Atmosphere, Volume 15, Issue 5 (May 2024) – 54 articles

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM_2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM_2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM_2.5 pollutions, as well as the differences in the contributions of different industries to PM_2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM_2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM_2.5 increased by 18.9~46.8 μg/m³ from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m³) after the holiday. In terms of spatial differences, the average concentrations of PM_2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM_2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM_2.5 generated by residential sources contributed the vast majority (60~100 μg/m³) of PM_2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources. Full article

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations. Full article

Precise water management is essential for the efficient development of irrigated agricultural crops in the Hetao Irrigation Area of Inner Mongolia. Given the severe water scarcity in the region and the significant use of intercropping as a cropping method, the development of rational irrigation scheduling is crucial. The objective of this work was to combine the ISAREG model with wheat–sunflower intercropping crops in order to enhance the effectiveness of irrigation scheduling in intercropping systems. This was achieved by changing and verifying crucial parameters for simulating irrigation patterns in intercropping. We conducted an assessment of nine irrigation schedules for a wheat–sunflower intercropping system in order to provide a range of irrigation scenarios that effectively fulfill the water requirements of the system. In light of this, we suggested implementing restrictions on the dates and volumes of irrigation based on the demand for agricultural irrigation. This approach aimed to establish irrigation schedules that are highly efficient and tailored to the specific crops in the area. As a result, we achieved a water use efficiency rate of 100%, saved 28.78% of water resources, optimized crop irrigation schedules, and enhanced crop economics by 6.7%. This study presents a novel and efficient method to optimize agricultural irrigation schedules, boost agricultural water use efficiency, and maximize crop yields in order to promote sustainable agricultural development. Full article

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

Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R² = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R² = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution. Full article

In early 2020, China experienced a mass outbreak of a novel coronavirus disease (COVID-19). With an aim to evaluate the impact of emission variations on toxic element species in PM_2.5 and the health risks associated with inhalation exposure during COVID-19, we collected PM_2.5 filter samples in Beijing from January 1 to February 28, 2020. Positive matrix factorization (PMF) and a health risk (HR) assessment model were used to assess the health risks of the toxic elements and critical risk sources. The total concentration of eight toxic elements (Se, Cd, Pb, Zn, As, Cu, Ni, and Cr) in Beijing showed a trend of first increasing and then decreasing: full lockdown (322.9 ng m⁻³) > pre-lockdown (264.2 ng m⁻³) > partial lockdown (245.3 ng m⁻³). During the lockdown period, stringent control measures resulted in significant reductions (6−20%) in Zn, Pb, Cd, and Ni levels, while concentrations of Se, As, Cu, and Cr were unexpectedly elevated (14−348%). A total of five sources was identified: traffic emission, coal combustion, dust emission, industrial emission and mixed source of biomass burning and firework combustion. Total carcinogenic risk (TCR) of the selected toxic elements exceeded the US EPA limits for children and adults. As and Cr (IV) were the main contributors to non-carcinogenic and carcinogenic risks, respectively. For source-resolved risks, coal combustion was the main contributor to HI (43%), while industrial emissions were the main cause of TCR (45%). Additionally, increased contributions from coal combustion, biomass burning, and firework combustion during the full lockdown elevated the HI and TCR values. Full article

There is a 50% possibility that global temperatures will have risen by more than 5 °C by the year 2100. As demands on Earth’s systems grow more unsustainable, human security is clearly at stake. This narrative review provides an overview and synthesis of findings in relation to climate change, air pollution, and human health within the Global South context, focusing on case study geographic locations in South Africa and Brazil. Two case study regions—the Kruger to Canyons Biosphere region of South Africa and the Amazon region of Brazil—were the subjects of PubMed literature searches. Technical reports, policy briefs, and grey literature were also narratively synthesized. The burning of wood for fuel, as witnessed in Agincourt, and forest fires, such as those seen in the Amazon rainforest, release air pollutants such as methane and black carbon, which are strong short-lived climate pollutants (SLCPs) which fuel climate change and adversely affect human health. SLCPs have a brief lifetime in the atmosphere, but they frequently have a far larger potential for global warming than carbon dioxide (CO²). Most air pollution in geographic case study areas, that are home to human settlements, is due to the burning of wood and other biomasses that are pollutants. These areas are seen to be important for climate and health responses, and if constructive action is taken to switch to other modes of electricity generation (such as solar power) and the prevention of deforestation, the worst of the impacts may still be mitigated in these regions. Authorities should also establish a monitoring strategy for air quality, as well as enforce air quality regulations that safeguard public health. Full article

