Satellite Column Research Articles

Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Abstract. Machine learning (ML) models are becoming a meaningful tool for modeling air pollutant concentrations. ML models are capable of learning and modeling complex nonlinear interactions between variables, and they require less computational effort than chemical transport models (CTMs). In this study, we used gradient-boosted tree (GBT) and multi-layer perceptron (MLP; neural network) algorithms to model near-surface nitrogen dioxide (NO2) and ozone (O3) concentrations over Germany at 0.1∘ spatial resolution and daily intervals. We trained the ML models using TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements combined with information on emission sources, air pollutant precursors, and meteorology as feature variables. We found that the trained GBT model for NO2 and O3 explained a major portion of the observed concentrations (R2=0.68–0.88 and RMSE=4.77–8.67 µg m−3; R2=0.74–0.92 and RMSE=8.53–13.2 µg m−3, respectively). The trained MLP model performed worse than the trained GBT model for both NO2 and O3 (R2=0.46–0.82 and R2=0.42–0.9, respectively). Our NO2 GBT model outperforms the CAMS model, a data-assimilated CTM but slightly underperforms for O3. However, our NO2 and O3 ML models require less computational effort than CTM. Therefore, we can analyze people's exposure to near-surface NO2 and O3 with significantly less effort. During the study period (30 April 2018 and 1 July 2021), it was found that around 36 % of people lived in locations where the World Health Organization (WHO) NO2 limit was exceeded for more than 25 % of the days during the study period, while 90 % of the population resided in areas where the WHO O3 limit was surpassed for over 25 % of the study days. Although metropolitan areas had high NO2 concentrations, rural areas, particularly in southern Germany, had high O3 concentrations. Furthermore, our ML models can be used to evaluate the effectiveness of mitigation policies. Near-surface NO2 and O3 concentration changes during the 2020 COVID-19 lockdown period over Germany were indeed reproduced by the GBT model, with meteorology-normalized near-surface NO2 having significantly decreased (by 23±5.3 %) and meteorology-normalized near-surface O3 having slightly increased (by 1±4.6 %) over 10 major German metropolitan areas when compared to 2019. Finally, our O3 GBT model is highly transferable to neighboring countries and locations where no measurements are available (R2=0.87–0.94), whereas our NO2 GBT model is moderately transferable (R2=0.32–0.64).

Concurrent measurements of atmospheric ammonia concentrations in the megacities of Beijing and Shanghai by using cavity ring-down spectroscopy

Ammonia (NH3) is a vital precursor of secondary aerosols, which play an important role in urban air pollution. The passive observation of NH3 in the urban atmosphere showed higher concentrations than conventionally believed; however, the NH3 dynamics remain limited due to the paucity of active measurements with fast response instruments. In this study, intensive online measurements of ambient NH3 were conducted using cavity ring-down spectroscopy in late spring and early summer 2020 at the megacities of Beijing and Shanghai in northern and southern China. The results show that the average NH3 concentration in Beijing (23.1 ± 10.3 ppb) was nearly double that in Shanghai (12.0 ± 5.0 ppb). This spatial difference was confirmed by satellite column observations and bottom-up emission patterns. In addition, there was a significant increase in NH3 concentrations during the morning in our study. This special diurnal profile was a universal feature, with an occurrence frequency exceeding 50% in both cities. These frequent morning increases were mainly caused by motor vehicle emissions, with an additional contribution from the residual layer breakup for one-quarter of the days in the morning increase. On these days, the NH3 concentration increased by approximately 20% due to the downward mixing of the residual layer rupture. The unique data observed here could be used to validate the chemistry and transport models at a higher time resolution and improve the understanding of the current status of NH3 pollution in the urban atmosphere.

Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia

Even though there are expert monitoring and assessment stations in large cities, air quality monitoring and measurement have a high cost and face significant issues. Data on air pollution can be acquired from remote sensing satellites for large areas and at a reasonable expense to compensate for monitoring stations on the ground. This research presented a method for retrieving PM10 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Thickness (AOT) records. The study depends on a previously established equation to retrieve PM10 over Malaysia in September 2014. In Contrast Reduction Technique, we used Aerosol Robotic Network AERONET AOT to define the reference. The conversion factors, representing the relationship between AOT and PM10 satellite columns, were determined using a mathematical approach. The size and type of aerosol, relative humidity, and boundary layer height vary globally and seasonally, thus the AOT–PM10 relationship fluctuates spatially and temporally. The conversion factor was then applied to the MODIS image to predict the surface level of PM10 concentrations in micrograms via cubic meter (μg/m3). Moreover, the achieved correlation coefficient R2 of calculated PM10 based on ground truth data was equal to 0.92. Based on the results obtained from the thematic maps, PM10 levels are significantly higher in specific cities such as Kuala Lumpur and Johor. Where PM10 ranged from (26.5 to 72) µg/m3, while AOT values were between (0.12 and 0.83). It raises concerns about the environmental health threats and their relationship to air quality in these regions as a research topic.

Characterization of errors in satellite-based HCHO ∕ NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties

Abstract. The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO2 + RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO/NO2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LROx/LNOx. FNRs show a strongly linear (R2=0.94) relationship with LROx/LNOx, but only on the logarithmic scale. Following the baseline (i.e., ln(LROx/LNOx) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the ROx–HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO × NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO × NO2 becomes more pronounced when moving towards NOx-sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km2). A pixel with a grid size of 216 km2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns, should be above 1.2–1.5 × 1016 molec. cm−2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation

• Extending a surface O 3 estimation framework (LESO) from China to broader regions. • LESO captured surface O 3 spatiotemporal variations well in Europe and US. • Deep learning techniques improved the estimation performance by 30 %. • Satellite columns have relatively small impacts on estimating surface O 3 concentrations. • Spatially well-distributed in-situ sites are vital for global surface O 3 estimation. Surface O 3 pollution severely threats human health and crop production across the globe. Data-driven machine learning approaches are broadly used for estimating surface O 3 concentrations in China by integrating site-level measurements, meteorological reanalysis data, and satellite remote sensing observations. However, the feasibility of extending these approaches from China to the globe requires further investigation, and the sensitivities of different model drivers remain unknown. By using the LEarning Surface Ozone (LESO) framework, this study explores the effectiveness of estimating daily surface O 3 over Europe, the United States, and India and analyses the impacts of satellite columns, climate drivers, machine learning algorithms, and in-situ sites on the estimation performance. A cross-validation of the LESO framework against in-situ sites exhibited a promising performance with an mean R 2 higher than 0.9 and uncertainty smaller than 4 µg/m 3 in Europe and the United States. Due to its success in capturing the O 3 variability with a user-friendly interface and less computing resources occupancy, LESO is expected to promote efforts in global O 3 pollution monitoring. However, the estimation performance in India was not good enough (R 2 < 0.5 and uncertainty > 9 µg/m 3 ). Regarding potential factors affecting the estimation performance, the satellite O 3 columns and the number of in-situ sites did not affect the estimation performance significantly; the use of the deep learning technique improved the performance by 30 % than using a conventional non-deep learning algorithm; and the spatial sampling density of in-situ sites dominantly affected the estimation performance. This can be the reason causing the unsatisfactory performance in India. The findings of this study are beneficial to promoting global surface O 3 estimation and can bring evidence on the location selection for deploying new in-situ sites.

