A decadal atmospheric ammonia reanalysis product in China

Atmospheric ammonia has great environmental implications due to its important role in ecosystem and nitrogen cycle, as well as contribution to formation of secondary particles. China is recognized as a hotspot of NH3 pollution owing to agricultural and livestock intensification. In the quest to achieve a comprehensive understanding of atmospheric ammonia load and to quantify its environmental impacts in China, relying solely either on existing measurements or on model simulations falls short. Their limitations, either in spatial coverage and integrity or in data quality, fails to meet the needs. Available reanalysis products exhibit a marked deficiency in ammonia data. We therefore aim to propose an integrated ammonia reanalysis product in China, adeptly melding satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) NH3 retrievals with chemical transport model simulation, capitalizing on the robust Ensemble Kalman Filter (EnKF) data assimilation methodology. The product is validated in high quality via the comparison against independent measurements from ground monitoring stations. Spanning a decade from 2013 to 2022, our reanalysis uncovers not just the spatial intricacies of NH3 concentrations but also their temporal dynamics. Our findings pinpointed the spatial disparities in atmospheric ammonia intensities, highlighting regional hotspots in the NCP, SCB, and Northeast China, and identified annual and seasonal patterns. Our research provides crucial insights for shaping future NH3 pollution prevention and control strategies in China.

Atmospheric ammonia in China: Long-term spatiotemporal variation, urban-rural gradient, and influencing factors

In recent years, atmospheric ammonia (NH₃) concentrations have increased in China. Ammonia control has become one of the next hot topics in air pollution mitigation with the increasing cost of acid gas emission reduction. In this study, using Infrared Atmospheric Sounding Interferometer (IASI) satellite observations, we analyzed the spatiotemporal distribution, the urban-rural gradient of the vertical column densities (VCDs) of NH₃ and the contribution of influencing factors (meteorology, social, atmospheric acid gases, and NH₃ emissions) in China from 2008 to 2019 using hotspot analysis, circular gradient analysis, geographical and temporal weighted regression, and some other methods. Our results showed that NH₃ VCDs in China have significantly increased (31.88 %) from 2008 to 2019, with the highest occurring in North China Plain. The average NH₃ VCDs in urban areas were significantly higher than those in rural areas, and the urban-rural gap in NH₃ VCDs was widening. The results of circular gradient analysis showed an overall decreasing trend in NH₃ VCDs along the urban-rural gradient. We used a geographically and temporally weighted regression model to analyze the contribution of various influencing factors to NH₃ VCDs: meteorology (30.13 %), social (27.40 %), atmospheric acid gases (23.20 %), and NH₃ emissions (19.28 %) factors. The results showed substantial spatiotemporal differences in the influencing factors. Atmospheric acid gas was the main reason for the increase in NH₃ VCDs from 2008 to 2019. A more thorough understanding of the spatiotemporal distribution, urban-rural variations, and factors influencing NH₃ in China will aid in developing control strategies to reduce PM_2.5.

Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data

Several anthropogenic activities have undergone major changes following the spread of the COVID-19 pandemic, which in turn has had consequences on the environment. The effect on air pollution has been studied in detail in the literature, although some pollutants, such as ammonia (NH₃), have received comparatively less attention to date. Focusing on the case of Lombardy in Northern Italy, this study aimed to evaluate changes in NH₃ atmospheric concentration on a temporal scale (the years from 2013 to 2019 compared to 2020) and on a spatial scale (countryside, city, and mountain areas). For this purpose, ground-based (from public air quality control units scattered throughout the region) and satellite observations (from IASI sensors on board MetOp-A and MetOp-B) were collected and analyzed. For ground-based measurements, a marked spatial variability is observed between the different areas while, as regards the comparison between periods, statistically significant differences were observed only for the countryside areas (+31% in 2020 compared to previous years). The satellite data show similar patterns but do not present statistically significant differences neither between different areas, nor between the two periods. In general, there have been no reduction effects of atmospheric NH₃ as a consequence of COVID-19. This calls into question the role of the agricultural sector, which is known to be the largest responsible for NH₃ emissions. Even if the direct comparison between the two datasets shows little correlation, their contextual consideration allows making more robust considerations regarding air pollutants.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long-term homogeneous satellite-derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level-1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT-released IASI level-2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Record high levels of atmospheric ammonia over India: Spatial and temporal analyses

Atmospheric ammonia (NH₃) is an alkaline gas and a prominent constituent of the nitrogen cycle that adversely affects ecosystems at higher concentrations. It is a pollutant, which influences all three spheres such as haze formation in the atmosphere, soil acidification in the lithosphere, and eutrophication in water bodies. Atmospheric NH₃ reacts with sulfur (SO_x) and nitrogen (NO_x) oxides to form aerosols, which eventually affect human health and climate. Here, we present the seasonal and inter-annual variability of atmospheric NH₃ over India in 2008-2016 using the IASI (Infrared Atmospheric Sounding Interferometer) satellite observations. We find that Indo-Gangetic Plains (IGP) is one of the largest and rapidly growing NH₃ hotspots of the world, with a growth rate of +1.2% yr^-1 in summer (June-August: Kharif season), due to intense agricultural activities and presence of many fertilizer industries there. However, our analyses show insignificant decreasing trends in annual NH₃ of about -0.8% yr^-1 in all India, about -0.4% yr^-1 in IGP, and -1.0% yr^-1 in the rest of India. Ammonia is positively correlated with total fertilizer consumption (r = 0.75) and temperature (r = 0.5) since high temperature favors volatilization, and is anti-correlated with total precipitation (r = from -0.2, but -0.8 in the Rabi season: October-February) as wet deposition helps removal of atmospheric NH₃. This study, henceforth, suggests the need for better fertilization practices and viable strategies to curb emissions, to alleviate the adverse health effects and negative impacts on the ecosystem in the region. On the other hand, the overall decreasing trend in atmospheric NH₃ over India shows the positive actions, and commitment to the national missions and action plans to reduce atmospheric pollution and changes in climate.

Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative solutions to such RTM inversion problems. This is in large part due to the confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.

Estimating exposure to hydrogen sulfide from animal husbandry operations using satellite ammonia as a proxy: Methodology demonstration

Husbandry trace gases that have climate change implications such as carbon dioxide (CO₂), methane (CH₄) and ammonia (NH₃) can be quantified through remote sensing; however, many husbandry gases with health implications such as hydrogen sulfide (H₂S), cannot. This pilot study demonstrates an approach to derive H₂S concentrations by coupling in situ and remote sensing data. Using AMOG (AutoMObile trace Gas) Surveyor, a mobile air quality and meteorology laboratory, we measured in situ concentrations of CH₄, CO₂, NH₃, H₂S, and wind at a southern California university research dairy. Emissions were 0.13, 1.93, 0.022 and 0.0064 Gg yr^-1; emission factors (E_F) were 422, 6333, 74, and 21 kg cow^-1 yr^-1, respectively, for the 306 head herd. Contributing to these strong E_F were spillway emissions from a grate between the main cowshed and the waste lagoon identified in airborne remote sensing data acquired by the hyperspectral thermal infrared imager, Mako. NH₃ emissions from the Chino Dairy Complex, also in southern California, were calculated from Infrared Atmospheric Sounding Interferometer (IASI) satellite data for 2008-2017 using average morning winds, yielding a flushing time of 2.7 h, and 8.9 Gg yr^-1. The ratio of E_F(H₂S) to E_F(NH₃) for the research dairy from AMOG data were applied to IASI NH₃ emissions to derive H₂S exposure concentration maps for the Chino area, which ranged to 10-30 ppb H₂S for many populated areas. Combining remote sensing with in situ concentrations of multiple emitted gases can allow derivation of emissions at the sub-facility, facility, and larger scales, providing spatial and temporal coverage that can translate into exposure estimates for use in epidemiology studies and regulation development. Furthermore, with high fidelity information at the sub-facility level we can identify best practices and opportunities to sustainably and holistically reduce husbandry emissions.

Remote sensing and in situ measurements of methane and ammonia emissions from a megacity dairy complex: Chino, CA

Methane (CH₄) and ammonia (NH₃) directly and indirectly affect the atmospheric radiative balance with the latter leading to aerosol generation. Both have important spectral features in the Thermal InfraRed (TIR) that can be studied by remote sensing, with NH₃ allowing discrimination of husbandry from other CH₄ sources. Airborne hyperspectral imagery was collected for the Chino Dairy Complex in the Los Angeles Basin as well as in situ CH₄, carbon dioxide (CO₂) and NH₃ data. TIR data showed good spatial agreement with in situ measurements and showed significant emissions heterogeneity between dairies. Airborne remote sensing mapped plume transport for ∼20 km downwind, documenting topographic effects on plume advection. Repeated multiple gas in situ measurements showed that emissions were persistent on half-year timescales. Inversion of one dairy plume found annual emissions of 4.1 × 10⁵ kg CH₄, 2.2 × 10⁵ kg NH₃, and 2.3 × 10⁷ kg CO₂, suggesting 2300, 4000, and 2100 head of cattle, respectively, and Chino Dairy Complex emissions of 42 Gg CH₄ and 8.4 Gg NH₃ implying ∼200k cows, ∼30% more than Peischl et al. (2013) estimated for June 2010. Far-field data showed chemical conversion and/or deposition of Chino NH₃ occurs within the confines of the Los Angeles Basin on a four to six h timescale, faster than most published rates, and likely from higher Los Angeles oxidant loads. Satellite observations from 2011 to 2014 confirmed that observed in situ transport patterns were representative and suggests much of the Chino Dairy Complex emissions are driven towards eastern Orange County, with a lesser amount transported to Palm Springs, CA. Given interest in mitigating husbandry health impacts from air pollution emissions, this study highlights how satellite observations can be leveraged to understand exposure and how multiple gas in situ emissions studies can inform on best practices given that emissions reduction of one gas could increase those of others.

A Multi-Channel Method for Retrieving Surface Temperature for High-Emissivity Surfaces from Hyperspectral Thermal Infrared Images

The surface temperature (ST) of high-emissivity surfaces is an important parameter in climate systems. The empirical methods for retrieving ST for high-emissivity surfaces from hyperspectral thermal infrared (HypTIR) images require spectrally continuous channel data. This paper aims to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR data. With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800–1200 cm⁻¹ and a spectral sampling frequency of 0.25 cm⁻¹. We have analyzed the sensitivity of the proposed method to spectral sampling frequency and instrumental noise, and evaluated the proposed method using satellite data. The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency. The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product.