Volcanic glass has been used extensively as a paleoaltimeter. Deuterium (²H) concentrations in glass have been found to be stable over geologic timescales, making δ²H (also known as δD) a reliable proxy for ancient water chemistry. However, continued work revolves around better understanding how different factors affect preserved water in volcanic ash. Here, we analyze δD in the Rattlesnake Tuff (RST), a widespread ca. 7 Ma ash-flow tuff, and create a paleoisoscape to assess variations in δD across Oregon during that time. To this end, 16 ash samples were collected across central and eastern Oregon from various flow units within the RST. Samples were analyzed for δD using a temperature conversion elemental analyzer (TC/EA) connected to a mass spectrometer and elemental composition using a scanning electron microscope (SEM). We compared the isotopic results to modern water and published ancient water proxy data to better constrain changes in climate and topography across Oregon throughout the Neogene. We also estimated wt. % H₂O by calculating excess (non-stoichiometric) oxygen from SEM elemental data. We did not observe significant variations in δD among the flow units from single locations, nor was there a significant relationship between the prepared glass shard composition and wt. % H₂O or δD, supporting the use of volcanic glass as a reliable paleoenvironmental indicator. Our results show significant spatial variation in δD_water values of RST, ranging from −107‰ to −154‰. δD values of ancient glass were similar to modern water near the Cascade Mountains but became relatively negative to the east near the inferred eruptive center of the RST, suggesting that a significant topographic feature existed in the vicinity of the RST eruptive center that has since subsided. Full article

Severe ozone (O₃) pollution has been recorded in China in recent years. The key precursor, volatile organic compounds (VOCs), is still not well understood in Nanning, which is a less developed city compared to other megacities in China. In this study, a year-long measurement of VOCs was conducted from 1 October 2020 to 30 September 2021, to characterize the ambient variations and apportion the source contributions of VOCs. The daily-averaged concentration of VOCs was measured to be 26.4 ppb, ranging from 3.2 ppb to 136.2 ppb across the whole year. Alkanes and oxygenated VOCs (OVOCs) were major species, contributing 46.9% and 25.2% of total VOC concentrations, respectively. Propane, ethane, and ethanol were the most abundant in Nanning, which differed from the other significant species, such as toluene (3.7 ppb) in Guangzhou, ethylene (3.8 ppb) in Nanjing, and isopentane (5.5 ppb), in Chengdu. The positive matrix factorization (PMF) model resolved six source factors, including vehicular emission (contributing 33% of total VOCs), NG and LPG combustion (19%), fuel burning (17%), solvent use (16%), industry emission (10%), and biogenic emission (5%). This indicated that Nanning was less affected by industrial emission compared with other megacities of China, with industry contributing 12–50%. Ethylene, m/p-xylene, butane, propylene, and isoprene were key species determined by ozone formation potential (OFP) analysis, which should be priority-controlled. The variations in estimated OFP and observed O₃ concentrations were significantly different, suggesting that VOC reactivity-based strategies as well as meteorological and NO_x effects should be considered collectively in controlling O₃ pollution. This study presents a year-long dataset of VOC measurements in Nanning, which gives valuable implications for VOC control in terms of key sources and reactive species and is also beneficial to the formulation of effective ozone control strategies in other less developed regions of China. Full article

Based on the hourly surface ozone (O₃) observations and meteorological data from Zhuzhou in 2021, the pollution characteristics and influencing factors of O₃ in Zhuzhou were investigated in the study. In addition, the Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) analysis methods were employed to analyze the transmission paths and potential pollution sources of O₃ pollution in Zhuzhou. The results showed that the total number of days with O₃ exceeding the standard at all monitoring stations in Zhuzhou was 142 days in 2021. The overall air quality was less affected by SO₂, NO₂, and CO, and the trend of O₃ pollution was still increasing. The concentrations of O₃, CO, and NO₂ varied significantly in different months, and the variation of O₃ exhibited a “double-peak” pattern, with the peak value occurring in September. The O₃ concentration in urban areas was significantly higher than that in suburban areas. Meteorological conditions had a significant impact on the degree of O₃ pollution in Zhuzhou. The average wind speed in Zhuzhou throughout the year was 1.7 m/s, and the prevailing wind direction in summer was southeast, with a frequency of 16%. O₃ pollution was mainly transported by short-distance airflow during the over-standard periods in 2021, accounting for 37.64%. The main source of O₃ pollutant was from Jiangxi Province in the east, with the shortest distance of regional transport and the highest O₃ concentration. In addition, transportation from central Guangdong Province, western Jiangxi Province, and central Hubei Province also had a significant impact. Full article

