Atmosphere, Volume 15, Issue 6 (June 2024) – 92 articles

Polychlorinated diphenyl ethers (PCDEs), persistent environmental pollutants, are found in flue gas from incinerators. While air pollution control systems (APCSs) capture pollutants, the resulting sludge/fly ash (SFA) requires further treatment due to residual PCDEs and other harmful substances. This study investigated a hazardous waste thermal treatment system (HAWTTS) utilizing flameless combustion technology alongside a multistage APCS (scrubbers, cyclone demisters, bag houses). SFA from the APCS was recirculated for secondary combustion. PCDE levels were measured before and after each unit within the HAWTTS. The HAWTTS achieved a remarkable overall PCDE removal efficiency of 99%. However, the incinerator alone was less effective for low-chlorine PCDEs. Scrubbers and bag houses exhibited lower removal efficiencies (17.8% and 30.9%, respectively) due to the memory effect. Conversely, the cyclone demister achieved a high removal rate (98.2%). Following complete APCS treatment, PCDE emissions were significantly reduced to 1.02 ng/Nm³. While SFA still contained some PCDEs, the flameless combustion’s uniform temperature distribution enhanced combustion efficiency, minimizing overall PCDE emissions. This system demonstrates significant potential for mitigating PCDE pollution from incinerators. Further research could focus on optimizing treatment processes to address residual PCDEs in SFA. Full article

Climatic classification is essential for evaluating climate parameters that allow sustainable urban planning and resource management in countries with difficult access to meteorological information. Clustering methods are on trend to identify climate zoning; however, for microclimate, it is necessary to apply a double clustering technique to reduce the variability from former clusters. This research raised a climate classification of an emerging country, Colombia, using climatological models based on freely available satellite image data. A double clustering approach was applied, including climatological, geographic, and topographic patterns. The research was divided into four stages, covering the collection and selection of climatic and geographic data, and multivariate statistical analysis including principal components analysis (PCA) and agglomerative hierarchical clustering (HAC). The meteorological data were from reliable sources from the Center for Hydrometeorology and Remote Sensing (CHRS) and the National Renewable Energy Laboratory (NREL). The results showed that a total of 17 microclimates distributed across the country were identified, each characterized by a different threshold of the climatic and geographic factors evaluated. This subdivision provided a detailed understanding of local climatic conditions, especially in the mountain chains of the Andes. Full article

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

In this study, a diagnostic model for evaporation ducts was established based on the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) and the Naval Postgraduate School (NPS) models. Utilizing this model, four sensitivity tests were conducted over the South China Sea from 21 September to 5 October 2008, when four tropical cyclones affected the study domain. These tests were designed with different roughness schemes to investigate the impact mechanisms of wave processes on evaporation duct simulation under extreme weather conditions. The results indicated that wave processes primarily influenced the evaporation duct heights by altering sea surface roughness and dynamical factors. The indirect impacts of waves without dynamical factors were rather weak. Generally, a decrease in local roughness led to increased wind speed, decreased humidity, and a reduced air–sea temperature difference, resulting in the formation of evaporation ducts at higher altitudes. However, this affecting mechanism between roughness and evaporation ducts was also greatly influenced by changes in regional circulation. In the eastern open sea areas of the South China Sea, changes in evaporative ducts were more closely aligned with local impact mechanisms, whereas the changes in the central and western areas demonstrated greater complexity and fewer local impacts due to variations in regional circulation. Full article

In atmospheric chemistry, the Henry’s law constant (HLC) is crucial for understanding the distribution of organic compounds across gas, particle, and aqueous phases. Quantitative structure–property relationship (QSPR) models described in scientific research are generally tailored to specific groups or categories of substances and are often developed using a limited set of experimental data. This study developed a machine learning model using an extensive dataset of experimental HLCs for approximately 1100 organic compounds. Molecular descriptors calculated using alvaDesc software (v 2.0) were used to train the models. A hybrid approach was adopted for feature selection, ensuring alignment with the domain knowledge. Based on the root mean squared error (RMSE) of the training and test data after cross-validation, Gradient Boosting (GB) was selected as a model for predicting HLC. The hyperparameters of the selected model were optimized using the automated hyperparameter optimization framework Optuna. The impact of features on the target variable was assessed using the SHapley Additive exPlanations (SHAP). The optimized model demonstrated strong performance across the training, evaluation, and test datasets, achieving coefficients of determination (R²) of 0.96, 0.78, and 0.74, respectively. The developed model was used to estimate the HLC of compounds associated with carbon capture and storage (CCS) emissions and secondary organic aerosols. Full article

