Atmosphere, Volume 15, Issue 1 (January 2024) – 141 articles

Polycyclic aromatic compounds (PAHs) have been noted to generate a high risk for human health. Their presence and concentration have not been equally distributed in the environment and various anthropic activities favored the environmental presence of specific pollution components. The economic sector of bakery, as well as intensive animal breeding, are well spread worldwide and they represent a priority economic sector due to their direct link to the food industry. In this study, particulate matter (PM) and PAH pollutant compounds were monitored and their presence and concentration were correlated with specific anthropic activities such as bakery and animal husbandry. For the first time, the data analysis established correlations between PM10 or PM2.5 sizes and concentrations with a specific anthropic activity (bakery vs. animal husbandry). PM10 seemed to be more present at sites of animal husbandry activities than bakery ones. The vast majority of high PAH concentrations were detected in industrial sites such as bakeries. Spearman statistical correlation tests of intensive breeding of animals and bakery fields showed a moderate correlation between dimensional fractions of particulate matters, which indicated several emission sources, with different characteristics. Full article

During the SARS-CoV-2 pandemic, the air quality and infection risk in classrooms were the focus of many investigations. Despite general recommendations for sufficient ventilation, quantitative analyses were often lacking due to the large number of combinations of boundary conditions. Hence, in this paper, we describe a computational fluid dynamics model that predicts the time-resolved airflow for a typical 45 $\min$ classroom scenario. We model 28 students and a teacher, each emitting CO₂ and an individual aerosol. We investigated 13 ventilation setups with window or mechanical ventilation and different positions and operating conditions of an additional air purifier. The ventilation performance is assessed by evaluating the ventilation effectiveness, aerosol removal effectiveness, local air exchange efficiency and overall inhaled aerosol mass of the occupants, which is a measure of the infection risk. If the window is opened according to the “20-5-20” recommendation, the incoming airflow reduces both the CO₂ and aerosol concentration whilst decreasing the thermal comfort at low ambient temperatures. An active air purifier enhances aerosol removal, but, depending on the position, the local air exchange efficiency and individual aerosol inhalation vary. If mechanical ventilation with 700 m³/h is utilised, the CO₂ concentration is kept below 1250 ppm while also effectively removing aerosol from the classroom. Full article

This paper explores the effects of climate change on the wind energy input (WEI) to the surface waves (SWs) in the northern Indian Ocean (NIO), a region with great potential for green renewable energy from waves and wind. We used the newly developed Coupled Model Intercomparison Project Phase 6 (CMIP6) model data to predict the spatiotemporal variations of the WEI to the SW. We found that, under the global warming scenario, the WEI to the SWs decreased significantly in most of the NIO, and it will drop by 18% to 27% in the central and southern regions by the end of the 21st century under the SSP5–8.5 scenario. However, the WEI to the SWs increased in the Red Sea, Persian Gulf, northwestern Arabian Sea, and northern Bay of Bengal, with the largest increase in the Persian Gulf region (up to 27%). We also examined the interannual and interdecadal variability characteristics of the WEI to the SW after the accumulation of the whole study region and found that it showed a long-term increasing trend only under the SSP1–2.6 scenario, while it showed a significant decreasing trend under the SSP2–4.5 and SSP5–8.5 scenarios. Furthermore, we show that the WEI to the SWs in the Indian Ocean mainly occurs in summer, followed by winter. Full article

High-resolution precipitation products have been crucial for hydrology, meteorology, and environmental ecosystems over the Tibetan Plateau (TP). However, these products are usually subject to systematic errors, which may vary with time and topographic conditions. The study evaluated the suitability of four satellite-derived products (GPM IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and four fusion precipitation products (ERA5-Land, CHIRPS, CMFD, and TPHiPr) by comparing with 22 rain gauges at a daily scale from 1 January 2014 to 31 December 2018 over the hinterland of the TP. The main findings are as follows: (1) TPHiPr and CMFD are better than the satellite-derived products, while the performance of CHIRPS is worse; (2) among the satellite-derived products, the quality of GPM IMERG is the highest on different time scales, and PERSIANN-CDR is better in the months of June to October, while GSMaP and CMORPH have poor performance; (3) the eight precipitation products have weaker detection capability for heavy precipitation events, and the quality of each product decreases with the increase in the precipitation threshold, while the rate of descent of fusion precipitation products is slower than that of satellite-derived products. This study demonstrates the performance of eight precipitation products over the hinterland of the TP, which is expected to provide valuable information for hydrometeorology applications. Full article

