Air Quality is among the most pressing environmental issues impacting upon urban populations. Traditional air quality networks can assess time trends and assess compliance to air quality regulations, but lack the spatial and temporal resolution to understand individual exposure to air pollution. The RI-URBANS project is a European initiative aiming to develop new strategies and enhance the existing tools to address the air quality challenges and societal needs in European cities. This paper presents an overview of the pilots of the RI-URBANS project associated with air quality mapping and pollution hotspot identification using modelling, novel measurement methodologies and mapping techniques. Special focus is given on the discussion of the novel measurement methodologies introduced with the use of low-cost sensors, mobile measurements and citizen participation in the data collection process. The findings highlight the significance of participatory science, technological advancements in air quality measurement, adoption of novel measurement and modelling strategies and the potential for policy integration. The project's outcomes suggest that integrating stationary sensor networks, mobile monitoring platforms, and citizen engagement can significantly enhance urban air quality management alongside traditional monitoring and modelling approaches. However, harmonisation of data collected using these different methods is essential to ensure comparable outcomes across projects, which was one of the primary aims of RI-URBANS. The project also highlights several practices that improve both data collection and citizen participation. In particular, it emphasises the importance of careful campaign design and clear, direct communication with citizen scientists throughout both the monitoring process and the dissemination of project outcomes. This study highlights the important work undertaken by the participating cities and the novel approaches used to disentangle the complicated air pollution patterns and improve the air quality for everyone, while making this crucial information easily obtainable.
We present a method to derive stratospheric aerosols up to 30 km from Frequency Scanning Lidar (FSL) measurements. The lidar itself is designed as a universal instrument for the middle atmosphere and is capable of doing Doppler-Mie, Doppler-Rayleigh and Doppler-resonance measurements, for simultaneous retrievals of wind, temperature and aerosols. The system leverages a narrowband Alexandrite ring laser operating at the 770 nm potassium (K) line with a 3.3 MHz spectral width and high-resolution spectroscopy. By scanning a 100 MHz spectral window with sub-MHz sampling, the filter chain efficiently suppresses the Rayleigh background and spectrally resolves the Mie peak, enabling the separation of aerosol and molecular scattering. The FSL method's solar-blind Mie channel allows for measurements both day and night, while its compact design (approximately one cubic meter in volume) facilitates mobile deployment. With a vertical resolution of 200 m and a temporal resolution of 20 min, as achieved for the data presented here using the instrument configuration described in this study, the FSL method provides high-resolution observations of aerosol distributions in the stratosphere. The uncertainties of the FSL method for the backscatter coefficient are approximately 1.5×10-10 m−1 sr−1 at 20 km, both during day and night. We demonstrate the method's capabilities by presenting backscatter coefficient profiles measured during selected periods from 2022 to 2024. These profiles show good agreement with satellite-derived profiles from the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) and the Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) with a mean absolute deviation of ∼25 % at altitudes of 15–25 km. This demonstrates the potential of the FSL method for providing high-resolution, long-term observations of stratospheric aerosols.
The WaLiNeAs campaign took place along the north-western Mediterranean coast between October 2022 and January 2023. This period was marked by unusual weather conditions associated with dry autumn and winter. In such conditions and for the first time, eight ground-based stations equipped with water vapour Raman lidars were strategically deployed by four European countries. We studied the consistency of this network with the water vapour mixing ratio (WVMR) products derived from the Infrared Atmospheric Sounding Interferometer (IASI) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), which assimilate IASI radiances. The statistical metrics used in the comparison are the mean bias (MB, defined as lidar – IASI or ERA5), the root mean square error (RMSE) and the correlation coefficient (COR). A positive MB of approximately ∼ 0.4 g kg−1 (respectively ∼ 0.2 g kg−1) between 0.2 and 5 km above mean sea level (a.m.s.l.) indicates a systematic underestimation of the WVMR by IASI (respectively ERA5). RMSE values range from 1 to 2 g kg−1 across all lidar stations for IASI and ERA5, while the measurement uncertainties of the lidars are typically below 0.4 g kg−1. COR presents little variation between stations; it ranges from 0.7 to 0.8 and remains almost constant between 0.2 and 5 km a.m.s.l. Both the IASI and the ERA5 products appear to accurately reproduce the temporal variability of the vertical structure of water vapour in the low troposphere. Nevertheless, they show MB and RMSE significantly above the uncertainties of lidar measurements.
