Bias correction of OMI HCHO columns based on FTIR and aircraft measurements and impact on top-down emission estimates

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives Ω$_{OMI}$=0,651 Ω$_{airc}$+2,95 x 10$^{15}$, molec. cm$^{-2}$ , with Ω$_{OMI}$ and Ω$_{airc}$ the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives Ω$_{OMI}$= 6,59 Ω$_{FTIR}$ + 2.02 x 10$^{15}$, molec. cm$^{-2}$ . Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions ($\sim$ 445 Tgyr$^{-1}$) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated

TROPOMI–Sentinel-5 Precursor formaldehyde validation using an extensive network of ground-based Fourier-transform infrared stations

TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth\u27s atmosphere since October 2017 with an unprecedented horizontal resolution (initially 7 km$^{2}$×3.5 km$^{2}$, upgraded to 5.5 km$^{2}$×3.5 km$^{2}$ in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). The pre-launch accuracy requirements of TROPOMI HCHO are 40 %–80 %. We observe that these requirements are well reached, with the BIAS found below 80 % at all the sites and below 40 % at 20 of the 25 sites. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations except for the highest-HCHO-level site, where it is found to be underestimated. We find that while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26±5 %) of TROPOMI is observed for very low HCHO levels (8.0×10$^{15}$ molec. cm$^{-2}$). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias in the regions of emissions and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI =+1.10±0.05 ×10$^{15}$+0.64±0.03 × FTIR; in molecules per square centimetre). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful for minimizing a possible additional collocation error. The precision requirement of 1.2×10$^{16}$ molec. cm$^{-2}$ for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be 2 times better (0.5–0.8×10$^{15}$ molec. cm$^{-2}$ for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R=0.88 for 3 h mean coincidences; R=0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products, which is well within the pre-launch requirements for both accuracy and precision. This paper makes suggestions for the refinement of the TROPOMI random uncertainty budget and TROPOMI quality assurance values for a better filtering of the remaining outliers

NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances

Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50% can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations. For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12% and 27% and between 1 and 11x1014 moleccm-2, respectively. The median values among all stations are 13% and 2.9x1014 moleccm-2 for the total systematic and random uncertainties. This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013moleccm-2) to highly polluted levels (7x1016moleccm-2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

Study of the greenhouse gas balance in the amazonic basin using total column measures through the ftir technique

A intervenção humana no ciclo do carbono vem provocando alterações nos ciclos biogeoquímicos. Um dos efeitos mais significativos é o efeito estufa que está provocando um aumento da temperatura média do planeta. O presente trabalho, tem como objetivo a caracterização química dos gases da atmosfera, considerados Gases de Efeito Estufa (CH4, N2O) e Compostos Orgânicos Voláteis (C2H2, C2H6, H2CO e HCN) e o CO, (traçador de queimadas) que podem interferir no clima e no ecossistema da Bacia Amazônica, e correlacioná-las com fatores climáticos: previsão de chuva e chuva efetiva. A concentração dos gases: foi medida aplicando a técnica da espectroscopia de infravermelho com transformada de Fourier FTIR, utilizando um espectrômetro de absorção Bruker 125M FTIR, num período de 4 anos por meio de medidas de perfil vertical em Porto Velho (8,8°S, 63,9°O), Rondônia. Local privilegiado pela sua posição na América do Sul e por ser caminho das massas de ar que transportam humidade para diversas regiões do Brasil. Para obtenção dos dados climáticos desenvolveu-se um sistema (software) utilizando a metodologia de desenvolvimento por prototipação (Robô de banco de dados). A análise dos dados, obtidos pelos diferentes equipamentos, utilizou um processo que combina diversas áreas de descoberta do conhecimento: Aprendizagem de Máquina, Reconhecimento de Padrões, Estatística e Inteligência Artificial. Com os resultados encontrados, observamos que a concentração dos gases, possuem relação com a movimentação atmosférica relativa às altitudes estudadas (troposfera). Os valores de acurácia encontrados nas análises, são considerados promissores para uma nova metodologia de análise e estudos climáticos. Ressaltase a necessidade da continuidade e aprofundamento dos estudos, aplicando a metodologia criada, aperfeiçoando as análises e utilizando dados recentes, considerando que este método pode contribuir para as previsões climáticas da região.Human intervention in the carbon cycle has been causing changes in biogeochemical cycles. One of the most significant effects is the greenhouse effect, which is causing an increase in the planets average temperature. This work aims at the chemical depiction of atmospheric gases, considered Greenhouse Gases (CH4, N2O) and Volatile Organic Compounds (C2H2, C2H6, H2CO and HCN) and the CO, (burnings tracer) which may interfere on the climate and the ecosystem of Amazon Basin, and associate them with some climatic factors: rainfall forecast and effective rainfall. Gases concentration: reasoned by applying the Fourier Transform Infrared Spectroscopy Technique FTIR, using a Bruker 125M FTIR absorption spectrometer, for over a period of 4 years by means of vertical profile measurements in Porto Velho (8.8° S, 63.9°C), Rondonia. A privileged site for its position in South America and for being the pathway of the air masses that drive humidity to different regions of Brazil. In order to reach climate info a system (software) has been conceived, by using the prototyping development methodology (database robot). The analysis of the data obtained by different equipment, used a process that combines several areas of knowledge discovery: Machine Learning, Pattern Recognition, Statistics and Artificial Intelligence. By the results obtained, we noticed that the concentration of gases related to the atmospheric movement relative to the considered altitudes (troposphere). The accuracy values found in the analysis are promising for a new methodology of analysis and climate studies. It emphasizes the need for continuity and deepening of studies, applying the methodology, enhancing the analysis and using recent data, taking into consideration that this method can contribute to climate forecasts in the region

NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances

Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50% can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations. For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12% and 27% and between 1 and 11x1014 moleccm-2, respectively. The median values among all stations are 13% and 2.9x1014 moleccm-2 for the total systematic and random uncertainties. This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013moleccm-2) to highly polluted levels (7x1016moleccm-2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions

Universal Dependencies 2.6

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.5

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.7

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.8.1

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). Version 2.8.1 fixes a bug in 2.8 where a portion of the Dutch Alpino treebank was accidentally omitted