Many cities in coastal areas are prone to typhoon disasters due to their location on the Pacific storm path, and the direct effect of catastrophic winds can lead to the destruction of low-rise buildings and severe damage to high-rise structures. The purpose of this study was to enhance the understanding of boundary layer wind profiles of strong typhoons in coastal areas and reduce property losses and casualties caused by wind disasters. Based on the field measurements of wind profile acoustic radar in coastal areas, the variation characteristics of the boundary layer wind profile during the passage of super typhoon Lekima were first studied in depth, and the evolution law of the typhoon boundary layer profile was summarized. Then, the effects of typhoon horizontal structure, topography, wind speed, and time distance on the characteristics of the typhoon profile were discussed, respectively. Finally, the evolution characteristics of wind profile parameters were obtained by fitting three wind profile theoretical models. Due to the strong variability of typhoon profile morphology, the theoretical model of wind profile is only applicable to the wind profile from the bottom to the low-level jet height of typhoons, while wind parameters are closely related to the spatial location of the typhoon wind field. Full article

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

The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based catalysts and continuously monitoring its evolution for a weather event that occurred on 15 December 2019 in Henan Province, China. Satellite measurements are also used to verify an operational catalyst T&D algorithm. The results show that FY-4A exhibits a remarkable capability of identifying the cloud-seeding tracks and continuously tracing their evolution for a period of over 3 h. About 60 min after the cloud seeding, the cloud crystallization track became clear in the FY-4A tri-channel composite cloud image and lasted for about 218 min. During this time period, the cloud track moved with the cloud system about 153 km downstream (northeast of the operation area). An operational catalyst T&D model was run to simulate the cloud track, and the outputs were extensively compared with the satellite observations. It was found that the forecast cloud track closely agreed with the satellite observations in terms of the track widths, morphology, and movement. Finally, the FY-4A measurements show that there were significant differences in the microphysical properties across the cloud track. The effective cloud radius inside the cloud track was up to 15 μm larger than that of the surrounding clouds; the cloud optical thickness was about 30 μm smaller; and the cloud-top heights inside the cloud track were up to 1 km lower. These features indicate that the cloud-seeding catalysts led to the development of ice-phase processes within the supercooled cloud, with the formation of large ice particles and some precipitation sedimentation. Full article

An oxidizing and harmful pollutant gas, tropospheric ozone is a product of a complex set of photochemical reactions that can make it difficult to enact effective control measures. A better understanding of its precursors including volatile organic compounds (VOCs) and nitrogen oxides (NO_x) and their spatial distribution can enable policymakers to focus their control efforts. In this study we used low-cost sensors (LCSs) to increase the spatial resolution of an existing NO₂ monitoring network in addition to VOC sampling to better understand summer ozone formation in Maricopa County, Arizona, and observed that afternoon O₃ values at the downwind sites were significantly correlated, ~0.27, to the morning NO₂ × rate values at the urban sites. Additionally, we looked at the impact of wildfire smoke on ozone exceedances and compared non-smoke days to smoke days. The average O₃ on smoke days was approximately 20% higher than on non-smoke days, however, the average NO₂ concentration multiplied by estimated photolysis rate (NO₂ × rate) values were only 2% higher on smoke days. Finally, we evaluated the ozone sensitivity of the region by calculating HCHO/NO₂ ratios using three different datasets: ground, satellite, and model. Although the satellite dataset produced higher HCHO/NO₂ ratios than the other datasets, when the proper regime thresholds are applied the three datasets consistently show transition and VOC-limited O₃ production regimes over the Phoenix metro area. This suggests a need to implement more VOC emission controls in order to reach O₃ attainment in the county. Full article

