Atmosphere, Volume 15, Issue 6 (June 2024) – 65 articles

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

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM_2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM_2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM_2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM_2.5 exposure. Our results show wildfires significantly influence PM_2.5 in California and nearly all exceedances of the daily US EPA PM_2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM_2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks. Full article

Oceanic biogenic emissions exert a significant impact on the atmospheric environment within the marine boundary layer (MBL). This study employs the extreme gradient boosting (XGBoost) machine learning method and clustering method combined with satellite observations and model simulations to discuss the effects of marine biogenic emissions on MBL formaldehyde (HCHO). The study reveals that HCHO columnar concentrations peaked in summer with 8.25 × 10¹⁵ molec/cm², but the sea–air exchange processes controlled under the wind and sea surface temperature (SST) made marine biogenic emissions represented by isoprene reach their highest levels in winter with 95.93 nmol/m²/day. Analysis was conducted separately for factors influencing marine biogenic emissions and affecting MBL HCHO. It was found that phytoplankton functional types (PFTs) and biological degradation had a significant impact on marine biogenic emissions, with ratio range of 0.07~15.87 and 1.02~5.42 respectively. Machine learning methods were employed to simulate the conversion process of marine biogenic emissions to HCHO in MBL. Based on the SHAP values of the learning model, the importance results indicate that the factors influencing MBL HCHO mainly included NO₂, as well as temperature (T) and relative humidity (RH). Specifically, the influence of NO₂ on atmospheric HCHO was 1.3 times that of T and 1.6 times that of RH. Wind speed affected HCHO by influencing both marine biogenic emission and the atmospheric physical conditions. Increased marine biogenic emissions in air masses heavily influenced by human activities can reduce HCHO levels to some extent. However, in areas less affected by human activities, marine biogenic emissions can lead to higher levels of HCHO pollution. This research explores the impact of marine biogenic emissions on the HCHO status of the MBL under different atmospheric chemical conditions, offering significant insights into understanding chemical processes in marine atmospheres. Full article

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO₂ sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland > temperate meadow steppe > temperate typical steppe > temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios. Full article

The global shift towards sustainable transportation, primarily through vehicle electrification, is critical in addressing climate change. Qatar presents a knowledge gap with specific challenges and opportunities in this transition. This study calculates the potential reduction in CO₂-eq, NOx, and PM2.5 emissions resulting from substituting Internal Combustion Engine Vehicles (ICEVs) with Battery Electric Vehicles (BEVs) in Qatar, considering ICEV ban scenarios in 2030, 2035, and 2040, alongside five policy pathways. A Vehicle Stock Model (VSM) simulates Qatar’s fleet evolution from 2022 to 2050, focusing on the vehicle’s operational phase. An ICEV ban in 2030 would result in a 34% cumulative emission reduction in road transport between 2022 and 2050 compared with the Business-as-Usual (BAU) scenario. For NOx and PM2.5, cumulative emissions for the 2030 ICEV ban pathways are approximately 20% and 9% lower, respectively, compared with BAU. This study underscores the necessity of localising environmental strategies to meet Qatar’s specific needs and climate commitments, where results indicate significant emission reductions are possible through BEVs. Full article

The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By optimizing the urban construction layout to reduce obstruction and leave a passageway for wind to blow in, the natural ventilation environment could be improved. This paper presents a promising approach for natural ventilation planning at both the city and community scales. Based on the assessment of wind environment, heat island intensity, and ventilation potential, the results revealed that winds blowing from the western and northern mountainous area of Shijiazhuang play a natural ventilation inlet role which can provide clean air. The SSHI and SHI were mainly distributed within the Second Ring Road, which has a large proportion of the low ventilation potential level. Thus, six first-class ventilation corridors and thirteen secondary corridors were recommended, which were set to be adapted to the dominant wind direction. Subsequently, an urban climate analysis map (UCAnMap) was developed considering climate sensitivity, and planning recommendations were provided for different climate zones. The relationship between architectural spatial structure and ventilation efficiency was analyzed; the results revealed that increasing the height of the buildings will decrease the proportion of comfortable wind zones, and the overall ventilation efficiency will weaken, so the average building height of a typical block should be controlled within 45 m, which matches ventilation performance requirements. The ventilation efficiency of the block has a certain negative correlation with the building density, and as the building density decreased by more than 10%, the proportion of the comfortable wind zones could increase by 4–5%. Full article

