Secondary organic aerosol (SOA) represents a major component of urban air pollution. This study presents observational evidence from summer 2017 in urban Beijing, supported by model simulations and a case study from summer 2023, demonstrating the crucial role of nighttime organic nitrates (ONs) production in subsequent daytime SOA formation. Our measurements revealed that total reactive nitrogen compound (NO z ) concentrations exceeded 40 ppb at night, resulting from nitrate radical (NO3)-initiated oxidation of volatile organic compounds (VOCs) in the surface layer and through aloft production followed by downward transport. While these NO z existed primarily in the gas phase during nighttime, they underwent atmospheric aging processes the following day, significantly contributing to SOA growth and potentially new particle formation. Model simulations identified reactive terpenoids as the dominant VOC precursors for nighttime ON formation. These findings underscore the need for an improved understanding of nocturnal ONs production mechanisms given their substantial impact on daytime SOA production.

Fog formation over tropical forests remains poorly characterized, despite its potential role in bioaerosol dispersion and ecosystem processes. Here, we analyzed fog samples collected at the Amazon Tall Tower Observatory using flow cytometry and culture-based techniques to characterize viable microbial communities. Microbial cell concentrations varied over an order of magnitude across 13 fog events, reaching up to 8 & times; 104 cells per ml of fog water. Flow cytometry consistently detected metabolically active cells, while culturing and mass spectrometry-based identification yielded eight viable bacterial species and seven fungal taxa. The bacteria Serratia marcescens, Ralstonia pickettii and Sphingomonas paucimobilis exhibited seasonal variations in prevalence. The fungal species identified were primarily mesophilic saprophytes and endophytes, commonly associated with soil and plant surfaces. Our findings indicate that fog harbors viable microbes, including Serratia marcescens and Ralstonia pickettii, which may imply a relevance of fog for microbial dispersal, colonization and nutrient cycling in the Amazon rainforest.

India experiences severe air pollution driven by human activities. The role of anthropogenic chlorine is significant yet underexplored, with its mechanisms poorly understood and impacts largely unquantified, despite its importance in atmospheric oxidation and secondary pollutant formation. Using the GEOS-Chem chemical transport model, we quantify the impact of human-derived chlorine emissions on particulate chloride (pCl−), particulate matter (PM2.5), ClNO2, and O3 in the boundary layer over India. Comprehensive model simulations reveal major chlorine hotspots affecting nearly ~ 700 million people across the Indo-Gangetic Plain (IGP). The PM2.5 concentration increases due to pCl− formation (principally NH4Cl). Annual mean pCl− and ClNO2 increase by 4-fold and 3-fold, respectively. Regionally and seasonally, enhancements range from 0.04 − 3.6 μg m−3 for pCl−, 7-273 ppt for ClNO2, and -0.47-0.44 ppb for O3 with strongest effects in autumn and winter. Compared to other polluted hotspots in the world, for example China, O3 showed a lower sensitivity to chlorine emissions over India. Anthropogenic chlorine significantly influences India’s air quality, underscoring the need to include chlorine emission inventories and chemistry in models.

Secondary organic aerosol (SOA) represents a major component of urban air pollution. This study presents observational evidence from summer 2017 in urban Beijing, supported by model simulations and a case study from summer 2023, demonstrating the crucial role of nighttime organic nitrates (ONs) production in subsequent daytime SOA formation. Our measurements revealed that total reactive nitrogen compound (NOz) concentrations exceeded 40 ppb at night, resulting from nitrate radical (NO3)-initiated oxidation of volatile organic compounds (VOCs) in the surface layer and through aloft production followed by downward transport. While these NOz existed primarily in the gas phase during nighttime, they underwent atmospheric aging processes the following day, significantly contributing to SOA growth and potentially new particle formation. Model simulations identified reactive terpenoids as the dominant VOC precursors for nighttime ON formation. These findings underscore the need for an improved understanding of nocturnal ONs production mechanisms given their substantial impact on daytime SOA production.

