in August. Both the climatology of the WSA and the features of its drivers are investigated as well. [ABSTRACT FROM AUTHOR]

One of the principal sources of pollution, on a local scale for a water city, with a tourist and commercial port, is certainly the port. Monitoring what is happening here is essential in order to implement suitable measures to control and contain emissions with consideration for the increasingly delicate environmental problem. This paper details the methods and results of an experimental campaign of local-scale emission measurements conducted in the port of Naples for two weeks in 2021. The chosen instrumentation, its setup, post-processing of the data, and an analysis critique of the results will be presented in detail. The campaign is part of broader research attempting to superimpose the concentrations of pollutants measured ashore in the port area with what is emitted by moored ships. [ABSTRACT FROM AUTHOR]

Appropriately designed and implemented climate mitigation actions have multiple co-benefits (yet some trade-offs cannot be excluded) that result in substantial social and economic value beyond their direct impact on reducing energy consumption and GHG emissions. Despite their wider acknowledgement by the research community, decision makers and the public have incomplete information on these multiple effects. This paper has a twofold objective: First, through analytical bottom-up approaches, it assesses, in quantitative terms, the macroeconomic effects and the public health benefits attributed to a variety of mitigation actions under consideration in the context of the Greek Energy and Climate Plan. Second, it investigates, through a social survey, how citizens perceive climate change and value these multiple impacts of mitigation actions, and to what extent they are willing to pay for them and support the adoption of policy measures aiming at the green transition of the Greek economy. We show that mitigation actions bring about significant health benefits, particularly in cities, and generate significant positive macroeconomic effects, particularly if mitigation actions focus on the decarbonization of the building sector and on the exploitation of local renewable sources. We also argue that most people do not realize that climate mitigation actions can have wider benefits for society, such as tackling energy poverty, improving public health, and creating new jobs. Unwillingness to pay tends to be the prominent attitude. People who are more reluctant to cover a part of the cost of environmental protection are less likely to perceive that climate change is one of the main challenges at global and national level and support the adoption of climate mitigation policies. In this context, the national strategy for climate change should focus on effectively informing and engaging the public in climate mitigation strategies, strengthening the public trust in government institutions, promoting mutually acceptable solutions with the local communities, and providing incentives for changing citizens' behavior towards climate-related actions. [ABSTRACT FROM AUTHOR]

During the winter of 2019, the global outbreak of COVID-19 prompted extensive research on urban air pollution under lockdown measures. However, these studies predominantly focused on winter conditions, thereby limiting investigations into changes in urban air pollutants during other seasons that were also subject to lockdown restrictions. Shanghai, China, has undergone two COVID-19 lockdown periods in two seasons: winter 2019 and spring 2022. The seasonal variations and human activities were represented by meteorological factors and nighttime light brightness in this paper, respectively. The reduction in human-related emissions during the two lockdown periods was estimated based on the targets outlined in China's Air Pollution Prevention and Control Action Plan. The results showed significant reductions in NO2 and PM particles during the two lockdown periods, both accompanied by a notable increase in O3 concentration. In comparison to the winter lockdown, there was an approximate 40% decrease in the NO2 and PM2.5 concentrations in the spring, while the O3 concentration exhibited an increase of 48.81%. Furthermore, due to shifting wind patterns during the two lockdowns from winter to spring, the high-pollution core areas shifted 20–25 km southeastward in the spring. The PM particles and NO2 concentrations exhibited a considerable impact from human activities, whereas the O3 concentration was affected mostly by seasonal change and interactions among air pollutants. Compared to the corresponding non-lockdown condition, the concentration of CO decreased during the winter lockdown; however, it increased during the spring lockdown. The different change in CO concentration during the two lockdown periods was found to have a lower effect on the O3 concentration than that caused by changes in meteorological factors and nitrogen oxide (NO, NO2) concentrations. In summary, the impact of COVID-19 lockdown periods on urban air pollutants was more pronounced in spring compared to winter, and the interactions among air pollutants also underwent alterations. [ABSTRACT FROM AUTHOR]