Warm zone rainstorms and frontal rainstorms are two types of rainstorms that often occur in the rainy season in Jiangxi (located in the eastern part of China). The ability to correctly identify the type of rainstorms is important for accurate forecasting of rainstorms. Two heavy rainstorms took place in Jiangxi province. The first heavy rainstorm occurred from 20:00 BJT (Beijing Time) on 6 June to 20:00 BJT on 9 June (referred to as the “6.9” process) and another heavy rainstorm occurred from 20:00 BJT on 21 June to 20:00 BJT on 22 June (referred to as the “6.9” process), 2019. We analyzed the two rainstorms’ processes by using ground-based observation data, NCEP/FNL reanalysis data, ECMWF and CMA-SH9 numerical forecasting products. The results show that: “6.9” process is a warm area rainstorm, and a strong northeast cold vortex exists at 500 hPa geopotential height. The northwesterly flow behind the northeast cold vortex trough is stronger. The position of the northern edge of the subtropical high pressure is more south than that at “6.22” process. The rainstorm is in the precipitation zone of the warm temperature ridge over 925 hPa geopotential height, and with more convective character than “6.22” process. The process of “6.22” is a frontal rainstorm. The convective character of precipitation is weaker. The rainstorm precipitation zones are in a strong temperature front area at 925 hPa geopotential height and there is a tendency for vertical convection to develop into oblique upward convection in the late stage of the rainstorm. The precipitation location and intensity forecast by CMA-SH9 at the “6.9” process is better than that of ECMWF, while ECMWF’s prediction of the precipitation zone and weather condition of the “6.22” process is better. Full article

Potential evapotranspiration (PET) is a crucial variable for implementing adaptation measures to mitigate the potential impacts of climate change on water resources. In the context of global warming, PET is essential for predicting water resource supply and demand, guiding irrigation and water management decisions. However, there is limited understanding of the spatiotemporal changes in PET and its driving factors in the hyper-arid regions of Northwest China. In this study, the Hargreaves model was employed to estimate PET in the Hami region from 1991 to 2020. By combining relevant climate data and partial correlation analysis, we investigated the spatiotemporal distribution patterns of PET within the study area and analyzed the factors influencing these patterns. The results showed the following: (1) From 1991 to 2020, the overall PET in the Hami region demonstrated a tendency to rise. The interannual trend rates of PET for the full year, spring, summer, autumn, and winter were 0.933, 2.744, 0.906, 0.488, and −0.406 mm·a-1, respectively. Despite a decreasing trend in winter PET, the other seasonal PET values and the annual PET values exhibited an increasing trend. (2) The spatial distribution of both annual and seasonal PET showed significant regional heterogeneity, following a consistent pattern marked by lower values in the central part and higher values in the surrounding areas. The southern region tended to have relatively high PET, while the northwestern region experienced comparatively low PET. (3) Partial correlation analysis indicated significant differences in the impact of various climatic factors on PET. The maximum temperature emerged as the dominant factor influencing annual PET variation, while precipitation played a leading role in influencing autumn PET variation. This study underscores the influence of climate change on PET in the Hami region, contributing to an enhanced comprehension of PET variations. Full article

