In the polar regions, the extreme cold and dark atmosphere of the winter season imposes stringent conditions on the planetary boundary layer (PBL) leading to limited vertical atmospheric mixing and increasing the severity of air pollution episodes. Understanding the physical and chemical transformations affecting air pollution critically depends on our ability to accurately describe the dynamics of the PBL. This requires an adequate combination of modeling, ground-based observations, and vertical profile measurement systems to assess emission dispersion and transport in stratified environments. We present an overview of the meteorological conditions and PBL observations collected during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fairbanks, Alaska, in winter 2022. Surface and vertical profile observations of radiation, turbulence, dynamics, and atmospheric composition were collected to account for surface and elevated emissions. The study area is the Tanana Valley in the interior of Alaska, which experiences persistent synoptic anticyclonic conditions resulting in stagnant flow and limited ventilation, exacerbating air pollution levels. These conditions are interspersed with periodic transits of cyclonic air masses influencing the PBL through radiative forcing that erodes low-level temperature inversion layers and mixes local air masses into the free troposphere. This paper highlights the research objectives, experimental findings, and first results linking meteorological conditions with the observed structure and composition of the PBL under the challenging experimental conditions of the Alaskan winters. It also highlights the need for integrated surface and profiling observations of PBL dynamics and composition, which are critical for advancing across-scale modeling and enhancing predictive capabilities for air pollution episodes locally and throughout the Arctic air shed. SIGNIFICANCE STATEMENT: This article provides an overview of the meteorology during the Alaskan Layered Pollution and Chemical Analysis (ALPACA)-2022 winter field experiment. It also presents observations designed to better understand the physical processes influencing the planetary boundary layer (PBL) and their role in wintertime Arctic air pollution. It describes the synoptic meteorological conditions throughout the experiment and the diverse instrumental platforms used to study the dynamics and composition of the Arctic polluted PBL for the first time. The article emphasizes the importance of multi-instrumental platforms to improve understanding of the PBL composition and dynamics in the context of Arctic air pollution. This is particularly relevant in conditions with limited photochemistry and in areas experiencing very cold, stable environments that exacerbate pollution levels. The observations are used to improve meteorological and air quality model simulations.

Highly oxygenated organic molecules (HOMs) can significantly contribute to new particle formation (NPF). HOM-derived NPF in preindustrial (PI) environments provides the baseline for calculating radiative forcing, yet global model studies examining this are lacking. Here, we use a global climate model with a semi-explicit HOM chemistry and the associated nucleation scheme to systematically quantify the effect of HOM-derived NPF on cloud condensation nuclei (CCN) formation and effective radiative forcing due to aerosol-cloud interactions (ERFaci). The model shows better agreement with measured CCN numbers after including organic NPF mechanisms. Aerosols generated from organic NPF nearly double the globally averaged CCN burden in PI (39 %) compared to present-day (PD) (18 %) experiments. This weakens the ERFaci by 0.4 W m-2, corresponding to a 16 % reduction, with most of this reduction occurring in tropical regions where the pure organic nucleation rate shows a larger value in the PI atmosphere. The reduction is mainly driven by a greater enhancement of the sub-20 nm growth rate (GR) in the PI atmosphere compared to PD, in contrast to the findings of Gordon et al. (2016) that the similar to 1 nm nucleation rate (j1.7nm) drives the reduction. The greater enhancement of GR is due to higher HOM concentrations in the PI atmosphere, while the greater j1.7nm in the PD environment results from higher sulfuric acid concentrations, leading to higher heteromolecular nucleation rates involving sulfuric acid and organics. The significant reduction underscores the critical role of biogenic NPF in CCN formation, particularly in the PI climate when cloud droplet concentrations and albedo are more sensitive to aerosol changes.