Alternating current can result in flickering—or pulsing—in the brightness of light emitted by luminaires. Lighting flicker typically occurs in the range of 100 to 140 cycles per second (Hz), which is too fast for visual perception by most organisms. However, evidence indicates that many organisms perceive flicker with non-visual photoreceptors present on the retinas. Exposure to flickering lights at night disrupts the circadian rhythm of organisms, leading to symptoms similar to blue light exposure at night. The traditional method for detecting flickering is with a flickermeter held near a single light. In this paper, we explore the use of high-speed camera data in the collection of temporal profiles for groups of luminaires simultaneously at distances ranging from several meters to several kilometers. Temporal profiles are extracted for individual lighting features and the full scene. The identification of luminaire types is based on their spectral signatures. With the camera data, it is possible to identify flickering and non-flickering lights, to determine the flicker frequency, to calculate percent flicker and the flicker index, and to identify groups of lights whose flickers are synchronized. Both flickering and non-flickering luminaires can be found for LED, metal halide, fluorescent, and compact fluorescent lights. To date, flickering has been detected in all of the incandescent, high-pressure sodium, and low-pressure sodium luminaires that we measured. We found that flicker synchronization is often present for lights installed within a single facility and also for strings of streetlights. We also found that flicker exposure can come from the light reflected off of the earth's surface. Luminaires designed to illuminate large areas often saturate high-speed camera data collection. This saturation can be reduced or eliminated using neutral density filters on the camera. Published experimental data on the impacts of flicker on organisms remains sparse. Many studies have drawn inferences on the impacts of spectral and lighting brightness on organisms without controlling for flicker. Our conclusion is that lighting flicker is a type of light pollution. The use of high-speed camera data makes it easier to include flicker as a variable in studies regarding the impacts of lighting on organisms. [ABSTRACT FROM AUTHOR]

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0   ° C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow. [ABSTRACT FROM AUTHOR]

Air quality in school environments is of particular interest due to the significant amount of time children spend in these settings. Children, being a particularly sensitive demographic, are exposed to various pollutants at school or kindergarten. In this regard, our studies have focused on monitoring the concentrations of three main categories of pollutants: VOCs (volatile organic compounds), VICs and PM (particulate matter). We conducted two experimental campaigns in seven classrooms within public educational institutions. The average concentration values of TVOC (total volatile organic compounds) ranged from 554 µg/m3 to 2518 µg/m3, of CO2 from 1055 ppm to 2050 ppm, of NH₃ (Ammonia) from 843.2 µg/m3 to 1403.4 µg/m3, of PM2.5 from 25.1 µg/m3 to 89.9 µg/m3, and of PM10 from 63.7 µg/m3 to 307.4 µg/m3. In most instances, the registered values exceeded the limit values set by national or international regulations. Furthermore, this study highlights the significant impact of a heat recovery ventilation system in improving indoor air quality by substantially reducing the levels of CO2 and PM. However, it also underscores the need for further measures to more efficiently reduce TVOC concentrations. The aim of our paper was to enhance the understanding of pollution levels in school environments, increase awareness of the importance of indoor air quality, and highlight the adverse effects of polluted air on the health of occupants. [ABSTRACT FROM AUTHOR]

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process. [ABSTRACT FROM AUTHOR]

Madden–Julian Oscillation (MJO) modulates the generation of typhoons (TYs) in the western North Pacific (WNP). Using IBTrACS v04 tropical cyclone best path data, ERA5 reanalysis data, and the MJO index from the Climate Prediction Center (CPC), this paper defines an index to describe the persistent anomalies of the MJO and to examine the statistical characteristics of TYs over 44 years (1978–2021), focusing on the analysis of major differences in environmental conditions after the removal of the ENSO signal over the WNP. The results indicate that the persistent anomalous state of the MJO influences the change in large-scale environmental factors, which, in turn, affects the generation of TYs, as follows: (1) For the I high-value years, the center of the MJO stagnates in the Indian Ocean–South China Sea (SCS), the monsoon trough retreats westward, the warm pool becomes warmer, and the Walker circulation is enhanced. There is stronger upper-level divergence and low-level convergence, larger low-level relative vorticity, higher mid-level relative humidity, and smaller vertical wind shear in the SCS and the seas near the Philippines. Consequently, these conditions foster a conducive environment for TY genesis in the SCS and the seas near the Philippines. (2) For the I low-value years, the center of the MJO stagnates in the WNP–North America region, the monsoon trough extends eastward, the warm pool becomes colder, and the Walker circulation is weakened. Consequently, these conditions are more likely to facilitate TY genesis in the central–eastern WNP. The results show that persistent anomalies in MJO active centers can effectively improve the predictive ability of TY frequency. [ABSTRACT FROM AUTHOR]