Clumped isotopic measurements of nitrous oxide (N2O) have the potential to offer unique constraints on the processes governing N2O production and destruction, building on the information provided by δ15N, δ18O and 15N site preference (SP). Extending their application requires a robust absolute reference frame. Here we show that thermal equilibration of N2O over γ-Al2O3 provides such a reference frame for measurements of the isotopologues 14N15N18O and 15N14N18O, as well as for SP. Using a quantum cascade laser absorption spectroscopy (QCLAS) platform, we simultaneously quantify seven isotopologues of N2O, including 14N15N18O, 15N14N18O, and 15N15N16O. Experiments starting from isotopically-distinct starting materials show convergence to time-invariant compositions that are in agreement with theoretically predicted temperature dependencies. These results demonstrate that γ-Al2O3 activated at ≥550 °C catalyzes isotope exchange among isotopologues of N2O at equilibration temperatures between 153 and 218 °C and thereby define an absolute stochastic reference frame for Δ14N15N18O and Δ15N14N18O. In contrast, 15N15N16O does not equilibrate under these conditions, suggesting selective activation of N–O but not N–N bonds. Comparison of equilibrium SP values with theoretical predictions reveals a systematic offset relative to the current reference scale, which will require future work to reconcile.
This study assesses the impact of assimilating high-volume Radio Occultation (RO) data from the RO Modeling EXperiment (ROMEX) on the Navy's global operational Naval Global Environment Model (NAVGEM). A series of observing system experiments were conducted, including a control run, a standard assimilation of all ROMEX data, and two sensitivity tests: one with an empirical bias correction and another with a modified refractivity coefficient. Results indicate that while the standard assimilation of ROMEX data improved free-tropospheric moisture forecasts, it amplified existing model biases in temperature and geopotential height, leading to forecast degradation. In contrast, both sensitivity experiments led to substantial improvements in forecast skill. The empirical bias correction method proved most effective, yielding consistent forecast improvements across temperature, moisture, and geopotential height. A Forecast Sensitivity to Observation Impact (FSOI) analysis confirmed the positive contribution of all ROMEX missions, with Spire missions providing the largest total impact and COSMIC-2 showing the highest per-observation effectiveness. The findings underscore that an adjustment to the current treatment of observations was critical to fully realize the benefits of the large volume of RO observations. While the empirical bias correction delivers the greatest forecast improvements, it may obscure and reinforce persistent model biases. The refractivity coefficient adjustment offers an alternative that preserves the unbiased nature of RO observations.
Accurate calibration is essential for spaceborne polarization-sensitive lidars, as biases in depolarization ratio measurements can significantly affect the retrieval of cloud and aerosol properties. A polarization calibration technique based on solar background signals scattered by optically thick ice clouds (OTIC) provides a semi-continuous daytime calibration capability that complements onboard pseudo-depolarizer (PD) methods. This method was successfully applied to data from the Cloud-Aerosol Transport System (CATS) lidar at 1064 nm, where molecular scattering effects are negligible. However, at shorter wavelengths, molecular scattering of sunlight between the lidar and the OTIC layer polarizes the background signal and introduces systematic biases. We present a molecular scattering correction (MSC) scheme based on vector radiative transfer modeling (VRTM) to account for this effect and demonstrate its performance using observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results show that molecular scattering introduces a daytime bias of approximately 1 % at 532 nm, which is effectively removed by the VRTM-based MSC, yielding close agreement with onboard PD calibrations. For the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) Atmospheric Lidar (ATLID) operating at 355 nm, model calculations indicate that molecular scattering contributions can be more than five times larger than at 532 nm, underscoring the necessity of applying an MSC when the OTIC calibration technique is employed. Together, these results establish the OTIC calibration technique, combined with MSC, as a robust approach for achieving accurate polarization calibration across current and future spaceborne lidar missions.
