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Below‐cloud scavenging (BS) is often underestimated in chemical transport models (CTMs) due to inaccurate parameterizations of BS coefficient for fine particle (Λ) caused by a shortage of high‐time resolution field observations. Rainfall ions and related air pollutants were measured hourly in Central China (CC) during 2019. BS contributed to 37%–68% of wet deposition for SO42– ${\text{SO}}_{4}^{2\mbox{--}}$, NO3– ${\text{NO}}_{3}^{\mbox{--}}$, and NH4+ ${\text{NH}}_{4}^{+}$ (SNA). By a bulk method coupled with brute‐force search, the Λ (10−2–10 hr−1) was parameterized for SNA in PM2.5, which was 1–3 orders of magnitudes higher than theoretical calculations in CTMs. These chemical‐specific Λ parameterizations were validated by EMEP model. Compared to baselines, updated simulations for annual SNA wet deposition increased by 3.3%–20.4% and for mean PM2.5 SNA concentrations reduced by 1.2%–40%, capturing measurements better. The contributions of scavenged gases to wet deposition below cloud were calculated as 9%–73%, exhibiting discrepancies (2%–17% for HNO3 and 19%–90% for SO2) with previous modeling results as different Λ schemes adopted in CTMs. The nonlinearity between Λ and precipitation intensity causes frequency exerting stronger impact on aerosol burden than intensity and duration. Periodic light rain with a precipitation amount of 1–10 mm per event can eliminate 60% of SNA in PM2.5 and is suggested as a routine procedure to improve local air quality. Analyzing a typical washout process after a haze event in CC, BS could reduce PM2.5 SNA concentrations by 44%–54% derived from improved parameterizations. Plain Language Summary: The below‐cloud scavenging (BS) is a key process for removing atmospheric aerosols. An underestimation of BS for fine particle (PM2.5) is common in chemical transport models (CTMs) and biases simulations. A comprehensive observation was conducted for 1 year to improve parameterizations of BS coefficient for PM2.5 compounds (Λ). These parameterizations were assessed with a CTM. The updates increased the modeled wet deposition fluxes and decreased aerosol concentrations. These results mimicked observations better than baselines. The diversity of BS parameterizations in CTMs enhanced uncertainties of numerical simulation evaluations. The nonlinearity pattern of Λ between precipitation intensity implies frequency imposing stronger impact on aerosol burden than intensity and duration. This principle determines periodic light rain more suitable for cleaning air pollutants routinely. Utilizing the observational parameterizations, the importance of washout on clearing haze was revealed. This work can improve numerical simulations and air pollution control policies. Key Points: The parameterizations of below‐cloud scavenging coefficients for fine particle compounds were improvedThe reliability of chemical‐specific parameterizations was verified by a chemical transport modelThe implications of improved parameterizations for numerical simulations and air pollution control were elucidated [ABSTRACT FROM AUTHOR]

Fluxes of nitrogen oxides (NOx=NO+NO2) and carbon dioxide (CO2) were measured using eddy covariance at the British Telecommunications (BT) Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 73 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled, identifying that transport NOx emissions had reduced by >73 % since 2017. This was attributed in part to the success of air quality policy in central London but crucially due to the substantial reduction in congestion that resulted from pandemic-reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which, until now, has been primarily focused on the emissions from diesel exhausts. [ABSTRACT FROM AUTHOR]