Based on the characteristics of space particle radiation in the Sun-synchronous orbit (SSO), a space particle radiation effect comprehensive measuring instrument (SPRECMI) was installed on the orbital platform of the upper stage of the Chinese CZ-4C carrier rocket, which can acquire the high-energy proton energy spectra, linear energy transfer (LET) spectra of particles, and radiation dose rate. The particle radiation detection data were obtained at 1000 km altitude for the first time, which can be used mainly for scientific research of the space environment, in-orbit fault analysis, and the operational control management of spacecraft, and can also serve as reference data for component validation tests. After SPRECMI’s development, accelerator calibration and simulations were conducted, and the results demonstrated that all the measured indicators, including the high-energy proton spectra (energy range: 21.8–275.0 MeV, precision: <3.3%), total radiation dose (dose range: 0–1.04 × 10⁶ rad, sensitivity: 6.2 µrad/h), and the LET spectra (range: 0.001–37.20 MeV/(mg/cm²), >37.2 MeV/(mg/cm²)), met the relevant requirements. Furthermore, the in-orbit flight test revealed that the detection results of the load components were consistent with the physical characteristics of the particle radiation environment of the spacecraft’s orbit. Full article

This study aims to examine the particle number (PN) emissions of a retrofitted heavy-duty spark ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and particle size distribution (PSD) upstream and downstream of the three-way catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm on the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region as is used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. The findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 s of the cycle, linked to the most intense phase of the WHTC. Full article

This study concerns the application of statistical learning (SL) in thermal stress assessment compared to the results accomplished by an international expert group when developing the Universal Thermal Climate Index (UTCI). The performance of diverse SL algorithms in predicting UTCI equivalent temperatures and in thermal stress assessment was assessed by root mean squared errors (RMSE) and Cohen’s kappa. A total of 48 predictors formed by 12 variables at four consecutive 30 min intervals were obtained as the output of an advanced human thermoregulation model, calculated for 105,642 conditions from extreme cold to extreme heat. Random forests and k-nearest neighbors closely predicted UTCI equivalent temperatures with an RMSE about 3 °C. However, clustering applied after dimension reduction (principal component analysis and t-distributed stochastic neighbor embedding) was inadequate for thermal stress assessment, showing low to fair agreement with the UTCI stress categories (Cohen’s kappa <0.4). The findings of this study will inform the purposeful application of SL in thermal stress assessment, where they will support the biometeorological expert. Full article

Fine-scale models for the transport of marine aerosols are of great interest for the study of micro-climates and air quality in areas of complex topography, such as in urbanized coastal areas. To this end, the MIO laboratory implemented the Meso-NH model in its LES version over the northwest Mediterranean coastal zone using a recent sea-spray source function. Simulated meteorological parameters and aerosol concentrations are compared to experimental data acquired in the Mediterranean coastal zone in spring 2008 on board the R/V Atalante. Key findings indicate that the large eddy simulation (LES) mode closely matches with the experimental data, enabling an in-depth analysis of the numerical model ability to predict variations in aerosol concentrations. These variations are influenced by different wind directions, which lead to various fetch distances typical of coastal zones. Full article

Building materials, such as brick and concrete, are known indoor radon (²²²Rn) and thoron (²²⁰Rn) sources. Most radon and thoron exhalation studies are based on the laboratory testing of pieces and blocks of such materials. To discuss if laboratory findings can be applied to a real-world environment, we conducted intensive in situ exhalation tests on two solid concrete interior walls of an apartment in Japan for over a year. Exhalation rates of radon (J_Rn) and thoron (J_Tn) were measured using an accumulation chamber and dedicated monitors, alongside monitoring indoor air temperature (T) and absolute humidity (AH_in). There were weak correlations between J_Rn or J_Tn and T or AH_in at one tested wall, and moderate correlations of J_Rn and strong correlations of J_Tn with T or AH_in at the other wall, meaning more or less seasonal variations. The findings aligned with previous laboratory experiments on J_Rn but lacked corresponding data for J_Tn. Additionally, a moderate or strong correlation between J_Rn and J_Tn was observed for both tested walls. Comparison with theoretical calculations revealed a new issue regarding the impact of each process of emanation and migration within concrete pores on radon and thoron exhalation. Overall, this study provides insight into parameterizing radon and thoron source inputs in modeling the spatiotemporal dynamics of indoor radon and thoron. Full article

