The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe.

Measurements of OH, HO₂, complex RO₂ (alkene- and aromatic-related RO₂) and total RO₂ radicals taken during the integrated Study of AIR Pollution PROcesses in Beijing (AIRPRO) campaign in central Beijing in the summer of 2017, alongside observations of OH reactivity, are presented. The concentrations of radicals were elevated, with OH reaching up to $\mathrm{2.8}\times {\mathrm{10}}^{\mathrm{7}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ , HO₂ peaking at $\mathrm{1}\times {\mathrm{10}}^{\mathrm{9}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ and the total RO₂ concentration reaching $\mathrm{5.5}\times {\mathrm{10}}^{\mathrm{9}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ . OH reactivity (k(OH)) peaked at 89 s⁻¹ during the night, with a minimum during the afternoon of $\approx \mathrm{22}{\mathrm{s}}^{-\mathrm{1}}$ on average. An experimental budget analysis, in which the rates of production and destruction of the radicals are compared, highlighted that although the sources and sinks of OH were balanced under high NO concentrations, the OH sinks exceeded the known sources (by 15 ppbv h⁻¹) under the very low NO conditions (<0.5 ppbv) experienced in the afternoons, demonstrating a missing OH source consistent with previous studies under high volatile organic compound (VOC) emissions and low NO loadings. Under the highest NO mixing ratios (104 ppbv), the HO₂ production rate exceeded the rate of destruction by $\approx \mathrm{50}\mathrm{ppbv}{\mathrm{h}}^{-\mathrm{1}}$ , whilst the rate of destruction of total RO₂ exceeded the production by the same rate, indicating that the net propagation rate of RO₂ to HO₂ may be substantially slower than assumed. If just 10 % of the RO₂ radicals propagate to HO₂ upon reaction with NO, the HO₂ and RO₂ budgets could be closed at high NO, but at low NO this lower RO₂ to HO₂ propagation rate revealed a missing RO₂ sink that was similar in magnitude to the missing OH source. A detailed box model that incorporated the latest Master Chemical Mechanism (MCM3.3.1) reproduced the observed OH concentrations well but over-predicted the observed HO₂ under low concentrations of NO (<1 ppbv) and under-predicted RO₂ (both the complex RO₂ fraction and other RO₂ types which we classify as simple RO₂) most significantly at the highest NO concentrations. The model also under-predicted the observed k(OH) consistently by $\approx \mathrm{10}{\mathrm{s}}^{-\mathrm{1}}$ across all NO_x levels, highlighting that the good agreement for OH was fortuitous due to a cancellation of missing OH source and sink terms in its budget. Including heterogeneous loss of HO₂ to aerosol surfaces did reduce the modelled HO₂ concentrations in line with the observations but only at NO mixing ratios <0.3 ppbv. The inclusion of Cl atoms, formed from the photolysis of nitryl chloride, enhanced the modelled RO₂ concentration on several mornings when the Cl atom concentration was calculated to exceed $\mathrm{1}\times {\mathrm{10}}^{\mathrm{4}}\mathrm{atoms}{\mathrm{cm}}^{-\mathrm{3}}$ and could reconcile the modelled and measured RO₂ concentrations at these times. However, on other mornings, when the Cl atom concentration was lower, large under-predictions in total RO₂ remained. Furthermore, the inclusion of Cl atom chemistry did not enhance the modelled RO₂ beyond the first few hours after sunrise and so was unable to resolve the modelled under-prediction in RO₂ observed at other times of the day. Model scenarios, in which missing VOC reactivity was included as an additional reaction that converted OH to RO₂, highlighted that the modelled OH, HO₂ and RO₂ concentrations were sensitive to the choice of RO₂ product. The level of modelled to measured agreement for HO₂ and RO₂ (both complex and simple) could be improved if the missing OH reactivity formed a larger RO₂ species that was able to undergo reaction with NO, followed by isomerisation reactions reforming other RO₂ species, before eventually generating HO₂. In this work an α-pinene-derived RO₂ species was used as an example. In this simulation, consistent with the experimental budget analysis, the model underestimated the observed OH, indicating a missing OH source. The model uncertainty, with regards to the types of RO₂ species present and the radicals they form upon reaction with NO (HO₂ directly or another RO₂ species), leads to over an order of magnitude less O₃ production calculated from the predicted peroxy radicals than calculated from the observed peroxy radicals at the highest NO concentrations. This demonstrates the rate at which the larger RO₂ species propagate to HO₂, to another RO₂ or indeed to OH needs to be understood to accurately simulate the rate of ozone production in environments such as Beijing, where large multifunctional VOCs are likely present.