China's national air quality monitoring network has revealed a rapid improvement in air quality during the 2010s, during which fine particulate matter (PM2.5) and other priority pollutant levels fell, except for ozone, which concurrently increased. However, recent changes in China's economic outlook mean that the future trajectory of China's air quality is highly uncertain. Here we analyse the last 10 years of air quality monitoring data to assess whether China's air quality has continued to improve in recent years. We find that the period of steep negative trends in PM2.5 observed during 2014-2019 (-2.47 mu g m(-3) year(-1)) has ended, slowing to -0.18 mu g m(-3) year(-1) during 2021-2024. Meanwhile, ozone levels continued to increase during 2021-2024, with a trend of 2.06 mu g m(-3) year(-1). We demonstrate that population PM2.5 exposure in China can be accurately constrained using only surface monitoring station data, and we use this to estimate future health impacts under three observationally-based future PM2.5 scenarios. We show that the current government PM2.5 reduction target is insufficient to sustain the decrease in PM2.5-attributed mortality that was achieved during 2014-2019, and a similar to 2 times more ambitious target is needed to offset the effects of China's ageing population.

Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.

Electrochemical gas sensors (EGSs) have been used to measure the surface distributions and vertical profiles of trace gases in the wintertime Arctic boundary layer during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fairbanks, Alaska, in January–February 2022. The MICRO sensors for MEasurements of GASes (MICROMEGAS) instrument set up with CO, NO, NO2, and O3 EGSs was operated on the ground at an outdoor reference site in downtown Fairbanks for calibration, while on board a vehicle moving through the city and its surroundings and on board a tethered balloon, the helikite, at a site at the edge of the city. To calibrate the measurements, a set of machine learning (ML) calibration methods were tested. For each method, learning and prediction were performed with coincident MICROMEGAS and reference analyser measurements at the downtown site. For CO, the calibration parameters provided by the manufacturer led to the best agreement between the EGS and the reference analyser, and no ML method was needed for calibration. The Pearson correlation coefficient R is 0.82, and the slope of the linear regression between MICROMEGAS and reference data is 1.12. The mean bias is not significant, but the root mean square error (290 ppbv, parts per billion by volume) is rather large because of CO concentrations reaching several ppmv (parts per million by volume) in downtown Fairbanks. For NO, NO2, and O3, the best agreements for the prediction datasets were obtained with an artificial neural network, the multi-layer perceptron. For these three gases, the correlation coefficients are higher than 0.95, and the slopes of linear regressions with the reference data are in the range 0.93–1.04. The mean biases, which are 1 ± 3, 0 ± 4, and 3 ± 12 ppbv for NO2, O3, and NO, respectively, are not significant. Measurements from the car round of 21 January are presented to highlight the ability of MICROMEGAS to quantify the surface variability in the target trace gases in Fairbanks and the surrounding hills. MICROMEGAS flew 11 times from the ground up to a maximum of 350 m above ground level (a.g.l.) on board the helikite at the site at the edge of the city. The statistics performed over the helikite MICROMEGAS dataset show that the median vertical gas profiles are characterized by almost constant mixing ratios. The median values over the vertical are 140, 8, 4, and 32 ppbv for CO, NO, NO2, and O3. Extreme values are detected with low-O3 and high-NO2 and NO concentrations between 100 and 150 m a.g.l. O3 minimum levels (5th percentile) of 5 ppbv are coincident with NO2 maximum levels (95th percentile) of 40 ppbv, which occur around 200 m a.g.l. The peaks aloft are linked to pollution plumes originating from Fairbanks power plants such as those documented during the flight on 20 February.