Traditional bioclimatic classification schemes have several inherent shortcomings; they do not represent anthropogenic impact, they contain a bias for global north representation, and they lack flexibility regarding novel climates that may arise due to climate change. Here we present an alternative approach, using a machine learning approach. We combine European Space Agency Land Cover Classification data with traditional bioclimate classification climate variables, and additional variables; latitude, elevation, and topography. We utilise a random forest algorithm to create a classification system that overcomes the limitations and biases of the traditional schemes. The algorithm produced is able to predict land cover classification globally at 0.5-degree resolution with 93% accuracy. The resulting classifications account for human impact, particularly via agriculture, are informed by the topography of a region, and avoids the biases that traditional bioclimatic schemes contain. The algorithm can provide insights into the drivers of land cover change, the spatial distribution of land cover change, the potential impacts on ecosystem services and human well-being. Furthermore, the random forest model serves as a novel approach to the prediction of future land cover, and can be used to identify regions at risk of a land cover transition. Our data-based machine learning approach produces larger land-cover changes due to climate change than a traditional bioclimatic scheme, especially in sensitive regions such as Amazonia. Overall, our new approach projects approximately 17.4 million square kilometre of land-cover change per degree celsius of global warming. Full article

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran’s I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM_2.5 and PM₁₀ in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O₃. Seasonal peaks for O₃ occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies. Full article

Increased warming in the Arctic is of great concern. This is particularly due to permafrost degradation, which is expected to accelerate microbial breakdown of soil organic carbon, with its further release into the atmosphere as carbon dioxide (CO₂). The fine-scale variability of CO₂ fluxes across highly mosaic Arctic tundra landscapes can provide us with insights into the diverse responses of individual plant communities to environmental change. In the paper, we contribute to filling existing gaps by investigating the variability of CO₂ flux rates within different landscape units for dominant vegetation communities and plant species across typical tundra of the southern part of the Taimyr Peninsula, Siberia. In general, the variability of soil CO₂ flux illustrates a four-fold increase from non-vascular vegetation, mainly lichens and mosses (1.05 ± 0.36 µmol m⁻² s⁻¹), towards vascular plants (3.59 ± 0.51 µmol m⁻² s⁻¹). Barren ground (“frost boils”) shows the lowest value of 0.79 ± 0.21 µmol m⁻² s⁻¹, while considering the Arctic “browning” phenomenon, a further substantial increase of CO₂ flux can be expected with shrub expansion. Given the high correlation with top soil temperature, well-drained and relatively dry habitats such as barren ground and non-vascular vegetation are expected to be the most sensitive to the observed and projected temperature growth in the Arctic. For mixed vegetation and vascular species that favor wetter conditions, soil moisture appears to play a greater role. Based on the modeled seasonal pattern of soil CO₂ flux and precipitation records, and applying the rainfall simulations in situ we outlined the role of precipitation across enhanced CO₂ emissions (i.e., the “Birch” effect). We found that a pulse-like growth of soil CO₂ fluxes, observed within the first few minutes after rainfall on vegetated plots, reaches 0.99 ± 0.48 µmol m⁻² s⁻¹ per each 1 mm of precipitation, while barren ground shows 55–70% inhibition of CO₂ emission during the first several hours. An average additive effect of precipitation on soil CO₂ flux may achieve 7–12% over the entire growing season, while the projected increased precipitation regime in the Arctic may strengthen the total CO₂ release from the soil surface to the atmosphere during the growing season. Full article

