Search

The present study focuses on the investigation of both near-surface and vertical variability of carbon monoxide (CO) concentrations and their potential sources obtained from both in situ and satellite Measurements of Pollution in the Troposphere (MOPITT) over a semiarid region (Anantapur, India) from January 2016 to December 2017. The diurnal variation of CO shows sharp morning (07:00-09:00 LT) and evening (07:00-09:00 LT) peaks associated to local anthropogenic activities as well as the impact of the mixed layer height, and low concentrations during daytime (12:00-15:00 LT). The low levels during afternoon hours may be due to the increase of the mixed layer height and the decrease of anthropogenic sources. The seasonal mean CO showed no obvious variation, with highest levels observed in winter (329 ± 52 ppbv), followed by the pre-monsoon (327 ± 57 ppbv), post-monsoon (234 ± 36 ppbv) and monsoon (192 ± 22 ppbv). The high levels of CO during the winter are attributed to increased emissions from anthropogenic sources and a shallow mixed layer height. The vertical distribution of CO showed secondary peaks at high-pressure levels (300-200 hPa) during winter, pre-monsoon, and post-monsoon, which indicates CO transport from different source regions. These findings are reasonably confirmed through the air mass Concentrated Weighted Trajectory (CWT) analysis obtained from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This study suggests that except for the monsoon, air masses transported from Indo-Gangetic Basin region also contribute to the enhancement of CO concentrations at the receptor site

This study analyzed the long-term variations in carbon monoxide (CO) mixing ratios from January 2006 to December 2017 at the Lin'an regional atmospheric background station (LAN; 30.3∘ N, 119.73∘ E, 138 m a.s.l.) in China's Yangtze River Delta (YRD) region. The CO mixing ratios were at their highest (0.69 ± 0.08 ppm) and lowest (0.54 ± 0.06 ppm) in winter and summer, respectively. The average daily variation in CO exhibited a double-peaked pattern, with peaks in the morning and evening and a valley in the afternoon. A significant downward trend of −11.3 ppb yr−1 of CO was observed from 2006 to 2017 at the LAN station, which was in accordance with the negative trends of the average CO mixing ratios and total column retrieved from the satellite data (Measurements of Pollution in the Troposphere, MOPITT) over the YRD region during the same period. The average annual CO mixing ratio at the LAN station in 2017 was 0.51 ± 0.04 ppm, which was significantly lower than that (0.71 ± 0.12 ppm) in 2006. The decrease in CO levels was largest in autumn (−15.7 ppb yr−1), followed by summer (−11.1 ppb yr−1), spring (−10.8 ppb yr−1), and winter (−9.7 ppb yr−1). Moreover, the CO levels under relatively polluted conditions (the annual 95th percentiles) declined even more rapidly (−22.4 ppb yr−1, r=-0.68, p) from 2006 (0.91 ppm) to 2017 (0.58 ppm), and the CO levels under clean conditions (the annual 5th percentiles) showed decreasing evidence but not statistically significant (r=-0.41, p=0.19) throughout the years. The long-term decline and short-term variations in the CO mixing ratios at the LAN station were mainly attributed to the implementation of the anthropogenic pollution control measures in the YRD region and to events like the Shanghai Expo in 2010 and Hangzhou G20 in 2016. The decreased CO level may influence atmospheric chemistry over the region. The average OH reactivity of CO at the LAN station is estimated to significantly drop from 4.1 ± 0.7 s−1 in 2006 to 3.0 ± 0.3 s−1 in 2017.

South Asian outflow over the adjoining marine regions is most pronounced during winter season, whereas the prevalence of strong winds aloft can transport the influences from inter-continental sources. Here, we investigate the tropospheric carbon monoxide (CO) distribution over the northern Indian Ocean (IO) combining shipborne measurements carried out during the Integrated Campaign for Aerosols, gases, and Radiation Budget (ICARB) campaign (January–February 2018), retrievals from Measurements of Pollution in the Troposphere (MOPITT), and Copernicus Atmosphere Monitoring Service (CAMS) model. Surface CO varied from ~ 50 to 365 ppbv (179 ± 67 ppbv) with higher levels over the coastal region of southeast Arabian Sea and lower levels over the equatorial IO. We observed lower CO levels (200 ± 43 ppbv) than those during the Indian Ocean Experiment-1999 (229 ± 40 ppbv) supporting the reported decreasing trend of tropospheric CO. In situ CO observations are found to be in a good agreement with the satellite retrievals (MOPITT-version 8) as well as the CAMS model results (r2 = 0.60‒0.65). The upper-tropospheric CO (300‒200 hPa) over the equatorial IO is observed to be higher by up to 30% during February-2018 as compared to the decadal mean, coinciding with anomalous westerlies resulting from a disturbed Walker cell over the equatorial IO and deeper penetration of the sub-tropical jet. The influences of African forest fires are suggested to have enhanced the upper-tropospheric CO over the IO during February-2018. Our study highlights the importance of strong large-scale dynamics and global biomass-burning emissions in the wintertime pollution loading over the IO, besides the South Asian outflow.

The investigation of atmospheric conditions in the steppe, forest-steppe, and semi-desert zones of Kazakhstan is relevant, the observations of air pollution are carried out in all big settlements. Satellite data are most suitable for solving the problems of quantitative and qualitative assessment of propagation of atmospheric pollutants. The contamination of the atmosphere over large territories which depends on the natural and anthropogenic sources of emission of trace gases can be determined from space. The statistical analysis of ground-based data on the atmospheric concentration of carbon monoxide (CO) over the most polluted big industrial cities is performed. The CO content in the atmosphere over Kazakhstan is analyzed using MOPITT satellite IR radiometer data for the period of 2007–2017. The seasonal and long-term variability of the total CO is revealed. The CO maximum falls on March-April, the minimum is registered in July to October. The average CO content in the atmosphere over Kazakhstan has decreased by 4.7% in the recent decade.

The results of systematic (2003–2017) measurements of the total content and height-averaged relative volume concentration of CO at st. Novolazarevskaya with a spectrometer with a resolution of 0.2 cm–1 are presented. The inverse problem of determining the total content of CO as well as interfering gases (H2O and N2O) was solved using the SFIT4 software package. A data analysis indicated that during the measurements the average total CO content at st. Novolazarevskaya was (8 ± 2) 1017 molecules/cm2 and the height-averaged volume concentration was (37 ± 8) ppb. The resulting data were compared with variations in the total CO content at st. Arrival-Heights, MOPITT satellite data, and CO surface concentrations at st. Syowa. The maximum and minimum values of CO were observed in September and January–February, respectively. For all the data series considered, the trends are insignificant; in this case, an increased CO content was observed in 2010, and an increasing trend of the minimum values of CO was observed in recent years (2014–2017). Both stations (Novolazarevskaya and Arrival-Heights) are characterized by an excess of satellite data over ground-based measurement data (19% and 14%, respectively); here, a seasonal dependence of the deviation was observed with minimal deviations in December–January. Data of surface measurements of the total content at st. Novolazarevskaya and Arrival-Heights are rather well consistent, and the average deviation since 2010 was 2.4%. The average concentration of CO at st. Syowa (51 ppb) is higher than the height-averaged concentration at st. Novolazarevskaya. According to spectral, wavelet, and composite analyzes, all data series considered include oscillations in the range of 6–45 months with almost identical periods and phase relationships.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Given its relatively long lifetime in the troposphere, carbon monoxide (CO) is commonly employed as a tracer for characterizing airborne pollutant distributions. The present study aims to estimate the spatiotemporal distributions of ground-level CO concentrations across China during 2013–2016. We refined the random-forest–spatiotemporal kriging (RF–STK) model to simulate the daily CO concentrations on a 0.1∘ grid based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT CO). The RF–STK model alleviated the negative effects of sampling bias and variance heterogeneity on the model training, with cross-validation R2 of 0.51 and 0.71 for predicting the daily and multiyear average CO concentrations, respectively. The national population-weighted average CO concentrations were predicted to be 0.99±0.30 mg m−3 (μ±σ) and showed decreasing trends over all regions of China at a rate of -0.021±0.004 mg m−3 yr−1. The CO pollution was more severe in North China (1.19±0.30 mg m−3), and the predicted patterns were generally consistent with MOPITT CO. The hotspots in the central Tibetan Plateau where the CO concentrations were underestimated by MOPITT CO were apparent in the RF–STK predictions. This comprehensive dataset of ground-level CO concentrations is valuable for air quality management in China.