We present 3 years (2022–2024) of polarisation lidar observations of polar stratospheric clouds (PSCs) and tropospheric cirrus above Concordia Station (Dome C, Antarctica). Layer-mean lidar ratios (LR) at 532 nm are retrieved using the Young inversion method applied to an elastic backscatter and depolarisation (Rayleigh) lidar. The measurements are classified in the (1-1/R,δT) phase space, allowing us to separate supercooled ternary solution (STS), nitric-acid trihydrate (NAT) and ice PSC, as well as upper-tropospheric cirrus. To quantify the impact of the Young assumptions, we analyse both the full set of cloud detections and a Young-optimized subset of clouds that satisfy stricter homogeneity conditions above and below the cloud layer. The comparison between these two datasets allows us to separate the effective climatological variability of lidar ratio values from those retrieved under idealised conditions that strictly satisfy the Young inversion assumptions. For PSCs, the full dataset yields optically weighted median LR values (25–75 percentiles) of 38 (31–52) sr for STS, 49 (37–67) sr for NAT, and 52 (41–66) sr for ice PSC. For cirrus, the median LR is 50 (33–52) sr. These values are consistent with microphysical expectations and with previous ground-based and spaceborne lidar studies. The Young-optimized subset yields 38 (31–59) sr for STS, 59 (38–74) sr for NAT, 38 (32–38) sr for the few remaining ice PSC, and 40 (32–40) sr for cirrus although for this latter case the number of observations is not statistically significant. The subset thus provides a conservative methodological benchmark for conditions that most closely satisfy the Young inversion assumptions, while the full dataset captures the broader range of cloud variability. The comparison between the full dataset and the Young-optimized subset shows that the retrieved LR statistics are not controlled only by particle type, but also by cloud structural complexity and mixing. In particular, ice PSC and some cirrus layers frequently violate the vertical homogeneity assumptions of the Young method, so that their layer-mean LR should be interpreted as an effective value representative of mixed or vertically structured clouds rather than as a pure microphysical signature. These values provide a physically consistent reference for PSC and cirrus retrievals over Dome C and can be used in radiative-transfer modelling and satellite-lidar validation.
This study presents a comprehensive laboratory and field-based evaluation of dimethyl sulfoxide (DMSO) as a non-flammable working fluid for condensation particle counters (CPCs), directly compared to a butanol-operated counterpart across a wide range of pressures, temperatures, and aerosol types. Modifications to the instrument’s automatic refilling system ensured reliable operation over six months. Particle growth in the DMSO-CPC is strongly depending on the saturator temperature Tsat and the temperature difference ΔT between saturator and condenser, with optimal growth achieved at high Tsat and large ΔT values. Measurements with an optical particle counter downstream of the condenser, along with saturation and droplet size simulations, confirmed these trends and emphasized the importance of CPC internal settings for reliable particle growth. The DMSO-CPC achieved counting efficiencies and cutoff diameters comparable to the Butanol-CPC. The mean cutoff diameter was (5.8±0.9) nm for the DMSO-CPC and (5.6±0.5) nm for the Butanol-CPC. At the same time, the DMSO-CPC substantially reduced working fluid consumption and enabled stable long-term operation. The use of DMSO–H2O mixtures further extended the operational range and improved safety, making the CPC suitable for airborne measurements and remote monitoring. Recommendations regarding instrument modification, operational conditions, and hardware adjustments are made for operating a DMSO-CPC to gain results comparable to a Butanol-CPC. Overall, DMSO-based CPCs provide safe, efficient, and regulation-compliant operation without compromising measurement quality under challenging environmental conditions.