Rainfall, or more generally the precipitation process (flux), is a clear example of chaotic variables resulting from a highly nonlinear dynamical system, the atmosphere, which is represented by a set of physical equations such as the Navier–Stokes equations, energy balances, and the hydrological cycle, among others. As a generalization of the Euclidean (ordinary) measurements, chaotic solutions of these equations are characterized by fractal indices, that is, non-integer values that represent the complexity of variables like the rainfall. However, observed precipitation is measured as an aggregate variable over time; thus, a physical analysis of observed fluxes is very limited. Consequently, this review aims to go through the different approaches used to identify and analyze the complexity of observed precipitation, taking advantage of its geometry footprint. To address the review, it ranges from classical perspectives of fractal-based techniques to new perspectives at temporal and spatial scales as well as for the classification of climatic features, including the monofractal dimension, multifractal approaches, Hurst exponent, Shannon entropy, and time-scaling in intensity–duration–frequency curves. Full article

This study aimed to investigate the effect of human heart-rate synchronization with variations in the geomagnetic field (GMF) (“biogeophysical synchronization effect”). We analyzed 403 electrocardiogram (ECG) recordings of 100 or 120 min that were obtained in 2012–2023 from two middle-aged female volunteers in good health. The minute-value series of the GMF vector from the INTERMAGNET network was used. Each ECG recording was individually examined using cross-correlation and wavelet analysis. The findings from two separate experimental sets (306 recordings from Volunteer A and 97 from Volunteer B) displayed notable similarity in all aspects analyzed: (1) For both participants, the biogeophysical synchronization effect is observed in 40–53% of the recordings as a statistically significant (p < 0.0045) correlation between minute heart-rate (HR) time-series values and at least one of the horizontal components of the GMF, with a time shift between values of [−5, +5] min. (2) Wavelet analysis indicates that the spectra of the HR series and at least one GMF component exhibit similarity in 58–61% of cases. (3) The synchronization is most evident within the period range between 8–13 min. The probability of the synchronization effect manifestation was independent of the geomagnetic activity (GMA) level, which was recorded during the observations. Full article

Regional air quality and major sources can be reflected by dust. 87 dust samples in Nanchang (four residential areas and three roadside points) were collected, with particle size and carbon components determined to discuss the distribution characteristics and the sources. The distribution of dust particle size in different sampling areas was similar, composed mainly of particles larger than 10 μm (over 69.8%). Dust particle size showed a decreasing trend with increasing horizontal distance from the main road and vertical height from the ground. EC in road dust was higher than that in residential dust. EC outdoors was higher than EC indoors in the same area. OC in indoor dust was higher than that in atmospheric dust when there were obvious indoor OC emission sources. The main carbon fractions in residential dust were OC3 and EC1, and in road dust were EC2 and OC3. The distribution of carbon fractions showed that OC3 and EC2 were mainly affected by human activities and motor vehicle emissions, respectively. The ratio of OC/EC and SOC in dust decreased from autumn to winter. SOC in the dust of Nanchang was at a medium level compared to other cities/regions around world. Clustering analysis and principal component analysis indicated that combustion sources (coal and biomass combustion, etc.), motor vehicle exhaust sources (gasoline and diesel vehicles), and human sources (cooking fumes, cigarette smoking, etc.) were the main contributors to the carbon components in dust. Full article

Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations. Full article

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM_2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM_2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM_2.5 concentrations in the BTH region. The findings reveal that PM_2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM_2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM_2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development. Full article

Microscopic analyses of the effects of ash on particulate matter oxidation are rather scarce. In this study, three different lubricating oils with varying ash contents were used to investigate their effects on the nanostructure of diesel particulate matter. The nanostructure and nanostructure parameters, including fringe length, fringe separation distance, and fringe tortuosity, were studied using high-resolution transmission electron microscopy. The results show that all samples obtained from blending with different lubricant oil present typical core–shell structures. The inner cores remain relatively unchanged, whereas the thickness of the outer shells increases with the increasing ash content in the lubricant oil under the same working conditions. The fringe length increases and the fringe separation distance decreases with the rising ash content in the lubricant oil operating in the same working conditions. The fringe tortuosity decreases when the ash content in the lubricant oil increases from 0.92% to 1.21%, but shows little change when the ash content in the lubricant oil increases from 1.21% to 1.92%. Based on the effects of ash on the nanostructure parameters, it can be inferred that the oxidation activity of particles decreases with increasing ash content in the lubricant oil. Full article