Urban PM2.5 samples were collected in Manaus, a major city in the Amazon rainforest, to analyze dicarboxylic acids (DCA) (C2-C9), black carbon (BC), levoglucosan (LEV), and sulfate (SO4 2-) during daytime and nighttime, in periods with and without smoke over the city from biomass burning (BB). Oxalic acid (C2) was the dominant DCA, followed by malonic acid (C3) and succinic acid (C4). The 12.5% nighttime increase in oxalic acid (C2) and higher C2/TDCA ratio at night indicate ongoing secondary formation, not just accumulation from a lower boundary layer. BC showed a moderate correlation with C4, and, since no correlation was observed between BC and LEV, this suggests that fossil fuel combustion-a recognized source of BC-is one of the possible primary sources of C4 in the studied atmosphere. The phthalic acid (Ph) to azelaic acid (C9) ratio (Ph/C9), an indicator of the dominant oxidative pathway for DCA formation, was lowest at night, and C9 exhibited a strong correlation with levoglucosan (LEV) (r = 0.89), suggesting that BB serves as a major source of primary C9 and may also provide precursors that contribute to the secondary formation of PM2.5. During smoke periods, LEV increased by 479%, while C2 and C3 increased by 247% and 259%, and the ratios of C2/C4 and C2/SO4 2- increased by 40% and 259%, respectively. Biomass burning has modified the composition of DCAs, possibly influencing aerosol hygroscopicity and their potential to act as cloud condensation nuclei, with possible implications for regional climate. This study highlights the critical role of biomass burning in the formation of organic aerosols and their influence on oxidative pathways, as well as their broader environmental impacts. This study shows biomass burning increases dicarboxylic acids in Manaus air, altering aerosol formation and composition, impacting urban air quality and atmospheric processes in the Amazon region.

Aerosols influence Earth's energy balance and hydrological cycle as cloud condensation nuclei (CCN), yet uncertainties persist in how anthropogenic emissions alter their abundance and climate-relevant properties. Abrupt, large-scale reductions in human activities provided a natural experiment to quantify anthropogenic impact on aerosol-cloud-climate interactions in coastal India. Combining chemical and microphysical measurements under drastically reduced and subsequently reintroduced emission scenarios, we reveal that CCN concentrations increased by 80-250% postlockdown. This surge coincided with increased new particle formation (NPF) event frequency and enhanced particle growth rates. Postlockdown air masses shifted from marine to continental sources, revealing that anthropogenic organic matter (OM), despite lower hygroscopicity, dominated particle growth to CCN-active sizes, offsetting hygroscopicity limitations. These findings demonstrate how shifts in anthropogenic activity can strongly impact aerosol-cloud interaction potential, even under varying air mass influences, and provide a reference for understanding the atmospheric effects of future air quality interventions.

Aerosols, with their direct and indirect effects impacting the climate, have been established to significantly perturb Earth's radiative budget and hydrological cycle. The climate impact of aerosols is complex and multifaceted, with various factors influencing the combined net effect. The intricacies of aerosol effects, mainly through aerosol-cloud interactions, necessitate precise measurements to reduce the uncertainty in forecasting future climate fluctuations1. Studying their characteristics in pristine settings can provide an enhanced scientific understanding of aerosol impact in background conditions, as opposed to polluted ones2. With this motivation, we conducted a comprehensive field measurement campaign during the second phase of the COVID-induced lockdown in Munnar, a relatively clean high-altitude site in the Western Ghats of India. Munnar is surrounded by lush tea plantations and extensive forest reserves, and tea production and tourism are the major human activities in the area. However, suspended tourist activities due to the pandemic and frequent precipitation during monsoon enabled us to study the ambient aerosol characteristics in near-natural conditions3. This study presents results from the size-resolved Cloud Condensation Nuclei (SR-CCN) measurements conducted along with aerosol size distribution and chemical composition at the Natural Aerosol and Bioaerosol High Altitude Laboratory (NABHA; 10.09 N, 77.06 E; 1605m asl) during the Southwest Monsoon season between June-October 2021. The median number concentration for 10–450nm particles was observed to be 533cm-3, with 357cm-3and 908cm-3 as first and third quartiles, respectively, similar to other pristine locations, such as Amazonia during the wet season4. The average non-refractory particulate matter (NR-PM1) concentration was 2.28±1.81 µg/m3 (mean ± one standard deviation). The SR-CCN measurements were carried out for set supersaturations between 0.1% and 0.85% for particles ranging between 20-350 nm in diameter. The critical dry diameter varied from 60 to 150nm for highest to lowest supersaturation, similar to previously reported studies elsewhere4,5. During the campaign, the efficiency spectra of CCN often reached unity despite organic aerosols dominating the submicron aerosol composition.Further, hygroscopicity, a particle size and composition function, was investigated using the kappa-Köhler theory. The hygroscopicity parameter, kappa, derived from SR-CCN measurements(kCCN) varied between 0.26 and 0.57. kCCN did not exhibit much variation in the Aitken mode regime (60-80nm) but increased in the accumulation mode (100-160nm), suggesting higher hygroscopic fraction in larger (aged) particles. Assuming a linear mixing of organic and inorganic aerosols, chemically derived hygroscopicity (kchem) was comparable to kCCN, following similar diurnal variation. Further details will be presented.References:1.Lohmann, U. & Ferrachat, S. Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect. AtmosChemPhys (2010).2.Andreae, M. O. Aerosols Before Pollution. Science (2007).3.Navasakthi, S., Pandey, A., Bhari, J. S. & Sharma, A. Significant variation in air quality in South Indian cities during COVID-19 lockdown and unlock phases. EnvironMonitAssess (2023).4.Gunthe, S. S. et al. Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity. AtmosChemPhys (2009).5.Singh, A. et al. Rapid growth and high cloud-forming potential of anthropogenic sulfate aerosol in a thermal power plant plume during COVID lockdown in India. NPJClimAtmosSci (2023).