The Sahara Desert is the largest contributor of global atmospheric dust aerosols impacting regional climate, health, and ecosystems. The climate effects of these dust aerosols remain uncertain due, in part, to climate model uncertainty of Saharan source region contributions and aerosol microphysical properties. This study distinguishes source region elemental signatures of Saharan dust aerosols sampled during the 2015 Aerosols Ocean Sciences Expedition (AEROSE) in the tropical Atlantic. During the 4-week campaign, cascade impactors size-dependently collected airborne Saharan dust particulate upon glass microfiber filters. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis differentiated metal isotope concentrations within filter samples from various AEROSE dust sampling periods. Back-trajectory analysis and NOAA satellite aerosol optical depth retrievals confirmed source regions of AEROSE ’15 dust samples. Pearson correlational statistics of source region activity and dust isotope concentrations distinguished the elemental signatures of North African potential source areas (PSAs). This study confirmed that elemental indicators of these PSAs remain detectable within dust samples collected far into the marine boundary layer of the tropical Atlantic. Changes detected in dust elemental indicators occurred on sub-weekly timescales across relatively small sampling distances along the 23W parallel of the tropical Atlantic. PSA-2 emissions, covering the western coast of the Sahara, were very strongly correlated (R² > 0.79) with Ca-44 isotope ratios in AEROSE dust samples; PSA-2.5 emissions, covering eastern Mauritania and western Mali, were very strongly correlated with K-39 ratios; PSA-3 emissions, spanning southwestern Algeria and eastern Mali, were very strongly correlated with Fe-57 and Ti-48 ratios. The abundance of Ca isotopes from PSA-2 was attributed to calcite minerals from dry lakebeds and phosphorous mining activities in Western Sahara, based on source region analysis. The correlation between K isotope ratios and PSA-2.5 was a likely indicator of illite minerals near the El Djouf Desert region, according to corroboration with mineral mapping studies. Fe and Ti ratio correlations with PSA-3 observed in this study were likely indicators of iron and titanium oxides from Sahelian sources still detectable in Atlantic Ocean observations. The rapid changes in isotope chemistry found in AEROSE dust samples provide a unique marker of Saharan source regions and their relative contributions to desert outflows in the Atlantic. These elemental indicators provide source region apportionments of Sahara Desert aerosol flux and deposition into the Atlantic Ocean, as well as a basis for model and satellite validation of Saharan dust emissions for regional climate assessments. Full article

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F₁ score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O₃ predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article

It has been demonstrated that benzene, toluene, and xylene are carcinogens. Its combined effects with other contaminants have the potential to harm several ecosystem components. Since most human benzene exposure takes place inside, it is important to understand how outdoor benzene emissions from traffic and industry affect interior concentrations. However, this area of study has not received enough attention to date. Herein, we examine the outdoor concentrations of benzene, toluene, and xylene (BTX) in a Steelpoort mining area. BTX pollutants were passively sampled on the first seven days of the month, from January to December 2021 using Radiello samplers. The effects of meteorological parameters such as temperature, relative humidity, wind speed, and solar radiation on BTX concentrations were also statistically tested. For all seasons, BTX concentrations were greater in the winter than in the summer with concentrations of 0.69 µg/m³, 2.97 µg/m³ and 0.80 µg/m³ for benzene, toluene and xylene, respectively. In addition, toluene was the most common BTX compound with the highest concentrations when compared to benzene and xylene. Benzene, toluene and xylene, had yearly average concentrations of 0.61 µg/m³, 1.48 µg/m³ and 0.64 µg/m³, respectively. The benzene and xylene concentrations were below international exposure limits (annual, 5 µg/m³ for benzene; weekly, 260 µg/m³ for toluene), as in comparison to the World Health Organization, as well as within South African exceedance limits. Both positive and negative correlations between BTX and meteorological parameters were demonstrated by statistical models. Temperature, wind speed, and relative humidity depicted a weak negative correlation with benzene of 0.003, 0.019 and 0.006, respectively. Toluene showed a positive correlation with wind speed (1.90) and relative humidity (0.041). Overall, the concentration of benzene is of major concern since it is an agent of cancer and it is there in the atmosphere. Full article