In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only requires the coordinates of the region of interest and range with the desired spatio-temporal resolutions. The model ionosphere is formed using spherical voxels in a lexicographical order so that a 4-D ionosphere can be mapped to a 2-D matrix. The model matrix is formed automatically using a background ionospheric model with an optimized retrospective or near-real time manner. The singular value decomposition is applied to extract a subset of significant singular values and corresponding signal subspace basis vectors. The measurement vector is filled automatically with the optimized number of ground-based and space-based paths. The reconstruction is obtained in closed form in the least squares sense. When the performance of IONOLAB-Fusion across Europe was compared with ionosonde profiles, a 26.51% and 32.33% improvement was observed over the background ionospheric model for quiet and disturbed days, respectively. When compared with GIM-TEC, the agreement of IONOLAB-Fusion was 37.89% and 31.58% better than those achieved with the background model for quiet and disturbed days, respectively. Full article

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River. Full article

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future₁₆ (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios. Full article

The detection and evaluation of biomass burning and dust events are critical for understanding their impact on air quality, climate, and human health, particularly in the Mediterranean region. This research pioneers an innovative methodology that uses Sentinel-2 multispectral (MS) imagery to meticulously pinpoint and analyze long-transport dust outbreaks and biomass burning phenomena, originating both locally and transported from remote areas. We developed the dust/biomass burning (DBB) composite normalized differential index, a tool that identifies clear, dusty, and biomass burning scenarios in the selected region. The DBB index jointly employs specific Sentinel-2 bands: B2-B3-B4 for visible light analysis, and B11 and B12 for short-wave infrared (SWIR), exploiting the specificity of each wavelength to assess the presence of different aerosols. A key feature of the DBB index is its normalization by the surface reflectance of the scene, which ensures independence from the underlying texture, such as streets and buildings, for urban areas. The differentiation involves the comparison of the top-of-atmosphere (TOA) reflectance values from aerosol events with those from clear-sky reference images, thereby constituting a sort of calibration. The index is tailored for urban settings, where Sentinel-2 imagery provides a decametric spatial resolution and revisit time of 5 days. The average values of DBB achieve a 96% match with the coarse-mode aerosol optical depths (AOD), measured by a local station of the AERONET network of sun-photometers. In future studies, the map of DBB could be integrated with that achieved from Sentinel-3 images, which offer similar spectral bands, albeit with much less fine spatial resolution, yet benefit from daily coverage. Full article

With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions. Full article

Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies. Full article

Ethanol as a renewable fuel has been applied in fuel vehicles (FVs), and it is promising in hybrid electric vehicles (HEVs). This work aims to investigate the emission characteristics of ethanol applied in both FVs and plug-in hybrid electric vehicles (PHEVs). The paper conducted a real-road test of an internal combustion FV and PHEV, respectively, based on the world light vehicle test cycle (WLTC) by using gasoline and regular gasoline under different temperature conditions. The use of E10 and E20 in FVs has been effective in reducing the conventional emissions of the vehicles. At 23 °C, E10 and E20 reduced the conventional emissions including carbon monoxide (CO), total hydrocarbon compound (THC), non-methane hydrocarbon compound (NMHC), particulate matter (PM), and particulate number (PN) by 15.40–31.11% and 11.00–44.13% respectively. At 6 °C, E10 and E20 reduced conventional emissions including THC, CO, and PM by 2.15–8.61% and 11.02–13.34%, respectively. However, nitrogen oxide (NO_X) emissions increased to varying degrees. The reduction trend of non-conventional emissions including methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂) from FVs fueled with E10 and E20 is not significant for vehicles. Overall, the emission reduction effect of E20 is better than that of E10, and the emission reduction effect of ethanol gasoline on vehicle emissions is reduced at low temperatures. Lower ambient temperatures increase vehicle emissions in the low-speed segment but decrease vehicle emissions in the ultra-high-speed segment. HEV emissions of THC, CO, PN, and PM are reduced by 25.28%, 12.72%, 77.34%, and 64.59%, respectively, for E20 compared to gasoline, and the use of E20 in HEVs contributes to the reduction of overall vehicle emissions. Full article

The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m², the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m², respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances. Full article

Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with severe typhoon Khanun (2017) and super-typhoon Mangkhut (2018). The satellite products include the CPC MORPHing technique (CMORPH) data, Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement Mission (GPM IMERG). Eight precision evaluation indexes and statistical methods were used to analyze and evaluate the monitoring capabilities of CMORPH, TRMM 3B42, and GPM IMERG satellite precipitation products. The results indicated that the monitoring capability of TRMM satellite precipitation products was superior in capturing the spatial distribution, and GPM products captured the temporal distributions and different category precipitation observed from gauge stations. In contrast, the CMORPH products performed moderately during two heavy rainfall events, often underestimating or overestimating precipitation amounts and inaccurately detecting precipitation peaks. Overall, the three satellite precipitation products showed low POD, high FAR, low TS, and high FBIAS for heavy rainfall events, and the differences in monitoring torrential TRP may be related to satellite retrieval algorithms. Full article

Freshwater availability in Pacific equatorial atolls is highly variable because of the influence of El Niño–Southern Oscillation (ENSO) on rainfall. IPCC projections for the central and western tropical Pacific suggest annual rainfall (P_a) will increase as sea surface temperature (SST) rises. Future changes in ENSO frequency and intensity and in hydrological droughts, however, are uncertain. Here, trends in monthly, seasonal, annual, annual maximum, and minimum rainfall in two equatorial atolls in the eastern and central tropical Pacific are compared with trends in the SST of the surrounding Nino regions from 1951 to 2023. Significant increasing trends in the warm season, annual, and annual maximum SST in the Nino1 + 2, Nino3, and Nino4 regions were of order +1.0 °C/100 y. There were no significant trends in the cool season or annual minimum SST. Despite ocean warming, there were no significant trends in atoll P_a, in intra-annual or interannual variability over 7 decades for either SST or P_a, or in the relative strengths of warm/cool and wet/dry seasons. Extreme, large P_a only occurred after 1987, indicative of ocean warming. Extreme, small P_a happened throughout the period, suggesting no change in drought frequency. Correlations between 12-month P and SST were very strong, with historic rates of increases in P_a of around 1200 mm/y/°C, consistent with projections. The results indicate that the recharge of atoll groundwater will increase as oceans warm, but droughts will remain a major challenge. Full article

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM₁₀ and PM_2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear. Full article

Sensor-based air monitoring instruments (SAMIs) can provide high-resolution air quality data by offering a detailed mapping of areas that air quality monitoring stations (AQMSs) cannot reach. This enhances the precision of estimating PM_2.5 concentration levels for areas that have not been directly measured, thereby enabling an accurate assessment of exposure. The study period was from 30 September to 2 October 2019 in the Guro-gu district, Seoul, Republic of Korea. Four models were applied to assess the suitability of the SAMIs and visualize the temporal and spatial distribution of PM_2.5. Assuming that the PM_2.5 concentrations measured at a SAMI located in the center of the Guro-gu district represent the true values, the PM_2.5 concentrations estimated using QGIS spatial interpolation techniques were compared. The SAMIs were used at seven points (S₁–S₇) according to the distance. Models 3 and 4 accurately estimated the unmeasured points with higher coefficients of determination (R²) than the other models. As the distance from the AQMS increased from S₁ to S₇, the R² between the observed and estimated values decreased from 0.89 to 0.29, respectively. The auxiliary installation of SAMIs could resolve regional concentration imbalances, allowing for the accurate estimation of pollutant concentrations and improved risk assessment for the population. Full article

From 2001 to 2020, three typhoons with similar moving paths and intensities were found to trigger markedly different cooling off northeastern Taiwan. They were typhoons Utor (2001), Nuri (2008), and Hagupit (2008), which led to maximum sea surface temperature (SST) cooling temperatures of 8.8, 2.7, and 1.4 °C, respectively. The drastic cooling discrepancy implies that the existing understanding of the key mechanism leading to the cooling off northeastern Taiwan could be insufficient. For further exploring the key reason(s) contributing to the marked discrepancy, a regional oceanic modeling system (ROMS) was used to reconstruct the background oceanic environment corresponding to three typhoon passages. Results show that the wide radius of maximum winds of typhoon Utor contributes to the strongest SST cooling by enhancing the Kuroshio intrusion (KI) onto the shelf northeast of Taiwan. Heat budget diagnostics explain why including tidal forcing can substantially promote SST cooling. The process was associated mainly with a stronger vertical advection tied to the influence of de-stratification by tidal mixing. Finally, warmer Taiwan Strait currents, driven by wind forcing the typhoons to pass zonally through the north South China Sea, intruded clockwise into the Longdong coast and accelerated the recovery of sea surface cooling around Longdong. Full article