The results of systematic (2003–2017) measurements of the total content and the volume mixing ratio of CO at Novolazarevskaya station with a spectrometer with a resolution of 0.2 cm– 1 are presented. The inverse problem of determining the total CO content, as well as interfering gases (H2O and N2O), was solved using the SFIT4 software package. Data analysis showed that over the measurement period the average total CO content at Novolazarevskaya amounted to (8 ± 2) 1017 molec/cm2, and the average volume mixing ratio amounted to (37 ± 8) ppb. The obtained data are compared with variations in the total content of CO in Arrival-Heights station, with MOPITT satellite data, as well as with surface values of CO concentration at Syova station. The maximum values of CO are observed in September, the minimum — in January–February. For all the considered series, the trends are insignificant, while there are periods of increased CO content (2010). In recent years (2014–2017) there is a tendency towards an increase in the minimum values of CO. For Novolazarevskaya and Arrival-Heights satellite data are characterized by the excess of over ground data, amounting to 19% and 14%, respectively, while there is a seasonal dependence of the deviation with the minimum in December–January. Surface measurements of the total CO content are in fairly good agreement at Novolazarevskaya and Arrival-Heights, and since 2010 the average deviation is 2.4%. The average value of the concentration of CO on Syova 51 ppb is higher than the average volume mixing ratio at Novolazarevskaya. According to the spectral, wavelet and composite analyzes, in all the considered series there are oscillations in the range of 6–45 months with closely coinciding periods and phases.

Atmospheric carbon monoxide (CO) concentrations have decreased since the beginning of the century, and the rate of decrease depends on the region. Depending on how regions are chosen, their boundaries may not always align with where there are differences in trends. To address this, we calculate trends within 0.4° × 0.4° grid cells independently throughout the globe using satellite CO retrievals from the Measurements Of Pollution In The Troposphere (MOPITT) satellite instrument from 2002 to 2017. These trends are found with the caveat that there are large singular biomass burning events somewhere nearly every year, and we include examples of large column CO anomalies during sporadic but large burning events in the North American and Eurasian boreal forests, the Amazon, Africa, and Indonesia. CO trends behave similarly within regions that span about a few thousand kilometers. Using TransCom region definitions, we find average trends between −0.9 and 0.1 ppb year⁻¹ (about −0.9 to 0.1% year⁻¹) for 2002–2017. Over 5‐year subsets, trends in TransCom regions vary between −3.6 and 1.8 ppb year⁻¹. This substantial spatial and temporal variability in trends is in agreement with other studies. With an understanding of regional trends, we compare with trends from urban areas. Generally, CO trends within urban areas are indistinguishable from regional trends. This may be because of a combination of noise in the data, the large footprint for MOPITT, or because anthropogenic CO reduction measures were implemented before the MOPITT record began. We provide case studies for a few cities, such as Los Angeles, and find long‐term variation in the rate of change of column CO.

Global coupled chemistry–climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea–United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the control run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O3 datasets and observationally constrained box model simulations of OH and HO2. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO2, and 27 % for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O3, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.

International audience; The large peatland carbon stocks in the land use change-affected areas of equatorial Asia are vulnerable to fire. Combining satellite observations of active fire, burned area, and atmospheric concentrations of combustion tracers with a Bayesian inversion, we estimated the amount and variability of fire carbon emissions in equatorial Asia over the period 1997-2015. Emissions in 2015 were of 0.51 ± 0.17 Pg carbon-less than half of the emissions from the previous 1997 extreme El Niño, explained by a less acute water deficit. Fire severity could be empirically hindcasted from the cumulative water deficit with a lead time of 1 to 2 months. Based on CMIP5 climate projections and an exponential empirical relationship found between fire carbon emissions and water deficit, we infer a total fire carbon loss ranging from 12 to 25 Pg by 2100 which is a significant positive feedback to climate warming.

Proper quantification of the aerosol vertical height is essential to constrain the atmospheric distribution and lifetime of aerosols, as well as their impact on the environment. We use globally distributed, daily averaged measurements of aerosol stereo heights of fire aerosols from the Multi-angle Imaging SpectroRadiometer (MISR) to understand the aerosol distribution. We also connect these results with a simple plume rise model and a new multi-linear regression model approach based on daily measurements of NO2 from OMI and CO from MOPITT to understand and model the global aerosol vertical height profile over biomass burning regions. First, plumes associated with the local dry-burning season at midlatitudes to high latitudes frequently have a substantial fraction lofted into the free troposphere and in some cases even the stratosphere. Second, plumes mainly associated with less-polluted regions in developing countries and heavily forested areas tend to stay closer to the ground, although they are not always uniformly distributed throughout the boundary layer. Third, plumes associated with more serious loadings of pollution (such as in Africa, Southeast Asia and northeast China) tend to have a substantial amount of smoke transported uniformly through the planetary boundary layer and up to around 3 km. Fourth, the regression model approach yields a better ability to reproduce the measured heights compared to the plume rise model approach. This improvement is based on a removal of the negative bias observed from the plume model approach, as well as a better ability to work under more heavily polluted conditions. However, over many regions, both approaches fail, requiring deeper work to understand the physical, chemical and dynamical reasons underlying the failure over these regions.

Assimilation of atmospheric composition retrievals presents computational challenges due to their high data volume and often sparse information density. Assimilation of compact phase space retrievals (CPSRs) meets those challenges and offers a promising alternative to assimilation of raw retrievals at reduced computational cost (Mizzi et al., 2016). This paper compares analysis and forecast results from assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) CPSRs with independent observations. We use MetOp-A/Infrared Atmospheric Sounding Interferometer (IASI) CO retrievals and Measurement of OZone, water vapor, carbon monoxide, and nitrogen oxides by in-service AIrbus airCraft (MOZAIC) in situ CO profiles for our independent observation comparisons. Generally, the results confirm that assimilation of MOPITT CPSRs improves the Weather Research and Forecasting model with chemistry coupled to the ensemble Kalman filter data assimilation from the Data Assimilation Research Testbed (WRF-Chem/DART) analysis fit and forecast skill at a reduced computational cost compared to assimilation of raw retrievals. Comparison with the independent observations shows that assimilation of MOPITT CO generally improved the analysis fit and forecast skill in the lower troposphere but degraded it in the upper troposphere. We attribute that degradation to assimilation of MOPITT CO retrievals with a possible bias of ∼ 14 % above 300 hPa. To discard the biased retrievals, in this paper, we also extend CPSRs to assimilation of truncated retrieval profiles (as opposed to assimilation of full retrieval profiles). Those results show that not assimilating the biased retrievals (i) resolves the upper tropospheric analysis fit degradation issue and (ii) reduces the impact of assimilating the remaining unbiased retrievals because the total information content and vertical sensitivities are changed.