This study presents a performance evaluation of eight Atmotube PRO sensors using US Environmental Protection Agency (US EPA) guidelines. The Atmotube PRO sensors were collocated side by side with a reference-grade Fidas monitor in an outdoor setting for a 14-week period in the city centre of Leeds, UK. We assessed the linearity and bias for PM1, PM2.5, and PM10. The result of the PM2.5 assessment showed the Atmotube PRO sensors had particularly good precision with a coefficient of variation (CoV) of 28 %, 18 %, and 15 % for PM2.5 data averaged every minute, hour, and day, respectively. The inter-sensor variability assessment showed two sensors with low bias and one sensor with a higher bias in comparison with the sensor average. Simple univariate analysis was sufficient to obtain good fitting quality to a Fidas reference-grade monitor (R2>0.7) at hourly averages, although poorer performance was observed using a higher time resolution of 15 min averaged PM2.5 data (R2 of 0.48–0.53). The average error bias, root mean square error (RMSE), and normalized root mean square error (NRMSE) were 3.38 µg m−3 and 0.03 %, respectively. While there were negligible influences of temperature on Atmotube PRO-measured PM2.5 values, substantial positive biases (compared to a reference instrument) occurred at relative humidity (RH) values > 80 %. The Atmotube PRO sensors correlated well with the PurpleAir sensor (R2 of 0.88, RMSE of 2.9 µg m−3). In general, the Atmotube PRO sensors performed well and passed the base-testing metrics as stipulated by recommended guidelines for low-cost PM2.5 sensors. Calibration using the multiple linear regression model was enough to improve the performance of the PM2.5 data of the Atmotube PRO sensors.

Exposure to fine particulate matter (PM2.5) pollution, both outdoors and indoors poses a significant health burden in Africa, where concentrations are often high, but there are limited measurements. Two types of low-cost sensors were used during two distinct measurement phases conducted in Ibadan, Nigeria. In Phase I, indoor and outdoor PM2.5 concentrations were measured for a two-week period in twelve households using a total of twenty-four Atmotube PRO sensors. Phase II consisted of a seven-month extended monitoring study conducted in two households (each equipped with one indoor and one outdoor sensor) and a school (1 sensor only) using five PurpleAir sensors. Across the twelve households in Phase I, daily median PM₂.₅ concentrations ranged from 12.0 to 18.0 µgm−3 indoors, and from 12.2 to 20.0 µgm−3 outdoors. The overall PM2.5 indoor-outdoor (I/O) median ratio was 0.9 indicating that outdoor levels were typically slightly higher than indoors. In January (the dry harmattan season), daily median PM2.5 concentrations were 98.0 µgm−3 indoors and 109.3 µgm−3 outdoors. In contrast, lower PM2.5 concentrations of 21.4 µgm−3 indoors and 24.5 µgm−3 outdoors were recorded in May, a rainy season. In Phase II, we find that a substantial part ( 90

Fairbanks, Alaska, is a sub-Arctic city that frequently suffers from the non-attainment of national air quality standards in the wintertime due to the coincidence of weak atmospheric dispersion and increased local emissions. As part of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign, we deployed a Chemical Analysis of Aerosol Online (CHARON) inlet coupled with a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF MS) and an Aerodyne high-resolution aerosol mass spectrometer (AMS) to measure organic aerosol (OA) and non-refractory submicron particulate matter (NR-PM1), respectively. We deployed a positive matrix factorization (PMF) analysis for the source identification of NR-PM1. The AMS analysis identified three primary factors: biomass burning, hydrocarbon-like, and cooking factors, which together accounted for 28 %, 38 %, and 11 % of the total OA, respectively. Additionally, a combined organic and inorganic PMF analysis revealed two further factors: one enriched in nitrates and another rich in sulfates of organic and inorganic origin. The PTRCHARON factorization could identify four primary sources from residential heating: one from oil combustion and three from wood combustion, categorized as low temperature, softwood, and hardwood. Collectively, all residential heating factors accounted for 79 % of the total OA. Cooking and road transport were also recognized as primary contributors to the overall emission profile provided by PTRCHARON. All PMF analyses could apportion a single oxygenated secondary organic factor. These results demonstrate the complementarity of the two instruments and their ability to describe the complex chemical composition of PM1 and related sources. This work further demonstrates the capability of PTRCHARON to provide both qualitative and quantitative information, offering a comprehensive understanding of the OA sources. Such insights into the sources of submicron aerosols can ultimately assist environmental regulators and citizens in improving the air quality in Fairbanks and in rapidly urbanizing regional sub-Arctic areas.