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM_2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM_2.5 and PM₁₀ atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM_2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM_2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM_2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM_2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM_2.5 concentration simulation, and how to better couple the two models is the next research direction. Full article

Mountain lee waves present significant hazards to aviation, often inducing turbulence and aircraft icing. The current study focuses on understanding the potential impact of global climate change on the precursor environments to mountain lee wave cloud episodes over central Iberia. We examine the suitability of several Global Climate Models (GCMs) from CMIP6 in predicting these environments using the ERA5 reanalysis as a benchmark for performance. The dataset is divided into two periods: historical data (2001–2014) and projections for the SSP5–8.5 future climate scenario (2015–2100). The variations and trends in precursor environments between historical data and future climate scenarios are exposed, with a particular focus on the expansion of the Azores High towards the Iberian Peninsula, resulting in increased zonal winds throughout the Iberian Peninsula in the future. However, the increase in zonal wind is insufficient to modify the wind pattern, so future mountain lee wave cloud events will not vary significantly. The relative humidity trends reveal no significant changes. Moreover, the risk of icing precursor environments connected with mountain lee wave clouds is expected to decrease in the future, due to rising temperatures. Our results highlight that the EC-EARTH3 GCM reveals the closest alignment with ERA5 data, and statistically significant differences between the historical and future climate scenario periods are presented, making EC-EARTH3 a robust candidate for conducting future studies on the precursor environments to mountain lee wave cloud events. Full article

There is a need for monitoring air pollution associated with black carbon (BC) using a passive monitor is required in remote areas where the measurements are absent. In this pilot study, we developed a quantitative method to determine dry deposition submicron BC using dual-wavelength ultraviolet–visible spectroscopy. Furthermore, we measured the levels of dry deposition BC on plant leaves from 30 plant species located in urban Nanjing using the established method. The oxidative potential of BC on plant leaves as passive bio-monitoring samplers was assessed. The concentrations of black carbon (BC) on tree leaves varied from 0.01 to 1.6 mg m⁻². Significant differences in levels of BC across leaves from different tree types were observed. The values of oxidative potential in deposited particles of leaf samples were observed to be in the range of 33–46 nmol min⁻¹ mg⁻¹ using the dithiothreitol (DTT) assay and 18–32 nmol min⁻¹ mg⁻¹ using the ascorbic acid (AA) assay, respectively. In comparison, the oxidative potential of BC-dominated mass in water extracts of leaf samples was in the range of 5–35 nmol min⁻¹ mg⁻¹ measured using the DTT assay and 2 to 12 nmol min⁻¹ mg⁻¹ using the AA assay, respectively. We found variations in the levels of OP across the leaves of different tree types were not large, while the levels of OP in terms of BC-dominated mass varied greatly. These results indicate that the established method with dual-wavelength ultraviolet–visible spectroscopy could provide a simple tool to determine submicron BC in plant leaves of the passive monitor. Full article

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation. Full article

Citizens in urban areas are affected by the urban heat island (UHI) effect, resulting in increased thermal heat compared to rural areas. This threat is exacerbated by global climate change. Therefore, it is necessary to assess human thermal comfort and risk for decision making. This is important for planners (climate resilience), the health sector (information for vulnerable people), tourism, urban designers (aesthetics), and building architects. Urban structures modify local meteorological parameters and thus human thermal comfort at the microscale. Knowledge of the pattern of a city’s UHI is typically limited. Based on previous research, generalized additive models (GAMs) were built to predict the spatial pattern of the UHI in the city of Karlsruhe. The models were trained with administrative, remotely sensed, and land use and land cover geodata, and validated with measurements in Freiburg. This identified the hot and cold spots and the need for further urban planning in the city. The model had some limitations regarding water bodies and anthropogenic heat production, but it was well suited for applications in mid-latitude cities which are not topographically characterized. The model can potentially be used for other cities (e.g., in heat health action plans) as the training data are freely available. Full article