A comparative study was carried out to explore carbon monoxide total columnar amount (CO TC) in background and polluted atmosphere, including the stations of ZSS (Zvenigorod), ZOTTO (Central Siberia), Peterhof, Beijing, and Moscow, during 1998–2014, on the basis of ground- and satellite-based spectroscopic measurements. Interannual variations of CO TC in different regions of Eurasia were obtained from ground-based spectroscopic observations, combined with satellite data from the sensors MOPITT (2001–14), AIRS (2003–14), and IASI MetOp-A (2010–13). A decreasing trend in CO TC (1998–2014) was found at the urban site of Beijing, where CO TC decreased by 1.14%±0.87% yr−1. Meanwhile, at the Moscow site, CO TC decreased remarkably by 3.73%±0.39% yr−1. In the background regions (ZSS, ZOTTO, Peterhof), the reduction was 0.9%–1.7% yr−1 during the same period. Based on the AIRSv6 satellite data for the period 2003–14, a slight decrease (0.4%–0.6% yr−1) of CO TC was detected over the midlatitudes of Eurasia, while a reduction of 0.9%–1.2% yr−1 was found in Southeast Asia. The degree of correlation between the CO TC derived from satellite products (MOPITTv6 Joint, AIRSv6 and IASI MetOp-A) and ground-based measurements was calculated, revealing significant correlation in unpolluted regions. While in polluted areas, IASI MetOp-A and AIRSv6 data underestimated CO TC by a factor of 1.5–2.8. On average, the correlation coefficient between ground- and satellite-based data increased significantly for cases with PBL heights greater than 500 m.

Rapid industrial and economic development over the past two decades in India leads the high levels of air pollution. Carbon monoxide (CO) is one of the main pollutants, is not only harmful for huma...

The CHRONOS space mission concept provides time-resolved abundance for emissions and transport studies of the highly variable and highly uncertain air pollutants carbon monoxide and methane, with sub-hourly revisit rate at fine (∼ 4 km) horizontal spatial resolution across a North American domain. CHRONOS can provide complete synoptic air pollution maps (snapshots) of the continental domain with less than 10 min of observations. This rapid mapping enables visualization of air pollution transport simultaneously across the entire continent and enables a sentinel-like capability for monitoring evolving, or unanticipated, air pollution sources in multiple locations at the same time with high temporal resolution. CHRONOS uses a compact imaging gas filter correlation radiometer for these observations, with heritage from more than 17 years of scientific data and algorithm advances by the science teams for the Measurements of Pollution in the Troposphere (MOPITT) instrument on NASA's Terra spacecraft in low Earth orbit. To achieve continental-scale sub-hourly sampling, the CHRONOS mission would be conducted from geostationary orbit, with the instrument hosted on a communications or meteorological platform. CHRONOS observations would contribute to an integrated observing system for atmospheric composition using surface, suborbital and satellite data with atmospheric chemistry models, as defined by the Committee on Earth Observing Satellites. Addressing the U.S. National Academy's 2007 decadal survey direction to characterize diurnal changes in tropospheric composition, CHRONOS observations would find direct societal applications for air quality management and forecasting to protect public health.

Measurements made by the MOPITT (“Measurements of Pollution in the Troposphere”) instrument on the NASA Terra polar-orbiting platform enable the retrieval of tropospheric carbon monoxide (CO) concentrations. As determined by the Terra orbit and MOPITT swath width, the frequency of MOPITT observations at a specific location, or measurement sampling frequency, is typically about once every three to four days. However, because the MOPITT retrieval algorithm is only applicable to clear-sky scenes, MOPITT retrieval sampling frequency strongly depends on regional cloudiness and can be much smaller than the measurement sampling frequency. Moreover, highly polluted scenes, characterized by high aerosol optical depths, can be confused with cloudy scenes and thus be discarded unnecessarily by the MOPITT cloud detection algorithm. Herein are described revisions to this algorithm which substantially increase retrieval sampling over land in varying pollution conditions. The performance of the revised cloud detection algorithm is evaluated through validation, case studies, and continental-scale maps of retrieval sampling frequency. Presented case studies illustrate (1) why the current operational MOPITT cloud detection algorithm excludes extended areas of potentially valuable cloud-free MOPITT observations and (2) how, for the same scenes, improved retrieval coverage benefits analyses of regional CO variability. Maps of retrieval sampling frequency for South America and Asia exhibit well-defined improvements, especially in regions with poor sampling frequency in the current product.

In this study, we present and compare regional correlations between CO total column density (TCD CO ) from the data set of Measurement of Pollution in the Troposphere (MOPITT), and high-absorbing BC dominant aerosol optical depth (AOD BC ) from the retrieval algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) (MODIS-OMI algorithm, MOA). TCD CO shows positive relationship to both fine-mode AOD (AOD FM ) and AOD BC in general, but TCD CO better correlates with AOD BC than AOD FM . This enhanced correlation between TCD CO and AOD BC appears more clearly during spring and summer. Correlation between TCD CO and AOD BC is exceptionally poor in Northern Africa where the BC-dominated aerosols are frequently mixed with mineral dust particles from the Sahara. Another issue is also found in Southern Africa; the correlation between AOD BC and TCD CO in this region is not much higher than that between the AOD FM and TCD CO in spite of large occurrence of biomass burning and wildfire. This can be explained by the cloud perturbation near the source regions and dispersion effect due to the typical wind pattern. Correlations between AOD BC and TCD CO increase further when fire detected areas are only considered, but does not change much over the urban area. This difference clarifies the large contribution of burning events to the positive relationship between BC and CO. All findings in this study demonstrate a possible use of satellite CO product in evaluating the BC-dominated aerosol product from satellite remote sensing over the globe.

In this study, the long-term trend in atmospheric carbon monoxide (CO) concentration was analyzed using the CO levels measured (intermittently) at an air quality monitoring (AQM) station in Seoul, Korea, between the years 1987 and 2013. Temporal trends in CO were analyzed on an annual and seasonal basis in reference to other important air pollutants such as methane (CH4), particulate matter (PM10), sulfur dioxide (SO2), nitrogen monoxide (NO), nitrogen dioxide (NO2), mercury (Hg), and ozone (O3). The annual mean of CO for the entire period was 0.93 ± 0.22 ppm. CO levels were reduced by 83% from 3.25 ± 0.78 ppm (1987) to 0.51 ± 0.31 ppm (2013). Its relative reduction was compared over three periods chosen arbitrarily as period 1 (fast reduction, 1987–1988), period 2 (intermediate reduction, 1999–2000), and period 3 (slow reduction, 2004–2013). The concentrations of CO were strongly correlated with others (e.g., SO2, NO, NO2, O3, and Hg), suggesting the effects of similar source processes (e.g., fuel combustion). The reduction in its level was marginally consistent with the decreasing trend in the total CO column concentration in Seoul by the Measurements of Pollution in the Troposphere (MOPITT) satellite between 2000 and 2013, indicating decreasing anthropogenic CO emissions (despite increasing anthropogenic CO2 emissions). The rapid relative reduction of CO in period 1 and the subsequent slower but moderate reduction thereafter appear to reflect the effects of both enforcement of administrative regulations and advances in emissions control technologies.