Potential evapotranspiration (PET) is a crucial parameter for forest development, having an important role in ecological, biometeorological, and hydrological assessments. Accurate estimations of PET using the FAO–56 Penman–Monteith (FAO–56 PM) benchmark method require a wide range of data parameters, which are not typically available at meteorological stations installed in forest environments. The aim of this study is to investigate the accuracy of various methods with low data requirements for assessing PET in a Mediterranean forest environment and propose appropriate alternatives for accurate PET estimation. Specifically, 50 temperature-based methods were evaluated against the FAO–56 PM method in a sub-humid forest in northern Greece, using high-quality daily meteorological data. The outcomes indicate that temperature-based methods offer a viable alternative for PET estimation when data availability is limited, with a considerable number of methods (22) presenting low deviations (up to 10%) compared to the benchmark method. Temperature-based models outperformed those incorporating water-related parameters (as relative humidity or precipitation) in Mediterranean forest environments. The top performing methods in the study site, based on several statistical indices, were the equations of Ravazzani et al., proposed in 2012, followed by Hargreaves–Samani in 1985 and Heydari and Heydari in 2014. Full article

Major explosive volcanic eruptions impact the climate by altering the radiative balance of the atmosphere and through feedback mechanisms in the climate system. The extent of the impact depends on the magnitude (aerosol mass loading) and the number or frequency of such eruptions. Multiple Antarctica ice core records of past volcanic eruptions reveal that the number (5) of major eruptions (volcanic sulfate deposition flux greater than 10 kg km⁻²) was the highest in the 13th century over the last two millennia. Signals of four of the five eruptions are dated to the second half of the century, indicating consecutive major eruptions capable of causing sustained climate impact via known feedback processes. The fact that signals of four corresponding eruptions have been found in a Greenland ice core indicates that four of the five 13th century eruptions were probably by volcanoes in the low latitudes (between 20° N and 20° S) with substantial aerosol mass loading. These eruptions in the low latitudes likely exerted the strongest volcanic impact on climate in the last two millennia. Full article

The seasonal variability of Southern Hemisphere (SH) synoptic-scale weather systems is analysed for the 20-year timespan 1997 to 2016. The relationships between the SH jet streams and storm tracks based on lower tropospheric circulation anomalies filtered into the high-pass (periods < 4 days) and band-pass (periods between 4 and 8 days) types are examined based on 6-hourly reanalysis data. Leading Empirical Orthogonal Functions (EOFs) and storm tracks based on all (growing and decaying) disturbances are determined. As well, the structure and standard deviations of streamfunction fluctuations are determined separately in three growth rate and three decay rate bins focusing on explosive growth and decay. In all cases, and in each season, the band-pass storm tracks are more zonally symmetric than the high-pass standard deviations and this is also reflected by the EOFs. Leading EOFs in both bands are monopole wavetrains of highs and lows located in the storm tracks with some band-pass disturbances having dipole structures consistent with blocking and northwest cloud bands. EOFs based on the bin with slow-growing fluctuations are structurally similar to the standard EOFs based on all disturbances. EOFs for moderately and explosively growing disturbances are increasingly displaced equatorward with a larger growth rate. Full article

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm², peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality. Full article

On 31 July 2021, Ningbo, an eastern coast city in China, experienced a severe convective rainstorm, characterized by intense short-duration precipitation extremes with a maximum rainfall rate of 130 mm h⁻¹. In this research, we first analyzed this rainstorm using Doppler radar and precipitation observation and then conducted high-resolution simulation for it. A three-dimensional precipitation diagnostic equation is introduced to quantitatively analyze the microphysical processes during the rainstorm. It is shown that this rainstorm was triggered and developed locally in central Ningbo under favorable large-scale quasi-geostrophic conditions and local conditions. In the early stage, the precipitation increase is mainly driven by the strong convergence of water vapor, and a noticeable increase in both the intensity and spatial extent of uplift promotes the upward transportation of water vapor. As the water vapor flux and associated convergence weaken in the later stage, the precipitation reduces accordingly. Cloud microphysical processes are also important in the entire precipitation process. The early stage updraft supports the escalations in raindrops, with the notable fluctuations in raindrop concentrations directly linked to variations in ground precipitation intensity. The behavior of graupel particles is intricately connected to their melting as they fall below the zero-degree layer. Although cloud water and snow exhibit changes during this period, the magnitudes of these adjustments are considerably less pronounced than those in raindrops and graupels, highlighting the differentiated response of various condensates to the convective dynamics. These results can help deepen the understanding of local severe rainstorms and provide valuable scientific references for practical forecasting. Full article

