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The characteristics of acoustic-gravity waves (waveforms, time durations, amplitudes, azimuths and horizontal phase speeds) from the eruption of the Hunga-Tonga-Hunga-Hapai volcano detected at different infrasound stations of the Infrasound Monitoring System and at a network of low-frequency microbarographs in the Moscow region are studied. Using the correlation analysis of the signals at different locations, six arrivals of signals from the volcano, which made up to two revolutions around the Earth, were detected. The Lamb mode of acoustic gravity waves from the volcano eruption is identified and the effect of this mode on generation of tsunami waves and variation of aerosol concentration is studied. The energy released from an underwater volcano into the atmosphere is estimated from the parameters of the Lamb wave and compared with the energy released from the most powerful nuclear bomb of 58 Mt TNT.

Aerosol–cloud interactions (ACI) are a major source of uncertainty in anthropogenic climate forcing. Anthropogenic aerosols modulate the cloud albedo by impacting the relative dispersion (ε) of cloud droplet size distributions (CDSDs), known as the dispersion effect. This effect can be either suppressive or enhancing, introducing considerable uncertainties in polluted continental clouds. Here, we developed in situ measurements in polluted East China and simultaneously measured the CDSDs and aerosol properties. A wide range of ε from 0.20 to 0.78 with a mean value of 0.43 was observed. Machine learning analysis shows that aerosol number concentration and hygroscopicity dominate the changes of ε in polluted continental stratiform clouds under weak vertical velocities (<0.5 m s−1) and low liquid water content (∼0.1 g m−3) conditions. Overall, the dispersion effect enhances the aerosol indirect effect by ∼11%. These results highlight the importance of aerosol properties in evaluating ACI in polluted environments.

Greater warming rates in the mountain areas (higher elevations) compared to other parts of the world have drawn the attention of the scientific community in recent years. In this study, we first analyzed elevation‐dependent warming (EDW) patterns based on maximum temperature trends along the south–north temperature gradients of Nepal and then focused on influencing factors of EDW. Nonparametric statistical test was used to identify the warming trend (1970–2016) in each meteorological station along the altitude gradients. Furthermore, aerosol optical depth data was used to observe aerosol concentrations in different seasons across Nepal. Overall, the EDW trend was found positive on the mean annual and seasonal cycle in the study area. It was observed that there was more significant positive correlation of warming rates with altitude below the middle parts of the Lower Hills while a less pronounced correlation above it. This different behavior is attributed to high aerosol concentration on the lower part of this region. Distinct elevation‐dependent warming (EDW) pattern was observed in the mountains of Nepal. The EDW pattern was found up to the middle part of Lower Hills. A deviation of EDW resulted due to more aerosol loading on the lower part of Lower Hills.

The sensitivity of aerosol effects on orographic precipitation from deep convective clouds to mountain upslope steepness is examined using the Weather Research and Forecasting (WRF) model coupled with a bin microphysics scheme. During the early stage, the sensitivity resembles that of warm, shallow orographic convection as discussed in Part I. As time progresses, interactions between vigorously developed lower-layer clouds and upstream-extending upper-layer clouds become crucial for enhancing surface precipitation via melting and direct sedimentation of ice-phase particles such as graupel and hail. In the simulations with a symmetric mountain shape, higher aerosol number concentration enhances surface precipitation through stronger condensational latent heating and more active mixed-phase processes (freezing, Wegener-Bergeron-Findeisen process, and riming). Under asymmetric mountain shapes, however, the sensitivities are non-monotonic. In the steep upslope cases, fast liquid drop growth in the clean case and strong latent heating in the polluted case both support cloud development and enhance precipitation. In contrast, the control case produces less precipitation because its drop growth is slower than in the clean case and its latent heating is weaker than in the polluted case. As a result, cloud interaction is suppressed. In the gentle upslope cases, the control case shows the most precipitation due to sufficient droplet supply and latent heating which promote vertical growth and cloud interaction. The clean case lacks enough droplets, while the polluted case suffers from weak convection despite strong aerosol-induced heating. Consequently, both cases exhibit suppressed cloud interaction and mixed-phase processes.

A key challenge in aerosol pollution studies and climate change assessment is to understand how atmospheric aerosol particles are initially formed 1 , 2 . Although new particle formation (NPF) mechanisms have been described at specific sites 3 – 6 , in most regions, such mechanisms remain uncertain to a large extent because of the limited ability of atmospheric models to simulate critical NPF processes 1 , 7 . Here we synthesize molecular-level experiments to develop comprehensive representations of 11 NPF mechanisms and the complex chemical transformation of precursor gases in a fully coupled global climate model. Combined simulations and observations show that the dominant NPF mechanisms are distinct worldwide and vary with region and altitude. Previously neglected or underrepresented mechanisms involving organics, amines, iodine oxoacids and HNO 3 probably dominate NPF in most regions with high concentrations of aerosols or large aerosol radiative forcing; such regions include oceanic and human-polluted continental boundary layers, as well as the upper troposphere over rainforests and Asian monsoon regions. These underrepresented mechanisms also play notable roles in other areas, such as the upper troposphere of the Pacific and Atlantic oceans. Accordingly, NPF accounts for different fractions (10–80%) of the nuclei on which cloud forms at 0.5% supersaturation over various regions in the lower troposphere. The comprehensive simulation of global NPF mechanisms can help improve estimation and source attribution of the climate effects of aerosols. Molecular-level experiments are described to develop a detailed assessment of 11 new particle formation mechanisms in a global climate model and, in comparison with simulations and observations, the dominant mechanisms worldwide are mapped.