On the weekly cycle of atmospheric ammonia over European agricultural hotspots

The presence of a weekly cycle in the abundance of an atmospheric constituent is a typical fingerprint for the anthropogenic nature of its emission sources. However, while ammonia is mainly emitted as a consequence of human activities, a weekly cycle has never been detected in its abundances at large scale. We expose here for the first time the presence of a weekend effect in the NH3 total columns measured by the IASI satellite sounder over the main agricultural source regions in Europe: northwestern Europe (Belgium-the Netherlands-northwest Germany), the Po Valley, Brittany, and, to a lesser extent, the Ebro Valley. A decrease of 15% relative to the weekly mean is seen on Sunday–Monday observations in northwestern Europe, as a result of reduced NH3 emissions over the weekend. This is confirmed by in situ NH3 concentration data from the National Air Quality Monitoring Network in the Netherlands, where an average reduction of 10% is found around midnight on Sunday. The identified weekend effect presents a strong seasonal variability, with two peaks, one in spring and one in summer, coinciding with the two main (manure) fertilization periods. In spring, a reduction on Sunday–Monday up to 53 and 26% is found in the NH3 satellite columns and in situ concentrations, respectively, as fertilization largely drives atmospheric NH3 abundances at this time of the year.

Long-term trends of ozone and precursors from 2013 to 2020 in a megacity (Chengdu), China: Evidence of changing emissions and chemistry

Elevated ozone (O3) pollution in the warm season is an emerging environmental concern affecting global highly urbanized megacities. In southwestern China, full characterization of causes for O3 pollution has been stymied by limited observations and the dominant factors that influence O3 variability on a long-term basis still lack understanding. Herein, we identified O3 variations and inferred trends in precursor emissions in Chengdu over 2013–2020 based on extensive ambient measurements, emission inventory, and satellite data. Numerical models were used to investigate the changes in meteorological variability and biogenic emissions. Trends of O3 in urban areas show deterioration (+14.0% yr−1) between 2013 and 2016 followed by a slight decrease over 2017–2020, while O3 levels in rural areas generally show a downward trend (−2.9% yr−1) during 2014–2020. Both emission inventory (−3.7% yr−1) and OMI satellite columns (−4.5% yr−1) depict strong decline trends in NOx emissions, while satellite HCHO columns exhibit a flattened downward trend of VOC emissions (−1.8% yr−1), which caused rural areas shifted from VOCs-limited to transitional or NOx-limited regime since 2016. Considering metropolitan Chengdu remains VOCs-limited regime over time, the existing regulatory framework involving simultaneous NOx and VOCs control would result in evident O3 improvements in the near future. Despite benefits from anthropogenic emission reductions, we demonstrate that meteorological conditions and enhanced biogenic emissions over the warm season could partially or even fully offset effects attributed to emission changes, making the net effects obscure. This finding provides robust evidence of reductions in NOx and VOCs emission and informs effective O3 mitigation policies for megacities which undergo similar emission pathways in Chengdu.

Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation

Abstract. Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses. The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellite columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.

Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany.

This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide (NO2) and ozone (O3) concentration changes in Germany during the COVID‐19 pandemic period using in‐situ surface and Sentinel‐5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS‐Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in‐situ (& column) NO2 concentrations by 23 % (& 16 %) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in‐situ O3 concentration increased by 4 % between March 21 and May 31, 2020, and decreased by 3% in June 2020, compared to 2019. In the winter and spring, the degree of NOX saturation of ozone production is stronger than in the summer. This implies that future reductions in NOX emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI NO2 concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North‐West Germany which can be attributed to enhanced NOX emissions from agricultural soils.

2019–20 Australian Bushfires and Anomalies in Carbon Monoxide Surface and Column Measurements

In Australia, bushfires are a natural part of the country’s landscape and essential for the regeneration of plant species; however, the 2019–20 bushfires were unprecedented in their extent and intensity. This paper is focused on the 2019–20 Australian bushfires and the resulting surface and column atmospheric carbon monoxide (CO) anomalies around Wollongong. Column CO data from the ground-based Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) site in Wollongong are used together with surface in situ measurements. A systematic comparison was performed between the surface in situ and column measurements of CO to better understand whether column measurements can be used as an estimate of the surface concentrations. If so, satellite column measurements of CO could be used to estimate the exposure of humans to CO and other fire-related pollutants. We find that the enhancements in the column measurements are not always significantly evident in the corresponding surface measurements. Topographical features play a key role in determining the surface exposures from column abundance especially in a coastal city like Wollongong. The topography at Wollongong, combined with meteorological effects, potentially exacerbates differences in the column and surface. Hence, satellite column amounts are unlikely to provide an accurate reflection of exposure at the ground during major events like the 2019–2020 bushfires.