The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument has been making observations of atmospheric carbon monoxide since 2000. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the version 7 (V7) product. Improvements include (1) representation of growing atmospheric concentrations of N2O, (2) use of meteorological fields from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) reanalysis for the entire MOPITT mission (instead of MERRA), (3) use of the MODIS (Moderate-Resolution Imaging Spectroradiometer) Collection 6 cloud mask product (instead of Collection 5), (4) a new strategy for radiance-bias correction and (5) an improved method for calibrating MOPITT's near-infrared (NIR) radiances. Statistical comparisons of V7 validation results with corresponding V6 results are presented, using aircraft in situ measurements as the reference. Clear improvements are demonstrated for V7 products with respect to overall retrieval biases, bias variability and bias drift uncertainty.

The Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) ensemble ozone simulations for the present day from the 2000 decade simulation results are evaluated by a state-of-the-art multi-constituent atmospheric chemical reanalysis that ingests multiple satellite data including the Tropospheric Emission Spectrometer (TES), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Measurement of Pollution in the Troposphere (MOPITT) for 2005–2009. Validation of the chemical reanalysis against global ozonesondes shows good agreement throughout the free troposphere and lower stratosphere for both seasonal and year-to-year variations, with an annual mean bias of less than 0.9 ppb in the middle and upper troposphere at the tropics and mid-latitudes. The reanalysis provides comprehensive spatiotemporal evaluation of chemistry-model performance that compliments direct ozonesonde comparisons, which are shown to suffer from significant sampling bias. The reanalysis reveals that the ACCMIP ensemble mean overestimates ozone in the northern extratropics by 6–11 ppb while underestimating by up to 18 ppb in the southern tropics over the Atlantic in the lower troposphere. Most models underestimate the spatial variability of the annual mean lower tropospheric concentrations in the extratropics of both hemispheres by up to 70 %. The ensemble mean also overestimates the seasonal amplitude by 25–70 % in the northern extratropics and overestimates the inter-hemispheric gradient by about 30 % in the lower and middle troposphere. A part of the discrepancies can be attributed to the 5-year reanalysis data for the decadal model simulations. However, these differences are less evident with the current sonde network. To estimate ozonesonde sampling biases, we computed model bias separately for global coverage and the ozonesonde network. The ozonesonde sampling bias in the evaluated model bias for the seasonal mean concentration relative to global coverage is 40–50 % over the western Pacific and east Indian Ocean and reaches 110 % over the equatorial Americas and up to 80 % for the global tropics. In contrast, the ozonesonde sampling bias is typically smaller than 30 % for the Arctic regions in the lower and middle troposphere. These systematic biases have implications for ozone radiative forcing and the response of chemistry to climate that can be further quantified as the satellite observational record extends to multiple decades.

We present two new products from near-infrared Greenhouse Gases Observing Satellite (GOSAT) observations: lowermost tropospheric (LMT, from 0 to 2.5 km) and upper tropospheric–stratospheric (U, above 2.5 km) carbon dioxide partial column mixing ratios. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial column mixing ratios significantly improve upon the Atmospheric CO2 Observations from Space (ACOS) and GOSAT (ACOS-GOSAT) initial guess and/or a priori, with distinct patterns in the LMT and U seasonal cycles that match validation data. For land monthly averages, we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U, and XCO2; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U, and XCO2. In the southern hemispheric biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and a CO–CO2 emission factor comparable to published values. The difference of LMT and U, useful for evaluation of model transport error, has also been validated with a monthly average error of 0.8 (1.4) ppm for ocean (land). LMT is more locally influenced than U, meaning that local fluxes can now be better separated from CO2 transported from far away.

In the present study, MOZART-4 (Model for Ozone and Related chemical Tracers-Version-4) simulation has been made from 2003 to 2007 and compared with satellite and in-situ observations with a specific focus on Indian subcontinent to illustrate the capabilities of MOZART-4 model. The model simulated CO have been compared with latest version (version-6) of MOPITT (Measurement Of Pollution In The Troposphere) carbon monoxide (CO) retrievals at 900, 800 and 700 hPa. Model reproduces major features present in satellite observations. However model significantly overestimates CO over the entire Indian region at 900 hPa and moderately overestimates at 800 hPa and 700 hPa. The frequency distribution of all simulated data points with respect to MOZART error shows maximum in the error range of 10–20% at all pressure levels. Over total Indian landmass, the percentage of gridded CO data that are being overestimated in the range of 0–30% at 900 hPa, 800 hPa and 700 hPa are 58%, 62% and 66% respectively. The study reflects very good correlation between two datasets over Central India (CI) and Southern India (SI). The coefficient of determination (r 2 ) is found to be 0.68–0.78 and 0.70–0.78 over the CI and SI respectively. The weak correlation is evident over Northern India (NI) with r 2 values of 0.1–0.3. Over Eastern India (EI), Good correlation at 800 hPa (r 2 = 0.72) and 700 hPa (r 2 = 0.66) whereas moderately weak correlation at 900 hPa (r 2 = 0.48) has been observed. In contrast, Over Western India (WI), strong correlation is evident at 900 hPa (r 2 = 0.64) and moderately weak association is found to be present at 800 hPa and 700 hPa. Model fairly reproduces seasonal cycle of CO in the lower troposphere over most of the Indian regions. However, during June to December, model shows overestimation over NI. The magnitude of overestimation is increasing linearly from 900 hPa to 700 hPa level. During April–June months, model results are coinciding with observed CO concentrations over SI region at 900 hPa. Model simulation has been compared with surface in-situ observations over ten Indian locations. Model performance is found to be moderate to good over various observational locations. However, over highly polluted megacities, model underestimates observed CO concentration by up to 3500 ppbv. A case study over the forest fire prone area reveals the clear increase of modeled and retrieved CO in February–March and a decrease in May which is coinciding with biomass burning emissions and fire counts. Model performance is found to be relatively poor over this region with r 2 of 0.29 and slope of 0.56.

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM10) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014–2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March–April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM10 concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM10 than Terra AODs-PM10 with correlation coefficients (r) of 0.72–0.83 and 0.57–0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65–0.73) than with CO total columns (r = 0.56–0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November–March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM10 and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.

EnvironmentAsia, 13, Special, 18-25

The performance of the Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions has not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we validate MOPITT products over urban regions using aircraft measurements from DISCOVER-AQ, SEAC4RS, ARIAs, A-FORCE, and KORUS-AQ campaigns. Overall, MOPITT performs reasonably well over both urban and non-urban regions, overall biases for V8J and V8T vary from −0.7 % to 0.0 %, and from 2.0 % to 3.5 %, respectively. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than that over urban regions with smaller biases and higher correlation coefficients. We find that the performance of MOPITT V8J and V8T at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily-polluted scenes. We test the sensitivities of validation results to assumptions and data filters applied during the comparisons of MOPITT retrievals and in-situ profiles. The results at the surface are insensitive to the model-based profile extension (required due to aircraft altitude limitations) whereas the results at levels with limited aircraft observations are more sensitive to the model-based profile extension. The validation results are insensitive to the allowed maximum time difference as criteria for co-location (12 hours, 6 hours, 3 hours, and 1 hour), and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km) and hence the MOPITT retrievals included in the validation become very small. Daytime MOPITT products have overall smaller biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in-situ profiles, likely due to the reduced number of MOPITT retrievals that result for comparison. Comparisons of MOPITT retrievals and in-situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate the some of that errors are due to CO representativeness with these sensitivity tests, but further quantification of validation errors due to CO variability within the MOPITT footprint will require future work.