Isoprene and monoterpene emissions to the atmosphere are generally dominated by biogenic sources. The oxidation of these compounds can lead to the production of secondary organic aerosol; however the impact of this chemistry in polluted urban settings has been poorly studied. Isoprene and monoterpenes can form secondary organic aerosol (SOA) heterogeneously via anthropogenic–biogenic interactions, resulting in the formation of organosulfate (OS) and nitrooxy-organosulfate (NOS) species. Delhi, India, is one of the most polluted cities in the world, but little is known about the emissions of biogenic volatile organic compounds (VOCs) or the sources of SOA. As part of the DELHI-FLUX project, gas-phase mixing ratios of isoprene and speciated monoterpenes were measured during pre- and post-monsoon measurement campaigns in central Delhi. Nocturnal mixing ratios of the VOCs were substantially higher during the post-monsoon (isoprene: (0.65±0.43) ppbv; limonene: (0.59±0.11) ppbv; α -pinene: (0.13±0.12) ppbv) than the pre-monsoon (isoprene: (0.13±0.18) ppbv; limonene: 0.011±0.025 (ppbv); α -pinene: 0.033±0.009) period. At night, isoprene and monoterpene concentrations correlated strongly with CO during the post-monsoon period. Filter samples of particulate matter less than 2.5 µm in diameter (PM 2.5) were collected and the OS and NOS content analysed using ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS 2). Inorganic sulfate was shown to facilitate the formation of isoprene OS species across both campaigns. Sulfate contained within OS and NOS species was shown to contribute significantly to the sulfate signal measured via AMS. Strong nocturnal enhancements of NOS species were observed across both campaigns. The total concentration of OS and NOS species contributed an average of (2.0±0.9) % and (1.8±1.4) % to the total oxidized organic aerosol and up to a maximum of 4.2 % and 6.6 % across the pre- and post-monsoon periods, respectively. Overall, this study provides the first molecular-level measurements of SOA derived from isoprene and monoterpene in Delhi and demonstrates that both biogenic and anthropogenic sources of these compounds can be important in urban areas. [ABSTRACT FROM AUTHOR]

The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain (IGP) during the winter of 2017–2018. Here, we report the first comparison in South Asia of high-temporal-resolution simulation of ammonia (NH 3) along with ammonium (NH 4+) and total NH x (i.e., NH 3+ NH 4+) using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and measurements made using the Monitor for AeRosols and Gases in Ambient Air (MARGA) at the WiFEX research site. In the present study, we incorporated the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme into WRF-Chem. Despite simulated total NH x values and variability often agreeing well with the observations, the model frequently simulated higher NH 3 and lower NH 4+ concentrations than the observations. Under the winter conditions of high relative humidity (RH) in Delhi, hydrogen chloride (HCl) was found to promote the increase in the particle fraction of NH 4+ (which accounted for 49.5 % of the resolved aerosol in equivalent units), with chloride (Cl -) (29.7 %) as the primary anion. By contrast, the absence of chloride (HCl / Cl -) chemistry in the standard WRF-Chem model results in the prediction of sulfate (SO 42-) as the dominant inorganic aerosol anion. To understand the mismatch associated with the fraction of NH x in the particulate phase (NH 4+ / NH x), we added HCl / Cl - to the model and evaluated the influence of its chemistry by conducting three sensitivity experiments using the model: no HCl, base case HCl (using a published waste burning inventory), and 3 × base HCl run. We found that 3 × base HCl increased the simulated average NH 4+ by 13.1 µ g m -3 and NH x by 9.8 µ g m -3 concentration while reducing the average NH 3 by 3.2 µ g m -3 , which is more in accord with the measurements. Thus HCl / Cl - chemistry in the model increases total NH x concentration, which was further demonstrated by reducing NH 3 emissions by a factor of 3 (- 3 × NH 3 _EMI) in the 3 × base HCl simulation. Reducing NH 3 emissions in the 3 × base HCl simulation successfully addressed the discrepancy between measured and modeled total NH x. We conclude that modeling the fate of NH 3 in Delhi requires a correct chemistry mechanism accounting for chloride dynamics with accurate inventories of both NH 3 and HCl emissions. [ABSTRACT FROM AUTHOR]

Fluxes of nitrogen oxides (NOx = NO + NO2) and carbon dioxide (CO2) were measured using eddy covariance at the BT Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 75 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled identifying that transport NOx emissions had reduced by > 75 % since 2017. This was attributed in part to the success of air quality policy in central London, but crucially due to the substantial reduction in congestion that resulted from pandemic reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which until now, has been primarily focused on the emissions from diesel exhausts. [ABSTRACT FROM AUTHOR]