Copter-type unmanned aerial vehicles (UAVs) have emerged as cutting-edge platforms for environmental research, offering rapid and cost-effective solutions for atmospheric sensing and sampling. This review highlights recent advancements and explores future prospects for their application in atmospheric chemistry. The UAV-based techniques have demonstrated excellent capabilities in characterizing the spatial distribution of gaseous pollutants using both real-time, low-cost sensors, and offline analytical methods. These platforms have also proven effective in profiling the physicochemical properties of airborne particulate matter, providing insights into its sources, chemical transformation, and environmental and climate impacts. In regard to the applications, this review underscores the diverse roles of UAVs related to atmospheric chemistry, including emission characterization (e.g., forest volatile and bioaerosol emissions), hazard assessment (e.g., wildfire and volcanic plume monitoring), and meteorology and climate research (e.g., aerosol-cloud interactions). The review also addresses key challenges in current UAV techniques, such as payload and battery limitations, regulatory constraints, propeller-induced disturbances, and explores emerging directions, such as UAVs in biosphere-atmosphere interactions and indoor air quality monitoring. Overall, UAVs are reshaping atmospheric chemistry research by providing high-resolution spatial data sets that complement traditional methods. These advancements further enhance our understanding of atmospheric processes and facilitate the development of precise and adaptable environmental monitoring strategies.

Following old-growth forest loss and subsequent land abandonment, secondary forest grows throughout the Amazon biome. For Amazonas, agricultural colonization was unsuccessful in many regions, leading to the regeneration of secondary forest and carbon storage under favorable climate conditions. Herein, the extent of regeneration in Amazonas and its timescale are investigated, including a granular analysis of its 62 municipalities, based on the MapBiomas dataset from 1985 to 2021. By 2021, 10,495 km2 of secondary forest had grown, corresponding to 28% of the lost old-growth forest. After normalization for algorithmic differences, this estimate was 17%-38% lower than prior studies for Amazonas that used earlier versions of the MapBiomas dataset, indicating increased accuracy in landcover assignments for more current versions of the dataset. For the northeastern microregion, representing the 15 municipalities of economic and population dominance in Amazonas, the growth of secondary forest varied from 3.0% to 9.8% of the total land area. For the southern microregion, constituting seven municipalities adjacent to large-scale deforestation of Mato Grosso and Rondônia, regeneration of secondary forest constituted 0.4%-1.2% of the land area. For the remaining interior municipalities, the regeneration was 0.0%-1.9%. Among the municipalities, the median regeneration interval, corresponding to the duration between the loss of old-growth forest and the appearance of secondary forest, ranged from 2 to 7 years. The median regeneration intervals of the interior, northeastern, and southern microregions were 3, 4, and 5 years, respectively. Even as the secular trend of deforestation continues in the Amazon biome and encroaches into the southern border of Amazonas state, the results herein indicate a possible resiliency toward secondary forest for undisturbed land on a timescale of several years, at least for mixed pasture-forest landscapes of kilometer-scale heterogeneity and assuming that a favorable climate persists for regeneration even as global change occurs.

Comprehending the intricate interplay between atmospheric aerosols and water vapour in subsaturated regions is vital for accurate modelling of aerosol–cloud–radiation–climate dynamics. But the microphysical mechanisms governing these interactions with ambient aerosols remain inadequately understood. Here we report results from high-altitude, relatively pristine site in Western-Ghats of India during monsoon, serving as a baseline for climate processes in one of the world’s most polluted regions. Utilizing a novel quartz crystal microbalance (QCM) approach, we conducted size-resolved sampling to analyse humidity-dependent growth factors, hygroscopicity, deliquescence behaviour, and aerosol liquid water content (ALWC). Fine-mode aerosols (≤2.5 μm) exhibited size-dependent interactions with water vapour, contributing significantly to ALWC. Deliquescence was observed in larger aerosols (>180 nm), influenced by organic species, with deliquescence relative humidity (DRH) lower than that of pure inorganic salts. This research highlights the significance of understanding ambient aerosol-water interactions and hygroscopicity for refining climate models in subsaturated conditions.