Part of the Springer Proceedings in Complexity book series; International audience; Atmospheric carbon monoxide (CO) emissions estimated from inverse modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties in CO emission estimates by constraining the modeled abundance of ozone (O3), nitrogen dioxide (NO2), nitric acid (HNO3), and formaldehyde (HCHO), which are constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system, we estimate CO emissions by assimilating observations of CO from the Measurement of Pollution In the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer (IASI), together with observations of O3 from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and IASI, NO2 and HCHO from the Ozone Monitoring Instrument (OMI), and HNO3 from the Microwave Limb Sounder (MLS). Although our focus is on quantifying CO emission estimates, we also infer surface emissions of nitrogen oxides (NOx = NO + NO2) and isoprene. Our results reveal that this multiple species chemical data assimilation produces a chemical consistent state that effectively adjusts the CO–O3–OH coupling in the model. The O3-induced changes in OH are particularly large in the tropics. We show that the analysis results in a tropospheric chemical state that is better constrained. Our experiments also evaluate the inferred CO emission estimates from major anthropogenic, biomass burning and biogenic sources.

La investigación y el monitoreo de calidad del aire fueron ganando iportancia en Argentina yLationamérica, principalmente en megaciudades donde la contaminación alcanza niveles críticos.Otro instrumento muy usado en el estudio de la composición atmosférica y sus procesos físicos yquímicos, y también para obtener el pronósstico de calidad de aire en una región, es un modelo de transporte químico. Este tipo de modelos estima las condiciones del aire que respiramos, la concentración de contaminantes en el nivel superficial y niveles superiores, y cómo sonconsumidos/producidos y transportados por las condiciones meteorológicas. El objetivo principal de la tesis es realizar mejoras en el sistema de modelado WRF-CHIMERE-EDGAR, ya implementado anteriormente en Argentina, que consideren una evaluación del sistema utilizando información satelital, y una implementación operativa para brindar pronóstico de calidad del aire a la población. El sistema se compone de un inventario de emisiones antropogénicas global EDGAR, el modelo de predicción numérica del tiempo WRF y el modelo de transporte químico CHIMERE. La evaluación del sistema fue realizada en términos de densidad diaria en columna total atmosférica de NO2, CO y espesor óptico de aerosoles, utilizando datos de los sensores OMI, MOPITT y MODIS, respectivamente. El dominio espacial definido incluye a toda la Argentina continental, y el temporal es de marzo a mayo del 2009 para evaluar NO2 en condiciones de emisiones regulares antropogénicas y al período diciembre 2016 a febrero 2017 para evaluar el destino de la emisión de contaminantes por incendios de vegetación en un gran evento en la Patagonia Nororiental. Para esto último, fue implementado el modelo de emisiones de incendios APIFLAME que estima la tasa de emisión para cada foco de incendios, en función de datos satelitales dinámicos y estáticos que brindan información sobre la ubicación, potencia radiativa, tipo de cobertura vegetal, cantidad de biomasa disponible y factores de emisión. La evaluación del sistema de modelado con información satelital da resultados optimistas en cuanto a la distribución espacial y temporal de los contaminantes presentes en la atmósfera, aunque aún hay una subestimación en las emisiones producto de los inventarios de emisiones antropogénicas y de incendios. Por otro lado, aún es necesaria una evaluación del perfil vertical de contaminantes. Finalmente, en el sitio de la CONAE http://meteo.caearte.conae.gov.ar/wrf/chimere.php ya se encuentra la implementación operativa del Pronóstico Experimental de Calidad del Aire WRF-CHIMERE-EDGAR, junto con la documentación descriptiva y publicaciones relacionadas; esperamos agregar a este sistema la implementación de las emisiones de incendios en los próximos meses.

The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument has been making nearly continuous observations of atmospheric carbon monoxide (CO) since 2000. Satellite observations of CO are routinely used to analyze emissions from fossil fuels and biomass burning, as well as the atmospheric transport of those emissions. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the version 8 (V8) product. V8 products benefit from updated spectroscopic data for water vapor and nitrogen used to develop the operational radiative transfer model and exploit a new method for minimizing retrieval biases through parameterized radiance bias correction. In situ datasets used for algorithm development and validation include the NOAA (National Oceanic and Atmospheric Administration) and HIPPO (HIAPER Pole-to-Pole Observations) datasets used for earlier MOPITT validation work in addition to measurements from the ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems – Cloud processes of the main precipitation systems in Brazil: A contribution to cloud resolving modeling and to the GPM (Global Precipitation Measurement)), KORUS-AQ (The Korea-United States Air Quality Study), and ATom (The Atmospheric Tomography Mission) programs. Validation results illustrate clear improvements with respect to long-term bias drift and geographically variable retrieval bias. For example, whereas bias drift for the V7 thermal-infrared (TIR)-only product exceeded 0.5 % yr−1 for levels in the upper troposphere (e.g., at 300 hPa), bias drift for the V8 TIR-only product is found to be less than 0.1 % yr−1 at all levels. Also, whereas upper-tropospheric (300 hPa) retrieval bias in the V7 TIR-only product exceeded 10 % in the tropics, corresponding V8 biases are less than 5 % (in terms of absolute value) at all latitudes and do not exhibit a clear latitudinal dependence.

An improved column averaged concentration (X) of greenhouse gases (GHGs) namely CO2, CH4, CO and N2O are retrieved using ground-based Fourier Transform Infrared (FTIR; model IFS125M) Spectrometer data collected at Atmospheric Science Lab (ASL) of National Remote Sensing Centre (NRSC), Shadnagar, India during 2016 period in clear sky days. Indium Antimonide (InSb) detector and Calcium Fluoride (CaF2) beam splitter in combination with the spectral range between 1800 cm−1 to 11000 cm−1 (5.50 μm to 0.90 μm) with 0.01 cm−1 spectral resolution (∆ν) are set for the present study. Atmospheric transmittance for each gas was computed using PcModWin6 (MODTRAN v6) and compared against measured spectrum. In this study, spectra are analyzed using non-linear least squares spectral fitting algorithm (GFIT) developed by the California Institute of Technology, U. S. A. The Total Carbon Column Observing Network (TCCON) identified standard spectral windows were selected for retrieving the GHGs over the study site. With the present retrieval scheme, precision of the FTIR achieved are 0.17 % to 0.52 % for CO2, 0.30 % to 0.77 % for CH4, 6.33 % to 8.92 % for CO and 0.41 % to 0.75 % for N2O, respectively. Observed little to clear diurnal and seasonal variations in XCO2, XCH4, XCO and XN2O respectively. In this work, we compared FTIR retrieved GHGs against Orbiting Carbon Observatory-2 (OCO-2) retrieved XCO2 and Measurements of Pollution in the Troposphere (MOPITT) retrieved XCO. With the present retrievals, comparative study yields a mean relative bias between ground-based FTIR retrieved XCO2 and XCO are −1.50 % and 0.60 % against OCO-2 (XCO2) and MOPITT (XCO) respectively. Pearson correlation coefficient (r) between FTIR retrieval and satellite retrievals are 0.80 (XCO2, N=14 co-located) and 0.85 (XCO, N=18), respectively.

