Many nontornadic supercell storms have times when they appear to be moving toward tornadogenesis, including the development of a strong low-level vortex, but never end up producing a tornado. These tornadogenesis failure (TGF) episodes can be a substantial challenge to operational meteorologists. In this study, a sample of 32 pre-tornadic and 36 pre-TGF supercells is examined in the 30 min pre-tornadogenesis or pre-TGF period to explore the feasibility of using polarimetric radar metrics to highlight storms with larger tornadogenesis potential in the near-term. Overall the results indicate few strong distinguishers of pre-tornadic storms. Differential reflectivity (Z_DR) arc size and intensity were the most promising metrics examined, with Z_DR arc size potentially exhibiting large enough differences between the two storm subsets to be operationally useful. Change in the radar metrics leading up to tornadogenesis or TGF did not exhibit large differences, though most findings were consistent with hypotheses based on prior findings in the literature. Full article

Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models. Full article

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models. Full article

The prediction of rapid intensification (RI) in tropical cyclones (TCs) is a challenging problem. In this study, the RI process and factors contributing to it are compared for two TCs: an axis-symmetric case (Hurricane Irma, 2017) and an asymmetric case (Hurricane Michael, 2018). Both Irma and Michael became major hurricanes that made significant impacts in the United States. The Hurricane Weather Research and Forecasting (HWRF) Model was used to examine the connection between RI with forcing from the large-scale environment and the subsequent evolution of TC structure and convection. The observed large-scale environment was reasonably reproduced by HWRF forecasts. Hurricane Irma rapidly intensified in an environment with weak-to-moderate vertical wind shear (VWS), typically favorable for RI, leading to the symmetric development of vortical convective clouds in the cyclonic, vorticity-rich environment. Conversely, Hurricane Michael rapidly intensified in an environment of strong VWS, typically unfavorable for RI, leading to major asymmetries in the development of vortical convective clouds. The tangential wind momentum budget was analyzed for these two hurricanes to identify similarities and differences in the pathways to RI. Results suggest that eddy transport terms associated with convective processes positively contributed to vortex spin up in the early stages of RI and inhibited spin up in the later stages of RI in both TCs. In the early stages of RI, the mean transport terms exhibited notable differences in these TCs; they dominated the spin-up process in Irma and were of secondary importance to the spin-up process in Michael. Favorable aspects of the environment surrounding Michael appeared to aid in the RI process despite hostile VWS. Full article

UV index (UVI) measurements were carried out by the hand-held instrument Solarmeter 6.5 onboard of MS Horyzont II during the cruise from Poland (Gdynia) to Spitsbergen (and back) in the period from 2 to 21 July 2017. A method is proposed to estimate the erythemal doses and sun-synthesized amount of vitamin D from a limited number of daily UVI observations. This study shows that the erythema could appear in a person with Caucasian type of skin characterized by Minimum Erythema Dose (MED) ~250 J m⁻² after ~1 h exposure near the polar circle and up to few hours in the Svalbard. During this time, it was possible to get the dose of vitamin D₃ equivalent to ~1000 IU of oral intake. The protection against UV overexposure should be applied even if UVI values during the cruise in the Arctic were always below the World Meteorological Organization (WMO) warning threshold of 3. To provide adequate amount of vitamin D, the exposure should be continued until getting 1 MED, after which the vitamin supplementation (or a diet rich in vitamin D) is necessary. Full article

We report the results of year-long PM_2.5 (particulate matter less than 2.5 µm in diameter) simulations over Northeast Asia for the base year of 2013 under the framework of the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project. LTP is a tripartite project launched by China, Japan, and Korea for cooperative monitoring and modeling of the long-range transport (LRT) of air pollutants. In the modeling aspect in the LTP project, each country’s modeling group employs its own original air quality model and options. The three regional air quality models employed by the modeling groups are WRF-CAMx, NHM-RAQM2, and WRF-CMAQ. PM_2.5 concentrations were simulated in remote exit-and-entrance areas associated with the LRT process over China, Japan, and Korea. The results showed apparent bias that remains unexplored due to a series of uncertainties from emission estimates and inherent model limitations. The simulated PM₁₀ levels at seven remote exit-and-entrance sites were underestimated with the normalized mean bias of 0.4 ± 0.2. Among the four chemical components of PM_2.5 (SO₄²⁻, NO₃⁻, organic carbon (OC), and elemental carbon (EC)), the largest inter-model variability was in OC, with the second largest discrepancy in NO₃⁻. Our simulation results also indicated that under considerable SO₄²⁻ levels, favorable environments for ammonium nitrate formation were found in exit-and-entrance areas between China and Korea, and gas-aerosol partitioning for semi-volatile species of ammonium nitrate could be fully achieved prior to arrival at the entrance areas. Other chemical characteristics, including NO₃⁻/SO₄²⁻ and OC/EC ratios, are discussed to diagnose the LRT characteristics of PM_2.5 in exit-and-entrance areas associated with transboundary transport over China, Japan, and Korea. Full article

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM_2.5 up to 300 µg/m³ (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk. Full article

Transported pollution plays an important role in the atmospheric environment of eastern China. This study analyzed the characteristics of surface winds at different air quality levels using meteorological station observations of both wind and mass concentrations of particulate matter with aerodynamic diameters <2.5 μm (PM_2.5) over Hong Kong and Beijing. In recent decades, wind directions at the surface exhibit a similar pattern for both good and poor air quality levels at all three stations, indicating a weak relationship between surface winds and air quality in Hong Kong. However, winds at a height of 1–2 km govern pollution accumulation. This dominant role is illustrated by a sudden change in wind direction within this layer and a simultaneous pollution accumulation stage on 8 January 2014. The controlling influence of winds at 1–2 km on both the deterioration and improvement of air quality is also supported by a distinct vertical wind distribution for all 21 monotonic increasing stages and 17 decreasing stages of PM_2.5. In contrast, air pollution is transported to Beijing throughout the atmospheric layer that extends from the surface to a height of more than 3 km. This key difference may be due to variations in meteorology, topography, and emission sources between Hong Kong and Beijing. The results that layer of 1–2 km in Hong Kong and of surface to 3 km in Beijing is the height where pollution transport is most likely to occur are critical for forecasting severe haze episodes in eastern China. Full article