The near-surface atmospheric electrostatic field plays a pivotal role in comprehending the global atmospheric circuit model and its influence on climate change. Prior to delving into the intricate interplay between solar activities, geological activities, and atmospheric electric field, a comprehensive examination of the diurnal fair atmospheric electric field’s baseline curve within a specific region is essential. Based on the atmospheric electric field network monitoring in Yunnan Province in the year 2022, this study systematically investigated the distribution of the atmospheric electric field under both fair-weather and disturbed weather conditions at a quadrilateral array encompassing Chuxiong Station, Mouding Station, Lufeng Station, and Dali Station. The primary focus was on elucidating the variations in the daily variation curves of fair atmospheric electric fields and conducting a comparative analysis with the Carnegie curves. The possible reasons for the differences among them are also discussed in this study, but more observational evidence is required to confirm the specific causes in the future. Full article

As the urban heat island effect has become a worldwide phenomenon commonly affecting densely built-up areas, public administrations need efficient strategies to mitigate its impact on human well-being and public health. The aim of this study was to define a replicable method to estimate the ecosystem services provided by public street trees as a supporting tool in the decision-making process of urban greenery management. We compared three street arrangements characteristic of a residential district in Pisa, Italy: (1) with large trees, (2) with small trees, and (3) without trees. First, the software i-Tree Eco was used to assess the benefits of public trees located in the case-study area when provided with the three scenarios. Second, the comparison was held on the field, and we collected data with a wet bulb globe temperature meter in order to evaluate the differences in pedestrian thermal comfort among the street arrangements. The results confirmed the importance of urban vegetation, as it has major impacts on carbon sequestration and storage, pollution removal, air humidity and quality, and shade, given bigger trees and canopy sizes. The loss of ecosystem services compared to the presence of large trees varied between 40% and 50% (no trees) and 30% and 40% (small trees). Full article

The Earth’s water resources, totalling 1.386 billion cubic kilometres, predominantly consist of saltwater in oceans. Groundwater plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as a drinking water source and 60–70% for irrigation. Climate change, with temperature increases and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies employ numerical models to assess climate change impacts on groundwater recharge that could help in the management of groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. This review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography and groundwater contamination processes, gradual sea level rise scenarios, and realistic representations of the region of study. With the advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence could assist in the efforts to improve models for assessing the impacts of climate change on hydrological modelling. Full article

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm. Full article

Ensemble weather forecasting involves the integration of multiple simulations to improve the accuracy of predictions by introducing a probabilistic approach. It is difficult to accurately predict heavy rainfall events that cause flash floods and, thus, ensemble forecasting could be useful to reduce uncertainty in the forecast, thus improving emergency response. In this framework, this study presents the efforts to develop and assess a flash flood forecasting system that combines meteorological, hydrological, and hydraulic modeling, adopting an ensemble approach. The integration of ensemble weather forecasting and, subsequently, ensemble hydrological-hydraulic modeling can improve the accuracy of flash flood predictions, providing useful probabilistic information. The flash flood that occurred on 26 January 2023 in the Evrotas river basin (Greece) is used as a case study. The meteorological model, using 33 different initial and boundary condition datasets, simulated heavy rainfall, the hydrological model, using weather inputs, simulated discharge, and the hydraulic model, using discharge data, estimated water level at a bridge. The results show that the ensemble modeling system results in timely forecasts, while also providing valuable flooding probability information for 1 to 5 days prior, thus facilitating bridge flood warning. The continued refinement of such ensemble multi-model systems will further enhance the effectiveness of flash flood predictions and ultimately save lives and property. Full article