Global surface emissions of nitrogen oxides (NOx) over a 10-year period (2005–2014) are estimated from an assimilation of multiple satellite data sets: tropospheric NO2 columns from Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), O3 profiles from Tropospheric Emission Spectrometer (TES), CO profiles from Measurement of Pollution in the Troposphere (MOPITT), and O3 and HNO3 profiles from Microwave Limb Sounder (MLS) using an ensemble Kalman filter technique. Chemical concentrations of various species and emission sources of several precursors are simultaneously optimized. This is expected to improve the emission inversion because the emission estimates are influenced by biases in the modelled tropospheric chemistry, which can be partly corrected by also optimizing the concentrations. We present detailed distributions of the estimated emission distributions for all major regions, the diurnal and seasonal variability, and the evolution of these emissions over the 10-year period. The estimated regional total emissions show a strong positive trend over India (+29 % decade−1), China (+26 % decade−1), and the Middle East (+20 % decade−1), and a negative trend over the USA (−38 % decade−1), southern Africa (−8.2 % decade−1), and western Europe (−8.8 % decade−1). The negative trends in the USA and western Europe are larger during 2005–2010 relative to 2011–2014, whereas the trend in China becomes negative after 2011. The data assimilation also suggests a large uncertainty in anthropogenic and fire-related emission factors and an important underestimation of soil NOx sources in the emission inventories. Despite the large trends observed for individual regions, the global total emission is almost constant between 2005 (47.9 Tg N yr−1) and 2014 (47.5 Tg N yr−1).

Carbon monoxide (CO) total columns over European Russia (ER) and western Siberia (WS) have been analyzed using MOPITT (V5, TIR/NIR, L3) IR-radiometer data obtained in 2000–2014. High CO contents are revealed over large urban and industrial agglomerations and over regions of oil-and-gas production. A stable local CO maximum is observed over the Moscow agglomeration. Statistical characteristics of CO total columns observed in the atmosphere over ER and WS in 2000–2014 are presented. An analysis of long-term changes in CO content reveals nonlinear changes in the CO total column over northern Eurasia in 2000–2014. Results of a comparative analysis of annual variations in atmospheric CO contents over ER and WS are given. Based on Fourier analysis, empirical models of annual variations in total CO contents over ER and WS are proposed. Relations between regional CO contents and fire characteristics and between spatial CO distributions and features of large-scale atmospheric dynamics under conditions of weather and climate anomalies in the summers of 2010 in ER and 2012 in WS are analyzed. Data on total CO contents measured with a MOPITT satellite radiometer and a ground-based spectrometer operating at the Zvenigorod Scientific Station of the Obukhov Institute of Atmospheric Physics are compared.

Springtime trans-Pacific transport of Asian air pollutants has been investigated in many ways to figure out its mechanism. Based on the Western Pacific (WP) pattern, one of climate variabilities in the Northern Hemisphere known to be associated with the pattern of atmospheric circulation over the North Pacific Ocean, in this study, we characterize the pattern of springtime trans-Pacific transport using long-term satellite measurements and reanalysis datasets. A positive WP pattern is characterized by intensification of the dipole structure between the northern Aleutian Low and the southern Pacific High over the North Pacific. The TOMS/OMI Aerosol Index (AI) and MOPITT CO show the enhancement of Asian pollutant transport across the Pacific during periods of positive WP pattern, particularly between 40 and 50°N. This enhancement is confirmed by high correlations of WP index with AI and CO between 40 and 50°N. To evaluate the influence of the WP pattern, we examine several cases of trans-Pacific transport reported in previous research. Interestingly, most trans-Pacific transport cases are associated with the positive WP pattern. During the period of negative WP pattern, reinforced cyclonic wave breaking is consistently found over the western North Pacific, which obstructs zonal advection across the North Pacific. However, some cases show the trans-Pacific transport of CO in the period of negative WP pattern, implying that the WP pattern is more influential on the transport of particles mostly emitted near ∼40°N. This study reveals that the WP pattern can be utilized to diagnose the strength of air pollutant transport from East Asia to North America.

Observations of carbon monoxide CO retrieved from Scanning Imaging Absorption SpectroMeter for Atmospheric Chartography SCIAMACHY, Measurement of Pollution in the Troposphere MOPITT, and Atmospheric Infrared Sounder AIRS are compared in this article. To better validate the retrieved data from SCIAMCHY, AIRS, and MOPITT, six surface stations at different locations and with various elevations were chosen. The results show these three instruments can all reflect CO spatial distribution well and show same temporal variations of CO concentration as well as six surface station measurements. MOPITT and AIRS have similar retrieval results with correlation coefficients being mostly over 0.70, except for a sixth field station on Crozet Island. The three satellites all have the ability to monitor CO concentration change on land, but SCIAMCHY results show a relatively larger bias than MOPITT and AIRS in low CO concentration areas because of systematic error.

Variability in the tropospheric distributions of carbon monoxide (CO) over five selected regions of India has been studied using the MOPITT data for the period of 2001–2014. The average seasonal profiles show highest mixing ratios at 900 hPa in the boreal winter and lowest in the Indian summer monsoon over all the study regions. We observe a slight increase in CO levels from 500 hPa to 200 hPa over all the locations. The CO mixing ratios are found to be higher by about 10–40% around 300–200 hPa as compared to 900 hPa over Ahmedabad, Hyderabad and Trivandrum during monsoon period. This could be due to lifting of polluted air by convection and getting trapped in the anticyclonic winds over the Indian region during the monsoon. Most of the 7 day back trajectories over these regions show transport of the polluted air mass from the major biomass burning regions of central Africa and SE Asia. The results show dominance of the seasonal amplitude at 900 hPa over all the regions, while inter-annual variability dominates mostly over Ahmedabad, Hyderabad and Trivandrum at 300 hPa. In order to check the ability of different models in capturing the observed variability, the results have been compared with simulations from two chemistry transport models (MOZART and EMAC). This comparison shows that both the models perform reasonably well in simulating the basic features such as annual variation as well as increase in CO around 300–200 hPa due to convection during the monsoon season.

This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of "compact phase space retrievals" of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals: (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain: (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of "compact phase space retrievals" (CPSRs). CPSRs are obtained by preprocessing retrieval datasets with an algorithm that: (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ~ 35 % while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.

The representation of air quality and meteorology over Asia remains challenging for chemical transport models as a result of the complex interactions between the East Asian monsoons and the large uncertainty (in space and time) of the high anthropogenic emissions levels over the region. High spatial resolution models allow resolving small-scale features induced by the complex topography of this region. In this study, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the spatial and seasonal variability of main physical and chemical variables over Asia for the year 2015 at 8-km horizontal resolution to enable resolving small-scale features induced by the region complex topography. The simulated atmospheric composition was evaluated against satellite retrievals (MOPITT, IASI + GOME2, MODIS and OMI) in addition to ground-based observations in China for the year 2015, while the meteorological variables were evaluated by several observational-based datasets (ERA5, CRU, MODIS, MTE). Results showed low to moderate seasonal biases for major meteorological variables, i.e. air temperature, relative humidity, precipitation, latent heat, sensible heat and snow cover fraction. Overall, WRF-Chem reproduced well the spatial and seasonal variability of lowermost tropospheric ozone content, total column carbon monoxide and aerosol optical depth, while large discrepancies were found for tropospheric nitrogen dioxide content, mainly during the warm season. In consistency with previous studies, the different biases between model-simulated and satellite-retrieved values can be mainly attributed to i) the large uncertainties in anthropogenic and natural nitrogen oxides emission estimates, as well as dust and sea salt emissions in the case of aerosol optical depth, and ii) some coarse parameterizations used to reproduce main small-scale features (e.g. meteorology, chemical processes, dry deposition to vegetation). Compared to ground-based observations, the WRF-Chem model reproduced well the mean annual cycle of surface nitrogen dioxide, ozone and fine particles concentrations in all seasons across China. Our results suggest that WRF-Chem provides reliable spatio-temporal patterns for most of the meteorological and chemical variables, adding thus confidence to its applicability in the context of air pollution risk assessment to human and ecosystems health.