Precise agricultural predictions of climate change effects on crop water productivity are essential to ensure food security and alleviate water scarcity. In this regard, the present study provides an overview of the future impacts of climate change on the irrigation of agricultural products such as rice, millet, maize, cassava, sorghum, and sugar cane. These crops are some of the most-consumed foodstuffs in countries of the Niger River basin. This study is realized throughout 2020 to 2080, and three Global Climate Models (GCMs) (CSIRO, MIROC5, and ECHAM. MPI-ESM-LR) have been used. The GCMs data have been provided by the IPCC5 database. The irrigation water requirement for each crop was calculated using Smith’s CROPWAT approach. The Penman–Monteith equation recommended by the FAO was used to calculate the potential evapotranspiration. The inter-annual results of the IWR, according to the set of models selected, illustrate that the largest quantities of water used for irrigation are generally observed between January and March, and the lowest quantities are the most often seen between July and September. The majority of models also illustrate a peak in the IWR between March and April. Sorghum and millet are the crops consuming the least amount of water for irrigation; followed by cassava, then rice and corn, and finally sugar cane. The most significant IWRs, which have been predicted, will be between 16.3 mm/day (MIROC5 model, RCP 4.5) and 45.9 mm/day (CSIRO model, RCP 4.5), particularly in Mali, Niger, Algeria, and rarely in Burkina-Faso (CSIRO model, RCP4.5 and 8.5). The lowest IWRs predicted by the models will be from 1.29 mm/day (MIROC5 model, RCP 4.5) to 33.4 mm/day (CSIRO model, RCP 4.5); they will be observed according to the models in Guinea, southern Mali, Ivory Coast, center and southern Nigeria, and Cameroon. However, models predict sugarcane to be the plant with the highest IWR, between 0.25 mm/day (Benin in 2020–2040) and 25.66 mm/day (Chad in 2060–2080). According to the models’ predictions, millet is the crop with the most IWR, between 0.20 mm/day (Benin from 2020 to 2060) and 19.37 mm/day (Chad in 2060–2080). With the results of this study, the countries belonging to the Niger River basin can put in place robust policies in the water resources and agriculture sectors, thus ensuring food security and high-quality production of staple crops, and avoiding water scarcity while facing the negative impacts of climate change. Full article

Over the Sahara in summer, the activity of the Saharan thermal low pressure system (SHL), which is linked to the West-African monsoon dynamics and the mid-latitude circulation, is modulated by dust concentration and water-vapor transport. In this context, the role of clouds over western Sahara remains under-investigated. Using Meteosat-Second-Generation geostationary satellite data, for the first time the variability of cloud occurrence over Sahara by type in summer, at diurnal, daily and intra-seasonal time scales for the 2008–2014 period is documented. Using European Center for Medium-range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim (ERAI) reanalysis, cloud cover occurrences are characterized in terms of regional circulation patterns and moisture balance. We show that, over West-Sahara and Hoggar, mid-top clouds are the most frequent cloud-type in summer. Their summit reaches between 500 hPa and 400 hPa and lies just above the top of the Saharan Atmospheric Boundary Layer (SABL). During the rest of the year, high-top clouds are the most frequent. The variations in the spatial distribution of mid-top cloud occurrence coincide with the seasonal displacement and strengthening of the SHL and, in the mid-troposphere, of the Saharan anticyclone. Mid-top clouds occur most frequently when, at large scale, mass and humidity converge in the lower SABL due to heating on an extensive surface, and diverge in the upper SABL. Their diurnal cycle, with minimal frequency around 10 UTC and maximum in the evening, is consistent with the diurnal development of the Saharan Convective-Boundary-Layer. The frequency of high cloud increases when anticyclonic circulations at mid-level and upper-level retreat to the southeast and upper-level trough from mid-latitudes can penetrate more southwards. Full article

Elements atmospheric deposition was studied by mosses method in Kosovo. Mosses are a very useful tool for atmospheric deposition of elements monitoring, owing to their physiological and morphological characteristics. Moss samples were collected from 45 locations, they were cleaned from leaves, twigs and other materials, they were grinded, digested in a microwave system and analyzed by spectroscopy Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). A total of 25 elements concentration in collected moss samples was determined: Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Sr, Ti, Tl, V and Zn. Extremely high concentrations of Pb and Zn were found in moss samples from the areas of Zveçan and Stanterg where the ore processing facilities are located, as well as the tailings landfill in Mitrovica which is nearby in the south, which reflects eminent air pollution. High concentrations of As and Cd were also measured in the same sampling points. The obtained results were processed by multivariate statistical analysis/factor analysis, which resulted in four factors, one anthropogenic and three geogenic or mixed geogenic-anthropogenic. The factor analysis revealed that the area with the highest atmospheric deposition of potentially toxic elements is the basin of Kosovo, from the north in Leposaviç to the south in Hani i Elezit because of the industrial plants laying in this line. Full article

This study proposed an aerosol characterization process using satellites for severe biomass burning events. In general, these severely hazy cases are labeled as “undecided” or “hazy.” Because atmospheric aerosols are significantly affected by factors such as air quality, global climate change, local environmental risk, and human and biological health, efficient and accurate algorithms for aerosol retrieval are required for global satellite data processing. Our previous classification of aerosol types was based primarily on near-ultraviolet (UV) data, which facilitated subsequent aerosol retrieval. In this study, algorithms for aerosol classification were expanded to events with serious biomass burning aerosols (SBBAs). Once a biomass burning event is identified, the appropriate radiation simulation method can be applied to characterize the SBBAs. The second-generation global imager (SGLI) on board the Japanese mission JAXA/Global Change Observation Mission-Climate contains 19 channels, including red (674 nm) and near-infrared (869 nm) polarization channels with a high resolution of 1 km. Using the large-scale wildfires in Kalimantan, Indonesia in 2019 as an example, the complementarity between the polarization information and the nonpolarized radiance measurements from the SGLI was demonstrated to be effective in radiation simulations for biomass burning aerosol retrieval. The retrieved results were verified using NASA/AERONET ground-based measurements, and then compared against JAXA/SGLI/L2-version-1 products, and JMA/Himawari-8/AHI observations. Full article

Experimental field campaigns are an essential part of atmospheric research, as well as of university education in the field of atmospheric physics and meteorology. Experimental field observations are needed to improve the understanding of the surface-atmosphere interaction and atmospheric boundary layer (ABL) physics and develop corresponding model parameterizations. Information on the ABL wind profiles is essential for the interpretation of other observations. However, wind profile measurements above the surface layer remain challenging and expensive, especially for the field campaigns performed in remote places and harsh conditions. In this study, we consider the experience of using two low-cost methods for the wind profiling, which may be easily applied in the field studies with modest demands on logistical opportunities, available infrastructure, and budget. The first one is a classical and well-known method of pilot balloon sounding, i.e., when balloon is treated as a Lagrangian particle and tracked by theodolite observations of angular coordinates. Second one is based on a vertical sounding with a popular and relatively cheap mass-market quadcopter DJI Phantom 4 Pro and utilizes its built-in opportunity to restore the wind vector from quadcopter tilt angles. Both methods demonstrated reasonable agreement and applicability even in harsh weather conditions and complex terrain. Advantages and shortcomings of these methods, as well as practical recommendations for their use are discussed. For the drone-based wind estimation, the importance of calibration by comparison to high-quality wind observations is shown. Full article