Isoprene and monoterpenes emissions to the atmosphere are generally dominated by biogenic sources. The oxidation of these compounds can lead to the production of secondary organic aerosol, however the impact of this chemistry in polluted urban settings has been poorly studied. Isoprene and monoterpenes can form SOA heterogeneously via anthropogenic-biogenic interactions resulting in the formation of organosulfates (OS) and nitrooxy-organosulfates (NOS). Delhi, India is one of the most polluted cities in the world, but little is known about the emissions of biogenic VOCs or the sources of SOA. As part of the DELHI-FLUX project, gas phase mixing ratios of isoprene and speciated monoterpenes were measured during pre- and post-monsoon measurement campaigns in central Delhi. Nocturnal mixing ratios of the VOCs were substantially higher during the post-monsoon (isoprene: (0.65 ± 0.43) ppbv, limonene: (0.59 ± 0.11) ppbv, a-pinene: (0.13 ± 0.12) ppbv) than the pre-monsoon (isoprene: (0.13 ± 0.18) ppbv, limonene: 0.011 ± 0.025 (ppbv), a-pinene: 0.033 ± 0.009) period. At night, isoprene and monoterpene concentrations correlated strongly with CO across during the post-monsoon period. This is one of the first observations in Asia, suggesting monoterpene emissions are dominated by anthropogenic sources. Filter samples of particulate matter less than 2.5 microns in diameter (PM2.5) were collected and the OS and NOS content analysed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS2). Inorganic sulfate was shown to facilitate the formation of isoprene OS species across both campaigns. Sulfate contained within OS and NOS species were shown to contribute significantly to the sulfate signal measured via AMS. Strong nocturnal enhancements of NOS species were observed across both campaigns. The total concentration of OS/NOS species contributed an average of (2.0 ± 0.9) % and (1.8 ± 1.4) % to the total oxidised organic aerosol, and up to a maximum of 4.2 % and 6.6 % across the pre- and post-monsoon periods, respectively. Overall, this study provides the first molecular level measurements of SOA derived from isoprene and monoterpene in Delhi and demonstrates that both biogenic and anthropogenic sources of these compounds can be important in urban areas. [ABSTRACT FROM AUTHOR]

Measurements of aerosol size distribution, chemical composition, and cloud condensation nuclei (CCN) concentration were performed during the Chemical Emission, Loss, Transformation, and Interactions with Canopies (CELTIC) field program at Duke Forest in North Carolina. A kinetic model of the cloud activation of ambient aerosol in the chamber of the CCN instrument was used to perform an aerosol-CCN closure study. This study advances prior investigations by employing a novel fitting algorithm that was used to integrate scanning mobility particle sizer (SMPS) measurements of aerosol number size distribution and aerosol mass spectrometer (AMS) measurements of the mass size distribution for sulfate, nitrate, ammonium, and organics into a single, coherent description of the ambient aerosol in the size range critical to aerosol activation (around 100-nm diameter). Three lognormal aerosol size modes, each with a unique internally mixed composition, were used as input into the kinetic model. For the two smaller size modes, which control CCN number concentration, organic aerosol mass fractions for the defined cases were between 58% and 77%. This study is also unique in that the water vapor accommodation coefficient was estimated based on comparing the initial timing for CCN activation in the instrument chamber with the activation predicted by the kinetic model. The kinetic model overestimated measured CCN concentrations, especially under polluted conditions. Prior studies have attributed a positive model bias to an incomplete understanding of the aerosol composition, especially the role of organics in the activation process. This study shows that including measured organic mass fractions with an assumed organic aerosol speciation profile (pinic acid, fulvic acid, and levoglucosan) and an assumed organic aerosol solubility of 0.02 kg [kg.sup.-1] still resulted in a significant model positive bias for polluted case study periods. The slope and y intercept for the CCN predicted versus CCN observed regression was found to be 1.9 and -180 [cm.sup.-3] respectively. The overprediction generally does not exceed uncertainty limits but is indicative that a bias exists in the measurements or application of model. From this study, uncertainties in the particle number and mass size distributions as the cause for the model bias can be ruled out. The authors are also confident that the model is including the effects of growth kinetics on predicted activated number. However, one cannot rule out uncertainties associated with poorly characterized CCN measurement biases, uncertainties in assumed organic solubility, and uncertainties in aerosol mixing state. Sensitivity simulations suggest that assuming either an insoluble organic fraction or external aerosol mixing were both sufficient to reconcile the model bias.