Nitrous oxide (N2O) is a potent greenhouse gas whose emissions are dominated by natural and agricultural soils and are highly heterogeneous and episodic, yet existing observational techniques lack the spatial coverage and near-surface sensitivity needed to resolve this variability. In this study, we evaluate a remote sensing framework that integrates shortwave infrared (SWIR) and thermal infrared (TIR) spectral bands to enhance the detectability of column-integrated N2O mixing ratio (XN2O). To implement this, we expand the capacity of Smithsonian PLanetary ATmosphere–Vector Linearized Discrete Ordinate Radiative Transfer (SPLAT–VLIDORT) model to jointly simulate both spectral regions and apply linear sensitivity analysis to quantify the XN2O measurement error and vertical sensitivity under realistic environmental conditions and instrumental designs. This framework is applied to both airborne and spaceborne instruments to evaluate the influence of platform characteristics on retrieval performance. The joint SWIR–TIR setting improves near-surface sensitivity relative to the TIR band alone while maintaining the low XN2O measurement error. It achieves single-sounding measurement error of approximately 3.2 ppb for an airborne instrument with a ground footprint size of 20 m and 1.1 ppb for spaceborne instrument with a footprint size of 0.7 km, while retaining sensitivity to the near-surface layers. Assuming XN2O variability is observable at twice the precision, natural XN2O variability inferred from in situ aircraft N2O observations in the US Midwest becomes observable beyond spatial aggregation scales of ∼2.5 km for airborne and ∼22 km for spaceborne instruments, subject to significant XN2O variation between flights. An independent, emission-based detectability analysis indicates that XN2O variability induced by uniform emissions of 5 nmol m−2 s−1 becomes observable beyond spatial averaging of about 2.1 km for airborne and 8.4 km for spaceborne instruments. Together, these results constitute a quantitative basis for N2O detectability using a joint SWIR–TIR setting, with a focus on diffuse soil emissions that are more difficult to detect yet dominate the global N2O budget, and they provide practical guidance for future N2O dedicated missions.
Abstract. The stable carbon isotopic ratio (δ13C) of atmospheric carbon dioxide (CO2) is a key tracer for understanding terrestrial carbon dynamics, yet its application in volume-limited systems remains constrained by analytical and sampling requirements. Here, we present a methodology for high-precision δ13C-CO2 analysis of ambient atmospheric CO2 from 1 mL air samples, tailored to the challenges of growth chamber experiments using microcosm model systems and other volume-limited systems. Our approach combines simple vial conditioning, dual-sealing using a malleable self-adhesive butyl-rubber compound to minimise gas leakage, low-temperature storage (−80 °C), and cryogenic pre-concentration coupled to continuous-flow isotope-ratio mass spectrometry (IRMS). The workflow is rapid, low-cost, relies on widely available materials, and avoids laborious sample preparation steps (i.e. purification), enabling other laboratories to reproduce the method easily. Using this approach, a precision of ±0.1 ‰ was achieved under controlled conditions, no statistically detectable isotopic drift for storage durations up to 1-week when vials were kept under low-temperature condition inside zip-lock bags filled with dry CO2-free air. Longer storage times or storage at ambient temperature reduces both precision and accuracy, emphasising the importance of short-term storage at negative temperature. This methodology allows high sampling frequency δ13C-CO2 measurements on 1 mL samples, while minimally perturbing the sampled system and maintaining analytical performance under the tested conditions. It provides a practical solution for studies constrained by sample volume.