The physicochemical properties of particles affect many important atmospheric processes; yet, they are challenging to measure in situ. Herein, fluorescence aerosol flow tube (F-AFT) spectroscopy is applied to directly probe the ionic strength and pH in model systems of inorganic aerosol particles. The pH-sensitive probe molecule quinaldine red (QR) is incorporated into aerosol particles of sodium chloride, ammonium sulfate, ammonium bisulfate, and their mixtures. Fluorescence spectra are collected for variable particle acidity and ionic strength as mediated by composition and relative humidity. Results show that shifts in fluorescence wavelength are driven by changes in both ionic strength and pH. A two-dimensional regression analysis of the sulfate particle fluorescence line shape as a linear function of both molality and pH results in R-2 = 0.75. For comparison, R-2 values of 0.17 and 0.64 are found for molality and pH considered separately, respectively. The regression model calculated from the sulfate particle system did not fit the sodium chloride particle data well, suggesting that there are chemically specific effects governing the pH and ionic strength interactions with the dye molecule. Overall, these findings indicate the potential of F-AFT spectroscopy for in situ elucidation of the physicochemical properties of submicrometer aerosol particles, contingent upon a detailed understanding of the controlling factors in the fluorescence behavior of the chosen probe molecules in common aerosol particle-phase chemical environments.

Worldwide, smoke from forest fires has deleterious health effects. Even so, because of the complexity of fire mechanics, public health authorities face challenges in forecasting and thus mitigating population exposure to smoke. The population in the Amazon basin regularly suffers from fire smoke tied to agriculture and land-use change. The people of Manaus, a city of two million in the center of the basin, suffer the consequences. The study herein evaluates the time lag between fire occurrence and hospital admission for cardiorespiratory illness. Understanding the time lag is key to forecasting and mitigating the public health effects. The study approach is sequential application of four increasingly complex methods of machine learning to examine the relationships among black carbon concentrations, fire count, meteorology, and hospital admissions. The mean absolute percentage error (MAPE) for predicting hospital admissions ranged from 27% to 38%. Furthermore, a one-day lag was observed between the detection of fires and the manifestations of respiratory health hazards. This finding suggests the potential for developing an early warning system, which could enable public health officials to issue advisories or implement preventive actions during the brief period before hospital admissions begin to rise. The findings have applicability not only to the population exposed to fires in the Amazon basin but also to populations where smoke is prevalent, notably increasingly in Australia, southern Europe, the western USA, southern Canada, and southeast Asia.

Notable anthropogenic heat sources such as coal-fired plants can alter the atmospheric boundary layer structure and the pollutant dispersion, thereby affecting the local environment and microclimate. Herein, in situ measurements inside a coal-fired steel plant were performed by multiple advanced lidars from 21 May to 21 June of 2021 in Yuncheng, Shanxi Province, China. Comparing with an adjacent meteorological site, we found a prominent nighttime dry heat island overhead of the factory, which was 3-10 degrees C hotter and 30%-60% drier than the surrounding fields. Large-eddy simulations constrained by the measured thermal contrast suggested that the heat-island-induced circulation could upward transport factory-discharged pollutants and horizontally spread them below the residual layer top, forming a mushroom-shaped cloud. The shape, size, and pollutant loading of the cloud were highly determined by thermodynamic variables such as aerodynamic wind and anthropogenic heat flux. Furthermore, these retained residual-layer pollutants can be convected downward to the ground after sunrise through the fumigation effect, causing the peaking phenomena aboveground. These peaks were statistically evidenced to be common in major urban agglomerations in China. The study provides a new insight regarding the origins of residual-layer pollutants and highlights the needs for programming representations of coal-fired heat emissions in mesoscale air-quality models.

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the US Department of Energy (DOE) Office of Science supported an instrumented Gulfstream 1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final 6 years of G-1 operations (2013 to 2018, 10.5439/1999133; Mei and Gaustad, 2024). The integrated dataset includes data collected from 236 flights (766.4 h), which covered the Arctic, the US Southern Great Plains (SGP), the US West Coast, the eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Cordoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability in the thermodynamic quantities and aerosol and cloud properties for addressing essential science questions in Earth system process studies. This paper describes the DOE ARM merged G-1 datasets, including information on the acquisition, data collection challenges and future potentials, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package.