Lagrangian tracer simulations are deployed to investigate processes influencing vertical and horizontal dispersion of anthropogenic pollution in Fairbanks, Alaska, during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) 2022 field campaign. Simulated concentrations of carbon monoxide (CO), sulfur dioxide ( S O 2 ), and nitrogen oxides ( N O x ), including surface and elevated sources, are the highest at the surface under very cold stable conditions. Pollution enhancements above the surface (50-300 m) are mainly attributed to elevated power plant emissions. Both surface and elevated sources contribute to Fairbanks' regional pollution that is transported downwind, primarily to the south-west, and may contribute to wintertime Arctic haze. Inclusion of a novel power plant plume rise treatment that considers the presence of surface and elevated temperature inversion layers leads to improved agreement with observed CO and N O x plumes, with discrepancies attributed to, for example, displacement of plumes by modelled winds. At the surface, model results show that observed CO variability is largely driven by meteorology and, to a lesser extent, by emissions, although simulated tracers are sensitive to modelled vertical dispersion. Modelled underestimation of surface N O x during very cold polluted conditions is considerably improved following the inclusion of substantial increases in diesel vehicle N O x emissions at cold temperatures (e.g. a factor of 6 at -30°C). In contrast, overestimation of surface S O 2 is attributed mainly to model deficiencies in vertical dispersion of elevated (5-18 m) space heating emissions. This study highlights the need for improvements to local wintertime Arctic anthropogenic surface and elevated emissions and improved simulation of Arctic stable boundary layers.

The Arctic is warming rapidly compared to the global average. As Arctic warming continues, urbanisation and industrial activities are predicted to increase, along with complex climate and ecosystem feedbacks. Therefore, local sources of air pollutants are expected to play an increasingly significant role in Arctic environmental changes in the coming years. Poor air quality is already a growing public health issue in Arctic and sub-Arctic cities. During wintertime, stable meteorological conditions and the persistence of strong surface-based temperature inversions suppress the dispersion of pollutants, which accumulate due to enhanced emissions linked to high energy demands. Fairbanks, in central Alaska, is an example of a sub-Arctic city that suffers from acute wintertime pollution episodes. The city’s topography (situated in a basin), strong stratification of the Arctic boundary layer (ABL), and high emissions, primarily from domestic heating at the surface, and power plant stacks aloft, are known to contribute to the problem. However, interactions between vertical stratification of the ABL and dispersal of pollutants from surface and elevated sources are poorly quantified due to a lack of observations and complexities of the ABL structure and dynamics. To address these uncertainties, comprehensive atmospheric composition and meteorological measurements were collected at the surface, and vertical profiles were obtained using a tethered balloon during the international ALPACA (Alaskan Layered Pollution and Chemical Analysis) field campaign in January and February 2022.Here, we explore the contribution of power plants and surface emission sources to pollution concentrations in the Fairbanks region. We use the FLEXPART-Weather Research and Forecasting (WRF) Lagrangian particle dispersion model, driven by meteorological fields from US Environmental Protection Agency (EPA) WRF simulations including data assimilation of meteorological observations, to simulate the evolution of selected emission tracers. Hourly power plant and sector-based surface EPA emissions at 1.3km resolution during ALPACA 2022 are included in the model runs. A novel model parameterisation of power plant plume injection heights accounts for the ABL structure, notably surface-based and elevated temperature inversions. Model results are evaluated against available observations from ALPACA 2022, and sensitivity to, for example, emissions and vertical mixing is explored. The simulation of pollution plume altitudes is significantly improved when ABL stratification is taken into account in the plume rise parameterisation since inversion layers can trap plumes. Variability in modelled surface pollutant concentrations is predominantly driven by meteorology, and the ability of the model to capture surface-based temperature inversions (as low as 10m). A cold-temperature dependence for NOx vehicle emissions, currently missing from the EPA emission inventory, is required to reproduce the magnitude of observed NOx surface concentrations at low temperatures below 0°C and needs to be considered in future emission inventories in the Arctic, and potentially in other wintertime environments. Finally, using the most realistic simulation, we estimate the contribution of power plant emissions to surface pollution in the Fairbanks region, addressing an important policy question. The results indicate preferential areas for downward transport of pollution from aloft and larger contributions to surface pollution under less stable meteorological conditions.