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h${}^{-1}$; 15.4 m s${}^{-1}$ 24 h${}^{-1}$) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecasts. Full article

Health risks from air pollution continue to be a major concern for residents in Mexico City. These health burdens could be partially alleviated through individual avoidance behavior if accurate information regarding the daily health risks of multiple pollutants became available. A split sample approach was used in this study to create and validate a multi-pollutant, health-based air quality index. Poisson generalized linear models were used to assess the impacts of ambient air pollution (i.e., fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ground-level ozone (O₃)) on a total of 610,982 daily emergency department (ED) visits for respiratory disease obtained from 40 facilities in the metropolitan area of Mexico City from 2010 to 2015. Increased risk of respiratory ED visits was observed for interquartile increases in the 4-day average concentrations of PM_2.5 (Risk Ratio (RR) 1.03, 95% CI 1.01–1.04), O₃ (RR 1.03, 95% CI 1.01–1.05), and to a lesser extent NO₂ (RR 1.01, 95% CI 0.99–1.02). An additive, multi-pollutant index was created using coefficients for these three pollutants. Positive associations of index values with daily respiratory ED visits was observed among children (ages 2–17) and adults (ages 18+). The use of previously unavailable daily health records enabled an assessment of short-term ambient air pollution concentrations on respiratory morbidity in Mexico City and the creation of a health-based air quality index, which is now currently in use in Mexico City. Full article

The Antarctic Peninsula belongs to the regions of the Earth that have seen the highest increase in air temperature in the past few decades. The warming is reflected in degradation of the cryospheric system. The impact of climate variability and interactions between the atmosphere and the cryosphere can be studied using numerical atmospheric models. In this study, the standard version of the Weather Research and Forecasting (WRF) model was validated on James Ross Island in the northern part of the Antarctic Peninsula. The aim of this study was to verify the WRF model output at 700 m horizontal resolution using air temperature, wind speed and wind direction observations from automatic weather stations on the Ulu Peninsula, the northernmost part of James Ross Island. Validation was carried out for two contrasting periods (summer and winter) in 2019/2020 to assess possible seasonal effects on model accuracy. Simulated air temperatures were in very good agreement with measurements (mean bias −1.7 °C to 1.4 °C). The exception was a strong air temperature inversion during two of the winter days when a significant positive bias occurred at the coastal and lower-altitude locations on the Ulu Peninsula. Further analysis of the WRF estimates showed a good skill in simulating near-surface wind speed with higher correlation coefficients in winter (0.81–0.93) than in summer (0.41–0.59). However, bias and RMSE for wind speed tended to be better in summer. The performance of three WRF boundary layer schemes (MYJ, MYNN, QNSE) was further evaluated. The QNSE scheme was generally more accurate than MYNN and MYJ, but the differences were quite small and varied with time and place. The MYNN and QNSE schemes tended to achieve better wind speed simulation quality than the MYJ scheme. The model successfully captured wind direction, showing only slight differences to the observed values. It was shown that at lower altitudes the performance of the model can vary greatly with time. The model results were more accurate during high wind speed southwestern flow, while the accuracy decreased under weak synoptic-scale forcing, accompanied by an occurrence of mesoscale atmospheric processes. Full article

A squall line is a type of strongly organized mesoscale convective system that can cause severe weather disasters. Thus, it is crucial to explore the dynamic structure and hydrometeor distributions in squall lines. This study analyzed a squall line over Guangdong Province on 6 May 2016 that was observed using a Ka-band millimeter-wave cloud radar (CR) and an S-band dual-polarization radar (PR). Doppler spectral density data obtained by the CR were used to retrieve the vertical air motions and raindrop size distribution (DSD). The results showed the following: First, the CR detected detailed vertical profiles and their evolution before and during the squall line passage. In the convection time segment (segment B), heavy rain existed with a reflectivity factor exceeding 35 dBZ and a velocity spectrum width exceeding 1.3 m s⁻¹. In the PR detection, the differential reflectivity factor (Z_dr) was 1–2 dB, and the large specific differential phase (K_dp) also represented large liquid water content. In the transition and stratiform cloud time segments (segments B and C), the rain stabilized gradually, with decreasing cloud tops, stable precipitation, and a 0 °C layer bright band. Smaller K_dp values (less than 0.9) were distributed around the 0 °C layer, which may have been caused by the melting of ice crystal particles. Second, from the CR-retrieved vertical air velocity, before squall line passage, downdrafts dominated in local convection and weak updrafts existed in higher-altitude altostratus clouds. In segment B, the updraft air velocity reached more than 8 m s⁻¹ below the 0 °C layer. From segments C to D, the updrafts changed gradually into weak and wide-ranging downdrafts. Third, in the comparison of DSD values retrieved at 1.5 km and DSD values on the ground, the retrieved DSD line was lower than the disdrometer, the overall magnitude of the DSD retrieved was smaller, and the difference decreased from segments C to D. The standardized intercept parameter (N_w) and shape parameter (μ) of the DSD retrieved at 1.8 km showed good agreement with the disdrometer results, and the mass-weighted mean diameter (D_m) was smaller than that on the ground, but very close to the PR-retrieved D_m result at 2 km. Therefore, comparing with the DSD retrieved at around 2 km, the overall number concentration remained unchanged and D_m got larger on the ground, possibly reflecting the process of raindrop coalescence. Lastly, the average vertical profiles of several quantities in all segments showed that, first of all, the decrease of N_w and D_m with height in segments C and D was similar, reflecting the collision effect of falling raindrops. The trends were opposite in segment B, indicating that raindrops underwent intense mixing and rapid collision and growth in this segment. Then, PR-retrieved D_m profiles can verify the rationality of the CR-retrieved D_m. Finally, a vertical velocity profile peak generated a larger D_m especially in segments C and D. Full article

The results of a preliminary investigation of the dust sources in the Arctic based on their geochemical properties by potential source contribution function (PSCF) analysis are presented in this paper. For this purpose, we considered one year of aerosol geochemical data from Ny-Ålesund, Svalbard, and a short list of chemical elements (i.e., Al, Fe, Mn, Ti, Cr, V, Ni, Cu, and Zn) variably related to the dust fraction. Based on PSCF analysis: (i) four different dust source areas (i.e., Eurasia, Greenland, Arctic-Alaska, and Iceland) were characterized by distinguishing geochemical ranges and seasonal occurrence; and (ii) a series of typical dust days from the distinct source areas were identified based on the corresponding back trajectory patterns. Icelandic dust samples revealed peculiar but very variable characteristics in relation to their geographical source regions marked by air mass back trajectories. The comparison between pure and mixed Icelandic dust samples (i.e., aerosols containing Icelandic dust along with natural and/or anthropogenic components) revealed the occurrence of different mixing situations. Comparison with Icelandic soils proved the existence of dilution effects related to the emission and the transport processes. Full article