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

Wildfires frequently occur in Ukraine during agricultural open-burning seasons in spring and autumn. High aerosol concentrations from fire emissions can significantly affect meteorological processes via direct and indirect aerosol effects. To study these impacts, we selected a severe wildfire episode from April 2020 in the Chornobyl Exclusion Zone (CEZ) and its surrounding area as a case study. We employed the Enviro-HIRLAM modeling system to simulate reference (REF) meteorological conditions, along with direct (DAE), indirect (IDAE), and combined (COMB) aerosol effects. In our simulations, black carbon (BC) and organic carbon (OC) comprised 70–80% of all aerosol mass in the region, represented in two layers of higher concentrations: one near the surface and the other 3–4 km above the surface. Our simulations showed that the inclusion of aerosol effects into the modeling framework led to colder (up to −3 °C) and drier (relative humidity drop up to −20%) conditions near the surface. We also observed localized changes in cloudiness, precipitation (mainly redistribution), and wind speed (up to ±4 m/s), particularly during the movement of atmospheric cold fronts. Larger uncertainties were observed in coarser model simulations when direct aerosol effects were considered. Quantifying the aerosol effects is crucial for predicting and promptly detecting changes that could exacerbate unfavorable weather conditions and wildfires. Such knowledge is essential for improving the effectiveness of emergency response measures. Full article

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents' daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors' impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development. Full article

Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented. Full article

Extracting statistics for turbulent flows directly from the Navier–Stokes equations poses a formidable challenge, particularly when dealing with unsteady or inhomogeneous flows. However, embracing Kolmogorov’s inertial range spectrum for isotropic turbulence as a dynamic equilibrium provides a conceptual starting point for perturbation theory. We review theoretical results, combining perturbation approaches, and phenomenological turbulence closures, which allow us to gain valuable insights into the statistics of unsteady and inhomogeneous turbulence. Additionally, we extend the ideas to the case of the mixing of a passive scalar. Full article

There has been an increasing awareness of indoor air quality (IAQ) management in green building designs, driven by the need to mitigate potential health risks and create sustainable and healthy indoor environments. The COVID-19 pandemic has further highlighted the critical role of ventilation and IAQ in reducing the risk of indoor airborne transmission. Governments and organisations worldwide have responded to this growing concern by implementing ventilation requirements and updating IAQ standards and guidelines. In the case of Hong Kong, a developed and densely populated city characterised by high-rise buildings, this study aims to provide a strategic framework for non-governmental agencies to address IAQ issues effectively. A comprehensive review of policies, regulations, and guidelines by international bodies and individual governments, along with an examination of the current IAQ management scheme in Hong Kong, has been conducted. Drawing inspiration from successful IAQ management strategies, the study aims to identify insights and potential pathways for the city’s future development of IAQ management strategies. Overall, this research highlights the importance of proactive IAQ management for buildings and offers a roadmap for Hong Kong’s pursuit of healthier indoor environments. Full article

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety. Full article

Since 2016, the Ministry of Ecology and Environment and the Beijing Municipal Government have adjusted the minimum concentration limit for ambient air dustfall several times, indicating that they attach great importance to dustfall. To grasp the pollution characteristics and sources of dustfall, in this work, the filtration method was used to determine the insoluble dustfall and water-soluble dustfall in the urban area of Beijing. From our analysis, the influence of the meteorological parameters on dustfall was found, and the chemical components of dustfall were determined. The positive matrix factorization (PMF) model was also utilized to analyze the sources of dustfall. The results indicated that the average amount of dustfall in 2021–2022 was 4.4 t·(km²·30 d)⁻¹, and the proportion of insoluble dustfall deposition was 82.4%. Dustfall was positively correlated with the average wind speed and temperature and negatively correlated with the relative humidity and rain precipitation. The impact of the meteorological parameters on insoluble dustfall and water-soluble dustfall was the opposite. The average proportions of crustal material, ions, organic matter, element carbon, trace elements, and unknown components were 48%, 16%, 14%, 1.4%, 0.20%, and 20%, respectively. The proportions of the crustal material and ions were the highest in spring (57%) and summer (37%). The contribution rates of fugitive dust source, secondary inorganic source, mobile source, coal combustion source, snow melting agent source, and other sources were 42.4%, 19.3%, 8.3%, 3.0%, 2.7%, and 24.3%, respectively. This study supported dustfall pollution control by analysing the pollutant characteristics and sources of dustfall from the standpoint of total chemical components. In order to better control dustfall pollution, control measures and evaluation standards for fugitive dust pollution should be formulated. Full article