This study investigates fractional contribution of different carbon monoxide (CO) sources over the Indian subcontinent at the surface in 2015 using a tagged tracer approach in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations are evaluated with respect to in-situ and satellite observations. The simulated CO levels reproduced in-situ observations in Pune, Anantapur, Udaipur, Ahmedabad, and Chennai reasonably well with mean bias ranging from −89.9 to 87.0 ppbv and RMSE ranging from 28 to 40% but a poor model performance was noticed in Hyderabad, Jabalpur, and Kanpur with larger mean bias (−259.0–190.9 ppbv) and RMSE (45–96%). Simulated CO concentrations are also compared with CO retrieved by MOPITT at 800 hPa and 200 hPa. At 800 hPa (200 hPa), the annual average WRF-Chem CO varies from 195 ± 69 to 224 ± 93 ppbv (102 ± 25 to 136 ± 21 ppbv) while MOPITT CO varies from 103 ± 26 to 114 ± 29 ppbv (103 ± 30 to 134 ± 33) over different Indian sub-regions. Over most of the regions, good correlation (coefficient of correlation > 0.7) is observed between simulated and satellite observed CO with mean bias of 92–118 ppbv at 800 hPa and −4 to 3 ppbv at 200 hPa. Over the total Indian region, we find the highest contribution from anthropogenic emissions (CO-ANT) and inflow into the model domain from domain boundaries (CO-BACK) with 45–46% contribution each and very small contribution (

A variance-maximization approach based on 19 years of weekly measurements of pollution in the troposphere carbon monoxide (CO) measurements quantifies the spatial-temporal distribution of global biomass burning. Seven regions consistent with existing datasets are discovered and shown to burn for longer, over a more widespread area. Each region has a unique and recurring burning season, with three dominated by inter- and intra-annual variation. The CO is primarily lofted to the free troposphere from where it spreads downwind at 800 to 700 mb with three exceptions: The Maritime Continent and South America where there is spread at 300 mb consistent with deep- and pyro-convection; and Southern Africa which reaches to 600 mb. The total mass of CO lofted into the free troposphere ranges from 46% over Central Africa to 92% over Australia. The global, annual emissions made using two different techniques lead to an increase of biomass burning CO emissions of 47TgCO/year and 99TgCO/year respectively. The larger increase is mainly due to two factors: first, a large amount of the emissions is lofted rapidly upwards over the biomass burning region and subsequently transported downwind, therefore not appearing near the biomass source in space and time and second, an increase in inter-annual variability. Consistently, there is an increase in variability year-to-year and during peak events, from which 35% to more than 80% of the total emissions is lofted into the free troposphere. The results demonstrate a significantly higher CO emission from biomass burning, a larger impact on the global atmospheric composition, and likely impacts on atmospheric chemistry and climate change.

We comprehensively integrated various remote sensing, modeling and meteorological datasets to assess and quantify the effects of Indonesia's forest fires in 2015 on the ambient atmosphere. When the forest fires occurred, the fire spots in Sumatra and Borneo increased sharply to 78,055 and fire radiative power (FRP) rose to 4.05 × 106 MW in September–October 2015. The Aerosol Optical Depth (AOD) and the retrieved concentration of PM2.5 around these two islands also peaked during this period. With the remote sensing data from 2016 and 2017 as the background, we found that the carbon monoxide (CO) anomalies along the equatorial Indian and Pacific Oceans (25°S to 25°N and 40°E to 160°E) reached 10.32 ± 0.58 Mt. and 25.05 ± 1.35 Mt. in September and October 2015, respectively. Meanwhile, the nitrogen dioxide (NO2) changes were not so obvious; the NO2 anomalies within Sumatra and Borneo were only 0.46 ± 0.14 Kt and 0.49 ± 0.17 Kt, respectively. All the four indicators (AOD, PM2.5 concentration, CO and NO2 anomalies) revealed that the ambient air quality in October 2015 was even worse than that in September. The precipitation anomaly (PA) of these two islands decreased to −1.89 mm/day in September 2015, which is assumed to be the main meteorological factor to induce the forest fires. During the 1997/1998 El Nino event, the PA decreased to −3.04 mm/day on October 1997 and temperature anomaly (TA) increased to 1.52 °C on March 1998, the variation of which is more significant than other two El Nino events in 1982/1983 and 2015/2016.

Monsoonal dynamics strongly impacts the composition over the South Asian region, however, in situ measurements remain limited mostly to the continental region. Whereas, remote oceanic regions of the Bay of Bengal (BoB) remain largely unexplored where shipping emissions can infuse into the South Asian outflow. In this direction, we performed ship-based measurements of ozone (O3) and carbon monoxide (CO) over the BoB during July‒August 2018 and corroborated the analyses with model results and satellite retrievals. Surface level mixing ratios of O3 and CO varied in the range of 14–45 and 50–164 ppbv, respectively during the expedition. Elevated O3 and CO levels were observed in coastal region downwind of a tropical megacity (Chennai), referred to as the Chennai-plume. Analysis of the Copernicus Atmosphere Monitoring Service (CAMS) model simulations suggests volatile organic compound (VOC)-limited O3 production in morning over most of the plume and east coast followed by gradual shift to NOx-limited regime over the BoB in afternoon. While the southern BoB (7–10° N) received generally pristine airmasses from the equatorial Indian Ocean, impacts of shipping emissions near 6° N are observed. Consequently, higher values of CO (as much as 120 ppbv) and net O3 production by 1.1–1.3 ppbv h−1 with a diurnal amplitude of 4–8 ppbv are observed over the southern BoB. Satellite-based observations from the Measurements of Pollution in the Troposphere (MOPITT) and results from CAMS model show decreasing trend (4.1–5.2 ± 1 ppbv decade−1) of surface CO, as also inferred from in situ measurements. Zonal fluxes of CO across the east coast show the outflow over the BoB with higher values (2–3 × 1019 molecules m−2 s−1) over the northern BoB. Positive vertical fluxes (~2 × 1016 molecules m−2 s−1) show the transport of CO from marine boundary layer to the free-troposphere. Due to strong updrafts, CO levels in upper and lower troposphere are seen to be comparable during monsoon. These findings in conjunction with higher methane and nitrogen dioxide in the upper-troposphere manifest the influences of monsoonal convection and trapping of trace gases in the anticyclone.

In this paper, WRF-Chem model response to horizontal resolution has been presented in simulating tropospheric ozone distribution during an intense biomass burning across Southeast Asia. Model resolution is varied between 100 km and 20 km. Enhanced fire emissions were also considered in the 20 km resolution simulation. Evaluations were made against observed meteorology such as temperature, relative humidity, wind speed and direction. Spatio-temporal distribution of ozone precursors such as NO2 and CO at the surface retrieved from OMI and MOPITT instruments respectively, were compared against model outputs. Ozonesonde datasets for ozone profile from SHADOZ campaign at Watukosek-Java, Hanoi and Kuala Lumpur were used in evaluating simulated results. All the model simulations adequately represented the observed meteorology. Except in Watukosek-Java where ozone levels were overrepresented, the levels in other locations such as Kuala Lumpur and Hanoi were captured adequately. For model simulations using low-resolution, high-resolution and high-resolution with enhanced fire emissions in Hanoi, Kuala Lumpur and Watukosek-Java region, normalized bias factors are around -0.06, 0.14 and 0.22; 0.01, 0.28 and 0.18, and; 1.20, 3.36 and 3.21, respectively. Normalized root mean square error obtained is as low as 0.09 in Hanoi, and as high as 1.02 in Watukosek-Java region.