Diesel exhaust is hazardous to human health. In time, this has led the EU to impose on manufacturers lower and lower emission standards. These limits are very challenging in particular for nitrogen oxides (NO_x) emitted by diesel-fueled vehicles. For the town of Milan (Italy), we used a complex modeling system that takes into account the NO_x emissions from vehicular traffic and other urban sources, as well as their dispersion and chemical transformations in the atmosphere related to meteorological parameters. The traffic emissions in the Milan urban area were estimated using the geometric and structural characteristics of the road network, whereas the traffic flows were provided by the Environment and Territory Mobility Agency. Car emissions were estimated by the official European method COPERT 5. The nitrogen dioxide (NO₂) concentrations were estimated under two scenarios: the actual scenario with real emissions and the Diesel Emission Standards Compliance (DESC) scenario. Using a recent meta-analysis, limited to European studies, we evaluated the relationship between NO₂ concentrations and natural mortality. For the actual scenario, the NO₂ annual concentration mean was 44.3 µg/m³, whereas under the DESC hypothetical scenario, this would have been of 37.7 µg/m³. This “extra” exposure of 6.6 µg/m³ of NO₂ leads to a yearly excess of 574 “natural” deaths. Diesel emissions are very difficult to limit and are harmful for exposed people. This suggests that specific policies, including traffic limitations, need to be developed and enforced in urban environments. Full article

A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and services. Recent research on data analysis using radar data has concentrated on applying machine learning techniques to solve complicated problems, including quality control, quantitative precipitation estimation, and convective storm prediction. Convective storms, which consist of heavy rains and winds, are closely related to real-life and cause significant loss of life and property. This paper proposes a novel approach utilizing the given convective storms’ temporal properties based on machine learning models to predict future locations. The experimental results showed that the machine learning-based prediction models are capable of nowcasting future locations of convective storms with a slight difference. Full article

In January 2017, historic forest fires occurred in south-central Chile. Although their causes and consequences on health and ecosystems were studied, little is known about their atmospheric effects. Based on chemistry-transport modeling with WRF-CHIMERE, the impact of the 2017 Chilean mega-fires on regional atmospheric composition, and the associated meteorological feedback, are investigated. Fire emissions are found to increase pollutants surface concentration in the capital city, Santiago, by +150% (+30 µg/m${}^3$) for PM${}_{2.5}$ and +50% (+200 ppb) for CO on average during the event. Satellite observations show an intense plume extending over 2000 km, well reproduced by the simulations, with Aerosol Optical Depth at 550 nm as high as 4 on average during the days of fire activity, as well as dense columns of CO and O${}_3$. In addition to affecting atmospheric composition, meteorology is also modified through aerosol direct and indirect effects, with a decrease in surface radiation by up to 100 W/m${}^2$ on average, leading to reductions in surface temperatures by 1 K and mixing layer heights over land by 100 m, and a significant increase in cloud optical depth along the plume. Large deposition fluxes of pollutants over land, the Pacific ocean and the Andes cordillera are found, signaling potential damages to remote ecosystems. Full article

Vulcanian eruptions (short-lived explosions consisting of a rising thermal) occur daily in volcanoes around the world. Such small-scale eruptions represent a challenge in numerical modeling due to local-scale effects, such as the volcano’s topography impact on atmospheric circulation and near-vent plume dynamics, that need to be accounted for. In an effort to improve the applicability of Tephra2, a commonly-used advection-diffusion model, in the case of vulcanian eruptions, a number of key modifications were carried out: (i) the ability to solve the equations over bending plume, (ii) temporally-evolving three-dimensional meteorological fields, (iii) the replacement of the particle diameter distribution with observed particle terminal velocity distribution which provides a simple way to account for the settling velocity variation due to particle shape and density. We verified the advantage of our modified model (Tephra4D) in the tephra dispersion from vulcanian eruptions by comparing the calculations and disdrometer observations of tephra sedimentation from four eruptions at Sakurajima volcano, Japan. The simulations of the eruptions show that Tephra4D is useful for eruptions in which small-scale movement contributes significantly to ash transport mainly due to the consideration for orographic winds in advection. Full article

PM_2.5 level has decreased significantly in Beijing in recent years due to the strict air quality control measures taken in Jingjinji Region and the surrounding areas. However, the variation characteristics of the concentrations of PM_2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Beijing in recent years are still not so clear. In order to understand the pollution status of PM_2.5-bound PAHs in Beijing, fifteen PAHs were measured in a typical urban area of Beijing from 1 March to 20 March 2018. The average mass concentration of the 15 PAHs was 21 ng/m³ and higher in the nighttime than that in the daytime. The proportion of 4-ring PAHs in 15 PAHs was highest (43%), while 6-ring PAHs was lowest (10%). The levels of PAHs were higher during heavy pollution episodes than those in non-heavy pollution episodes, and the proportions of 5- and 6-ring PAHs were increased during a heavy pollution episode. PAHs posed obvious carcinogenic risks to the exposed populations, and the risk was higher during heavy pollution episodes than the average value of the whole monitoring period. The main sources of PAHs were traffic emissions and coal/biomass burning. Air masses from the south-southeast had a great influence on the PM_2.5 levels during a heavy pollution episode. It is recommended that not only the PM_2.5 levels but also the PAHs levels bounded in PM_2.5 should be controlled to protect human health in Beijing. Full article

While more and more studies are being conducted on carbonaceous fractions—organic carbon (OC) and elemental carbon (EC)—in urban areas, there are still too few studies about these species and their effects in polar areas due to their very low concentrations; further, studies in the literature report only data from intensive campaigns, limited in time. We present here for the first time EC–OC concentration long-time data records from the sea-level sampling site of Ny-Ålesund, in the High Arctic (5 years), and from Dome C, in the East Antarctic Plateau (1 year). Regarding the Arctic, the median (and the interquartile range (IQR)) mass concentrations for the years 2011–2015 are 352 (IQR: 283–475) ng/m³ for OC and 4.8 (IQR: 4.6–17.4) ng/m³ for EC, which is responsible for only 3% of total carbon (TC). From both the concentration data sets and the variation of the average monthly concentrations, the influence of the Arctic haze on EC and OC concentrations is evident. Summer may be interested by high concentration episodes mainly due to long-range transport (e.g., from wide wildfires in the Northern Hemisphere, as happened in 2015). The average ratio of EC/OC for the summer period is 0.05, ranging from 0.02 to 0.10, and indicates a clean environment with prevailing biogenic (or biomass burning) sources, as well as aged, highly oxidized aerosol from long-range transport. Contribution from ship emission is not evident, but this result may be due to the sampling time resolution. In Antarctica, a 1 year-around data set from December 2016 to February 2018 is shown, which does not present a clear seasonal trend. The OC median (and IQR) value is 78 (64–106) ng/m³; for EC, it is 0.9 (0.6–2.4) ng/m³, weighing for 3% on TC values. The EC/OC ratio mean value is 0.20, with a range of 0.06–0.35. Due to the low EC and OC concentrations in polar areas, correction for the blank is far more important than in campaigns carried out in other regions, largely affecting uncertainties in measured concentrations. Through the years, we have thus developed a new sampling strategy that is presented here for the first time: samplers were modified in order to collect a larger amount of particulates on a small surface, enhancing the capability of the analytical method since the thermo-optical analyzer is sensitive to carbonaceous aerosol areal density. Further, we have recently coupled such modified samplers with a sampling strategy that makes a more reliable blank correction of every single sample possible. Full article