Invasive alien insects directly or indirectly driven by climate change threaten crop production and increase economic costs worldwide. Ectomyelois ceratoniae (Zeller) is a highly reproductive invasive crop insect that can severely damage fruit commodities and cause significant economic losses globally. Estimating the global potentially suitable habitats (PSH) of E. ceratoniae is an important aspect of its invasive risk assessment and early warning. Here, we constructed an optimized MaxEnt model based on the global distribution records of E. ceratoniae, and nine environmental variables (EVs), to predict its global PSH under current and future climates. Our results showed that the RM value was 2.0 and the mean area under receiver operating characteristic curve (AUC) value was 0.972, indicating the high accuracy of the optimal MaxEnt model. The mean temperature of driest quarter (bio9, 50.2%), mean temperature of wettest quarter (bio8, 16.9%), temperature seasonality (bio4, 9.7%), and precipitation of coldest quarter (bio19, 9.1%) were the significant EVs affecting its distribution patterns. The global PSH of E. ceratoniae are mainly located in western Asia under current climate scenarios (687.57 × 10⁴ km²), which showed an increasing trend under future climate scenarios. The PSH of E. ceratoniae achieved the maximum under the shared socioeconomic pathway (SSP) 1–2.6 in the 2030s and under the SSP2-4.5 in the 2050s. The increased PSH of E. ceratoniae are mainly located in southwestern Asia, northwestern Europe, northwestern South America, northwestern North America, southern Oceania, and northwestern Africa. Our findings suggest that quarantine officials and governmental departments in the above high-risk invasion areas should strengthen monitoring and early warning to control E. ceratoniae; in particular, cultural measures should be taken in areas where its further expansion is expected in the future. Full article

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO₂) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm⁻¹ spectral resolution covering a 15 μm CO₂-absorbing band, providing a way of monitoring CO₂ with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R² of XCO₂ prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R² = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO₂. The deviation of XCO₂ predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO₂ concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R² = 0.9671, RMSE = 0.7072 ppmv), Europe (R² = 0.9703, RMSE = 0.8733 ppmv), North America (R² = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R² = 0.9558, RMSE = 0.4614 ppmv). Full article

This study investigates the interdecadal change in the covariability between the Tibetan Plateau (TP) east–west dipole precipitation and Indian precipitation during summer and primarily explores the modulation of atmospheric circulation anomalies on the covariability. The results reveal that the western TP precipitation (WTPP), eastern TP precipitation (ETPP), and northwestern Indian precipitation (NWIP) have covariability, with an in-phase variation between the WTPP and NWIP and an out-of-phase variation between the WTPP and ETPP. Moreover, this covariability was unclear during 1981–2004 and became significant during 2005–2019, showing a clear interdecadal change. During 2005–2019, a thick geopotential height anomaly, which tilted slightly northward, governed the TP, forming upper- and lower-level coupled circulation anomalies (i.e., anomalous upper-level westerlies over the TP and lower-level southeasterlies and northeasterlies around the southern flank of the TP). As such, the upper- and lower-tropospheric circulation anomalies synergistically modulate the summer WTPP, ETPP, and NWIP, causing the covariability of summer precipitation over the TP and India during 2005–2019. The upper- or lower-level circulation anomalies cannot independently result in significant precipitation covariability. During 1981–2004, the upper- and lower-level circulation anomalies were not strongly coupled, which caused precipitation non-covariability. The sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and tropical Atlantic (TA) may synergistically modulate the upper- and lower-level coupled circulation anomalies, contributing to the covariability of the WTPP, ETPP, and NWIP during 2005–2019. The modulation of the WNP and TA SSTs on the coupled circulation anomalies was weaker during 1981–2004, which was therefore not conducive to this precipitation covariability. This study may provide valuable insights into the characteristics and mechanisms of spatiotemporal variation in summer precipitation over the TP and its adjacent regions, thus offering scientific support for local water resource management, ecological environment protection, and social and economic development. Full article