As a high-latitude city, Fairbanks, Alaska, undergoes prolonged, cold winters with limited sunlight, and strong surface temperature inversions. These conditions coupled with its position at the bottom of the Tanana Valley lead to cold, dark, stagnant weather conditions, which when combined with demands for heating and transportation contribute to substantial degradations in air quality. These factors have led to Fairbanks exceeding the Environment Protection Agency PM2.5 standard and being classified as a serious nonattainment area for air quality. The ability to mitigate harmful pollution concentrations in Fairbanks is hampered by a lack of knowledge of the physicochemical processes which drive localised extreme pollution episodes during wintertime. For example, low levels of sunlight and ozone concentrations inhibit the well-established formation mechanisms of HOx via photolysis or radical reactions. However, nitrous acid (HONO) can be a major source of OH radicals even in cold, dark, polluted environments. Despite being a major source of OH radicals, the formation of HONO is poorly represented in models. HONO is directly emitted from vehicles or formed via gas-phase reactions or via heterogeneous reactions such as those occurring from the surface of aerosols.Using observations made during the Alaska Layered Pollution and Chemical Analysis Campaign (ALPACA), which took place in Fairbanks during January – February 2022, we conducted constrained chemical box model experiments to investigate HONO and oxidant sources during the ALPACA campaign. Our results show that gas-phase only reactions cannot account for observed HONO concentrations nor correctly reproduce diurnal trends. This suggests additional sources of HONO present in Fairbanks, potentially including formation from the surface of aerosols, which is not currently well constrained, especially at temperatures and relative humidities pertinent to wintertime Fairbanks.Here, we present laboratory results aimed at addressing the lack of studies into HONO formation on the surface of aerosols in cold, dark environments and provide a wider atmospheric context via chemical box modelling constrained to observations from the ALPACA campaign. We purpose-built a chamber designed to reach temperatures similar to wintertime conditions in Fairbanks to study the formation of HONO from aerosol samples collected on filters during the ALPACA campaign, as well as filters collected from idealised single-source emission experiments in the laboratory. The generated HONO was detected in the gas-phase following its photolysis at 355 nm to OH and NO, with the OH detected via OH laser-induced fluorescence spectroscopy. We comprehensively studied HONO formation from aerosol filter samples as a function of aerosol surface area, NO2 concentration, relative humidity, and temperature under actinic light levels applicable to wintertime conditions in Fairbanks. Inclusion of our experimental results into the chemical box model suggests enhancement of HONO concentrations over gas-phase only reactions alongside improved diurnal trends.

