Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry

Volatile organic compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). In this respect, long-term datasets of in situ atmospheric measurements are crucial for characterizing the variability of atmospheric chemical composition, its sources, and trends. The ongoing establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementation of the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOx), and aerosols have been carried out since 2012 at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory, located in the Paris region, France. Within the last decade, VOC measurements were conducted offline at SIRTA, until the implementation of real-time monitoring which started in January 2020 using a proton-transfer-reaction quadrupole mass spectrometer (PTR-Q-MS).The dataset acquired during the first 2 years of online VOC measurements provides insights into their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOx, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtained results. Results also include insights into VOC main sources and the influence of meteorological conditions and air mass origin on their levels in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably saw a quasi-total lockdown in France in spring and a lighter one in autumn. Therefore, the focus is placed on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. A comparison with gas chromatography data from the Paris city centre consolidates the regional representativity of the SIRTA station for benzene, while differences are observed for shorter-lived compounds with a notable impact of their local sources. This dataset could be further used as input for atmospheric models and can be found at 10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments

Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.

Sequential spatiotemporal distribution of PM2.5, SO2 and Ozone in China from 2015 to 2020

Currently, in the modeling of various atmospheric pollutants, the simulation of independent trace gases (SO2 and O3) is constrained by the insufficient resolution of key remote sensing products, resulting in insufficient simulation reliability. In this study, spatial sampling and parameter convolution are combined to optimize LightGBM by utilizing ground observations, remote sensing products, meteorological data, assistance data, and random ID. Through the above techniques and an sequentialsimulation of air pollutants, we produce seamless daily 1-km-resolution products of PM2.5, SO2 and O3 for most parts of China from 2015 to 2020. Through random sampling, random site sampling, area-specific validation, comparisons of different models, and a cross26 sectional comparison of different studies, we verified that our simulations of the spatial distribution of multiple atmospheric pollutants are reliable and effective. The CV of the random sample yielded an R² of 0.88 and an RMSE of 9.91 ㎍/m³ for PM2.5, an R² of 0.89 and an RMSE of 4.62 ㎍/m³ for SO2, and an R² of 0.91 and an RMSE of 6.88 ㎍/m3 for O3. Combined with the SHapley Additive exPlanations (SHAP) approach, the roles of different parameters in the simulation process were clarified, and the positive role of parameter convolution was confirmed. Our dataset was used to assess the changes in the Air Pollution Index (API) in China before and after the outbreak of COVID-19, and the results indicate that these 34 changes were relatively small huge, suggesting that the epidemic control measures in 2020 were effective. The study demonstrates that the multipollutant datasets produced with the proposed models are of great value for long-term, large-scale, and regional-scale air pollution monitoring and prediction, as well as population health evaluation. [ABSTRACT FROM AUTHOR] Copyright of Earth System Science Data Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Two years of Volatile Organic Compounds online in-situ measurements at SIRTA (Paris region, France) using Proton-Transfer-Reaction Mass Spectrometry

Volatile Organic Compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry, as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). To this respect, long-term datasets of in-situ atmospheric measurements are crucial to characterize the variability of atmospheric chemical composition, its sources and trends. The on-going establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementing the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOX) and aerosols have been carried out since 2012 at the SIRTA observatory, located in the Paris region, France. Within the last decade, VOC measurements have been conducted offline at SIRTA, until the implementation of a real-time monitoring which started in January 2020, using a Proton-Transfer-Reaction Quadrupole Mass-Spectrometer (PTR-Q-MS). The dataset acquired during the first two years of online VOC measurements provides insights on their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOX, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtain results. Results also include insights on VOC's main sources and the influence of meteorological conditions and air mass origin on their levels, in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably comprised a quasi-total lockdown in France in Spring, and a lighter one in Autumn. Therefore, a focus is made on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. This dataset could be further used as input for atmospheric models and can be found under https://doi.org/10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al, 2022). [ FROM AUTHOR]

Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

The aim of this paper is to highlight how TROPOspheric Monitoring Instrument (TROPOMI) trace gas data can best be used and interpreted to understand event-based impacts on air quality from regional to city scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO, and CHOCHO) detected by the Sentinel-5P TROPOMI instrument and driven by reductions in anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in TROPOMI NO2 column amounts on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India, supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in 2-week-averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short timescale are detectable from space is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China, which is in concert with the other trace gas reductions observed during lockdown;however, large interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19-lockdown-driven reductions in satellite-observed trace gas column amounts using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction in anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future the combined use of inverse modeling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