The official Landsat 8 surface reflectance (SR) product, generated by the Land Surface Reflectance (LaSRC) algorithm, is the most extensively utilized medium-resolution dataset and serves as a benchmark to cross-validate the accuracy of other SR products. However, the accuracy of the Landsat 8 SR products did not meet the expectations of the previous studies under specific conditions. Consequently, it is necessary to analyze the Urban Clean aerosol-type assumption implemented in the LaSRC algorithm and comprehensively re-evaluate the accuracy of the Landsat 8 SR. Therefore, this study leverages Landsat 8 data over 600 scenes acquired at 100 Aerosol Robotic Network (AERONET) sites globally and conducts a comprehensive analysis of how different dynamic aerosol types – MOD04-based (used in the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-2 Aerosol Optical Depth Product), MOD09-based (used in MODIS Terra Atmospherically Corrected Surface Reflectance Product), and Urban Clean (used in LaSRC) – affect the accuracy of atmospheric correction (AC) for the first time. The results indicated that, in terms of aerosol optical depth (AOD), the MOD04 aerosol type exhibited the highest accuracy, with a coefficient of determination (R2_AerT) of 0.7236, Root Mean Square Error (RMSE) of 0.0437, and bias of 0.0052. The accuracy (A), precision (P), and uncertainty (U) of the four evaluated SR products ranged from −2.9754 × 10−4 to 3.0145 × 10−3, from 2.3184 × 10−2 to 2.6020 × 10−2, and from 2.3366 × 10−2 to 2.6040 × 10−2, respectively. The MOD04-based aerosol type demonstrated the highest overall accuracy in the visible and near-infrared (VNIR) bands. The MOD09-based aerosol type outperformed the others in the bright surface regions. The Urban Clean aerosol type showed a comparable but slightly inferior performance to that of the MOD09-based aerosol type, with limited advantages in specific reflectance ranges. Moreover, LaSRC-derived SR demonstrated higher stability and accuracy in the shortwave infrared (SWIR) bands compared to its inferior performance in the VNIR. These findings emphasize the critical importance of aerosol-type assumptions in AC workflow. A mixed strategic implementation framework is proposed as follows: (1) adopt MOD04-based aerosol types for AOD retrieval and VNIR SR retrieval, (2) use MOD09-based aerosol types for scenes dominated by very high-reflectance surfaces, and (3) leverage SWIR SR products derived by LaSRC. Our findings provide actionable guidelines for dynamic aerosol-type selection to enhance the AC performance across diverse environments.
Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation in inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase as this metric increases. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN for long-term evaluation of models and aerosol–cloud interactions.
Ocean-atmosphere exchange plays an important but uncertain role for many volatile organic compounds (VOCs). Airborne eddy covariance (EC) enables direct flux quantification over large areas, but VOC applications have largely been performed over land. Here we combine the EC methodology with aircraft-based measurements from the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) and use the results to characterize air-sea VOC fluxes and to elucidate random and systematic drivers of error. Using perturbation experiments, we show that uncorrelated sensor noise (USN) causes flux biases by obscuring the sensor-wind time lag; such biases are avoided by imposing a time-lag constraint (e.g., from a higher-flux compound or time). We define the flux signal-to-noise ratio SNRf and characterize its dependence on USN and sampling regime. Results show a transition from a USN-dominated regime to one where SNRf is limited by turbulent stochasticity. The NAAMES VOC fluxes are noise-limited, whereas H2O and sensible heat fluxes lie respectively in turbulence-limited and transitional regimes. We provide a methodology for determining sensor noise levels needed for robust flux detection: for the NAAMES subset examined here, a factor of 7–23 USN reduction would enable 75 % (rather than 17 %) of measured VOC fluxes to attain SNRf>3. The airborne NAAMES results reveal VOCs with universally upward (e.g., dimethyl sulfide), downward (e.g., acetone), bidirectional (e.g., acetaldehyde), and undetectable (e.g., monoterpenes) air-sea exchange, with controls including wind speed and planktonic activity. Findings highlight the importance of USN for VOC flux quantification by airborne EC and lay a foundation for expanded use of this technique.