A multi-radar analysis of the 20 May 2013 Moore, Oklahoma, U.S. supercell is presented using three Weather Surveillance Radars 1988 Doppler (WSR-88Ds) and PX-1000, a rapid-scan, polarimetric, X-band radar, with a focus on the period between 1930 and 2008 UTC, encompassing supercell maturation through rapid tornado intensification. Owing to the 20-s temporal resolution of PX-1000, a detailed radar analysis of the hook echo is performed on (1) the microphysical characteristics through a hydrometeor classification algorithm (HCA)—inter-compared between X- and S-band for performance evaluation—including a hail and debris class and (2) kinematic properties of the low-level mesocyclone (LLM) assessed through $\Delta V_r$ analyses. Four transient intensifications in $\Delta V_r$ prior to tornadogenesis are documented and found to be associated with two prevalent internal rear-flank downdraft (RFD) momentum surges, the latter surge coincident with tornadogenesis. The momentum surges are marked by a rapidly advancing reflectivity ($Z_H$) gradient traversing around the LLM, descending reflectivity cores (DRCs), a drop in differential reflectivity ($Z_{DR}$) due to the advection of smaller drops into the hook echo, a decrease in correlation coefficient (${\rho }_{hv}$), and the detection of debris from the HCA. Additionally, volumetric analyses of $Z_{DR}$ and specific differential phase ($K_{DP}$) signatures show general diffusivity of the $Z_{DR}$ arc even after tornadogenesis in contrast with explosive deepening of the $K_{DP}$ foot downshear of the updraft. Similarly, while the vertical extent of the $Z_{DR}$ and $K_{DP}$ columns decrease leading up to tornadogenesis, the phasing of these signatures are offset after tornadogenesis, with the $Z_{DR}$ column deepening the lagging of $K_{DP}$. Full article

Two low-cost fine particulate matter (PM_2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM_2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM_2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events. Full article

A total of 68 PUF samples were collected seasonally from 17 sampling sites in Harbin, China from May 2016 to April 2017 for analyzing 15 congeners of gaseous polycyclic aromatic hydrocarbons (Σ₁₅PAHs). An improved non-negative matrix (NMF) model and a positive matrix factorization (PMF) model were used to apportion the sources of PAHs. The carcinogenic risk due to exposure to PAHs was estimated by the toxicity equivalent of BaP (BaPeq). The results showed that the average concentration of Σ₁₅PAHs was 68.3 ± 22.3 ng/m³, and the proportions of 3-ring, 4-ring, 5-ring, and 6-ring PAHs were 64.4%, 32.6%, 2.10%, and 0.89%, respectively. Among the six typical functional areas in Harbin, the Σ₁₅PAHs concentrations were 98.1 ± 76.7 ng/m³, 91.2 ± 76.2 ng/m³, 71.4 ± 75.6 ng/m³, 67.9 ± 65.6 ng/m³, 42.6 ± 34.7 ng/m³, and 38.5 ± 38.0 ng/m³ in the wastewater treatment plant, industrial zone, business district, residential area, school, and suburb, respectively. During the sampling period, the highest concentration of Σ₁₅PAHs was in winter. The improved NMF model and PMF model apportioned the PAHs into three sources including coal combustion, biomass burning, and vehicle exhaust. The contributions of coal combustion, biomass burning, and vehicle exhausts were 34.6 ± 3.22%, 48.6 ± 4.03%, and 16.8 ± 5.06%, respectively. Biomass burning was the largest contributor of Σ₁₅PAHs concentrations in winter and coal combustion contributed significantly to the concentrations in summer. The average ΣBaPeq concentration was 0.54 ± 0.23 ng/m³ during the sampling period, high concentrations occurred in the cold season and low levels presented in the warm period. Vehicle exhaust was the largest contributor to the ΣBaPeq concentration of PAHs in Harbin. Full article

This study presents the PM₁₀ concentration, respiratory and cardiovascular disease hospital admissions evolution in the Ciuc basin for a period of 9 years (2008–2016), taking into consideration different meteorological conditions: boundary layer, lifting condensation level, temperature-humidity index, and wind chill equivalent chart index. The PM₁₀ and hospital admissions evolution showed a very fluctuated hourly, weekly, monthly, yearly tendency. The PM₁₀ concentration in winter (34.72 μg/m³) was 82% higher than the multiannual average (19.00 μg/m³), and almost three times higher than in summer (11.71 μg/m³). During the winter, PM₁₀ concentration increased by an average of 9.36 μg/m³ due to the increased household heating. Climatological parameters have a demonstrable effect on the PM₁₀ concentration variation. Children, the elderly and men are more sensitive to air pollution, the calculated relative risk for men was (RR = 1.45), and for women (RR = 1.37), respectively. A moderate correlation (0.51) was found between PM₁₀ and pneumonia (P), while a relatively weak correlation (0.39) was demonstrated in the case of PM₁₀ and upper respiratory tract infections (URTI). Furthermore, except thermal humidity index (THI), strong negative correlations were observed between the multiannual monthly mean PM₁₀ and the meteorological data. The PM₁₀ followed a moderate negative correlation with the boundary layer (−0.61). In the case of URTI and P, the highest number of hospital admissions occurred with a 5 to 7-day lag, while the 10 μg/m³ PM₁₀ increase resulted in a 2.04% and 8.28% morbidity increase. For lung cancer (LC) and cardiovascular diseases (AMI, IHD, CCP), a maximum delay of 5–6 months was found. Three-month delay and an average growth of 1.51% was observed in the case of chronic obstructive pulmonary disease (COPD). Overall, these findings revealed that PM₁₀ was and it is responsible for one-third of the diseases. Full article