Recent advances in thermal infrared remote sensing include the increased availability of airborne hyperspectral imagers (such as the Hyperspectral Thermal Emission Spectrometer, HyTES, or the Telops HyperCam and the Specim aisaOWL), and it is planned that an increased number spectral bands in the long-wave infrared (LWIR) region will soon be measured from space at reasonably high spatial resolution (by imagers such as HyspIRI). Detailed LWIR emissivity spectra are required to best interpret the observations from such systems. This includes the highly heterogeneous urban environment, whose construction materials are not yet particularly well represented in spectral libraries. Here, we present a new online spectral library of urban construction materials including LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer. FTIR emissivity measurements need to be carefully made, else they are prone to a series of errors relating to instrumental setup and radiometric calibration, which here relies on external blackbody sources. The performance of the laboratory-based emissivity measurement approach applied here, that in future can also be deployed in the field (e.g. to examine urban materials in situ), is evaluated herein. Our spectral library also contains matching short-wave (VIS–SWIR) reflectance spectra observed for each urban sample. This allows us to examine which characteristic (LWIR and) spectral signatures may in future best allow for the identification and discrimination of the various urban construction materials, that often overlap with respect to their chemical/mineralogical constituents. Hyperspectral or even strongly multi-spectral LWIR information appears especially useful, given that many urban materials are composed of minerals exhibiting notable reststrahlen/absorption effects in this spectral region. The final spectra and interpretations are included in the London Urban Micromet data Archive (LUMA; http://LondonClimate.info/LUMA/SLUM.html)

The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns – a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing and (ii) Pinggu in rural Beijing during 10 November–10 December 2016 (winter) and 21 May–22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme and sets the scene for more focused papers addressing specific aspects, processes and effects of air pollution in Beijing.

We report on more than 3 years of measurements of fluxes of methane (CH₄), carbon monoxide (CO) and carbon dioxide (CO₂) taken by eddy-covariance in central London, UK. Mean annual emissions of CO₂ in the period 2012–2014 (39.1 ± 2.4 ktons km⁻² yr⁻¹) and CO (89 ± 16 tons km⁻² yr⁻¹) were consistent (within 1 and 5 % respectively) with values from the London Atmospheric Emissions Inventory, but measured CH₄ emissions (72 ± 3 tons km⁻² yr⁻¹) were over two-fold larger than the inventory value. Seasonal variability was large for CO with a winter to summer reduction of 69 %, and monthly fluxes were strongly anti-correlated with mean air temperature. The winter increment in CO emissions was attributed mainly to vehicle cold starts and reduced fuel combustion efficiency. CO₂ fluxes were 33 % higher in winter than in summer and anti-correlated with mean air temperature, albeit to a lesser extent than for CO. This was attributed to an increased demand for natural gas for heating during the winter. CH₄ fluxes exhibited moderate seasonality (21 % larger in winter), and a spatially variable linear anti-correlation with air temperature. Differences in resident population within the flux footprint explained up to 90 % of the spatial variability of the annual CO₂ fluxes and up to 99 % for CH₄. Furthermore, we suggest that biogenic sources of CH₄, such as wastewater, which is unaccounted for by the atmospheric emissions inventories, make a substantial contribution to the overall budget and that commuting dynamics in and out of central business districts could explain some of the spatial and temporal variability of CO₂ and CH₄ emissions. To our knowledge, this study is unique given the length of the data sets presented, especially for CO and CH₄ fluxes. This study offers an independent assessment of "bottom-up" emissions inventories and demonstrates that the urban sources of CO and CO₂ are well characterized in London. This is however not the case for CH₄ emissions which are heavily underestimated by the inventory approach. Our results and others point to opportunities in the UK and abroad to identify and quantify the "missing" sources of urban methane, revise the methodologies of the emission inventories and devise emission reduction strategies for this potent greenhouse gas.

Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM₁), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM₁ variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM₁ at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM₁ concentrations (coefficient of determination (R²) of 0.58), with model performance depending on the individual PM₁ species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m³ for MLHs below <500 m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM₁ concentrations of as much as ∼9 µg/m³. Northeasterly winds are found to contribute ∼5 µg/m³ to predicted PM₁ concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM₁ concentrations in summer are temperatures above ∼25 ^∘C (contributions of up to ∼2.5 µg/m³), dry spells of several days (maximum contributions of ∼1.5 µg/m³), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m³), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.