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia. Full article

Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production. The aim of the present work was to contribute to policy and climate adaptation, thus reducing the vulnerability of maize production to climate change over Equatorial Africa. This study firstly examined long-term trends of precipitation (PRE), soil moisture (SM), actual evapotranspiration (E), and potential evapotranspiration (Ep), as well as surface air temperatures, including the minimum (TMIN) and maximum (TMAX). Secondly, the relationship between maize production and these climate variables was quantified for 18 Equatorial African countries (EQCs) over 1980−2021. To assess the linear trends, Mann–Kendall and Sen’s slope tests were used to quantify the magnitude of the hydro-climatic variable trends at the 5% significance level, and Pearson’s correlation coefficient was used to evaluate the relation of these climate parameters with the maize production. The annual mean PRE declined at 0.03 mm day⁻¹10a⁻¹. Other climate variables increased at different rates: SM at 0.02 mmday⁻¹10a⁻¹, E at 0.03 mm day⁻¹10a⁻¹, Ep at 0.02 mm day⁻¹ 10a⁻¹, TMIN and TMAX at 0.01 °C day⁻¹10a⁻¹. A regional analysis revealed heterogeneous significant wet–dry and warm–cool trends over the EQCs. While, spatially, dry and warm climates were observed in the central to eastern areas, wet and warm conditions dominated the western regions. Generally, the correlations of maize production with the E, Ep, TMAX, and TMIN were strong (r > 0.7) and positive, while moderate (r > 0.45) correlations of maize production with PRE and SM were obvious. These country-wide analyses highlight the significance of climate change policies and offer a scientific basis for designing tailored adaptation strategies in rainfed agricultural regions. Full article

This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity. Full article

Since about 1997, the realisation that the finite Reynolds number (FRN) effect needs to be carefully taken into account when assessing the behaviour of small-scale statistics came to the fore. The FRN effect can be analysed either in the real domain or in the spectral domain via the scale-by-scale energy budget equation or the transport equation for the energy spectrum. This analysis indicates that the inertial range (IR) is established only when the Taylor microscale Reynolds number $R{e}_{\lambda }$ is infinitely large, thus raising doubts about published power-law exponents at finite values of $R{e}_{\lambda }$, for either the second-order velocity structure function $\overline{{\left(\delta u\right)}^2}$ or the energy spectrum. Here, we focus on the transport equation of $\overline{{\left(\delta u\right)}^2}$ in decaying grid turbulence, which represents a close approximation to homogeneous isotropic turbulence. Regarding small-scale effects, the large-scale forcing term associated with streamwise advection decreases as $R{e}_{\lambda }$ increases and finally disappears when $R{e}_{\lambda }$ is sufficiently large. An approach based on the dual scaling of $\overline{{\left(\delta u\right)}^2}$, i.e., a scaling based on the Kolmogorov scales (when the separation r is small) and another based on the integral scales (when r is large), yields $\overline{{\left(\delta u\right)}^2}\sim r^{2/3}$ when $R{e}_{\lambda }$ is infinitely large. This approach also yields $\overline{{\left(\delta u\right)}^n}\sim r^{n/3}$ when $R{e}_{\lambda }$ is infinitely large. These results seem to be supported by the trend as $R{e}_{\lambda }$ increases according to the available experimental data. Overall, the results for decaying turbulence strongly suggest that a tendency towards the predictions of K41 cannot be dismissed at least at Reynolds numbers that are currently beyond the reach of experiments and direct numerical simulations. Full article

This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located in the European continent and two in the Asian continent, with measurement periods ranging from 72 to 93 years, wet and dry days have been identified, statistics on precipitation conditions during the warm and cold seasons have been generated, categorization based on precipitation intensities has been performed, and analyses have been conducted using extreme precipitation indices. At stations located in the northern part of the city, higher annual total precipitation has been observed compared to those in the south. A similar situation applies to the number of wet days. While during the cold season, the wet and dry day counts are nearly the same across all stations, this condition exhibits significant differences in favor of dry days during the warm season. Apart from dry conditions, “moderate” precipitation is the most frequently observed type across all stations. However, “extreme” events occur significantly more often (6%) during the warm season compared to the cold season (2%). Long-term anomalies in terms of annual precipitation totals have shown similarity between stations in the north and south, which has also been observed in longitudinally close stations. Despite the longer duration of the cold season and stronger temperature gradients, extreme rainfall events are more frequent during the warm season, primarily due to thunderstorm activity. While trend analyses revealed limited significant trends in precipitation intensity categories and extreme indices, the study highlights the importance of comprehensive examination of extreme rainfall events on both station-based and regional levels, shedding light on potential implications for regional climate change. Lastly, during the cold season, the inter-station correlation in terms of annual total precipitation amounts has been considerably higher compared to the warm season. Full article