Background and Aims: Ambient particulate matter (PM) is causing respiratory symptoms of individuals at all ages and reducing their lung functions. These individuals could develop chronic pulmonary disease. Recent studies have shown that short-term exposure to PM affects acute exacerbation of respiratory disease. However, evidence about the association between long-term exposure and progression of respiratory diseases remains insufficient. The purpose of this study was to examine the association between long-term exposure of air pollution (PM_2.5) and the effect on lung imaging phenotype in dust-exposed Korean adults living near cement factories. Methods: We conducted a cross-sectional analysis on the Chronic Obstructive Pulmonary Disease (COPD) in Dusty Areas (CODA) cohort, which was recruited from 2012 to 2014. Emphysema index and mean wall area were measured using an in-house software program developed by the Korean obstructive lung disease study group based on chest CT scan. A satellite-based model was used to estimate the long-term PM_2.5 concentration at each participant’s address. Results: Of 504 eligible participants, 400 participants were analyzed. Their mean age was 71.7 years. Most participants were men (N = 301, 75.3%). The emphysema index of the whole group was 6.63 ± 0.70, and the mean wall area was 68.8 ± 5.2. Image measurement and PM_2.5 concentration showed no significant difference in the whole group; however, in the group of subjects with normal lung function, there were significant associations between long-term PM_2.5 exposure and emphysema index measurement: 1-year (ß = 0.758, p = 0.021), 3-year (ß = 0.629, p = 0.038), and 5-year (ß = 0.544, p = 0.045). There was no significant association between long-term PM_2.5 exposure and mean wall area measurement: 1-year (ß = −0.389, p = 0.832), 3-year (ß = −3.677, p = 0.170), and 5-year (ß = −3.769, p = 0.124). Conclusions: This study suggests that long-term exposure of PM2.5 may affect the emphysematous change in patients with normal lung functions. Full article

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h⁻¹ (−4.4%) and root mean square error (RMSE) of 0.09 mm h⁻¹ (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%. Full article

Mercury (Hg) is one of the trace toxic and bioaccumulative global pollutants, and due to its long atmospheric lifetime, it presents a significant global challenge. The present study (1) utilizes total gaseous mercury (TGM) measurements made around a former Hg-cell chlor-alkali plant (CAP) located in Pavlodar, Kazakhstan, and predicts the spatial distribution of Hg over its premises and the nearby city. It then (2) estimates the environmental repartition of Hg deposited by the CAP using three fugacity models of varying complexity: Level I, QWASI, and HERMES. Finally, it (3) predicts long-range Hg transport via forward trajectory-based cluster analysis. The atmospheric Hg levels measured in Pavlodar and around Lake Balkyldak were elevated: in the range of 1–37 ng/m³ with an urban background level at 4.9 ng/m³. Specifically, concentrations up to 37 ng/m³ close to Lake Balkyldak and up to 22 ng/m³ nearby the city’s industrial zone (where the CAP was located) had been observed. Interpolation maps created using kriging also suggest these locations as the primary sources of atmospheric Hg in the city. The Level I fugacity model indicated that almost all of Hg is expected to end up in the atmosphere. The modeling results obtained using more complex QWASI and HERMES models showed that some significant quantity of Hg would still be associated with the sediments of Lake Balkyldak (a large wastewater discharge pond nearby the CAP). The forward trajectory-based cluster analysis method revealed the long-range atmospheric transportation routes and local, regional, and global impact zones. Furthermore, a source-receptor relationship using air transportation pathways to identify “areas of impact” was addressed. During both heating and non-heating seasons, the frequency-based analysis identified the distribution of Hg reaching the territories of Mongolia, northwest China, southwest Kazakhstan. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT-4) model forward trajectory analysis has confirmed similar patterns during heating and non-heating seasons, except with shorter impact distances during the non-heating period. Even though the CAP was closed more than 30 years ago and those past remediation efforts cleaned up the site, the residual Hg pollution seems significant and should be further investigated in different environmental media. Full article

The impact of the reduced atmospheric emissions due to the COVID-19 lockdown on ambient air quality in the Po Valley of Northern Italy was assessed for gaseous pollutants (NO₂, benzene, ammonia) based on data collected at the monitoring stations distributed all over the area. Concentration data for each month of the first semester of 2020 were compared with those of the previous six years, on monthly, daily, and hourly bases, so that pre, during, and post-lockdown conditions of air quality could be separately analyzed. The results show that, as in many other areas worldwide, the Po Valley experienced better air quality during 2020 spring months for NO₂ and benzene. In agreement with the reductions of nitrogen oxides and benzene emissions from road traffic, estimated to be −35% compared to the regional average, the monthly mean concentration levels for 2020 showed reductions in the −40% to −35% range compared with the previous years, but with higher reductions, close to −50%, at high-volume-traffic sites in urban areas. Conversely, NH₃ ambient concentration levels, almost entirely due the emissions of the agricultural sector, did not show any relevant change, even at high-volume-traffic sites in urban areas. These results point out the important role of traffic emissions in NO₂ and benzene ambient levels in the Po Valley, and confirm that this region is a rather homogeneous air basin with urban area hot-spots, the contributions of which add up to a relatively high regional background concentration level. Additionally, the relatively slow response of the air quality levels to the sudden decrease of the emissions due to the lockdown shows that this region is characterized by a weak exchange of the air masses that favors both the build-up of atmospheric pollutants and the development of secondary formation processes. Thus, air quality control strategies should aim for structural interventions intended to reduce traffic emissions at the regional scale and not only in the largest urban areas. Full article

PM_2.5 (fine particles with diameters 2.5 micrometers and smaller) and PM_>2.5 were separately collected in Kanazawa, Japan in every season, from the spring of 2017 to the winter of 2018, and nine polycyclic aromatic hydrocarbons (PAHs) and six nitropolycyclic aromatic hydrocarbons (NPAHs) were respectively determined using high-performance liquid chromatography (HPLC) with fluorescence and chemiluminescence detections. The atmospheric concentrations of both the PAHs and NPAHs showed seasonal changes (highest in the winter and lowest in the summer), which differed from the variations in the total suspended particulate matter (TSP) and PM_2.5 amounts (which were highest in the spring). The contributions of major sources to the combustion-derived particulate (P_c) in the PM_2.5 were calculated using the 1-nitropyrene-pyrene (NP) method, using pyrene and 1-nitropyrene as the representative markers of PAHs and NPAHs, respectively. The annual average concentration of P_c accounted for only 2.1% of PM_2.5, but showed the same seasonal variation as PAHs. The sources of P_c were vehicles (31%) and coal heating facilities/industries (69%). A backward trajectory analysis showed that the vehicle-derived P_c was mainly from Kanazawa and its surroundings, and that coal heating facilities/industry-derived P_c was transported from city areas in central and northern China in the winter, and during the Asian dust event in the spring. These results show that large amounts of PAHs were transported over a long range from China during the winter. Even in the spring, after the coal heating season was over in China, PAHs were still transported to Japan after Asian dust storms passed through Chinese city areas. By contrast, the main contributors of NPAHs were vehicles in Kanazawa and its surroundings. The recent P_c concentrations were much lower than those in 1999. This decrease was mostly attributed to the decrease in the contribution of vehicle emissions. Thus, the changes in the atmospheric concentrations of P_c, PAHs and NPAHs in Kanazawa were strongly affected not only by the local emissions but also by long-range transport from China. Full article