In the context of global climate change, lightning disasters have emerged as a serious environmental factor that restricts the sustainable development of megacities. This paper provides a review of the research on the impact of urbanization on thunderstorm processes and lightning activity, exploring various aspects, such as aerosols, urban thermal effects, urban dynamic effects, and building morphology. Despite numerous significant achievements in the study of the impact of air pollutants on lightning activity, there is no consensus on whether aerosols serve to enhance or inhibit lightning activity. The temperature difference between the urban underlying surface and the natural underlying surface could sustain and promote the occurrence and development of convective systems, thus enhancing lightning activity. In terms of urban dynamics, the barrier effect has led to the maximum center of lightning appearing at the edge of a built-up area, which might be associated with factors, such as urban heat island (UHI) intensity, wind speed, synoptic background, and city size. Additionally, the size of a city and the height of the buildings was also an influencing factor on lightning activity. In summary, scholars have made progress in understanding the characteristics and drivers of urban lightning activity in recent years, but there are still some urgent problems that need to be solved: (1) How to analyze, comprehensively, the spatiotemporal patterns of urban lightning activity under different thunderstorm intensity backgrounds? (2) How to conduct analysis to investigate the influence of alterations in the boundary layer structure, water–heat energy balance, and water vapor circulation processes on urban lightning activity in the context of urbanization? (3) How to couple numerical models of different scales to enhance the understanding of the impact of complex underlying surfaces on urban lightning activity? Future studies could investigate the relationship between urbanization and thunderstorm/lightning activity using a combination of observational data, numerical modeling, and laboratory experiments, which holds promise for providing valuable theoretical insights and technical support to enhance the prediction, nowcasting, early warning, and risk assessment of thunderstorms and lightning in urban areas. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear dynamics of atmospheric aerosols during the transition from laminar to turbulent flows using the framework of Scale Relativity Theory. It is proposed that the transition from multifractal to non-multifractal scales (in the dynamics of the atmospheric aerosols) can be assimilated to the transition between laminar and turbulent states. These transitions are determined by the multifractal diffusion and deposition processes. The methodology used involves the application of the principle of scale covariance, which states that the laws of atmospheric physics remain invariant with respect to spatial and temporal transformations as well as scale transformations. Based on this principle, several conservation laws are constructed. In such context, the conservation law of the density of states associated with the multifractal-non-multifractal scale transition in a one-dimensional case is then considered. The model describes the non-linear behaviour of atmospheric aerosols undergoing diffusion and deposition processes. The theoretical approach was correlated using experimental data from a ceilometer and radar reflectivity factor data. [ABSTRACT FROM AUTHOR]

The changing climate has a serious bearing on agriculture, particularly livestock production in Botswana. Therefore, studying the relationship between climate and livestock, which at present is largely missing, is necessary for the proper formulation of government policy and interventions. This is critical in promoting the adoption of relevant mitigation strategies by farmers, thereby increasing resilience. The aim of this research is to establish associations between climate variability and livestock production in Botswana at the national level. The paper employs time series data from 1970 to 2020 and the Vector Autoregression with Exogenous Variables (VARX) model for statistical analysis. The trend shows that both cattle and goat populations are decreasing. The VARX model results reveal that cattle and goat populations are negatively associated with increasing maximum temperatures. Cattle respond negatively to increased minimum temperatures as well, while goats tend to respond positively, implying that livestock species react differently to climatic conditions due to their distinct features. The results of the roots of the companion matrix for cattle and goat production meet the stability condition as all the eigenvalues lie inside the unit circle. The study recommends further intervention by the government to deal with increasing temperatures, thereby addressing the dwindling populations of goats and cattle, which have significant contributions to the household economies of smallholders and the national economy, respectively. [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]

Rainfall, with its intensity, duration, and seasonal distribution, is among the main factors causing soil erosion, which is a widespread environmental problem in Bulgaria. Rainfall erosivity shows the potential of precipitation to generate erosion processes and is an essential indicator of the climate vulnerability of a region. This paper aims to evaluate rainfall erosivity in a part of the Eastern Rhodopes Mountains, an area that is characterised by high-intensity erosion processes and high erosion risk. Local peculiarities of rainfall erosivity were revealed by the calculation of some precipitation indices based on the monthly precipitation for the period 2000–2021, such as the precipitation concentration index (PCI), Angot precipitation index, Fournier index (FI), and modified Fournier index (MFI). The analysis of the extremely wet and extremely dry months at the annual and seasonal (October–March and April–September) levels was performed to evaluate the susceptibility to erosion. The results from the study show that rainfall erosivity in the studied area varies from low to moderate in the northern part of the study area and from high to very high in the south. According to the MFI, high and very high erosivities have been observed mainly since 2012. The erosivity increases from north to south, to the area with a complex relief, where the combination of orography and atmospheric circulation make favourable conditions for the occurrence of extreme precipitation. The analyses of the calculated indices show that the precipitations in most of the studied area generally have from a low to a moderate erosivity, but this does not exclude the occurrence of cases with high and very high erosivities, which are characteristic of recent years and are related to the increase in annual precipitations and extreme precipitation months. The results of this study can contribute to the development and implementation of measures and preventive activities for the reduction and possible elimination of the negative impacts of extreme precipitation. [ABSTRACT FROM AUTHOR]