The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign was conducted during the winter months of January and February 2022 to examine urban pollution sources and transformations in Fairbanks, Alaska. Several data collection sites were set up throughout the city to investigate the less-explored dynamic, physical, and chemical mechanisms governing air pollution events during the cold and dark winter.The vertical dispersion of pollutants was investigated from an observation site in the suburban area just outside downtown Fairbanks. It featured ground-based measurements, a ten-meter mast for eddy covariance measurements, and a tethered balloon for vertical profiling of the atmosphere. Sampling included measurements of aerosol microphysical characteristics and trace gases (CO, CO2, O3, NOx). Meteorological parameters were also continuously measured at 2m and 10m from the mast, and also during the balloon flights. The tethered balloon was deployed to assess the vertical mixing of pollutants under stable atmospheric conditions from sources located at the surface but also at higher elevations, such as emissions from high power plant stacks.A total of 148 individual profiles (up to a maximum altitude of 350 m above ground level) from 24 flights were collected between January 26 and February 25, 2022. The atmospheric conditions featured surface-based temperature inversions (SBI) in 86% of the cases due to the upwelling longwave radiation dominating the surface energy budget. Interestingly, eight flights captured elevated pollution plumes from power plants located downtown. The analysis of profiles reveals that the atmospheric stability and mixing of the surface layer was affected by two mechanisms. On one hand, radiative cooling promoted strong SBI locally, suppressing turbulence. On the other hand, a drainage flow at the surface from a nearby valley increased the shear stress at the surface, promoting mechanical turbulence near the surface. The measurements show how these two competing mechanisms affect the mixing of the surface layer.The second part of the study focuses on the vertical dispersion of elevated plumes. The vertical mixing of pollutant plumes and their potential to contribute to surface pollution are investigated using the chemical and physical signature of the plumes and their vertical extents.Together, the results of this study contribute to improving our understanding of pollution mixing under the very stable conditions typical of the Arctic winter and can help to design pollution mitigation strategies by identifying the conditions and mechanisms leading to high pollution events.

Vertical in situ measurements of aerosols and trace gases were conducted in Fairbanks, Alaska, during winter 2022 as part of the Alaskan Layered Pollution and Chemical Analysis campaign (ALPACA). Using a tethered balloon, the study explores the dispersion of pollutants in the continental high-latitude stable boundary layer (SBL). Analysis of 24 flights revealed a stratified SBL structure with different pollution layers in the lowest tens of meters of the atmosphere, offering unprecedented detail. Surface emissions generally accumulated in a surface mixing layer (ML) extending to an average of 51 m, with a well-mixed sublayer (MsL) reaching 22 m. The height and concentrations within the ML were strongly influenced by a local wind driven by nearby topography under anticyclonic conditions. During strong radiative cooling, a drainage flow increased turbulence near the surface, altering the temperature profile and deepening the ML. Above the ML, pollution concentrations decreased but showed clear signs of freshly released anthropogenic emissions. Higher in the atmosphere, above elevated inversions, pollution levels were similar to previously reported Arctic haze concentrations, even though Fairbanks' outflow concentrations below elevated inversions were up to 6 times higher, likely due to power plant emissions. In situ measurements indicated that gas and particle tracer ratios in elevated power plant plumes differed significantly from those near the surface. Overall, pollution layers were strongly correlated with the temperature stratification and emission heights, emphasizing the need for improved representation of temperature inversions and emission sources in air quality models to enhance pollution forecasts.

Changes in the availability of a subset of aerosol known as ice-nucleating particles (INPs) can substantially alter cloud microphysical and radiative properties. Despite very large spatial and temporal variability in INP properties, many climate models do not currently represent the link between (i) the global distribution of aerosols and INPs and (ii) primary ice production in clouds. Here we use the UK Earth System Model to simulate the global distribution of dust, marine-sourced, and black carbon INPs suitable for immersion-mode freezing of liquid cloud droplets over an annual cycle. The model captures the overall spatial and temporal distribution of measured INP concentrations, which is strongly influenced by the world's major mineral dust source regions. A negative bias in simulated versus measured INP concentrations at higher freezing temperatures points to incorrectly defined INP properties or a missing source of INPs. We find that the ability of the model to reproduce measured INP concentrations is greatly improved by representing dust as a mixture of mineralogical and organic ice-nucleating components, as present in many soils. To improve the agreement further, we define an optimized hypothetical parameterization of dust INP activity (ns(T)) as a function of temperature with a logarithmic slope of −0.175 K−1, which is much shallower than existing parameterizations (e.g. −0.35 K−1 for the K-feldspar data of Harrison et al., 2019). The results point to a globally important role for an organic component associated with mineral dust.

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by −9.7 % for CH4, −21 % for CO, and −18 % for O3. Results for CH4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons.

