Global atmospheric emissions of 16 polycyclic aromatic hydrocarbons (PAHs) from 69 major sources were estimated for a period from 1960 to 2030. Regression models and a technology split method were used to estimate country and time specific emission factors, resulting in a new estimate of PAH emission factor variation among different countries and over time. PAH emissions in 2007 were spatially resolved to 0.1°× 0.1° grids based on a newly developed global high-resolution fuel combustion inventory (PKU-FUEL-2007). The global total annual atmospheric emission of 16 PAHs in 2007 was 504 Gg (331-818 Gg, as interquartile range), with residential/commercial biomass burning (60.5%), open-field biomass burning (agricultural waste burning, deforestation, and wildfire, 13.6%), and petroleum consumption by on-road motor vehicles (12.8%) as the major sources. South (87 Gg), East (111 Gg), and Southeast Asia (52 Gg) were the regions with the highest PAH emission densities, contributing half of the global total PAH emissions. Among the global total PAH emissions, 6.19% of the emissions were in the form of high molecular weight carcinogenic compounds and the percentage of the carcinogenic PAHs was higher in developing countries (6.22%) than in developed countries (5.73%), due to the differences in energy structures and the disparities of technology. The potential health impact of the PAH emissions was greatest in the parts of the world with high anthropogenic PAH emissions, because of the overlap of the high emissions and high population densities. Global total PAH emissions peaked at 592 Gg in 1995 and declined gradually to 499 Gg in 2008. Total PAH emissions from developed countries peaked at 122 Gg in the early 1970s and decreased to 38 Gg in 2008. Simulation of PAH emissions from 2009 to 2030 revealed that PAH emissions in developed and developing countries would decrease by 46-71% and 48-64%, respectively, based on the six IPCC SRES scenarios.

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O 2 ) Aband at 0.765 microns and the carbon dioxide (CO 2 ) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO 2 dry-air mole fraction, XCO 2 . Variations of XCO 2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO 2 . This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO 2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO 2 distribution could be misinterpreted as evidence for CO 2 fluxes.To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s −1 across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO 2 . Each of these soundings has an unprecedented combination of spatial resolution (< 3 km 2 /sounding), spectral resolving power (λ/ λ > 17 000), dynamic range (∼ 10 4 ), and sensitivity (continuum signal-to-noise ratio > 400).The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

Abstract. Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100-1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.

Ammoniated aerosols are important for urban air quality, but emissions of the key precursor NH are not well quantified. Mobile laboratory observations are used to characterize fleet-integrated NH emissions in six cities in the U.S. and China. Vehicle NH:CO emission ratios in the U.S. are similar between cities (0.33-0.40 ppbv/ppmv, 15% uncertainty) despite differences in fleet composition, climate, and fuel composition. While Beijing, China has a comparable emission ratio (0.36 ppbv/ppmv) to the U.S. cities, less developed Chinese cities show higher emission ratios (0.44 and 0.55 ppbv/ppmv). If the vehicle CO inventories are accurate, NH emissions from U.S. vehicles (0.26 ± 0.07 Tg/yr) are more than twice those of the National Emission Inventory (0.12 Tg/yr), while Chinese NH vehicle emissions (0.09 ± 0.02 Tg/yr) are similar to a bottom-up inventory. Vehicle NH emissions are greater than agricultural emissions in counties containing near half of the U.S. population and require reconsideration in urban air quality models due to their colocation with other aerosol precursors and the uncertainties regarding NH losses from upwind agricultural sources. Ammonia emissions in developing cities are especially important because of their high emission ratios and rapid motorizations.

Simultaneous measurements of aerosol size, distribution of number, mass, and chemical compositions were conducted in the winter of 2007 in Beijing using a Twin Differential Mobility Particle Sizer and a Micro Orifice Uniform Deposit Impactor. Both material density and effective density of ambient particles were estimated to be 1.61 ± 0.13 g cm(-3) and 1.62 ± 0.38 g cm(-3) for PM(1.8) and 1.73 ± 0.14 g cm(-3) and 1.67 ± 0.37 g cm(-3) for PM(10). Effective density decreased in the nighttime, indicating the primary particles emission from coal burning influenced the density of ambient particles. Size-resolved material density and effective density showed that both values increased with diameter from about 1.5 g cm(-3) at the size of 0.1 μm to above 2.0 g cm(-3) in the coarse mode. Material density was significantly higher for particles between 0.56 and 1.8 μm during clean episodes. Dynamic Shape Factors varied within the range of 0.95-1.13 and decreased with particle size, indicating that coagulation and atmospheric aging processes may change the shape of particles.

