Simon Whitburn

Worldwide spatiotemporal atmospheric ammonia (NH3) columns variability revealed by satellite

We exploit six years of measurements from the IASI/MetOp-A instrument to identify seasonal patterns and inter-annual variability of atmospheric NH 3 . This is achieved by analyzing the time evolution of the monthly-mean NH 3 columns in 12 subcontinental areas around the world, simultaneously considering measurements from IASI morning and evening overpasses. For most regions, IASI has a sufficient sensitivity throughout the years to capture the seasonal patterns of NH 3 columns, and we show that each region is characterized by a well-marked and distinctive cycle, with maxima mainly related to underlying emission processes. The largest column abundances and seasonal amplitudes throughout the years are found in Southwestern Asia,with maxima twice as large as what is observed in Southeastern China. The relation between emission sources and retrieved NH 3 columns is emphasized at smaller regional scale by inferring a climatology of the month of maximum columns.

A flexible and robust neural network IASI‐NH3 retrieval algorithm

In this paper, we describe a new flexible and robust NH3 retrieval algorithm from measurements of the Infrared Atmospheric Sounding Interferometer (IASI). The method is based on the calculation of a spectral hyperspectral range index (HRI) and subsequent conversion to NH3 columns via a neural network. It is an extension of the method presented in Van Damme et al. (2014a) who used lookup tables (LUT) for the radiance-concentration conversion. The new method inherits the advantages of the LUT-based method while providing several significant improvements. These include the following: (1) Complete temperature and humidity vertical profiles can be accounted for. (2) Third-party NH3 vertical profile information can be used. (3) Reported positive biases of LUT retrieval are reduced, and finally (4) a full measurement uncertainty characterization is provided. A running theme in this study, related to item (2), is the importance of the assumed vertical NH3 profile. We demonstrate the advantages of allowing variable profile shapes in the retrieval. As an example, we analyze how the retrievals change when all NH3 is assumed to be confined to the boundary layer. We analyze different averaging procedures in use for NH3 in the literature, introduced to cope with the variable measurement sensitivity and derive global averaged distributions for the year 2013. A comparison with a GEOS-Chem modeled global distribution is also presented, showing a general good correspondence (within ±3 × 1015 molecules.cm−2) over most of the Northern Hemisphere. However, IASI finds mean columns about 1–1.5 × 1016 molecules.cm−2 (∼50–60%) lower than GEOS-Chem for India and the North China plain.

Doubling of annual ammonia emissions from the peat fires in Indonesia during the 2015 El Niño

In the autumn of 2015, thousands of square kilometers of forest and peatlands in Indonesia went up in flames. Among the primary species emitted by fires, ammonia (NH 3 ) is of special relevance for air quality. Here we derive daily and total NH 3 emission fluxes over the affected area using satellite measurements for the years 2008-2015. The 2015 fires emitted an estimated 1.4-8.2 Tg of NH 3 (with a maximum of 0.06-0.33 Tg day −1 ). On an annual basis, the 2015 NH 3 emissions are a factor 2-3 larger than in the previous seven years. We derive NH 3 emission factors for peat soils, which are found to be 2.5-8 times lower than those used in the GFASv1.2 emission inventory, but in excellent agreement with those reported in other recent studies. Finally, we estimate that 3.28×10 9 m 3 peat soil was consumed during these 2015 fires, corresponding to an average burn depth of 39 cm.In the autumn of 2015, thousands of square kilometers of forest and peatlands in Indonesia went up in flames. Among the primary species emitted by fires, ammonia (NH 3 ) is of special relevance for air quality. Here we derive daily and total NH 3 emission fluxes over the affected area using satellite measurements for the years 2008–2015. The 2015 fires emitted an estimated 1.4–8.2 Tg of NH 3 (with a maximum of 0.06–0.33 Tg d −1 ). On an annual basis, the 2015 NH 3 emissions are a factor 2–3 larger than in the previous 7 years. We derive NH 3 emission factors for peat soils, which are found to be 2.5–8 times lower than those used in the GFASv1.2 emission inventory but in excellent agreement with those reported in other recent studies. Finally, we estimate that 3.28 × 109 m 3 peat soil was consumed during these 2015 fires, corresponding to an average burn depth of 39 cm.

Ammonia Emissions May Be Substantially Underestimated in China

China is a global hotspot of atmospheric ammonia (NH3) emissions and, as a consequence, very high nitrogen (N) deposition levels are documented. However, previous estimates of total NH3 emissions in China were much lower than inference from observed deposition values would suggest, highlighting the need for further investigation. Here, we reevaluated NH3 emissions based on a mass balance approach, validated by N deposition monitoring and satellite observations, for China for the period of 2000 to 2015. Total NH3 emissions in China increased from 12.1 ± 0.8 Tg N yr-1 in 2000 to 15.6 ± 0.9 Tg N yr-1 in 2015 at an annual rate of 1.9%, which is approximately 40% higher than existing studies suggested. This difference is mainly due to more emission sources now having been included and NH3 emission rates from mineral fertilizer application and livestock having been underestimated previously. Our estimated NH3 emission levels are consistent with the measured deposition of NHx (including NH4+ and NH3) on land (11-14 Tg N yr-1) and the substantial increases in NH3 concentrations observed by satellite measurements over China. These findings substantially improve our understanding on NH3 emissions, implying that future air pollution control strategies have to consider the potentials of reducing NH3 emission in China.

Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-based measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), and ozone (O3)), aerosol optical properties (aerosol optical depth (AOD) and Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI) and temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD × AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatial variability in skill of simple generalized nucleation schemes in reproducing observed NPF. In contrast to more simple proxies developed in prior studies (e.g., based on AOD, AE, SO2, and UV), use of additional predictors (NO2, NH3, HCHO, LAI, T, and O3) increases the explained temporal variance of UFP concentrations at all sites.

Validation of mobile in situ measurements of dairy husbandry emissions by fusion of airborne/surface remote sensing with seasonal context from the Chino Dairy Complex

Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context. For the Chino dairies, MISTIR-AMOG ammonia (NH3) agreement was within 5% (15.7 versus 14.9 Gg yr-1, respectively) using all information. Methane (CH4) emissions were 30 Gg yr-1 for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported. Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH4, NH3, and CO2 emissions were 91, 209, and 8200 Mg yr-1, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion. NH3 downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

Observation of Air Pollution over China Using the IASI Thermal Infrared Space Sensor

In this chapter we describe what is achievable in terms of pollutant tracking from space using observations provided by thermal infrared remote sensors. After a general introduction on infrared remote sensing, we exploit the data provided by the Infrared Atmospheric Sounding Interferometer (IASI) missions onboard the Metop series of satellite to illustrate pollution detection at various spatial and temporal scales. Then, we focus on air pollution over China and discuss three case studies involving different pollutants. The first example discusses the geophysical conditions for detection of ammonia (NH3) and sulfur dioxide (SO2), both precursors of particulate matter (PM). The second case illustrates the seasonal variation of ozone (O3), in particular during the monsoon period. The third case shows the local accumulation of enhanced levels of carbon monoxide (CO) when pollution episodes occur.