This study examines the Vertical Total Electron Content (VTEC) estimation performance of multi-instruments on a global scale during different ionospheric conditions. For this purpose, GNSS-based VTEC data from Global Ionosphere Maps (GIMs), COSMIC (F7/C2)—Feng–Yun 3C (FY3C) radio occultation (RO) VTEC, SWARM–VTEC, and JASON–VTEC were utilized. VTEC assessments were conducted on three distinct days: geomagnetic active (17 March 2015), solar active (22 December 2021), and quiet (11 December 2021). The VTEC values of COSMIC/FY3C RO, SWARM, and JASON were compared with data retrieved from GIMs. According to the results, COSMIC RO–VTEC is more consistent with GIM–VTEC on a quiet day (the mean of the differences is 4.38 TECU), while the mean of FY3C RO–GIM differences is 7.33 TECU on a geomagnetic active day. The range of VTEC differences between JASON and GIM is relatively smaller on a quiet day, and the mean of differences on active/quiet days is less than 6 TECU. Besides the daily comparison, long-term results (1 January–31 December 2015) were also analyzed by considering active and quiet periods. Results show that Root Mean Square Error (RMSE) values of COSMIC RO, FY3C RO, SWARM, and JASON are 5.02 TECU, 6.81 TECU, 16.25 TECU, and 5.53 TECU for the quiet period, and 5.21 TECU, 7.07 TECU, 17.48 TECU, and 5.90 TECU for the active period, respectively. The accuracy of each data source was affected by solar/geomagnetic activities. The deviation of SWARM–VTEC is relatively greater. The main reason for the significant differences in SWARM–GIM results is the atmospheric measurement range of SWARM satellites (460 km–20,200 km (SWARM A, C) and 520 km–20,200 km (SWARM B), which do not contain a significant part of the ionosphere in terms of VTEC estimation. Full article

Bioaerosols are a potential risk of infection in densely populated indoor sites at university campuses. In this study, indoor bioaerosols from the canteen, classroom, and dormitory on campus were sampled and analyzed in the summer of 2021 to investigate the harmful risk. The results showed that bacteria are the predominant microbes, and the total number of bacteria detected in the classroom during no lesson in the morning (33% of samples) and in the canteen during meal times (55% of samples) was greater than the World Health Organization’s recommended value (1000 CFU/m³). The ranges of respirable bioaerosol (<3.3 µm) contributions in the classroom, dormitory, and canteen were 50–75%, 57–70%, and 64–80%, respectively. Bacteroidetes and Firmicutes were the most dominant phyla in all three indoor environments, with a relative abundance of both above 20%. At the family level, Muribaculaceae, Lachnospiraceae, and Bacteroidaceae had high relative abundance in all indoor sites. Some of the microbes carried by bioaerosols were conditionally pathogenic bacteria, such as Micrococcaceae and Enterococcaceae, which may have a harmful risk of causing various inflammatory infections. The results of this study provide basic data to improve indoor environments and control indoor bioaerosol pollution on campus. Full article

Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China’s ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China’s 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China’s carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China’s carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030. Full article

Agro-meteorological disasters are a significant cause of crop yield reduction. Northeast China is a major base for commodity grain production and is also highly sensitive to climate change. Early frost is one of the most significant meteorological disasters in Northeast China. The typical weather system serves as the primary meteorological cause of the occurrence of early frost. The Northeast Cold Vortex is a cyclonic system of certain intensity located in Northeast China, which has the potential to induce severe weather conditions such as extreme low temperatures and intense convection. Despite extensive research on the first occurrence of frost in Northeast China, the evolutionary characteristics under the combined influence of climate change and the Northeast Cold Vortex remain unclear. This limitation hinders the development of effective monitoring and early warning systems for early frost, as well as the formulation of disaster prevention and mitigation plans for crop production. Therefore, this study aims to objectively document the occurrence of early frost in maize crops in Northeast China from 1961 to 2021 under the influence of the Northeast Cold Vortex. It seeks to unveil the climatic characteristics and evolutionary patterns of early frost events in maize crops within this region, considering the impact of the Northeast Cold Vortex. Additionally, it endeavors to analyze the factors contributing to varying degrees of early frost caused by the Northeast Cold Vortex. The results showed that the occurrence of both early frost and frost influenced by the Northeast Cold Vortex exhibited a declining trend. Furthermore, there was also a decreasing proportion of initial frost attributed to the Northeast Cold Vortex, with a decline rate of 2% per decade, indicating a diminishing dominance of initial frost caused by this weather system. The onset date for the early frost under the influence of the Northeast Cold Vortex progressively advanced from southeast to northwest, occurring 4 days earlier than during the period from 1961 to 1990 between 1991 and 2021. While early frost displayed an increasing spatial distribution from southeast to northwest, it is noteworthy that the majority concentration of the Northeast Cold Vortex was observed in central regions, highlighting its predominant role in causing early frost in Northeast China. Full article

Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognosis of cloud formation, rainfall, energy levels reaching the surface, and air quality. Working with a spatial resolution of 1 km, we studied the influence of cumulus parameterization schemes coded in the Weather Research and Forecasting with Chemistry Version 3.2 (WRF-Chem 3.2) for modeling in an Andean city in Southern Ecuador (Cuenca, 2500 masl), during September 2014. To assess performance, we used meteorological records from the urban area and stations located mainly over the Cordillera, with heights above 3000 masl, and air quality records from the urban area. Firstly, we did not use any cumulus parameterization (0 No Cumulus). Then, we considered four schemes: 1 Kain–Fritsch, 2 Betts–Miller–Janjic, 3 Grell–Devenyi, and 4 Grell-3 Ensemble. On average, the 0 No Cumulus option was better for modeling meteorological variables over the urban area, capturing 66.5% of records and being the best for precipitation (77.8%). However, 1 Kain–Fritsch was better for temperature (78.7%), and 3 Grell–Devenyi was better for wind speed (77.0%) and wind direction (37.9%). All the options provided acceptable and comparable performances for modeling short-term and long-term air quality variables. The results suggested that using no cumulus scheme could be beneficial for holistically modeling meteorological and air quality variables in the urban area. However, all the options, including deactivating the cumulus scheme, overestimated the total amount of precipitation over the Cordillera, implying that its modeling needs to be improved, particularly for studies on water supply and hydrological management. All the options also overestimated the solar radiation levels at the surface. New WRF-Chem versions and microphysics parameterization, the other component directly related to cloud and rainfall processes, must be assessed. In the future, a more refined inner domain, or an inner domain that combines a higher resolution (less than 1 km) over the Cordillera, with 1 km cells over the urban area, can be assessed. Full article

Residential biomass combustion emits a large amount of organic gases into ambient air, resulting in the formation of secondary organic aerosol (SOA) and various environmental and health impacts. In this study, we investigated the emission characteristics of non-methane organic compounds (NMOCs) from residential biomass fuels during vigorous combustion (flaming) and stable combustion (smoldering) conditions. We quantified NMOC emission factors based on the CO concentration for different combustion phases and found that NMOC emissions were higher during the smoldering phase and approximately two to four times greater than those during flaming. NMOCs were categorized into volatile organic compounds (VOCs) and intermediate-volatility organic compounds (IVOCs) through the modeling of the organic compound volatility distribution. The photochemical aging of NMOCs revealed furans, phenolics, and certain IVOCs as significant non-traditional SOA precursors, with over half being consumed during a short aging period. A parametric function was established, indicating that accounting for non-traditional SOA precursors and IVOC yields improves the representation of the net enhancement of measured organic aerosol (OA). This study emphasizes the importance of differentiating emissions from various phases of residential biomass combustion and recognizing non-traditional SOA precursors and IVOCs for accurate SOA assessment and prediction. Full article

Precipitation is an essential component of the hydrological cycle that directly affects human lives. An accurate and early detection of a future rainfall event can help prevent social, environmental, and economic losses. Traditional methods for accurate rainfall prediction have faltered due to their weakness in quantifying nonlinear climatic conditions as they involve numerical weather prediction using radar to solve complex mathematical equations based on contemporary meteorological data. This study aims to develop a precise rainfall forecast model using machine learning (ML), and this model focuses on long short-term memory (LSTM) to enhance rainfall prediction accuracy. In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting extreme weather phenomena worldwide. For instance, long short-term memory (LSTM) is a forecast model that effectively estimates the amount of precipitation based on historical data. We analyzed 85,470 pieces of daily rainfall data from 1983 to 2021 collected from each of four synoptic stations in Rwanda (Kigali Aero, Ruhengeri Aero, Kamembe Aero, and Gisenyi Aero). Advanced ML algorithms, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and LSTM, were applied to predict extreme rainfall events. LSTM outperforms the CNN and GRU with 99.7%, 99.8%, and 99.7% accuracy. LSTM’s ability to filter out noise showed important patterns by handling irregularities in rainfall data to improve forecast results. Our outcomes have significant implications for disaster preparedness and risk mitigation efforts in Rwanda, where frequent natural disasters, including floods, pose a challenge. Our research also demonstrates the superiority of LSTM-based ML algorithms in predicting extreme rainfall events, highlighting their potential to enhance disaster risk resilience and preparedness strategies in Rwanda. Full article