A novel strategy has been developed for analysis of wavelength-scanned, wavelength modulation spectroscopy (WMS) with tunable diode lasers (TDLs). The method simulates WMS signals to compare with measurements to determine gas properties (e.g., temperature, pressure and concentration of the absorbing species). Injection-current-tuned TDLs have simultaneous wavelength and intensity variation, which severely complicates the Fourier expansion of the simulated WMS signal into harmonics of the modulation frequency (fm). The new method differs from previous WMS analysis strategies in two significant ways: (1) the measured laser intensity is used to simulate the transmitted laser intensity and (2) digital lock-in and low-pass filter software is used to expand both simulated and measured transmitted laser intensities into harmonics of the modulation frequency, WMS-nfm (n = 1, 2, 3,…), avoiding the need for an analytic model of intensity modulation or Fourier expansion of the simulated WMS harmonics. This analysis scheme is valid at any optical depth, modulation index, and at all values of scanned-laser wavelength. The method is demonstrated and validated with WMS of H2O dilute in air (1 atm, 296 K, near 1392 nm). WMS-nfm harmonics for n = 1 to 6 are extracted and the simulation and measurements are found in good agreement for the entire WMS lineshape. The use of 1f-normalization strategies to realize calibration-free wavelength-scanned WMS is also discussed.

Abstract. Hydroxyl (OH) and peroxy radicals (HO2 and RO2) were measured in the Pearl River Delta, which is one of the most polluted areas in China, in autumn 2014. The radical observations were complemented by measurements of OH reactivity (inverse OH lifetime) and a comprehensive set of trace gases including carbon monoxide (CO), nitrogen oxides (NOx=NO, NO2) and volatile organic compounds (VOCs). OH reactivity was in the range from 15 to 80 s−1, of which about 50 % was unexplained by the measured OH reactants. In the 3 weeks of the campaign, maximum median radical concentrations were 4.5×106 cm−3 for OH at noon and 3×108 and 2.0×108 cm−3 for HO2 and RO2, respectively, in the early afternoon. The completeness of the daytime radical measurements made it possible to carry out experimental budget analyses for all radicals (OH, HO2, and RO2) and their sum (ROx). The maximum loss rates for OH, HO2, and RO2 reached values between 10 and 15 ppbv h−1 during the daytime. The largest fraction of this can be attributed to radical interconversion reactions while the real loss rate of ROx remained below 3 ppbv h−1. Within experimental uncertainties, the destruction rates of HO2 and the sum of OH, HO2, and RO2 are balanced by their respective production rates. In case of RO2, the budget could be closed by attributing the missing OH reactivity to unmeasured VOCs. Thus, the presumption of the existence of unmeasured VOCs is supported by RO2 measurements. Although the closure of the RO2 budget is greatly improved by the additional unmeasured VOCs, a significant imbalance in the afternoon remains, indicating a missing RO2 sink. In case of OH, the destruction in the morning is compensated by the quantified OH sources from photolysis (HONO and O3), ozonolysis of alkenes, and OH recycling (HO2+NO). In the afternoon, however, the OH budget indicates a missing OH source of 4 to 6 ppbv h−1. The diurnal variation of the missing OH source shows a similar pattern to that of the missing RO2 sink so that both largely compensate each other in the ROx budget. These observations suggest the existence of a chemical mechanism that converts RO2 to OH without the involvement of NO, increasing the RO2 loss rate during the daytime from 5.3 to 7.4 ppbv h−1 on average. The photochemical net ozone production rate calculated from the reaction of HO2 and RO2 with NO yields a daily integrated amount of 102 ppbv ozone, with daily integrated ROx primary sources being 22 ppbv in this campaign. The produced ozone can be attributed to the oxidation of measured (18 %) and unmeasured (60 %) hydrocarbons, formaldehyde (14 %), and CO (8 %). An even larger integrated net ozone production of 140 ppbv would be calculated from the oxidation rate of VOCs with OH if HO2 and all RO2 radicals react with NO. However, the unknown RO2 loss (evident in the RO2 budget) causes 30 ppbv less ozone production than would be expected from the VOC oxidation rate.

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.