The Pan-Eurasian Experiment Modelling Platform (PEEX-MP) is one of the key blocks of the PEEX Research Programme. The PEEX MP has more than 30 models and is directed towards seamless environmental prediction. The main focus area is the Arctic-boreal regions and China. The models used in PEEX-MP cover several main components of the Earth's system, such as the atmosphere, hydrosphere, pedosphere and biosphere, and resolve the physical-chemical-biological processes at different spatial and temporal scales and resolutions. This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system, online integrated, forward/inverse, and socioeconomical modelling, and other approaches with a particular focus on applications in the PEEX geographical domain. The employed high-performance computing facilities, capabilities, and PEEX dataflow for modelling results are described. Several virtual research platforms (PEEX-View, Virtual Research Environment, Web-based Atlas) for handling PEEX modelling and observational results are introduced. The overall approach allows us to understand better physical-chemical-biological processes, Earth's system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks, impact, consequences, etc. for population, environment and climate in the PEEX domain. This work was also one of the last projects of Prof. Sergej Zilitinkevich, who passed away on 15 February 2021. Since the finalization took time, the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

The formulation of chemical models is discussed. The component modules of chemical models (gas phase chemistry, heterogeneous chemistry, photolysis, deposition) are summarized and the chemical continuity equation is described. Trajectory, one-dimensional, two-dimensional and three-dimensional atmospheric models are discussed. Example applications in the stratosphere and troposphere are given.

The evaporative emissions of anthropogenic volatile organic compounds (AVOCs) are sensitive to ambient temperature. This sensitivity forms an air pollution-meteorology connection that has not been assessed on a regional scale. We parametrized the temperature dependence of evaporative AVOC fluxes in a regional air quality model and evaluated the impacts on surface ozone in the Beijing-Tianjin-Hebei (BTH) area of China during the summer of 2017. The temperature dependency of AVOC emissions drove an enhanced simulated ozone-temperature sensitivity of 1.0 to 1.8 μg m-3 K-1, comparable to the simulated ozone-temperature sensitivity driven by the temperature dependency of biogenic VOC emissions (1.7 to 2.4 μg m-3 K-1). Ozone enhancements driven by temperature-induced AVOC increases were localized to their point of emission and were relatively more important in urban areas than in rural regions. The inclusion of the temperature-dependent AVOC emissions in our model improved the simulated ozone-temperature sensitivities on days of ozone exceedance. Our results demonstrated the importance of temperature-dependent AVOC emissions on surface ozone pollution and its heretofore unrepresented role in air pollution-meteorology interactions.

China’s air quality has improved rapidly since the early 2010s, when the government launched an action plan focussed on reducing fine particulate matter (PM2.5) pollution, the pollutant species associated with the largest negative health impact. Measurements from China’s monitoring network, now consisting of >2000 surface stations, have shown a rapid fall in PM2.5 concentrations consistently over the period 2014–2019, while sulphur dioxide (SO2), carbon monoxide (CO) and nitrogen dioxide (NO2) concentrations have also significantly decreased. Concurrently there has been a rapid increase in ozone concentrations, which has been partly attributed to falling particulate matter concentrations. However, air quality data from China remains difficult to access, and recent changes in China’s economic outlook mean that the future trajectory of China’s air quality continues to be highly uncertain. Here we analyse 10 years of air quality monitoring data from May 2014 to April 2024 to assess whether China’s air quality has continued to improve in recent years, in the wake of the COVID-19 lockdowns and other economic challenges. After using an improved data cleaning algorithm to remove outliers from the dataset, we use a non-linear trend fitting technique to extract underlying trends and their uncertainties. We find that the steep negative trend in PM2.5 that was observed during 2014–2019 has now reversed, and since June 2022 there has been a significant (>95% confident) positive trend at over half of China’s air quality monitoring stations. This is mirrored by the average trend of ozone, which was positive during 2014–2019, but since December 2022 there has been a significant negative trend at almost half of monitoring stations. The increase in PM2.5 exposure has the potential to worsen air quality health impacts in China, particularly given its ageing population.

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.