Air Quality in Southeast Brazil during COVID-19 Lockdown: A Combined Satellite and Ground-Based Data Analysis

With the current COVID-19 pandemic being spread all over the world, lockdown measures are being implemented, making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of São Paulo (SP) and Rio de Janeiro (RJ) were addressed using a combination of satellite and ground-based daily data analysis. We explored nitrogen dioxide (NO2) and fine particulate matter (PM2.5) daily levels for the month of May from 2015–2020. Daily measurements of NO2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA’s Aura satellite were analyzed and decreases of 42% and 49.6% were found for SP and RJ, respectively, during the year 2020 compared to the 2015–2019 average. Besides NO2 column retrievals, ground-based data measured by the Brazilian States Environmental Institutions were analyzed and correlated with satellite retrievals. Correlation coefficients between year-to-year changes in satellite column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. Ground-based data showed 13.3% and 18.8% decrease in NO2 levels for SP and RJ, respectively, in 2020 compared to 2019. In SP, no significant change in PM2.5 was observed in 2020 compared to 2019. To further isolate the effect of emissions reduction due to the lockdown, meteorological data and number of wildfire hotspots were analyzed. NO2 concentrations showed negative and positive correlations with wind speed and temperature, respectively. PM2.5 concentration distributions suggested an influence by the wildfires in the southeast region of the country. Synergistic analyses of satellite retrievals, surface level concentrations, and weather data provide a more complete picture of changes to pollutant levels.

Contrasting effects of emission control on air pollution in Central China during the 2019 Military World Games based on satellite and ground observations

To ensure a good air quality during the 7th Military World Games (MWG) held in the megacity, Wuhan of central China, an unprecedented substantial emission reduction has been conducted during October 13–27, 2019. Here we present an observational overview on the effects of emission control measures as well as their implications by integrated satellite observations and surface measurements. Compared with air quality before control, there is a remarkable overall decline (~15–30%) in PM10 and SO2 concentration in control period. By contrast, NOx and O3 remain at high levels under persistent stagnant weather conditions since vehicle emission is not restricted. Despite high NOx (~100 μg/m3) in ground measurements, satellite column NO2 exhibits a considerable decline (~30–50%) over Wuhan, indicating that the large reduction of industrial emission from elevated chimneys has limited effects on changes of NOx near surface. The NO3− and NH4+ in PM2.5 reach peak values at the beginning of emergency control period, and then decrease steadily with a rising temperature. Meanwhile, OC and EC in PM2.5 have a continuous accumulation process on the premise of enhanced emission control, which can be caused by vehicle emissions. Generally, the stringent emission reduction focusing on industrial sources obviously mitigates particle pollution in Wuhan. Considering complicated relationship between atmospheric pollutants and their precursors, synchronized emission reduction measures based on their accurate roles are necessary in improving air quality effectively.

The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

Observation of column-averaged molar mixing ratios of carbon dioxide in Tokyo

Abstract The metropolitan area of Tokyo, the capital of Japan, emits a large amount of carbon dioxide (CO2), an anthropogenic greenhouse gas. We measured the day-time column-averaged dry-air molar mixing ratios of atmospheric CO2, XCO2, in the central area of Tokyo during September 2014 – August 2016 using a desktop optical spectrum analyzer. The observed seasonal cycle is compared with the seasonal cycle that is observed at the Total Carbon Column Observing Network (TCCON) site in Tsukuba, which is located 60 km to the northeastern direction of Tokyo. The differences in XCO2 between the two sites are high (∼5 ppm) during December – February and low (∼0.5 ppm) during June – September. The characteristic variations of XCO2 in Tokyo are interpreted in terms of local emission sources and surface meteorological data, by referring to the variations in the concentrations of surface CO2. The sharp peaks in XCO2 at both the Tokyo and Tsukuba sites in July are interpreted in terms of local air retention in the areas, as indicated by the aircraft profiles and wind-flow forward trajectory calculations. Finally, we compare the ground-based column measurements with the top-down satellite column observations.