Grapevine is a highly climate-sensitive plant. In the last few decades, an increase in the number and area of vineyards has been observed in the country, with the Zielona Góra region pioneering this revival. A comprehensive analysis of climatic and agroclimatic indicators for grapevines was conducted to assess the possibilities and limitations in this region. Based on data obtained from the Institute of Meteorology and Water Management—National Research Institute (IMGW-PIB) for stations in Zielona Góra, nine key indicators were identified. The analysis of agroclimatic conditions for the Zielona Góra winegrowing region from 1951 to 2022 revealed significant changes in air temperature, length of the vegetative period, and number of frosts. The average annual air temperature increased, while the number of days with temperatures below 8 °C decreased. The extension of the vegetative period (starting earlier and ending later) favours grapevine cultivation. The increase in temperature during the vegetative period and the lengthening of the frost-free period have a beneficial effect on grape production in the Zielona Góra region. Full article

The use of plant protection products (PPPs) has become fundamental to guarantee excellent field productivity. Nevertheless, their usage presents critical issues, such as the quantity of substances used, the relative toxicity, and the contamination of nearby fields caused by atmospheric drift. This study focuses on the characterization of aerosol droplets of PPPs produced by spraying a chemical marker, fluorescein, with an orchard airblast sprayer equipped with conventional hollow cone (HC) and anti-drift air inclusion (AI) nozzles, using a wind tunnel as a controlled environment. A particle/droplet image analysis was employed to study the droplet production of the nozzles, while a liquid chromatography tandem mass spectrometry (HPLC-MS/MS) analysis allowed us to evaluate samples collected using a cascade impactor located at 5 m, 10 m, and 20 m from the emission point. Overall, HC nozzles are very accurate at producing specific drop size distributions (DSDs), while AI nozzles produce a much wider DSD, concentrating the largest part of the distributed volume into droplets of a larger size. The marker concentration was much lower for the AI nozzles compared to the HC nozzles; moreover, the two nozzles show a similar trend in the coarse droplet range, while significantly differing in the fine droplet spectrum. Full article

A typical Madden–Julian Oscillation (MJO) generates a large region of enhanced rainfall over the equatorial Indian Ocean that moves slowly eastward into the western Pacific. Tropical cyclones often form on the poleward edges of the MJO moist-convective envelope, frequently impacting both southeast Asia and northern Australia, and on occasion Eastern Africa. This paper addresses the question of whether these MJO-induced tropical cyclones will become more numerous in the future as the oceans warm. The Lagrangian Atmosphere Model (LAM), which has been carefully tuned to simulate realistic MJO circulations, is used to study the sensitivity of MJO modulation of tropical cyclogenesis (TCG) to global warming. A control simulation for the current climate is compared with a simulation with enhanced radiative forcing consistent with that for the latter part of the 21st century under Shared Socioeconomic Pathway (SSP) 585. The LAM control run reproduces the observed MJO modulation of TCG, with about 70 percent more storms forming than monthly climatology predicts within the MJO’s convective envelope. The LAM SSP585 run suggests that TCG enhancement within the convective envelope could reach 170 percent of the background value under a high greenhouse gas emissions scenario, owing to a strengthening of Kelvin and Rossby wave components of the MJO’s circulation. Full article

The abundance of ions within the ionosphere makes it an important region for both long range and satellite communication systems. However, characterizing the complexity in the ionosphere within the equatorial region of Abuja, with geographic coordinates of 8.99° N and 7.39° E and a geomagnetic latitude of −1.60, and Lagos, with geographic coordinates of 3.27° E and 6.48° N and a dip latitude of −1.72°, is a challenging and daunting task due to the intrinsic and external forces involved. In this study, chaos theory was applied on data from both an HF Doppler sounding system and the Global Navigation Satellite System (GNSS) for the characterization of the ionosphere over these two tropical locations during 2020–2021 with respect to the quality of high-frequency radio signals between the two locations. Our results suggest that the ionosphere at the two locations is chaotic, with its largest Lyapunov exponent values being greater than 0 ($0.011\le \lambda \le 0.041$) and its correlation dimension being in the range of $1.388\le D_2\le 1.775$. Furthermore, it was revealed that there exists a negative correlation between the state of the ionosphere and signal quality at the two locations. Using transfer entropy, it was confirmed that the ionosphere interfered more with signals during 2020, a year of lower solar activity (sunspot number, 8.8) compared to 2021 (sunspot number, 29.6). On a monthly scale, the influence of the ionosphere on signal quality was found to be complicated. The results obtained in this study will be useful in communication systems design, modelling, and prediction. Full article