Distribution of hydrogen peroxide over Europe during the BLUESKY aircraft campaign

In this work we present airborne in situ trace gas observations of hydrogen peroxide (H2O2) and the sum of organic hydroperoxides over Europe during the Chemistry of the Atmosphere – Field Experiments in Europe (CAFE-EU, also known as BLUESKY) aircraft campaign using a wet chemical monitoring system, the HYdrogen Peroxide and Higher Organic Peroxide (HYPHOP) monitor. The campaign took place in May–June 2020 over central and southern Europe with two additional flights dedicated to the North Atlantic flight corridor. Airborne measurements were performed on the High Altitude and LOng-range (HALO) research operating out of Oberpfaffenhofen (southern Germany). We report average mixing ratios for H2O2 of 0.32 ± 0.25, 0.39 ± 0.23 and 0.38 ± 0.21 ppbv in the upper and middle troposphere and the boundary layer over Europe, respectively. Vertical profiles of measured H2O2 reveal a significant decrease, in particular above the boundary layer, contrary to previous observations, most likely due to cloud scavenging and subsequent rainout of soluble species. In general, the expected inverted C-shaped vertical trend with maximum hydrogen peroxide mixing ratios at 3–7 km was not found during BLUESKY. This deviates from observations during previous airborne studies over Europe, i.e., 1.64 ± 0.83 ppbv during the HOOVER campaign and 1.67 ± 0.97 ppbv during UTOPIHAN-ACT II/III. Simulations with the global chemistry–transport model EMAC partly reproduce the strong effect of rainout loss on the vertical profile of H2O2. A sensitivity study without H2O2 scavenging performed using EMAC confirms the strong influence of clouds and precipitation scavenging on hydrogen peroxide concentrations. Differences between model simulations and observations are most likely due to difficulties in the simulation of wet scavenging processes due to the limited model resolution.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and machine learning technique

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 - March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. The ML-trained anomalies fit very well with the wind-assigned anomalies with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory. Weekly variations of NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 24% at weekends in Riyadh, and high reductions are found near the city center and the areas along the highway. An average weekend reduction estimate of 30% in Madrid is found. The regions with dominant sources are located in the east of Madrid, where the residential areas and the Madrid-Barajas airport are located. Additionally, the NO2 emissions decreased by 21% in March-June 2020 compared to the same period in 2019 induced by the COVID-19 lockdowns in Riyadh. A much higher reduction (60%) is estimated for Madrid where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas and cover smaller areas during weekdays than at weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Though our analysis is limited to two cities as testing examples, the method has proved to provide reliable and consistent results. Therefore, it is expected to be suitable for other trace gases and other target regions. [ FROM AUTHOR] Copyright of Atmospheric Measurement Techniques Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Tropospheric ozone production and chemical regime analysis during the COVID-19 lockdown over Europe

The COVID-19 (coronavirus disease 2019) European lockdowns have led to a significant reduction in the emissions of primary pollutants such as NO (nitric oxide) and NO2 (nitrogen dioxide). As most photochemical processes are related to nitrogen oxide (NOx≡ NO + NO2) chemistry, this event has presented an exceptional opportunity to investigate its effects on air quality and secondary pollutants, such as tropospheric ozone (O3). In this study, we present the effects of the COVID-19 lockdown on atmospheric trace gas concentrations, net ozone production rates (NOPRs) and the dominant chemical regime throughout the troposphere based on three different research aircraft campaigns across Europe. These are the UTOPIHAN (Upper Tropospheric Ozone: Processes Involving HOx and NOx) campaigns in 2003 and 2004, the HOOVN1 -https://media.proquest.com/media/hms/PFT/1/Q2apM?_a=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&_s=4R%2BrSLBAOWkAv60BD6umfsLkEuQ%3D

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany;Kuopio, Finland;Leipzig, Germany;and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) COVID-19 campaign in May 2020. The reanalysis (fully quality-assured) lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (±0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch-dominant pollen periods, whereas for Hohenpeißenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods;the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

Interpreting the COVID effect on atmospheric constituents over the Indian region during the lockdown: chemistry, meteorology, and seasonality.