Governments around the world have implemented measures to slow down the spread of COVID-19, resulting in a substantial decrease in the usage of motorized transportation. The ensuing decrease in the emission of traffic-related heat and pollutants is expected to impact the environment through various pathways, especially near urban areas, where there is a higher concentration of traffic. In this study, we perform high-resolution urban climate simulations to assess the direct impact of the decrease in traffic-related heat emissions due to COVID-19 on urban temperature characteristics. One simulation spans the January–May 2020 period; two additional simulations spanning the April 2019–May 2020 period, with normal and reduced traffic, are used to assess the impacts throughout the year. These simulations are performed for the city of Montreal, the second largest urban centre in Canada. The mechanisms and main findings of this study are likely to be applicable to most large urban centres around the globe. The results show that an 80% reduction in traffic results in a decrease of up to 1 °C in the near-surface temperature for regions with heavy traffic. The magnitude of the temperature decrease varies substantially with the diurnal traffic cycle and also from day to day, being greatest when the near-surface wind speeds are low and there is a temperature inversion in the surface layer. This reduction in near-surface temperature is reflected by an up to 20% reduction in hot hours (when temperature exceeds 30 °C) during the warm season, thus reducing heat stress for vulnerable populations. No substantial changes occur outside of traffic corridors, indicating that potential reductions in traffic would need to be supplemented by additional measures to reduce urban temperatures and associated heat stress, especially in a warming climate, to ensure human health and well-being. Full article

Airborne backscatter lidar at 532 nm and in-situ measurements of black carbon (BC), carbon monoxide excess above background (ΔCO), and aerosol size distribution were carried out over Siberia in July 2013 and June 2017 in order to sample several kinds of aerosol sources. Aerosol types are derived using the Lagrangian FLEXible PARTicle dispersion model (FLEXPART) simulations and satellite observations. Six aerosol types could be identified in this work: (i) dusty aerosol mixture, (ii) Ob valley gas flaring emission, (iii) fresh forest fire, (iv) aged forest fire, (v) urban emissions over the Tomsk/Novosibirsk region (vi) long range transport of Northern China urban emission. The altitude range of aerosol layers is discussed for each aerosol type, showing transport above the boundary layer for long range transport of Northern China emissions or fresh forest fire. Comparisons of aerosol optical properties, BC and ΔCO are made between aged and fresh plumes for both the urban and forest fire emissions. An increase of aerosol optical depth at 532 nm (AOD${}_{532}$), aerosol particle size and ΔCO is found for aged forest fire plumes. Similar results are obtained when comparing the aged urban plume from Northern China with fresh urban emissions from Siberian cities. A flight above gas flaring emissions corresponds to the largest AOD${}_{532}$ and provides a possible range of 50–60 sr for the lidar ratio of these aerosol plumes often encountered in Siberia. Black carbon concentrations are relatively higher for the flaring plume (0.4–0.5 μ$\mathsf{\mu }\mathrm{g}.{\mathrm{m}}^{-3}$) than for the urban plume (0.2 μ$\mathsf{\mu }\mathrm{g}.{\mathrm{m}}^{-3}$). The largest BC concentrations are found for the fresh forest fire plume. The aerosol type identification and AOD${}_{532}$ provided by CALIOP Version 4.2 data products in air masses with similar origin generally agree with the results obtained from our detailed analysis of the aerosol plume origins. Full article

This study investigated the erosion of the nocturnal boundary layer (NBL) over the central Amazon using a high-resolution model of large-eddy simulation (LES) named PArallel Les Model (PALM) and observational data from Green Ocean Amazon (GoAmazon) project 2014/5. This data set was collected during four intense observation periods (IOPs) in the dry and rainy seasons in the years 2014 (considered a typical year) and 2015, during which an El Niño–Southern Oscillation (ENSO) event predominated and provoked an intense dry season. The outputs from the PALM simulations represented reasonably well the NBL erosion, and the results showed that it has different characteristics between the seasons. During the rainy season, the IOPs exhibited slow surface heating and less intense convection, which resulted in a longer erosion period, typically about 3 h after sunrise (that occurs at 06:00 local time). In contrast, dry IOPs showed more intensive surface warming with stronger convection, resulting in faster NBL erosion, about 2 h after sunrise. A conceptual model was derived to investigate the complete erosion during sunrise hours when there is a very shallow mixed layer formed close to the surface and a stable layer above. The kinematic heat flux for heating this layer during the erosion period showed that for the rainy season, the energy emitted from the surface and the entrainment was not enough to fully heat the NBL layer and erode it. Approximately 30% of additional energy was used in the system, which could come from the release of energy from biomass. The dry period of 2014 showed stronger heating, but it was also not enough, requiring approximately 6% of additional energy. However, for the 2015 dry period, which was under the influence of the ENSO event, it was shown that the released surface fluxes were sufficient to fully heat the layer. The erosion time of the NBL probably influenced the development of the convective boundary layer (CBL), wherein greater vertical development was observed in the dry season IOPs (~1500 m), while the rainy season IOPs had a shallower layer (~1200 m). Full article

Optical remote sensing data are used widely in many fields (such as agriculture, resource management and the environment), especially for the vast territory of China; however, the application of these data is usually limited by clouds. Although it is valuable to analyze the probability of acquiring cloud-free imagery (PACI), PACI using different sensors at the pixel level across China has not been reported. In this study, the PACI of China was calculated with daily Advanced Very High Resolution Radiometer (AVHRR) cloud mask data from 1990 to 2019. The results showed that (1) PACI varies dramatically in different regions and months in China. The value was larger in autumn and winter, and the largest figure reached 49.55% in October in Inner Mongolia (NM). In contrast, relatively small values occurred in summer, and the minimum value (5.26%) occurred in June in South China (SC). (2) As the climate changes, the PACI has increased significantly throughout the country and most regions, especially in North China (NC), with a growth rate of 1.9% per decade. The results can be used as a reference for selecting appropriate optical sensors and observation times in areas of interest. Full article