Fungal bio-aerosols are of concern due to their adverse health effects, especially in indoor environments. The aim of this study was to evaluate the relationship between the concentration and type of fungal bio-aerosols in the indoor and outdoor of Children’s Medical Center in Tehran, Iran. In the present descriptive-analytical study, the fungal bio-aerosols’ concentrations in both indoor and outdoor of the hospital air were measured. The measurements were carried out by the Anderson method using a Quick Take 30 pump at 28.3 L min −1 and 2.5 min sampling that was placed on a Sabouraud dextrose agar with chloramphenicol. The average concentrations of total fungal bio-aerosols in the hospital indoor and outdoor air were 40.48 and 119.6 CFU/m 3 , respectively. Onco-hematology and bone marrow transplantation wards were the most and least contaminated units, respectively (11.09 CFU/m 3 vs 1.47 CFU/m 3 ). The most common fungi isolated from the indoor environment were Penicillium spp. (45.86%) which was followed by C ladosporium spp. (31.92%), Aspergillus section Nigri (6.26%), sterilized mycelia (5.05%), and Aspergillus section Flavi (2.83%). Cladosporium spp. (61.10 CFU/m 3 ) and Penicillium spp. (18.56 CFU/m 3 ) had the highest mean concentrations in outdoor and indoor air, respectively. The indoor-to-outdoor ratio of fungal aerosols was < 1 at most sampling sites, indicating that the indoor fungal bio-aerosols may have originated from the outdoor environment.

The clean air actions implemented by the Chinese government in 2013 have led to significantly improved air quality in Beijing. In this work, we combined the in situ measurements of the chemical components of submicron particles (PM1) in Beijing during the winters of 2014 and 2017 and a regional chemical transport model to investigate the impact of clean air actions on aerosol chemistry and quantify the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the changes in aerosol chemical composition from 2014 to 2017. We found that the average PM1 concentration in winter in Beijing decreased by 49.5 % from 2014 to 2017 (from 66.2 to 33.4 µg m−3). Sulfate exhibited a much larger decline than nitrate and ammonium, which led to a rapid transition from sulfate-driven to nitrate-driven aerosol pollution during the wintertime. Organic aerosol (OA), especially coal combustion OA, and black carbon also showed large decreasing rates, indicating the effective emission control of coal combustion and biomass burning. The decreased sulfate contribution and increased nitrate fraction were highly consistent with the much faster emission reductions in sulfur dioxide (SO2) due to phasing out coal in Beijing compared to reduction in nitrogen oxides emissions estimated by bottom-up inventory. The chemical transport model simulations with these emission estimates reproduced the relative changes in aerosol composition and suggested that the reduced emissions in Beijing and its surrounding regions played a dominant role. The variations in meteorological conditions and regional transport contributed much less to the changes in aerosol concentration and its chemical composition during 2014–2017 compared to the decreasing emissions. Finally, we speculated that changes in precursor emissions possibly altered the aerosol formation mechanisms based on ambient observations. The observed explosive growth of sulfate at a relative humidity (RH) greater than 50 % in 2014 was delayed to a higher RH of 70 % in 2017, which was likely caused by the suppressed sulfate formation through heterogeneous reactions due to the decrease in SO2 emissions. Thermodynamic simulations showed that the decreased sulfate and nitrate concentrations have lowered the aerosol water content, particle acidity, and ammonium particle fraction. The results in this study demonstrate the response of aerosol chemistry to the stringent clean air actions and identify that the anthropogenic emission reductions are a major driver, which could help to further guide air pollution control strategies in China.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.

The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd,ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions which also comprises rapid adjustments. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd,ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large scale (hundreds of kilometres) ΔNd,ant remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and vertical wind and by droplet sink processes. These operate at scales on the order of 10s of metres at which only localized observations are available and at which no approach exists yet to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are three key uncertainties in determining ΔNd,ant, namely the quantification (i) of the cloud-active aerosol – the cloud condensation nuclei concentrations (CCN) at or above cloud base –, (ii) of Nd, as well as (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data. A fourth uncertainty, the anthropogenic perturbation to CCN concentrations, is also not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: In terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, the non-direct relation to the concentration of aerosols, the impossibility to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilizing colocated polarimeter and lidar instruments, ideally including high spectral resolution lidar capability at two wavelengths to maximize vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity, and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd – to – CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

While the elevated ambient levels of particulate matters with aerodynamic diameter of 2.5 µm or less (PM2.5) are alleviated largely with the implementation of effective emission control measures, an opposite trend with a rapid increase has been seen in surface ozone (O3) in the North China Plain (NCP) region over the past several years. It is critical to determine the real culprit causing such a large increase in surface O3. In this study, 7-year surface observations and satellite retrieval data are analyzed to determine the long-term change in surface O3 as well as driving factors. Results indicate that anthropogenic emission control strategies and changes in aerosol concentrations as well as aerosol optical properties such as single-scattering albedo (SSA) are the most important factors driving such a large increase in surface O3. Numerical simulations with the National Center for Atmospheric Research (NCAR) Master Mechanism (MM) model suggest that reduction of O3 precursor emissions and aerosol radiative effect accounted for 45 % and 23 % of the total change in surface O3 in summertime during 2013–2019, respectively. Planetary boundary layer (PBL) height with an increase of 0.21 km and surface air temperature with an increase of 2.1 ∘C contributed 18 % and 12 % to the total change in surface O3, respectively. The combined effect of these factors was responsible for the rest of the change. Decrease in SSA or strengthened absorption property of aerosols may offset the impact of aerosol optical depth (AOD) reduction on surface O3 substantially. While the MM model enables quantification of an individual factor's percentage contributions, it requires further refinement with aerosol chemistry included in the future investigation. The study indicates an important role of aerosol radiative effect in development of more effective emission control strategies on reduction of ambient levels of O3 as well as alleviation of national air quality standard exceedance events.