The dust has direct effects on people's health and climate change; so, this research studied the remotely sensed dust deposition in Africa from 1980 to 2018, and the dust's particulate matter of 2.5 μm size (or PM2.5), in particular, which pollutes the breathable air. PM2.5 is studied in comparison with multispectral carbon monoxide (CO), an abundant atmospheric air pollutant in central Africa. CO is an atmospheric gaseous pollutant for which the smoke, a gaseous aerosol from incomplete combustion processes, is the biggest source. The literature clarifies that both the particulate matter and the CO endanger human health while breathed in. The dust from the desert of Sahara is windblown all over the world. CO, in Africa, is from the anthropogenic fire and volcanic eruptions' smoke; these are two good reasons to have focused on Africa. Due to the big size of Africa, five sub-regions are set; these are the western, central, northern, eastern and southern sub-regions. The Goddard interactive online visualization and analysis infrastructure (GIOVANNI) has been a bridge to the collected remote sensing data, in this research. The data was collected online, from the measurement of pollution in the troposphere (MOPITT) as well as a second version of the modern era retrospective analysis for research and applications (MERRA-2); the analysis was done by a joint of the software tools, worth noting is the Arc GIS. As the amount of African dust dramatically increased by 2000; the heaviest in 2004, results are based on the selected dust deposition over 2000–2018: time-averaged maps, correlations, and quantitative estimations are reported in this research. The heaviest annual dust deposition reached 25.3 t/km2 over the year 2004, in Liberia, a focal point of study for the western sub-region. An important finding: the dust's PM2.5 positively correlated with multispectral CO from November to May; the positively high correlation coefficient was 0.86 in April 2018. The negative correlation between the two measurements started from June to October; the negatively high correlation was −0.68 in October 2015; this research discussed the possible reasons. This research recommends some onsite studies about the real figures and facts about the dust's effects on health, in all the seasons; thus, an alert to policymakers who would set some strategies to mitigate the dust hazards on the health of African inhabitants, neighbors, and visitors.

Aerosol loading and trace gas emissions from agriculture crop residue burning has been one of the main factors leading to the deterioration of air quality especially during the harvest time in China. Here, we evaluate the contribution of atmospheric aerosol loading from biomass burning and its relationship to the air pollution control policy. We focused on Shandong province, China because it has the highest level of agriculture burning pollution in October. Satellite-based data related to biomass burning including aerosol optical depth (AOD) and fire pixel counts from Moderate Resolution Imaging Spectroradiometer (MODIS), carbon monoxide (CO) from Measurements Of Pollution In The Troposphere (MOPITT), aerosol index (AI) and tropospheric NO2 from Ozone Monitoring Instrument (OMI), and aerosol layer depths from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). These metrics were synthetically utilized to evaluate spatial and temporal variation of atmospheric aerosols during the agriculture crop residue burning month from 2006 to 2017. Carbonaceous aerosol emission reduction and the impact of the air pollution control policy can be found in the CALIPSO aerosol types and MODIS fire pixel counts data. The AOD, NO2, CO, and AI over three different land covers (cropland, forest and impervious surfaces) were further obtained according to the land cover data set. The results indicated that there was an obvious decrease in all carbonaceous aerosol-related quantities after year 2012 when the local government began to enact and implement strict air pollution control policies, although a period (2–3 years) of transition after the policy implementation time was identified. We also found that CO is stable and low over the cropland versus forest and impervious surfaces; NO2 exhibits a continuously high value in the northern areas of the oil field. The impact of industry activity rather than straw burning should be also considered for NO2 and CO in certain areas.

Satellite measurements of tropospheric carbon monoxide (CO) enable a wide array of applications including studies of air quality and pollution transport. The MOPITT (Measurements of Pollution in the Troposphere) instrument on the Earth Observing System Terra platform has been measuring CO concentrations globally since March 2000. As indicated by the Degrees of Freedom for Signal (DFS), the standard metric for trace-gas retrieval information content, MOPITT retrieval performance varies over a wide range. We show that both instrumental and geophysical effects yield significant geographical and temporal variability in MOPITT DFS values. Instrumental radiance uncertainties, which describe random errors (or “noise”) in the calibrated radiances, vary over long time scales (e.g., months to years) and vary between the four detector elements of MOPITT's linear detector array. MOPITT retrieval performance depends on several factors including thermal contrast, fine-scale variability of surface properties, and CO loading. The relative importance of these various effects is highly variable, as demonstrated by analyses of monthly mean DFS values for the United States and the Amazon Basin. An understanding of the geographical and temporal variability of MOPITT retrieval performance is potentially valuable to data users seeking to limit the influence of the a priori through data filtering. To illustrate, it is demonstrated that calculated regional-average CO mixing ratios may be improved by excluding observations from a subset of pixels in MOPITT's linear detector array.

This study presents the distribution of tropospheric ozone and related species for South Asia using the Model for Ozone and Related chemical Tracers (MOZART-4) and Hemispheric Transport of Air Pollution version-2 (HTAP-v2) emission inventory. The model present-day simulated ozone (O3), carbon monoxide (CO) and nitrogen dioxide (NO2) are evaluated against surface-based, balloon-borne and satellite-based (MOPITT and OMI) observations. The model systematically overestimates surface O3 mixing ratios (range of mean bias about: 1–30 ppbv) at different ground-based measurement sites in India. Comparison between simulated and observed vertical profiles of ozone shows a positive bias from the surface up to 600 hPa and a negative bias above 600 hPa. The simulated seasonal variation in surface CO mixing ratio is consistent with the surface observations, but has a negative bias of about 50–200 ppb which can be attributed to a large part to the coarse model resolution. In contrast to the surface evaluation, the model shows a positive bias of about 15–20 × 1017 molecules/cm2 over South Asia when compared to satellite derived CO columns from the MOPITT instrument. The model also overestimates OMI retrieved tropospheric column NO2 abundance by about 100–250 × 1013 molecules/cm2. A response to 20% reduction in all anthropogenic emissions over South Asia shows a decrease in the anuual mean O3 mixing ratios by about 3–12 ppb, CO by about 10–80 ppb and NOX by about 3–6 ppb at the surface level. During summer monsoon, O3 mixing ratios at 200 hPa show a decrease of about 6–12 ppb over South Asia and about 1–4 ppb over the remote northern hemispheric western Pacific region.

A significant amount of satellite and ground-based data on the CO, CO2, and CH4 total contents for 2010–2013 was collected, classified, and analyzed. Transition relations between satellite and groundbased data on the content of impurities under study at different measuring sites (NDACC/ GAW and OIAP RAS stations) with different spatial and temporal resolutions have been found. A high correlation between daily average satellite-measured CO contents (AIRS v6 (R 2 = 0.48–0.96), IASI MetOp-A (R 2 = 0.25–0.86), and MOPITT v6 Joint (R 2 = 0.30–0.83) products, averaging over 1° × 1°) and the ground-based solar spectrometers’ data was ascertained for background conditions. In the case of high pollution of the mixing layer, a significant underestimation of the CO total content (by 1.7–4.7 times, depending on the sensor and observation point) by satellite sensors has been noted. Representative transition relations and correlation coefficients (R 2 ≥ 0.5) between satellite data on daily average CH4 contents and the data from ground-based diffraction spectrometers of A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS) and Fourier spectrometers of GAW stations have been found only for the AIRS sensor. The best correlation with ground-based measurement data on CO2 (R 2 = 0.25 for daily average values, averaging over 1° × 1°) was found for the IASI sensor. The daily average CH4 total contents from the IASI MetOp-A sensor weakly correlate with the ground-based data and with AIRS data.