In recent years, the importance and severity of weather-related disasters have escalated, attributed to rising temperatures and the occurrence of extreme weather events due to global warming. The focus of disaster management has shifted from crisis management (e.g., repairing and recovering from damage caused by natural disasters) to risk management (e.g., prediction and preparation) while concentrating on early warning, thanks to the development of media and communication conditions. The Rural Development Administration (Korea) has developed the “early warning service for weather risk management in the agricultural sector” that detects weather risks for crops from high-resolution weather information in advance and provides customized information to respond to possible disaster risks in advance in response to the increasing number of extreme weather events. The core technology of this service is damage prediction technology that determines the overall agricultural weather risk level by quantifying the current growth stage of cultivated crops and the probability of possible weather disasters according to the weather conditions of the farm. Agrometeorological disasters are damages caused by weather conditions that can affect crops and can be predicted by estimating the probability of damage that may occur from the interaction between hazardous weather and crop characteristics. This review introduces the classification of possible weather risks by their occurrence mechanisms, based on the developmental stage of crops and prediction techniques that have been developed or applied to date. The accumulated crop growth and weather risk information is expected to be utilized as support material for farming decision-making, which helps farmers proactively respond to crop damage due to extreme weather events by providing highly reliable disaster forecasts through the advancement of prediction technology. Full article

In this study, we reviewed smog chamber systems and methodologies used in secondary organic aerosol (SOA) formation studies. Many important chambers across the world have been reviewed, including 18 American, 24 European, and 8 Asian chambers. The characteristics of the chambers (location, reactor size, wall materials, and light sources), measurement systems (popular equipment and working principles), and methodologies (SOA yield calculation and wall-loss correction) are summarized. This review discussed key experimental parameters such as surface-to-volume ratio (S/V), temperature, relative humidity, light intensity, and wall effect that influence the results of the experiment, and how the methodologies have evolved for more accurate simulation of atmospheric processes. In addition, this review identifies the sources of uncertainties in finding SOA yields that are originated from experimental systems and methodologies used in previous studies. The intensity of the installed artificial lights (photolysis rate of NO₂ varied from 0.1/min to 0.40/min), SOA density assumption (varied from 1 g/cm³ to 1.45 g/cm³), wall-loss management, and background contaminants were identified as important sources of uncertainty. The methodologies developed in previous studies to minimize those uncertainties are also discussed. Full article

Sterile medical masks are essential in preventing infectious diseases. However, the ethylene oxide contained within these masks is a class I carcinogen. The standard method for measuring ethylene oxide is gas chromatography-mass spectrometry, which is not fit with the dynamic process of human inhalation. Thus, the amount of ethylene oxide volatilized from masks and inhaled by users is unknown. In this work, ethylene oxide was detected by using proton-transfer-reaction mass spectrometry, which can measure volatile quantities in milliseconds. We found that ethylene oxide was volatilized from masks during use. Within the first minute, the ethylene oxide concentration decreased by 84.65%, and then the rate of reduction gradually slowed. After 5 min, all ethylene oxide was effectively volatilized, and the average mass of ethylene oxide inhaled was 299.02 μg. We investigated three methods to reduce the concentration of ethylene oxide in masks before use: natural airing, shaking the mask, and blowing the mask with a hair dryer. The hair dryer method produced the best results: the ethylene oxide concentration decreased by 88.3% after only 10 s. The natural airing method was the least effective: the ethylene oxide concentration decreased by 60.7% even after 3 h. Full article

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NO_x from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality. Full article

The impact of elevated air temperature and heat stress on human health is a global concern. It not only affects our well-being directly, but also reduces our physical work capacity, leading to negative effects on society and economic productivity. Climate change has already affected the climate in Luxembourg and, based on the results of regional climate models, extreme heat events will become more frequent and intense in the future. To assess historical conditions, the micro-scaleRayManPro 3.1 model was used to simulate the thermal stress levels for different genders and age classes based on hourly input data spanning the last two decades. For the assessment of future conditions, with a special emphasis on heat waves, a multi-model ensemble of regional climate models for different emission scenarios taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX) was used. For both, the past and future conditions in Luxemburg, an increase in the heat stress levels was observed. Small differences for different age groups and genders became obvious. In addition to the increase in the absolute number of heat waves, an intensification of higher temperatures and longer durations were also detected. Although some indications of the adaptation to rising air temperatures can be observed for high-income countries, our results underscore the likelihood of escalating heat-related adverse effects on human health and economic productivity unless more investments are made in research and risk management strategies. Full article