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Low-cost air quality sensors are a promising supplement to current reference methods for air quality monitoring but can suffer from issues that affect their measurement quality. Interferences from environmental conditions such as temperature, humidity, cross-sensitivities with other gases and a low signal-to-noise ratio make them difficult to use in air quality monitoring without significant time investment in calibrating and correcting their output. Many studies have approached these problems utilising a variety of techniques to correct for these biases. Some use physical methods, removing the variability in environmental conditions, whereas most adopt software corrections. However, these approaches are often not standardised, varying in study duration, measurement frequency, averaging period, average concentration of the target pollutant and the biases that are corrected. Some go further and include features with no direct connection to the measurement such as the level of traffic nearby, converting the initial measurement into a modelled value. Though overall trends in performance can be derived when aggregating the results from multiple studies, they do not always match observations from individual studies, a phenomenon observed across many different academic fields and known as “Simpson’s Paradox”. The preference of performance metrics which utilise the square of the error, such as root mean squared error (RMSE) and r2, over ones which use the absolute error, such as mean absolute error (MAE), makes comparing results between models and studies difficult. Ultimately, comparisons between studies are either difficult or unwise depending on the metrics used, and this literature review recommends that efforts are made to standardise the reporting of calibration and correction studies. By utilising metrics which do not use the square of the error (e.g., MAE), models can be more easily compared within and between studies. By not only reporting the raw error but also the error normalised by multiple factors (including the reference mean and reference absolute deviation), the variabilities induced by environmental factors such as proximity to pollution sources can be minimised.

Isoprene is a key trace component of the atmosphere emitted by vegetation and other organisms. It is highly reactive and can impact atmospheric composition and climate by affecting the greenhouse gases ozone and methane and secondary organic aerosol formation. Marine fluxes are poorly constrained due to the paucity of long-term measurements; this in turn limits our understanding of isoprene cycling in the ocean. Here we present the analysis of isoprene concentrations in the atmosphere measured across the Southern Ocean over 4 months in the summertime. Some of the highest concentrations ( >500 ppt) originated from the marginal ice zone in the Ross and Amundsen seas, indicating the marginal ice zone is a significant source of isoprene at high latitudes. Using the United Kingdom Earth System Model we show that current estimates of sea-to-air isoprene fluxes underestimate observed isoprene by a factor >20. A daytime source of isoprene is required to reconcile models with observations. The model presented here suggests such an increase in isoprene emissions would lead to >8% decrease in the hydroxyl radical in regions of the Southern Ocean, with implications for our understanding of atmospheric oxidation and composition in remote environments, often used as proxies for the pre-industrial atmosphere.

While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget. Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers, but existing methods have not been well validated. Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions.

Low-cost air quality sensors have shown great promise as a complement to high-cost reference and equivalent methods. Though not currently as accurate, their low barrier of entry and smaller form factor allow them to be deployed in greater numbers, thus enabling air quality measurements to be made at a far higher spatial and temporal resolution than previously possible. However, their measurements require corrections as they suffer from both short-term biases (e.g., changes in environmental conditions such as temperature and humidity), and long-term measurement drift due to degradation. Many studies have focused on calibration and re-calibration of sensors, but fewer focus on correcting pre-calibrated sensor measurements. Correcting measurements is a likely scenario for people buying off-the-shelf devices, as they will not have access to the raw data that underpins the measurements, such as sensor voltages. Previous studies focused on a small range of correction techniques, without accounting for the variances that can occur between devices or locations. This work aimed to perform a comprehensive assessment of different correction techniques applied to air quality sensor systems. More than 470,000 unique measurement corrections were tested across two sites to determine best practices for correction campaigns going forward, resulting in a far more robust study than previous works. It highlights the large variances in results that occurred between sites, particularly for NO 2 , with results often more impacted by device type and location than the regression technique used. Simpler linear models were also found to perform just as well as, and sometimes better than, more complex non-parametric techniques. This study highlights that, though a strong focus is often put on comparing different regression methods, the choice of technique has less impact than the configuration of the device or the conditions of the co-location site. Therefore, future studies should focus less on small-scale comparisons of regression techniques and more on how to improve the transferability and applicability of results from a co-location campaign to another.

We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.

A correction for the undesirable effects of direct and indirect cross-interference from water vapour on ammonia (NH 3 ) measurements was developed using an optical laser sensor based on cavity ring-down spectroscopy. This correction relied on new measurements of the collisional broadening due to water vapour of two NH 3 spectral lines in the near infra-red (6548.6 and 6548.8 cm −1 ), and on the development of novel stable primary standard gas mixtures (PSMs) of ammonia prepared by gravimetry in passivated gas cylinders at 100 μmol mol −1 . The PSMs were diluted dynamically to provide calibration mixtures of dry and humidified ammonia atmospheres of known composition in the nmol mol −1 range and were employed as part of establishing a metrological traceability chain to improve the reliability and accuracy of ambient ammonia measurements. The successful implementation of this correction will allow the extension of this rapid on-line spectroscopic technique to exposure chamber validation tests under controlled conditions and ambient monitoring in the field.