Accurate quantification of urban greenhouse gas (GHG) emissions can benefit from path-averaged, high-precision, high-temporal-resolution measurements that complement point sensors and passive remote sensing. Among open-path techniques, dual-comb spectroscopy (DCS) stands out as a particularly capable candidate, offering simultaneous broadband coverage, an absolute SI-traceable frequency axis, and sufficient spectral radiance for multi-kilometer paths. Here we present an open-path dual-comb spectrometer using two commercial, self-referenced, turn-key frequency combs operated continuously in Heidelberg, Germany, over an urban landscape. The instrument allows to infer column-averaged dry-air mole fractions of CO2 along a 3.1 km absorption path. Within the evaluation period from September 2025 to February 2026 the system achieved a data coverage of 76 %, with losses primarily attributable to visibility-limiting weather conditions such as fog and heavy rain. The instrument precision, characterized by the overlapping Allan deviation under stable atmospheric conditions, reaches 6.13 ppm s for CO2, equivalent to 0.35 ppm at 5 min averaging time. These values are on par with or better than previous open-path DCS experiments and represent roughly one order of magnitude improvement over a co-deployed open-path Fourier transform spectrometer operating on the same path. The two instruments differ by a small bias of 0.50 ppm for CO2. These results demonstrate that commercial frequency-comb technology has matured enough to turn open-path DCS into an accessible tool for the broader atmospheric science community, without sacrificing performance: built exclusively from commercially available components, our instrument remains fully competitive with custom-built dual-comb spectrometers. This establishes a foundation for distributed path-averaged observations, from urban emission monitoring and network-scale deployments to the validation of spectroscopic databases.
Wastewater treatment facilities contribute ∼ 8 % of global anthropogenic methane (CH4) emissions. Accurate measurements of CH4 emissions not only improve greenhouse gas (GHG) emission estimates from the facilities but also expand our understanding of operational impact on emissions, thus enabling the development of effective mitigation strategies. In this study, CH4 emissions were measured during summer and winter seasons at an aerobic lagoon at a large sewage treatment plant in Australia. Line-averaged CH4 concentrations were measured by open-path lasers and CH4 fluxes were calculated using inverse-dispersion modelling. Methane fluxes showed temporal and spatial variations over the measurement periods, and correlated with wastewater dissolved methane, flow rate, and aerator operation. The annual GHG emission of 80 308 t CO2-e yr−1 represents ∼ 25 % of CH4 production captured by the anaerobic digestion pot and is approximately 2.0–2.3 times higher than the National Greenhouse and Energy Reporting Scheme (NGERS) reported emissions of the aerobic lagoon.
Abstract. Accurate prediction of wind speed is of great importance for stable and reliable operation of wind farms. However, the single numerical model forecast cannot provide precise wind speed outputs due to the defect of its physical parameterization scheme, whose error will gradually grow with increasing prediction time. Therefore, we proposed a model named Bi-clustered Recursive Bayesian Forest (BCRBR) for wind speed prediction and correction. The approach incorporated Sea-land Breeze and weather stability effects, integrating an atmospheric circulation index as input features; wind farm data underwent modal classification via bi-clustering to mitigate wind speed magnitude interactions, followed by machine learning-based correction of wind speed. The method was proved to be effective for wind speed prediction correction. Compared to forecasts from the Weather Research and Forecasting model, wind speed error indicators were reduced by more than 60 %; and the forecast precision increased from 30.2 % to 78.4 %, of which the improvement is more than twice. Compared to other models, the proposed model presented favorable correction results in different types of wind field, indicating its greater versatility and stronger competitiveness than other models.
Elevated concentrations of smoke within the planetary boundary layer (PBL) represent a significant health hazard, making its monitoring essential. This study demonstrates that a multi-channel fluorescence lidar can effectively analyze smoke–urban aerosol mixtures and retrieve smoke mass concentration. The method is based on the fundamentally distinct fluorescence spectra of the two aerosol types, with an estimated detection threshold on the order of 0.1 µg m−3. Measurements performed over Moscow with a five-channel fluorescence lidar in 2023–2024 captured numerous smoke episodes across a wide altitude range from spring through autumn. In 2024 alone, smoke was detected in 59 out of 67 measurement sessions between April to October. Back-trajectory analysis indicates that most events were associated with long-range transport over Atlantic, with only 12 episodes originating from fires in southern Russia. Focusing on smoke within the PBL, the results show that long-range transported smoke from North American wildfires can descend and mix with this layer, contributing mass concentrations on the order of 1 µg m−3. In contrast, regional wildfires in southern Russia led to substantially higher concentrations, with smoke mass in the PBL reaching up to 50 µg m−3 during observed episodes.