Molecular ionization potentials (IP) and photoionization cross sections (σ) can affect the sensitivity of photoionization detectors (PIDs) and other sensors for gaseous species. This study employs several methods of machine learning (ML) to predict IP and σ values at 10.6 eV (117 nm) for a dataset of 1251 gaseous organic species. The explicitness of the treatment of the species electronic structure progressively increases among the methods. The study compares the ML predictions of the IP and σ values to those obtained by quantum chemical calculations. The ML predictions are comparable in performance to those of the quantum calculations when evaluated against measurements. Pretraining further reduces the mean absolute errors (ε) compared to the measurements. The graph-based attentive fingerprint model was most accurate, for which εIP = 0.23 ± 0.01 eV and εσ = 2.8 ± 0.2 Mb compared to measurements and computed cross sections, respectively. The ML predictions for IP correlate well with both the measured IPs (R 2 = 0.88) and with IPs computed at the level of M06-2X/aug-cc-pVTZ (R 2 = 0.82). The ML predictions for σ correlated reasonably well with computed cross sections (R 2 = 0.66). The developed ML methods for IP and σ values, representing the properties of a generalizable set of volatile organic compounds (VOCs) relevant to industrial applications and atmospheric chemistry, can be used to quantitatively describe the species-dependent sensitivity of chemical sensors that use ionizing radiation as part of the sensing mechanism, such as photoionization detectors.

The COVID lockdown presented an interesting opportunity to study the anthropogenic emissions from different sectors under relatively cleaner conditions in India. The complex interplays of power production, industry, and transport could be dissected due to the significantly reduced influence of the latter two emission sources. Here, based on measurements of cloud condensation nuclei (CCN) activity and chemical composition of atmospheric aerosols during the lockdown, we report an episodic event resulting from distinct meteorological conditions. This event was marked by rapid growth and high hygroscopicity of new aerosol particles formed in the SO 2 plume from a large coal-fired power plant in Southern India. These sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions provides important new clues to delineate the contributions of different anthropogenic emission sectors and further to understand their perturbations of past and future climate forcing.

Photoionization detectors (PIDs) are lightweight and respond in real time to the concentrations of volatile organic compounds (VOCs), making them suitable for environmental measurements on many platforms. However, the nonselective sensing mechanism of PIDs challenges data interpretation, particularly when exposed to the complex VOC mixtures prevalent in the Earth's atmosphere. Herein, two approaches to this challenge are investigated. In the first, quantum-chemistry calculations are used to estimate photoionization cross sections and ionization potentials of individual species. In the second, machine learning models are trained on these calculated values, as well as empirical PID response factors, and then used for prediction. For both approaches, the resulting information for individual species is used to model the overall PID response to a complex VOC mixture. In complement, laboratory experiments in the Harvard Environmental Chamber are carried out to measure the PID response to the complex molecular mixture produced by α-pinene oxidation under various conditions. The observations show that the measured PID response is 15% to 30% smaller than the PID response modeled by quantum-chemistry calculations of the photoionization cross section for the photo-oxidation experiments and 15% to 20% for the ozonolysis experiments. By comparison, the measured PID response is captured within a 95% confidence interval by the use of machine learning to model the PID response based on the empirical response factor in all experiments. Taken together, the results of this study demonstrate the application of machine learning to augment the performance of a nonselective chemical sensor. The approach can be generalized to other reactive species, oxidants, and reaction mechanisms, thus enhancing the utility and interpretability of PID measurements for studying atmospheric VOCs.

New particle formation (NPF) is a global phenomenon that significantly influences climate. NPF also contributes to haze, with pronounced negative impacts on human health. Theory and observations both show that nucleation is favored during clean days and inhibited during haze episodes due to a high pre-existing condensation sink (CS). Here we show that the surprising occurrence of NPF during haze days in Beijing is associated with a high concentration of sulfuric acid dimers. With both field observations and model simulations, we demonstrate that downward mixing of sulfur dioxide (SO2) from the residual layer aloft enhances ground level SO2, which in turn elevates sulfuric acid dimer after rapid SO2 oxidation in the polluted air. Our results address a key gap between the source of SO2 and its atmospheric oxidation products during haze conditions in a megacity, Beijing, providing a missing link in a complete chain describing NPF in the polluted atmosphere.