To evaluate the detection performance of ozone lidars, the first comprehensive vertical ozone observation experiment in China was conducted at the Xilinhot National Climate Observatory in Inner Mongolia from August to December 2023. The ozone profiles and concentrations of four ozone lidars were systematically compared and assessed with ozone radiosonde measurements and ozone analyzer observations both at ground-based stations and on an Unmanned Aerial Vehicle. The results show that the relative deviations of four ozone lidars are less than 20% compared with ozone radiosonde measurements at a height between 150 and 400 m. Ozone lidars have better behavior between 400 m and 2000 m than the lower altitude, with the deviation within 10% and the correlation coefficient around 0.8. However, relative deviations of lidars increased with altitude above 2000 m. The surface ozone concentrations observed using ozone lidars agreed well with the ground-based ozone analyzer, especially during periods with ozone concentrations higher than 40 µg·m⁻³. The correlation coefficients for most models of ozone lidar are higher than 0.53. A further investigation of the influence of precipitation events on ozone lidar measurement has been conducted, which revealed that thick cloud layers, low cloud base, and an intensive precipitation event with large raindrop particles can result in high anomalies and reduce the inversion accuracy of the ozone lidar. During the experiment, four ozone lidars were assessed quantitatively according to the comprehensive performance, which could help to improve inversion algorithms and the system design of this promising technique. Full article

Accurate and rapid weather forecasting and climate modeling are universal goals in human development. While Numerical Weather Prediction (NWP) remains the gold standard, it faces challenges like inherent atmospheric uncertainties and computational costs, especially in the post-Moore era. With the advent of deep learning, the field has been revolutionized through data-driven models. This paper reviews the key models and significant developments in data-driven weather forecasting and climate modeling. It provides an overview of these models, covering aspects such as dataset selection, model design, training process, computational acceleration, and prediction effectiveness. Data-driven models trained on reanalysis data can provide effective forecasts with an accuracy (ACC) greater than 0.6 for up to 15 days at a spatial resolution of 0.25°. These models outperform or match the most advanced NWP methods for 90% of variables, reducing forecast generation time from hours to seconds. Data-driven climate models can reliably simulate climate patterns for decades to 100 years, offering a magnitude of computational savings and competitive performance. Despite their advantages, data-driven methods have limitations, including poor interpretability, challenges in evaluating model uncertainty, and conservative predictions in extreme cases. Future research should focus on larger models, integrating more physical constraints, and enhancing evaluation methods. Full article

Air pollution, particularly from particulate matter (PM_2.5) and black carbon (eBC), has been implicated in airway pathologies. This study aims to assess the relationship between exposure to these pollutants and respiratory function in various populations, including healthy individuals, while seeking an accurate assessment method. A cross-sectional study was conducted in Bogotá, evaluating respiratory function in the users of bicycles, minivans, and buses through spirometry and impulse oscillometry. Measurements were taken along two main avenues, assessing the PM_2.5 and eBC concentrations. The results reveal higher pollutant levels on AVE KR 9, correlating with changes in oscillometry values post-travel. Cyclists exhibited differing pre- and post-travel values compared to bus and minivan users, suggesting aerobic exercise mitigates pollutant impacts. However, no statistically significant spirometry or impulse oscillometry variations were observed among routes or modes. Public transport and minivan users showed greater PM_2.5 and eBC exposure, yet no significant changes associated with environmental contaminants were found in respiratory function values. These findings underscore the importance of further research on pollutant effects and respiratory health in urban environments, particularly concerning different transport modes. Full article

This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall. Full article

In the present study, a new geophysical model function (GMF) is developed for the X-band synthetic aperture radar (SAR) on board the Advanced Satellite with New System Architecture for Observation-2 (ASNARO-2) to retrieve accurate offshore wind speeds. Equivalent neutral wind speeds based on the local forecast model (LFM) are employed as reference wind vectors, and 12,259 matching points from 502 SAR images obtained with horizontal transmitting, horizontal receiving polarization around Japan are collected. To ensure convergence of the calculation, 8129 points are selected from the matching points to determine the basic formula for the GMF and 23 coefficients based on the relationships among the normalized radar cross section, wind speed, incidence angle, and relative wind direction. Compared with the reference wind speeds, the GMF wind speeds showed reproducibility with a bias of −0.10 m/s and an RMSD of 1.37 m/s. Additionally, it can be confirmed that the retrieved wind speed has the bias of 0.03 and the RMSD of 1.68 m/s when compared to the in situ wind speed from the Kuroshio Extension Observatory (KEO) buoy. The accuracy of these retrieved wind speeds is comparable to previous studies, and it is indicated that the developed GMF can be used to retrieve offshore winds from ASNARO-2 images. Full article