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability. [ABSTRACT FROM AUTHOR]

Ultraviolet radiation (UV) has strong chemical and biological effects on human health and ecosystems, and it plays an important role in the atmospheric environment by affecting photochemical processes, etc. Clouds and aerosols are the main factors affecting UV radiation and analyzing the quantitative impact of them on UV radiation is of great significance. Using the observation data of UV radiation in Beijing from 2005 to 2020, as well as the data of aerosol optical depth (AOD), single scattering albedo (SSA), and other related parameters, this paper simulated the surface UV radiation in two scenarios of cloudless without aerosol and cloudless with aerosol based on the TUV (Tropospheric Ultraviolet-Visible model), and quantitatively evaluated the attenuation of UV radiation by aerosol and cloud in the Beijing area. The results show that UV radiation is more sensitive to changes in AOD. Fixing the SSA value to 0.9, when the AOD increases from 0.2 to 1.0, the UV radiation decreases from 21.16 W/m2 to 12.64 W/m2 at 12:00; when AOD is maintained at 0.64, the SSA increases from 0.7 to 0.95, and the UV radiation increases from 14.55 W/m2 to 19.91 W/m2. The average annual attenuation rates of ultraviolet radiation by aerosols and clouds from 2005 to 2020 are 30.64% and 40.22%, respectively; the monthly averaged attenuation rates are 30.48% and 42.04%, respectively; and the daily averaged attenuation rates are 31.02% and 50.45%, respectively. [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]

Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) exist in the atmosphere in the vapor and particulate phases, as well as in solubilized form in fog/rain/cloud waters. In the current paper, fogwater samples are collected during 42 events between 2015 and 2021 at four different sites (Strasbourg, Geispolsheim, Erstein, Cronenbourg) in the Alsace region. Organics are extracted using liquid–liquid extraction (LLE) supported on a solid cartridge (XTR Chromabond), and then analyzed by gas chromatography–tandem mass spectrometry (GC-MS/MS). The total PAHs and PCBs concentrations in fog samples vary between 0.58 and 6.7 µg L−1 (average of 2.70 µg L−1), and 0.14 and 15.5 µg L−1 (average of 2.75 µg L−1). Low-molecular-weight (LMW) PAHs are predominant and highly detectable compared to high-molecular-weight (HMW) PAHs, while pentachloro-biphenyls are the dominant PCB congener. The PAHs and PCBs concentrations have increased over the sampling years at all sites, except for a slight decrease in PCBs level at Geispolsheim. A diagnostic ratio (DR) and principal component analysis (PCA) are applied to suggest potential contamination sources at Strasbourg metropolitan. Their results reveal that PAHs derive from a mixture of pyrogenic activities, while PCBs mainly come from industrial activities. The results also reveal, in some cases, inter-site variability for simultaneous and successive fog events. [ABSTRACT FROM AUTHOR]

Using multiple observational and reanalysis data, this paper investigates the impact of the interdecadal shift in summer Arctic Oscillation (AO) on precipitation in East Asia, by removing ENSO influences. The results indicate that the lower-layer activity center of summer AO in Atlantic shifted eastward after the mid-1980s. This regime shift of summer AO has a significant impact on precipitation in East Asia. Before the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were northern East Asia and Northeastern China and adjacent regions. After the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were central Inner Mongolia and Southern China. The mechanism of precipitation changes can be attributed to changes in atmospheric circulation and water vapor transport related to AO changes. After the mid-1980s, the influence of AO on geopotential height over northern East Asia weakened; meanwhile, the impact of AO on geopotential height over China increased. Consistent with the changes in atmospheric circulation, water vapor transport in East Asia also underwent interdecadal changes before and after the mid-1980s. The differences in atmospheric circulation and water vapor transport in East Asia can be traced back to the North Atlantic. Before the mid-1980s, wave activity flux related to summer AO tended to propagate in high latitudes and subtropics; after the mid-1980s, the wave activity flux changed in its subtropical path and propagated eastward from the North Atlantic through the Middle East to China, significantly affecting the summer precipitation in China. [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]