The ESTE system is running in nuclear crisis centers at various levels of emergency preparedness and response in Slovakia, the Czech Republic, Austria, Bulgaria, and Iran (at the Bushehr Nuclear Power Plant, monitored by the International Atomic Energy Agency (IAEA)). ESTE is a decision-support system that runs 24/7 and serves the crisis staff to propose actions to protect inhabitants against radiation in case of a nuclear accident. ESTE is also applicable as a decision-support system in case of a malicious act with a radioactive dispersal device in an urban or industrial environment. The dispersion models implemented in ESTE are the Lagrangian particle model (LPM) and the Puff trajectory model (PTM). We describe model approaches as implemented in ESTE. The PTM is applied in ESTE for the dispersion calculation near the point of release, up to 100 km from the point of a nuclear accident. The LPM for general atmospheric transport is applied for short-range, meso-scale and large-scale dispersion, up to dispersion on the global scale. Additionally, a specific micro-scale implementation of the LPM is applied for urban scale dispersion modeling. The dispersion models of ESTE are joined with radiological-consequences models to calculate a complete spectrum of radiological parameters—effective doses, committed doses, and dose rates by various irradiation pathways and by various radionuclides. Finally, radiation protective measures, like sheltering, iodine prophylaxis, or evacuation, evaluated on the base of predicted radiological impacts, are proposed. The dispersion and radiological models of the state-of-the-art ESTE systems are described. The results of specific analyses, like the number of particles applied, the initial spatial distribution of the source, and the height of the bottom reference layer, are presented and discussed. Full article

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)₁₀ concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO₂ “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM₁₀ and PM_2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources. Full article

Restrictions on human activities remarkably reduced emissions of air pollutants in China during the COVID-19 lockdown periods. However, distinct responses of O₃ concentrations were observed across China. In the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions, O₃ concentrations were enhanced by 90.21 and 71.79% from pre-lockdown to lockdown periods in 2020, significantly greater than the equivalent concentrations for the same periods over 2015–2019 (69.99 and 43.62%, p < 0.001). In contrast, a decline was detected (−1.1%) in the Pearl River Delta (PRD) region. To better understand the underlying causes for these inconsistent responses across China, we adopted the least absolute shrinkage and selection operator (Lasso) and ordinary linear squares (OLS) methods in this study. Statistical analysis indicated that a sharp decline in nitrogen dioxide (NO₂) was the major driver of enhanced O₃ in the BTH region as it is a NO_x-saturated region. In the YRD region, season-shift induced changes in the temperature/shortwave radiative flux, while lockdown induced declines in NO₂, attributable to the rise in O₃. In the PRD region, the slight drop in O₃ is attributed to the decreased intensity of radiation. The distinct regimes of the O₃ response to the COVID-19 lockdown in China offer important insights into different O₃ control strategies across China. Full article

The modelling of thermal exposure in outdoor urban environments is a highly topical challenge in modern climate research. This paper presents the results derived from a new micrometeorological model that employs an integrated biometeorology module to model Universal Thermal Climate Index (UTCI). This is PALM-4U, which includes an integrated human body-shape parameterization, deployed herein for a pilot domain in Prague, Czech Republic. The results highlight the key role of radiation in the spatiotemporal variability of thermal exposure in moderate-climate urban areas during summer days in terms of the way in which this directly affects thermal comfort through radiant temperature and indirectly through the complexity of turbulence in street canyons. The model simulations suggest that the highest thermal exposure may be expected within street canyons near the irradiated north sides of east–west streets and near streets oriented north–south. Heat exposure in streets increases in proximity to buildings with reflective paints. The lowest heat exposure during the day may be anticipated in tree-shaded courtyards. The cooling effect of trees may range from 4 °C to 9 °C in UTCI, and the cooling effect of grass in comparison with artificial paved surfaces in open public places may be from 2 °C to 5 °C UTCI. In general terms, this study illustrates that the PALM modelling system provides a new perspective on the spatiotemporal differentiation of thermal exposure at the pedestrian level; it may therefore contribute to more climate-sensitive urban planning. Full article

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM. Full article

Olive and vine cultivations are two of the most important crops in Europe, yielding high quality and value food products. The climate change over the Balkans may elevate the agroecological pressure for the established crops and shift their cultivations areas. One of the widely-used agroclimatic indices is the growing degree days (GDD) which accumulates the necessary thermal units for the selected crops. Despite the advances on the agroclimatic research, there are few available methods for spatiotemporal estimation of this useful index. So, this research is focused on the construction of simple and reliable equations for the calculation and projection of olive and vine cultivations’ GDD over the Balkans. The models’ input parameters are the time, the altitude, the distance from the seashore, and the latitude. Its assembly is made by the extracted spatial data, combined with the Agri4Cast dataset for the period of 1980 to 2018 incorporating the regional climate change trend. The results indicate that the most influential parameter is the time, followed by the latitude, for both cultivations. According to the projections, as quantified by GDD, a vast sprawl of olive and vine cultivation areas will have been formed to the northern parts of the studied area. To be more precise, the viticulture could expand spatially by 28.8% (of the Balkans area) by 2040, and by 15.1% to 2060, when the olive cultivations’ area could sprawl 23.9% by 2040 and 20.3% by 2060. Full article

We compare the relationship between the Arctic Oscillation (AO) and ozone concentration in the lower stratosphere over the Arctic during 1980–1994 (P1) and 2007–2019 (P2) in January and February using reanalysis datasets. The out-of-phase relationship between the AO and ozone in the lower stratosphere is significant in January during P1 and February during P2, but it is insignificant in January during P2 and February during P1. The variable links between the AO and ozone in the lower stratosphere over the Arctic in January and February are not caused by changes in the spatial pattern of AO but are related to the anomalies in the planetary wave propagation between the troposphere and stratosphere. The upward propagation of the planetary wave in the stratosphere related to the positive phase of AO significantly weakens in January during P1 and in February during P2, which may be related to negative buoyancy frequency anomalies over the Arctic. When the AO is in the positive phase, the anomalies of planetary wave further contribute to the negative ozone anomalies via weakening the Brewer–Dobson circulation and decreasing the temperature in the lower stratosphere over the Arctic in January during P1 and in February during P2. Full article

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO₂) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO₂, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO₂ reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO₂ data during the lockdown and the corresponding changes in tropospheric vertical NO₂ column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method. Full article

We investigate the distribution of dimethyl sulfide (DMS) in the Southern Ocean’s (50° W to 170° W) surface water, including the Antarctic Peninsula and the marginal sea ice zone (MIZ) in the Ross and Amundsen Seas. This is the first high-frequency observation conducted in the austral autumn (in April) in the Southern Ocean. The mean DMS concentration was 2.7 ± 2.5 nM (1 σ) for the entire study area. Noticeably enhanced DMS (5 to 28 nM) concentrations were observed in the MIZ around the Ross and Amundsen Seas and the coastal regions in the Antarctic Peninsula; this could be attributed to biological production of local ice algae, which appears to be supplied with nutrients from glacial or sea ice melt water. These observed DMS inventories were significantly higher (an order of magnitude) than current climatological DMS inventories. The local DMS sources being transported outward from the polynyas, where strong bloom occurs during summer, could result in larger discrepancies between observed DMS and climatological DMS in the MIZ area (in the Amundsen Sea). Overall, this study is the first to highlight the significance of the underestimation of current DMS fluxes in the austral autumn, which consequently results in significant errors in the climate models. Full article