The effects of solar activity and the accompanying space weather events on human pathological conditions, physiological parameters and other psycho-physiological disturbances have been analyzed in numerous recent investigations. Moreover, many of these studies have particularly focused on the different physical reactions humans have, according to their sex, during variations in the physical environment. In the framework of the above, this work analyses heart rate data obtained from volunteers (687 men and 534 women) from three different regions (Athens, Piraeus and Heraklion) of Greece in relation to the geophysical activity and variations of environmental factors. Dst index and Ap index data, along with cosmic ray intensity data derived from the Athens Neutron Monitor Station (A.Ne.Mo.S.), were used. The study expands from April 2011 to January 2018, covering solar cycle 24. The ANalysis Of Variance (ANOVA) and the superimposed epochs methods were used in order to examine heart rate variations depending on sex. Results revealed that women tend to be more sensitive to physical environmental changes. Statistically significant results are related to the geomagnetic activity but were not obtained for cosmic ray variations. Full article

The COVID-19 pandemic precipitated a unique period of social isolation, presenting an unprecedented opportunity to scrutinize the influence of human activities on urban air quality. This study employs ARIMA models to explore the impact of COVID-19 isolation measures on the PM₁₀ and PM_2.5 concentrations in a high-altitude Latin American megacity (Bogota, Colombia). Three isolation scenarios were examined: strict (5 months), sectorized (1 months), and flexible (2 months). Our findings indicate that strict isolation measures exert a more pronounced effect on the short-term simulated concentrations of PM₁₀ and PM_2.5 (PM₁₀: −47.3%; PM_2.5: −54%) compared to the long-term effects (PM10: −29.4%; PM_2.5: −28.3%). The ARIMA models suggest that strict isolation measures tend to diminish the persistence of the PM₁₀ and PM_2.5 concentrations over time, both in the short and long term. In the short term, strict isolation measures appear to augment the variation in the PM₁₀ and PM_2.5 concentrations, with a more substantial increase observed for PM_2.5. Conversely, in the long term, these measures seem to reduce the variations in the PM concentrations, indicating a more stable behavior that is less susceptible to abrupt peaks. The differences in the reduction in the PM₁₀ and PM_2.5 concentrations between the strict and flexible isolation scenarios were 23.8% and 12.8%, respectively. This research provides valuable insights into the potential for strategic isolation measures to improve the air quality in urban environments. Full article

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

Urban areas consist of various land cover types, with a high proportion of artificial surfaces among them. This leads to unfavorable thermal environments in urban areas. Continuous research on the thermal environment, specifically on the sensible heat flux (Q_h), has been conducted. However, previous research has faced temporal, spatial, and resolution limitations when it comes to detailed analysis of sensible heat flux in urban areas. Therefore, in this study, a computational fluid dynamics (CFD) model combined with the LDAPS and the VUCM was developed to simulate Q_h at one-hour intervals over a 1-month period in an urban area with various land cover types. Model validation was performed by comparing it with measurements, confirming the suitability of the model for simulating Q_h. The land cover was categorized into five types: building, road, bare land, grassland, and tree areas. Q_h exhibited distinct patterns depending on the land cover type. When averaging the Q_h distribution over the target period, buildings, roads, and bare land areas showed a predominance of upward Q_h values, while grassland and tree areas displayed dominant downward Q_h values. Additionally, even within the same land cover types, slight Q_h variations were identified based on their surroundings. The averaged Q_h value for building areas was the highest at 36.79 W m⁻², while that for tree areas was −3.04 W m⁻². Moreover, during the target period, the time-averaged Q_h showed that building, road, and bare land areas peaked at 14 LST, while grassland and tree areas exhibited very low Q_h values. Notably, buildings reached a maximum Q_h of 103.30 W m⁻² but dropped to a minimum of 1.14 W m⁻² at 5 LST. Full article