Temporal and spatial variability of tropospheric NO2 columns retrieved from OMI satellite data and comparison with ground based information in Thailand

Eight-year’s of satellite data were retrieved from the OMI satellite to investigate temporal and spatial variability of tropospheric NO2 columns during 2008-2015 over 6 regions of Thailand. The highest level of NO2 columns was observed in the central region followed by the eastern, northern, northeastern, and southern regions. Moderately increasing trends of NO2 columns were detected ranging from 16.62% to 39.48% over 7 years (ref. year 2008). In the north and northeast, the maximum levels of NO2 columns were revealed during the biomass burning period of January-April. In the central and eastern regions, the maximum levels were observed during wintertime, i.e., November-February. For the southern region, the west coast showed higher levels during November-April while the east coast showed elevated levels during May-October. Comparative analysis of seasonal patterns of OMI observations versus ground based information of NO2 concentrations and emissions revealed that they were generally in good agreement, particularly in the northern, eastern, and central regions. The results show that relative humidity has an effect on the correlations between OMI data and emissions. Overall, the OMI observations were shown to be useful in tracking atmospheric levels and emission sources of NO2, especially when ground based data are not available

Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations

BACKGROUND: Air pollution is associated with morbidity and premature mortality. Satellite remote sensing provides globally consistent decadal-scale observations of ambient nitrogen dioxide (NO2) pollution. OBJECTIVE: We determined global population-weighted annual mean NO2 concentrations from 1996 through 2012. METHODS: We used observations of NO2 tropospheric column densities from three satellite instruments in combination with chemical transport modeling to produce a global 17-year record of ground-level NO2 at 0.1° × 0.1° resolution. We calculated linear trends in population-weighted annual mean NO2 (PWMNO2) concentrations in different regions around the world. RESULTS: We found that PWMNO2 in high-income North America (Canada and the United States) decreased more steeply than in any other region, having declined at a rate of –4.7%/year [95% confidence interval (CI): –5.3, –4.1]. PWMNO2 decreased in western Europe at a rate of –2.5%/year (95% CI: –3.0, –2.1). The highest PWMNO2 occurred in high-income Asia Pacific (predominantly Japan and South Korea) in 1996, with a subsequent decrease of –2.1%/year (95% CI: –2.7, –1.5). In contrast, PWMNO2 almost tripled in East Asia (China, North Korea, and Taiwan) at a rate of 6.7%/year (95% CI: 6.0, 7.3). The satellite-derived estimates of trends in ground-level NO2 were consistent with regional trends inferred from data obtained from ground-station monitoring networks in North America (within 0.7%/year) and Europe (within 0.3%/year). Our rankings of regional average NO2 and long-term trends differed from the satellite-derived estimates of fine particulate matter reported elsewhere, demonstrating the utility of both indicators to describe changing pollutant mixtures. CONCLUSIONS: Long-term trends in satellite-derived ambient NO2 provide new information about changing global exposure to ambient air pollution. Our estimates are publicly available at http://fizz.phys.dal.ca/~atmos/martin/?page_id=232.Published versio

Airborne MAX-DOAS Measurements Over California: Testing the NASA OMI Tropospheric NO2 Product

Airborne Multi-AXis Differential Optical Absorption Spectroscopy (AMAX-DOAS) measurements of NO2 tropospheric vertical columns were performed over California for two months in summer 2010. The observations are compared to the NASA Ozone Monitoring Instrument (OMI) tropospheric vertical columns (data product v2.1) in two ways: (1) Median data were compared for the whole time period for selected boxes, and the agreement was found to be fair (R = 0.97, slope = 1.4 +/- 0.1, N= 10). (2) A comparison was performed on the mean of coincident AMAX-DOAS measurements within the area of the corresponding OMI pixels with the tropospheric NASA OMI NO2 assigned to that pixel. The effects of different data filters were assessed. Excellent agreement and a strong correlation (R = 0.85, slope = 1.05 +/- 0.09, N= 56) was found for (2) when the data were filtered to eliminate large pixels near the edge of the OMI orbit, the cloud radiance fraction was2 km, and a representative sample of the footprint was taken by the AMAX-DOAS instrument. The AMAX-DOAS and OMI data sets both show a reduction of NO2 tropospheric columns on weekends by 38 +/- 24% and 33 +/- 11%, respectively. The assumptions in the tropospheric satellite air mass factor simulations were tested using independent measurements of surface albedo, aerosol extinction, and NO2 profiles for Los Angeles for July 2010 indicating an uncertainty of 12%

Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

International audience[1] The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite spectrometer provides detailed information on the nitrogen dioxide (NO 2) content in the planetary boundary layer. NO 2 tropospheric column retrievals of SCIAMACHY and its predecessor Global Ozone Monitoring Experiment are characterized by errors of the order of 40%. We present here a new SCIAMACHY tropospheric retrieval data set for the year 2003. The cloud free satellite observations are compared to surface measurements and simulations over western Europe performed with the regional air-quality model CHIMERE. The model has a resolution of 50 km similar to the satellite observations. For these comparisons, averaging kernels are applied to the collocated model profiles to remove the dependency of the comparison on a priori NO 2 profile information used in the retrieval. The consistency of both SCIAMACHY and CHIMERE outputs over sites where surface measurements are available allows us to be confident in evaluation of the model over large areas not covered by surface observations. CHIMERE underestimates surface NO 2 concentrations for urban and suburban stations which we mainly attribute to the low representativeness of point observations. No such bias is found for rural locations. The yearly average SCIAMACHY and CHIMERE spatial NO 2 distributions show a high degree of quantitative agreement over rural and urban sites: a bias of 5% (relative to the retrievals) and a correlation coefficient of 0.87 (n = 2003). On a seasonal basis, biases are smaller than 20% and correlation coefficients are larger than 0.75. Spatial correlations between both the model and satellite columns and the European Monitoring and Evaluation Program (EMEP) emission inventory are high in summer (r = 0.74, n = 1779) and low in winter (r = 0.48, n = 1078), related to seasonal changes in lifetime and transport. On the other hand, CHIMERE and SCIAMACHY columns are mutually consistent in summer (r = 0.82) and in winter (r = 0.79). This shows that CHIMERE simulates the transport and chemical processes with a reasonable accuracy. The NO 2 columns show a high daily variability. The daily NO 2 pollution plumes observed by SCIAMACHY are often well described by CHIMERE both in extent and in location. This result demonstrates the capabilities of a satellite instrument such as SCIAMACHY to monitor the NO 2 concentrations over large areas on a daily basis. It provides evidence that present and future satellite missions, in combination with CTM and surface data, will contribute to improve quantitative air quality analyses at a continental scale. Citation: Blond, N., K. F. Boersma, H. J. Eskes, R. J. van der A, M. Van Roozendael, I. De Smedt, G. Bergametti, and R. Vautard (2007), Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe

The Distribution of Atmospheric Pollutants in Europe: Optimal Use of Models and Observations with a Data Assimilation Approach