Linking improvements in sulfur dioxide emissions to decreasing sulfate wet deposition by combining satellite and surface observations with trajectory analysis

Sulfur dioxide (SO2), a criteria pollutant, and sulfate (SO42−) deposition are major environmental concerns in the eastern U.S. and both have been on the decline for two decades. In this study, we use satellite column SO2 data from the Ozone Monitoring Instrument (OMI), and SO42− wet deposition data from the NADP (National Atmospheric Deposition Program) to investigate the temporal and spatial relationship between trends in SO2 emissions and the downward sulfate wet deposition over the eastern U.S. from 2005 to 2015. To establish the relationship between SO2 emission sources and receptor sites, we conducted a Potential Source Contribution Function (PSCF) analysis using HYSPLIT back trajectories for five selected Air Quality System (AQS) sites - (Hackney, OH, Akron, OH, South Fayette, PA, Wilmington, DE, and Beltsville, MD) - in close proximity to NADP sites with large downward SO42− trends since 2005. Back trajectories were run for three summers (JJA) and three winters (DJF) and used to generate seasonal climatology PSCFs for each site. The OMI SO2 and interpolated NADP sulfate deposition trends were normalized and overlapped with the PSCF, to identify the areas that had the highest contribution to the observed drop. The results suggest that emission reductions along the Ohio River Valley have led to decreases in sulfate deposition in eastern OH and western PA (Hackney, Akron and South Fayette). Farther to the east, emission reductions in southeast PA resulted in improvements in sulfate deposition at Wilmington, DE, while for Beltsville, reductions in both the Ohio River Valley and nearby favorably impacted sulfate deposition. For Beltsville, sources closer than 300 km from the site contribute roughly 56% observed deposition trends in winter, and 82% in summer, reflecting seasonal changes in transport pattern as well as faster oxidation and washout of sulfur in summer. This suggests that emissions and wet deposition are linked through not only the location of sources relative to the observing sites, but also to photochemistry and the weather patterns characteristic to the region, as evidenced by a west to east shift in the contribution between winter and summer. The method developed here is applicable to other regions with significant trends such as China and India, and can be used to estimate the potential benefits of emission reduction in those areas.

A Global Synthesis Inversion Analysis of Recent Variability in CO2 Fluxes Using GOSAT and In Situ Observations.

The precise contribution of the two major sinks for anthropogenic CO2 emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 are expected to benefit from the increasing measurement density brought by recent in situ and remote CO2 observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO2 retrievals to deduce the global distributions of natural CO2 fluxes during 2009-2010. Our objectives include evaluating bottom-up prior flux estimates, assessing the value added by the satellite data, and examining the impacts of inversion technique and assumptions on posterior fluxes and uncertainties. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and high-latitude ocean. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, spatial and temporal gaps in sampling still limit the ability to accurately resolve fluxes down to the sub-continental scale. The GOSAT inversion produces a shift in the global CO2 sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ~2 Pg C y-1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO2 distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO2 uptake in summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010-2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias estimation of flux interannual variability in this region.