The hydroxyl radical (OH) plays a crucial role in the chemistry of the atmosphere as it initiates the removal of most trace gases. A number of field campaigns have observed the presence of a “missing” OH sink in a variety of regions across the planet. A comparison of direct measurements of the OH loss frequency, also known as total OH reactivity (kOH), with the sum of individual known OH sinks (obtained via the simultaneous detection of species such as volatile organic compounds and nitrogen oxides) indicates that, in some cases, up to 80 % of kOH is unaccounted for. In this work, the UM-UKCA chemistry-climate model was used to investigate the wider implications of the missing reactivity on the oxidising capacity of the atmosphere. Simulations of the present-day atmosphere were performed and the model was evaluated against an array of field measurements to verify that the known OH sinks were reproduced well, with a resulting good agreement found for most species. Following this, an additional sink was introduced to simulate the missing OH reactivity as an emission of a hypothetical molecule, X, which undergoes rapid reaction with OH. The magnitude and spatial distribution of this sink were underpinned by observations of the missing reactivity. Model runs showed that the missing reactivity accounted for on average 6 % of the total OH loss flux at the surface and up to 50 % in regions where emissions of the additional sink were high. The lifetime of the hydroxyl radical was reduced by 3 % in the boundary layer, whilst tropospheric methane lifetime increased by 2 % when the additional OH sink was included. As no OH recycling was introduced following the initial oxidation of X, these results can be interpreted as an upper limit of the effects of the missing reactivity on the oxidising capacity of the troposphere. The UM-UKCA simulations also allowed us to establish the atmospheric implications of the newly characterised reactions of peroxy radicals (RO2) with OH. Whilst the effects of this chemistry on kOH were minor, the reaction of the simplest peroxy radical, CH3O2, with OH was found to be a major sink for CH3O2 and source of HO2 over remote regions at the surface and in the free troposphere. Inclusion of this reaction in the model increased tropospheric methane lifetime by up to 3 %, depending on its product branching. Simulations based on the latest kinetic and product information showed that this reaction cannot reconcile models with observations of atmospheric methanol, in contrast to recent suggestions.

Field campaigns have been carried out with the FAGE (fluorescence assay by gas expansion) technique in remote biogenic environments in the last decade to quantify the in situ concentrations of OH, the main oxidant in the atmosphere. These data have revealed concentrations of OH radicals up to a factor of 10 higher than predicted by models, whereby the disagreement increases with decreasing NO concentration. This was interpreted as a major lack in our understanding of the chemistry of biogenic VOCs (volatile organic compounds), particularly isoprene, which are dominant in remote pristine conditions. But interferences in these measurements of unknown origin have also been discovered for some FAGE instruments: using a pre-injector, all ambient OH is removed by fast reaction before entering the FAGE cell, and any remaining OH signal can be attributed to an interference. This technique is now systematically used for FAGE measurements, allowing the reliable quantification of ambient OH concentrations along with the signal due to interference OH. However, the disagreement between modelled and measured high OH concentrations of earlier field campaigns as well as the origin of the now-quantifiable background OH is still not understood. We present in this paper the compelling idea that this interference, and thus the disagreement between model and measurement in earlier field campaigns, might be at least partially due to the unexpected decomposition of a new class of molecule, ROOOH, within the FAGE instruments. This idea is based on experiments, obtained with the FAGE set-up of the University of Lille, and supported by a modelling study. Even though the occurrence of this interference will be highly dependent on the design and measurement conditions of different FAGE instruments, including ROOOH in atmospheric chemistry models might reflect a missing piece of the puzzle in our understanding of OH in clean atmospheres.

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.

The iDirac is a new instrument to measure selected hydrocarbons in the remote atmosphere. A robust design is central to its specifications, with portability, power efficiency, low gas consumption and autonomy as the other driving factors in the instrument development. The iDirac is a dual-column isothermal oven gas chromatograph with photoionisation detection (GC-PID). The instrument is designed and built in-house. It features a modular design, with the novel use of open-source technology for accurate instrument control. Currently configured to measure biogenic isoprene, the system is suitable for a range of compounds. For isoprene measurements in the field, the instrument precision (relative standard deviation) is ±10 %, with a limit of detection down to 38 pmol mol−1 (or ppt). The instrument was first tested in the field in 2015 during a ground-based campaign, and has since shown itself suitable for deployment in a variety of environments and platforms. This paper describes the instrument design, operation and performance based on laboratory tests in a controlled environment as well as during deployments in forests in Malaysian Borneo and central England.