The research activity presented in this manuscript deals with the implementation of a methodology to merge in an optimal way atmospheric modelling and observations at different spatial scales. In particular, we approached the problem of assimilation of ground measurements and satellite columnar data and how the Data Assimilation (DA) could improve the chemical transport model (CTMs) and correct biases and errors in the chemical species forecast. The work focused on tropospheric ozone and the species linked to its formation, since they play a crucial role in chemical processes during photochemical pollution events. The study was carried out implementing and applying an Optimal Interpolation (OI) DA technique in the air quality model BOLCHEM and the CHIMERE CTM. The OI routine was chosen because it has given satisfactory results in air quality modelling and because it is relatively simple and computationally inexpensive. In the first part of the study we evaluated the improvement in the capability of regional model BOLCHEM to reproduce the distribution of tropospheric pollutants, using the assimilation of surface chemical observations. Among the many causes of uncertainties of CTMs simulations, a particular focus is given by uncertainties in emissions, that are known to be high. The scientific purpose was to analyse the efficacy of DA in correcting the biases due to perturbed emission. The work was performed using an Observing System Simulation Experiment (OSSE), which allowed the quantification of assimilation impact, through comparison with a reference state. Different sensitivity tests were carried out in order to identify how assimilation can correct perturbations on O3, induced by NOx emissions biased in flux intensity and time. Tests were performed assimilating different species, varying assimilation time window length and starting hour of assimilation. Emissions were biased quantitatively up to ± 50% and shifted temporally up to ± 2 hours. The analysis brought to the conclusions that NO2 assimilation significantly improves O3 maxima during the assimilation, making it almost independent on different emission scenarios. The assimilation impact lasts up to 36-40 hours after the end of the assimilation window. This is a considerable result, especially when it is taken into account that DA generally yields significantly better forecasts in the 6-12 hours range, but improvements vanish afterwards. The NO2 night-time chemistry has the role of maintaining the correction of O3 due to assimilation also in the following day. Assimilating NO2 and O3 simultaneously bring to rather better results, although the benefit lasts only a few hours after the end of the assimilation window. It was found that the best results are achieved assimilating observations during the photochemically active period (06-18 UTC). It was also found that temporally biased NOx emissions only slightly perturb O3 concentration during the photochemically active regime, while the perturbation is larger during night-time. Assimilation has a very low impact during the assimilation window and a negligible impact after its end. The second part of PhD research activity dealt with the evaluation of the impact of assimilation of satellite NO2 tropospheric columns on the distribution of pollutants at the ground level during photochemical pollution events at continental scale. In particular, we focused on the assimilation of observations from SCIAMACHY and from OMI, and its effect on ozone in the lowermost troposphere in Europe. For an effective improvement in assimilated fields it is particularly important the consistency between satellite and model resolution. SCIAMACHY and OMI have a considerable difference in spatial and temporal resolution, allowing to test the role of data resolution on the effectiveness of assimilation. The role of data resolution on the effectiveness of assimilation was investigated also changing the model resolutions. It was found the perturbation on NO2 field due to assimilation causes a modification on ozone field that appears more spatially variable and higher in some photochemical polluted areas. Similar effects are detected both for SCIAMACHY and OMI assimilation. Significative effects of assimilation on ozone can be appreciate in polluted areas at local scale. Focusing on specific subdomains, it was found that the effect of assimilation lasts, in general, 8 hours and in few cases until the reactivation of active photochemical period in the following day. This is a strong impact, considering that assimilation is performed at most once a day and it is probably linked to the model underestimate of ozone and its precursors in polluted areas with respect to those measured by SCIAMACHY and OMI. In wide and highly polluted areas assimilation achieves satisfactory results, comparing simulated ground ozone with independent ground measurements. In that region where OMI assimilation in the coarse and fine resolution simulations and SCIAMACHY assimilation were confronted, we could conclude that these different assimilation set-up are almost similar. Whereas, in more localised polluted areas (i.e. comparable to model and satellite resolution), OMI assimilation in the finer resolution simulation performs better with respect to OMI assimilation in the coarse resolution simulation and SCIAMACHY assimilation. As a general conclusive statement, assimilation can be an important tool to make the spatial and temporal distribution of pollutants more realistic and closer to the specific local differences with the caveat of horizontal resolution of the assimilated columns and model simulations

Development, enhancement, and evaluation of aircraft measurement techniques for criteria pollutants

The atmospheric contaminants most harmful to human health are designated Criteria Pollutants. To help Maryland attain the national ambient air quality standards (NAAQS) for Criteria Pollutants, and to improve our fundamental understanding of atmospheric chemistry, I conducted aircraft measurements in the Regional Atmospheric Measurement Modeling Prediction Program (RAMMPP). These data are used to evaluate model simulations and satellite observations. I developed techniques for improving airborne observation of two NAAQS pollutants, particulate matter (PM) and nitrogen dioxide (NO2). While structure and composition of organic aerosol are important for understanding PM formation, the molecular speciation of organic ambient aerosol remains largely unknown. The spatial distribution of reactive nitrogen is likewise poorly constrained. To examine water-soluble organic aerosol (WSOA) during an air pollution episode, I designed and implemented a shrouded aerosol inlet system to collect PM onto quartz fiber filters from a Cessna 402 research aircraft. Inlet evaluation conducted during a side-by-side flight with the NASA P3 demonstrated agreement to within 30%. An ion chromatographic mass spectrometric method developed using the NIST Standard Reference Material (SRM) 1649b Urban Dust, as a surrogate material resulted in acidic class separation and resolution of at least 34 organic acids; detection limits approach pg/g concentrations. Analysis of aircraft filter samples resulted in detection of 8 inorganic species and 16 organic acids of which 12 were quantified. Aged, re-circulated metropolitan air showed a greater number of dicarboxylic acids compared to air recently transported from the west. While the NAAQS for NO2 is rarely exceeded, it is a precursor molecule for ozone, America's most recalcitrant pollutant. Using cavity ringdown spectroscopy employing a light emitting diode (LED), I measured vertical profiles of NO¬2 (surface to 2.5 km) west (upwind) of the Baltimore/Washington, area in the morning, and east (downwind) in the afternoon. Column contents (altitude integrals of concentration) were remarkably similar (≈3x1015 molecules cm−2). These measurements indicate that NO2 is widely distributed over the eastern US and help quantify the regional nature of smog events and prove extensive interstate transport of pollutants. These results were used to help shape air pollution control policy based on solid science