Evaluation of the performance of Suomi-NPP OMPS nadir mapper products using station measurements and OMI data

Satellite remote sensing technology provides the only viable means for global monitoring of atmosphere systems, such as ozone. The ozone mapping and profiler suite (OMPS) onboard Suomi-NPP satellite, which was launched in the year 2011, has a primary purpose of measuring ozone. Suomi-NPP has been on operation for more than 3 years, and it is crucial to keep the satellite data precise and trusted. By using 17 months of satellite and ground-based total ozone column (TOC) data, this study performs an evaluation of OMPS products. Ozone monitoring instrument (OMI) data generated from a similar satellite instrument were also used to compare with the OMPS TOC data, and both the TOC products were generated using TOMS version 8.5 (TOMS-V8.5) algorithm. The evaluation consists of intercomparisons with ground-based Brewer measurements, similar satellite instruments, and accuracy analysis as a function of time and solar zenith angle. Results show that after 3 years of operation, OMPS-derived TOC data still have good correlation (R2 > 0.99, RMSE = 1.51 % ) with ground-based measurements. The results also give some evidence that the OMPS TOC data have better accuracy than those from OMI using the same algorithm.

A Conservative Downscaling of Satellite-Detected Chemical Compositions: NO2 Column Densities of OMI, GOME-2, and CMAQ

A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency’s (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies.

Changes in the Response of the Northern Hemisphere Carbon Uptake to Temperature Over the Last Three Decades

AbstractThe CO2 seasonal cycle amplitude (SCA) in the Northern Hemisphere has increased since the 1960s—a feature attributed mainly to enhanced vegetation activity along climate warming and CO2 increase. We identified a temporal change in the sign of the correlation between SCA and air temperature (T) from positive to negative around the year 2000 at most Northern Hemisphere ground stations, consistent with signals from satellite column CO2 measurements since the mid‐2000s. Further, we explored potential causes of this change using net biome productivity estimates from three atmospheric inversions for the period 1980–2015. The change in the SCA‐T relationship is primarily attributable to changes in the net biome productivity‐T relationship: positive correlations weakened in the spring in the high latitudes, confirming a limit to the “warmer spring‐bigger carbon sink” mechanism; negative correlations diminished in the autumn/winter in the mid‐to‐high latitudes, challenging the “warmer winter‐larger carbon release” assumption and highlighting the complexity of carbon processes outside the peak growing season.

Assessment of total columnar ozone climatological trends over the Indian sub-continent

ABSTRACTIn the present study, long term satellite and Dobson spectrophotometer Total Column Ozone (TCO) data have been used to study the interannual variability and also to assess climatological trends in TCO over different geographical locations of Indian sub-continent. TCO data were analyzed for the period 1957 to 2015 over New Delhi (28.63° N, 77.18° E), Varanasi (25.30° N, 83.02° E), Pune (18.53° N, 73.84° E) and Kodaikanal (10.0° N, 77.47° E). An extensive validation was performed for Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) retrieved TCO data independently with Dobson Spectrophotometer TCO measurements over New Delhi, Varanasi, Pune and Kodaikanal. The results of this exercise showed good correlation coefficient (r) of 0.87 (0.88), 0.84 (0.82), 0.91 (0.80) and 0.84 (Data not available) respectively. Climatological mean TCO over New Delhi, Varanasi, Pune and Kodaikanal are 275.02 ± 6.44 DU, 269.03 ± 7.34 DU, 260.78 ± 5.07 DU and 258.71 ± 6.36 DU respectively for the period 1957 to 2015. An increasing trend over New Delhi (0.20 DU year–1), Pune (0.18 DU year–1), Kodaikanal (0.14 DU year–1) and decreasing trend over Varanasi (0.01 DU year–1) were observed. High significance of TCO trend was found at New Delhi (p-value < 0.0001), Pune (p-value = 0.002) and Kodaikanal (p-value = 0.003) with negligible trend over Varanasi with p-value of 0.84. The TCO variations at different geographical locations associated with upper atmospheric meteorological parameters such as lower Stratospheric Temperature (ST) at 65 hPa and Tropopause Height (TH) were also addressed. Annual lower stratospheric temperature shows positive relationship with TCO and Stratospheric ozone over the study sites. Further, decadal variability in TCO with respect to solar activity at New Delhi was also analyzed.