During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017 and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the daytime by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped, and the areas around the measurement site responsible for differences in measured and simulated emissions were inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London and both the spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may be further improved with longer measurement time series to better understand temporal variation and improved temporal scaling factors to better simulate sub-annual emissions. [ABSTRACT FROM AUTHOR]

The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain during the winter of 2017-2018. Here, we report the first comparison in South Asia of the high temporal resolution measurements of ammonia (NH3) along with water-soluble inorganic ions in PM2.5 (Cl-, NO3-, SO42- and NH4+) and corresponding precursor gases (HCl, SO2, HONO, and HNO3) made at the WiFEX research site, using the Monitor for AeRosols and Gases in Ambient Air (MARGA) and high-resolution simulations with Weather Research and Forecasting model coupled with chemistry (WRF-Chem). The hourly measurements were used to investigate how well the model captures the temporal variation of gaseous and particulate water-soluble species and gas-to-particle partitioning of NH3, using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme. The model frequently simulated higher NH3 and lower NH4 + concentrations than the observations, while total NHx values/variability agreed well with the observations. Under the winter conditions of Delhi, high concentrations of hydrochloric acid (HCl) in the ambient air are found to dominate the gas-to-particle partitioning, as NH3 is usually in excess. The default model set-up of WRF-Chem excludes anthropogenic HCl emissions, so sulfuric acid (H2SO4) dominates the gas-to-particle partitioning with NH3 during the simulation period. The sensitivity experiments, including HCl emissions in the model, showed that the inclusion of HCl emissions improves the simulated gas-to-particle conversion rate of ammonia by 24 % (as indicated by NH4 + concentrations) while reducing the bias in gas phase NH3 by 10 %. Nevertheless, even with waste burning HCl emissions included, we find that WRF-Chem still overestimates sulfur dioxide (SO2) and nitrate (NO3 -34) formation and underestimates sulfate (SO4 2-35), nitrous acid (HONO), nitric acid (HNO3), and HCl concentration in which it interacts, thus limit the gas-to-particle conversion of NH3 to NH4 + in the model. This indicates that modeling of ammonia requires a correct chemistry mechanism with accurate emission inventories for the industrial HCl emissions. [ABSTRACT FROM AUTHOR]

Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using 8 years of continuous methane (CH 4) concentration data, this work assesses the United Kingdom's (UK) CH 4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 and 2.12 ± 0.26 Tg yr -1 between 2013 and 2020, an overall trend of - 0.05 ± 0.01 and - 0.06 ± 0.04 Tg yr -2 and a 2 %–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr -1 and the trend of - 0.03 Tg yr -2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013 and 2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method and the atmospheric transport model used. We find the decreasing trend to be due, primarily, to a reduction in emissions from England, which accounts for 70 % of the UK CH 4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, that the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion set-ups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH 4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend and is not necessarily a response to national lockdowns. [ABSTRACT FROM AUTHOR]

Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India, has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as a result of anthropogenic activities. Organic aerosols (OAs) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and their processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, where we would expect the apportionment and concentrations to vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need for multisite measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM 1) concentrations (ammonium, nitrate, sulfate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon, NO x and CO were performed. Positive matrix factorisation (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OAs (HOAs) related to traffic emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs (SOAs). A composition-based estimate of PM 1 is defined by combining black carbon (BC) and NR-PM 1 (C-PM 1= BC + NR-PM 1). No significant difference was observed in C-PM 1 concentrations between sites, OD (142 ± 117 µ g m -3) compared to ND (123 ± 71 µ g m 3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM 1 concentrations lower than 60 µ g m -3. A seasonal variation in C-PM 1 composition was observed; SO 42- showed a high contribution over pre-monsoon and monsoon seasons, while NO 3- and Cl - had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and Aethalometer model analysis. Thus, in order to reduce PM 1 concentrations in Delhi through local emission controls, traffic emission control offers the greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM 1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India. [ABSTRACT FROM AUTHOR]