Organic nitrates (ONs) are important temporary reservoirs of atmospheric NOx and, for sufficiently low-volatility species, contributors to secondary organic aerosol formation. However, online measurements of particle-phase ONs remain limited, hindering quantitative constraints on ON abundance and gas–particle partitioning. Here we present a five-channel thermal dissociation cavity-enhanced absorption spectrometer (TD-CEAS) for in situ, time-resolved measurements of NO2 and operationally defined ON classes in both the gas and particle phases. The instrument combines a room-temperature channel for ambient NO2 with thermal dissociation channels operated at 250 and 450 °C to quantify total peroxy nitrates (ΣPNs) and total alkyl nitrates (ΣANs), respectively. Gas–particle separation is achieved using paired inlet/filter configurations, and gas- and particle-phase ΣPNs and ΣANs are retrieved by channel differencing. The 1σ (1 s) detection limits are 49 pptv for gΣPNs, 49 pptv for pΣPNs, 48 pptv for gΣANs, and 68 pptv for pΣANs. Laboratory characterization included temperature-dependent dissociation measurements, cross-validation of PAN against GC–ECD (R2 = 0.988; slope = 0.987), and an operational calibration for particulate ΣANs using 2-ethylhexyl nitrate (recovery slope = 1.036 ± 0.028; method detection limit = 0.029 µg NO2). Dedicated interference experiments showed that NO and NO2 can introduce substantial nonlinear biases in ΣPN measurements; these effects were parameterized using a multiple nonlinear regression model. The instrument was deployed at an urban site in Guangzhou during September–October 2025 and provided 6 min measurements of NO2, gas- and particle-phase ΣPNs, and gas- and particle-phase ΣANs under high-NOx conditions. During the October intensive period, corrected gas-phase ΣPNs covaried well with independently measured PAN (R2 = 0.83), and PAN accounted for 78 % of daytime gΣPNs. This five-channel TD-CEAS provides a framework for continuous observations of ON phase partitioning and reactive nitrogen processing in polluted urban atmospheres.
An accurate characterization of cloud vertical motion is essential for understanding cloud microphysical and dynamical processes. The Cloud Profiling Radar (CPR) onboard the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, launched in May 2024, enables the first global measurements of Doppler velocity from space. The nadir-looking CPR operates in three observation modes – 16, 18, and 20 km modes – each characterized by a distinct pulse repetition frequency (PRF), which determines the Doppler velocity data quality, the maximum observable altitude, and the likelihood of spurious high-altitude echoes known as second-trip echo from mirror images and multi-scattering tails. This study quantitatively evaluates the applicability of these three modes using actual CPR observations, focusing on these three aspects. The standard deviation (SD) of Doppler velocity, used as an indicator of measurement noise, indicated that the 16 and 18 km modes provide more accurate Doppler measurements than the 20 km mode, with comparable SD values between the former two. Clouds above 16 km were primarily observed between 0 and 40° latitude, while clouds exceeding 18 km were rare, suggesting that the 18 or 20 km modes are suitable for observation in these regions. The risk of overlap between genuine cloud echoes and second-trip echoes at high altitudes was highest in the 16 and 18 km modes but was largely confined to low-latitude regions (approximately 0–40°). Accordingly, without considering second-trip echo-related risks, the 16 km mode is preferable at latitudes above 40°, where high clouds are infrequent and Doppler measurement accuracy is highest. In contrast, the 18 km mode provides an optimal balance between Doppler accuracy and vertical coverage at lower latitudes. It should be noted, however, that high-PRF modes inherently increase the likelihood of second-trip echo contamination. These results demonstrate, for the first time using actual EarthCARE observations, the trade-offs among Doppler measurement accuracy, observation height, and spurious echo contamination across CPR operational modes. Future work should involve continuous assessments of the balance between Doppler accuracy and second-trip echo contamination to determine the optimal implementation of each mode as a function of latitude.