Most of the published articles which document changes in atmospheric compositions during the various lockdown and unlock phases of COVID-19 pandemic have made a direct comparison to a reference point (which may be 1 year apart) for attribution of the COVID-mediated lockdown impact on atmospheric composition. In the present study, we offer a better attribution of the lockdown impacts by also considering the effect of meteorology and seasonality. We decrease the temporal distance between the impacted and reference points by considering the difference of adjacent periods first and then comparing the impacted point to the mean of several reference points in the previous years. Additionally, we conduct a multi-station analysis to get a holistic effect of the different climatic and emission regimes. In several places in eastern and coastal India, the seasonally induced changes already pointed to a decrease in PM concentrations based on the previous year data; hence, the actual decrease due to lockdown would be much less than that observed just on the basis of difference of concentrations between subsequent periods. In contrast, northern Indian stations would normally show an increase in PM concentration at the time of the year when lockdown was effected; hence, actual lockdown-induced change would be in surplus of the observed change. The impact of wind-borne transport of pollutants to the study sites dominates over the dilution effects. Box model simulations point to a VOC-sensitive composition.

Phase-wise analysis of the COVID-19 lockdown impact on aerosol, radiation and trace gases and associated chemistry in a tropical rural environment.

Phase-wise variations in different aerosol (BC, AOD, PM1, PM2.5 and PM10), radiation (direct and diffused) and trace gases (NO, NO2, CO, O3, SO2, CO2 and CH4) and their associated chemistry during the COVID-19 lockdown have been investigated over a tropical rural site Gadanki (13.5° N, 79.2° E), India. Unlike most of the other reported studies on COVID-19 lockdown, this study provides variations over a unique tropical rural environment located at a scientifically strategic location in the Southern Indian peninsula. Striking differences in the time series and diurnal variability have been observed in different phases of the lockdown. The levels of most species that are primarily emitted from anthropogenic activities reduced significantly during the lockdown which also impacted the levels and diurnal variability of secondary species like O3. When compared with the same periods in 2019, short-lived trace gas species such as NO, NO2, SO2 which have direct anthropogenic emission influence have shown the reduction over 50%, whereas species like CO and O3 which have direct as well as indirect impacts of anthropogenic emissions have shown reductions up to 10%. Long-lived species (CO2 and CH4) have shown negligible difference (<1%). BC and AOD have shown reductions over 20%. Particulate Matter (1, 2.5 and 10) reductions have been in the range of 40 to 50% when compared to the pre-lockdown period. The changes in shortwave downward radiation at the surface, diffuse component due to the scattering and diffuse fraction have been +2.2%, -4.1% and -2.4%, respectively, in comparison with 2019. In contrast with the studies over urban environments, air quality category over the rural environment remained same during the lockdown despite reduction in pollutants level. All the variations observed for different species and their associated chemistry provides an excellent demonstration of rural atmospheric chemistry and its intrinsic links with the precursor concentrations and dynamics.

Propagation of cloud base to higher levels during Covid-19-Lockdown.

Aerosol-cloud interactions and feedbacks play an important role in modulating cloud development, microphysical and optical properties thus enhancing or reducing precipitation over polluted/pristine regions. The lockdown enforced on account of Covid-19 pandemic is a unique opportunity to verify the influence of drastic reduction in aerosols on cloud development and its vertical distribution embedded in identical synoptic conditions. Cloud bases measured by ceilometer in Delhi, the capital of India, are observed to propagate from low level to higher levels as the lockdown progresses. It is explained in terms of trends in temporal variation of cloud condensation nuclei (CCN) and precursor gases to secondary hygroscopic aerosols. The large reduction (47%) in CCN estimated from aerosol extinction coefficient during the lockdown results in upward shift of cloud bases. Low clouds with bases located below 3 km are found to have reduced significantly from 63% (of total clouds distributed in the vertical) during pre-lockdown to 12% in lockdown period (less polluted). Cloud base height is found to have an inverse correlation with CCN (r = -0.64) and NO2/NH3 concentrations (r = -0.7). The role of meteorology and CCN in modulating the cloud vertical profiles is discussed in terms of anomalies of various controlling factors like lifting condensation level (LCL), precipitable water content (PWC) and mixing layer height (MLH).