We present the first real-time composition of submicron particulate matter (PM 1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM 1 concentrations reached ∼ 750 µ g m -3 during the most polluted period, the post-monsoon period, where PM 1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM 1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM 1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy. [ABSTRACT FROM AUTHOR]

Fluxes measured with the eddy covariance (EC) technique are subject to flux losses at high frequencies (low-pass filtering). If not properly corrected for, these result in systematically biased ecosystem–atmosphere gas exchange estimates. This loss is corrected using the system's transfer function which can be estimated with either theoretical or experimental approaches. In the experimental approach, commonly used for closed-path EC systems, the low-pass filter transfer function (H) can be derived from the comparison of either (i) the measured power spectra of sonic temperature and the target gas mixing ratio or (ii) the cospectra of both entities with vertical wind speed. In this study, we compare the power spectral approach (PSA) and cospectral approach (CSA) in the calculation of H for a range of attenuation levels and signal-to-noise ratios (SNRs). For a systematic analysis, we artificially generate a representative dataset from sonic temperature (T) by attenuating it with a first order filter and contaminating it with white noise, resulting in various combinations of time constants and SNRs. For PSA, we use two methods to account for the noise in the spectra: the first is the one introduced by (PSAI07), in which the noise and H are fitted in different frequency ranges, and the noise is removed before estimating H. The second is a novel approach that uses the full power spectrum to fit both H and noise simultaneously (PSAA21). For CSA, we use a method utilizing the square root of the H with shifted vertical wind velocity time series via cross-covariance maximization (CSAH,sync). PSAI07 tends to overestimate the time constant when low-pass filtering is low, whilst the new PSAA21 and CSAH,sync successfully estimate the expected time constant regardless of the degree of attenuation and SNR. We further examine the effect of the time constant obtained with the different implementations of PSA and CSA on cumulative fluxes using estimated time constants in frequency response correction. For our example time series, the fluxes corrected using time constants derived by PSAI07 show a bias between 0.1 % and 1.4 %. PSAA21 showed almost no bias, while CSAH,sync showed bias of ±0.4 %. The accuracies of both PSA and CSA methods were not significantly affected by SNR level, instilling confidence in EC flux measurements and data processing in set-ups with low SNR. Overall we show that, when using power spectra for the empirical estimation of parameters of H for closed-path EC systems the new PSAA21 outperforms PSAI07 , while when using cospectra the CSAH,sync approach provides accurate results. These findings are independent of the SNR value and attenuation level. [ABSTRACT FROM AUTHOR]

The eddy covariance (EC) technique has emerged as the prevailing method to observe the ecosystem–atmosphere exchange of gases, heat and momentum. EC measurements require rigorous data processing to derive the fluxes that can be used to analyse exchange processes at the ecosystem–atmosphere interface. Here we show that two common post-processing steps (time-lag estimation via cross-covariance maximisation and correction for limited frequency response of the EC measurement system) are interrelated, and this should be accounted for when processing EC gas flux data. These findings are applicable to EC systems employing closed- or enclosed-path gas analysers which can be approximated to be linear first-order sensors. These EC measurement systems act as low-pass filters on the time series of the scalar χ (e.g. CO2 , H2O), and this induces a time lag (tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence overestimates the travel time in the sampling line. This results in a phase shift between the time series of w and χ , which distorts the measured cospectra between w and χ and hence has an effect on the correction for the dampening of the EC flux signal at high frequencies. This distortion can be described with a transfer function related to the phase shift (Hp) which is typically neglected when processing EC flux data. Based on analyses using EC data from two contrasting measurement sites, we show that the low-pass-filtering-induced time lag increases approximately linearly with the time constant of the low-pass filter, and hence the importance of Hp in describing the high-frequency flux loss increases as well. Incomplete description of these processes in EC data processing algorithms results in flux biases of up to 10 %, with the largest biases observed for short towers due to the prevalence of small-scale turbulence. Based on these findings, it is suggested that spectral correction methods implemented in EC data processing algorithms are revised to account for the influence of low-pass-filtering-induced time lag. [ABSTRACT FROM AUTHOR]