Bottom-up Emission Research Articles

Assessment of a high-resolution NOX emission inventory using satellite observations: A case study of southern Jiangsu, China

To evaluate the bottom-up NOX emission inventories with different data sources and spatial scales, an updated product of tropospheric NO2 vertical column densities (VCDs) from Ozone Monitoring Instrument (OMI), POMINO, was applied in chemistry transport modeling (CTM) and Gaussian function model for southern Jiangsu, a typical developed and polluted region in eastern China. Compared to the national emission inventory (MEIC), better correlation was found between spatial distributions of the high-resolution provincial inventory (JS) and POMINO VCDs. When applied in CTM, the simulated VCDs using JS were closer to POMINO data than those using MEIC, indicating the advantage of the provincial inventory that incorporated detailed information of individual plants. The simulated VCDs, however, were generally larger than observed ones, particularly for regions with high NO2 levels, partly because the improved NOX control measures for power sector were not fully considered in both national and provincial inventories. The “top-down” NOX emissions were estimated for four cities/city combinations in southern Jiangsu, using a Gaussian function model based on POMINO NO2 VCDs. The results were found to be most consistent with the estimates in JS among bottom-up inventories with different data sources. To further harmonize emissions and satellite observations at relatively small spatial scale, the on-line emission measurement data for individual plants are recommended for emission inventory development, and the products of satellite observation data with finer horizontal resolution are encouraged.

Anthropogenic and biogenic CO2 fluxes in the Boston urban region.

With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model-measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m-2·y-1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model-measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Bottom-up anthropogenic dichloromethane emission estimates from China for the period 2005–2016 and predictions of future emissions

It has been shown that the short-lived halogenated hydrocarbons, such as dichloromethane (DCM), which are not regulated by the Montreal Protocol, have the potential to impact stratospheric ozone. DCM with increasing atmospheric mixing ratios in recent years, is widely used in solvent, foam-blowing and methyl fluoride production sectors in China, which is a large producer as well as consumer of DCM. This study develops a comprehensive emission inventory of anthropogenic DCM in China using a bottom-up method based on the industrial consumption of DCM in China from 2005 to 2016 and makes a projection to 2030. The inventory considered solvent use, polyurethane (PU) foam manufacturing, the chemical industry, and other end uses. Anthropogenic emissions of DCM increased from 101 (80–122) Gg/yr to 318 (254–384) Gg/yr between 2005 and 2016, a increase of 215% over the last 11 years. Solvent use and the manufacture of PU foam agents are the major contributors to total DCM emissions. Under a business as usual (BAU) scenario future Chinese emissions of DCM are predicted to increase to 708 (588–831) Gg/yr by 2030, more than twice the emission IHS, 2016. Chinese emission account for approximately 25%–37% of the 515 Gg/yr estimates by the WMO as average annual global emissions in 2008.

Current and future hot-spots and hot-moments of nitrous oxide emission in a cold climate river basin

An ecosystem in a cold climate river basin is vulnerable to the effects of climate change affecting permafrost thaw and glacier retreat. We currently lack sufficient data and information if and how hydrological processes such as glacier retreat, snowmelt and freezing-thawing affect sediment and nutrient runoff and transport, as well as N2O emissions in cold climate river basins. As such, we have implemented well-established, semi-empirical equations of nitrification and denitrification within the Soil and Water Assessment Tool (SWAT), which correlate the emissions with water, sediment and nutrients. We have tested this implementation to simulate emission dynamics at three sites on the Canadian prairies. We then regionalized the optimized parameters to a SWAT model of the Athabasca River Basin (ARB), Canada, calibrated and validated for streamflow, sediment and water quality. In the base period (1990–2005), agricultural areas (2662 gN/ha/yr) constituted emission hot-spots. The spring season in agricultural areas and summer season in forest areas, constituted emission hot-moments. We found that warmer conditions (+13% to +106%) would have a greater influence on emissions than wetter conditions (−19% to +13%), and that the combined effect of wetter and warmer conditions would be more offsetting than synergetic. Our results imply that the spatiotemporal variability of N2O emissions will depend strongly on soil water changes caused by permafrost thaw. Early snow freshet leads to spatial variability of soil erosion and nutrient runoff, as well as increases of emissions in winter and decreases in spring. Our simulations suggest crop residue management may reduce emissions by 34%, but with the mixed results reported in the literature and the soil and hydrology problems associated with stover removal more research is necessary. This modelling tool can be used to refine bottom-up emission estimations at river basin scale, test plausible management scenarios, and assess climate change impacts including climate feedback.

Study of the daily and seasonal atmospheric CH<sub>4</sub> mixing ratio variability in a rural Spanish region using <sup>222</sup>Rn tracer

Abstract. The ClimaDat station at Gredos (GIC3) has been continuously measuring atmospheric (dry air) mixing ratios of carbon dioxide (CO2) and methane (CH4), as well as meteorological parameters, since November 2012. In this study we investigate the atmospheric variability of CH4 mixing ratios between 2013 and 2015 at GIC3 with the help of co-located observations of 222Rn concentrations, modelled 222Rn fluxes and modelled planetary boundary layer heights (PBLHs). Both daily and seasonal changes in atmospheric CH4 can be better understood with the help of atmospheric concentrations of 222Rn (and the corresponding fluxes). On a daily timescale, the variation in the PBLH is the main driver for 222Rn and CH4 variability while, on monthly timescales, their atmospheric variability seems to depend on emission changes. To understand (changing) CH4 emissions, nocturnal fluxes of CH4 were estimated using two methods: the radon tracer method (RTM) and a method based on the EDGARv4.2 bottom-up emission inventory, both using FLEXPARTv9.0.2 footprints. The mean value of RTM-based methane fluxes (FR_CH4) is 0.11 mg CH4 m−2 h−1 with a standard deviation of 0.09 or 0.29 mg CH4 m−2 h−1 with a standard deviation of 0.23 mg CH4 m−2 h−1 when using a rescaled 222Rn map (FR_CH4_rescale). For our observational period, the mean value of methane fluxes based on the bottom-up inventory (FE_CH4) is 0.33 mg CH4 m−2 h−1 with a standard deviation of 0.08 mg CH4 m−2 h−1. Monthly CH4 fluxes based on RTM (both FR_CH4 and FR_CH4_rescale) show a seasonality which is not observed for monthly FE_CH4 fluxes. During January–May, RTM-based CH4 fluxes present mean values 25 % lower than during June–December. This seasonal increase in methane fluxes calculated by RTM for the GIC3 area appears to coincide with the arrival of transhumant livestock at GIC3 in the second half of the year.

The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic

The bottom-up traffic emission model EMISENS is used to calculate hourly black carbon (BC) and nitrogen oxides (NOx) emission rates on an average workday in Maribor (Slovenia), using emission factors that were previously determined with the on-road chasing measurements in Slovenia. From modeled NOx emission rates and in-situ NOx measurements we empirically determined the hourly dispersion of traffic emissions and applied it to model BC concentrations using BC emission rates. We compared the modeled BC concentrations with in-situ BC concentration measurements over three periods in winters 2011–2012 and 2012–2013, a total of 67 workdays. Measured BC concentrations were first apportioned to traffic using the top-down Aethalometer model. We found that the bottom-up modeled BC concentrations overestimated the top-down apportioned values by only 19%, 32% and 6% in each of the three investigated time periods, respectively. We did not find any influence of meteorology on the performance of the model. This is the first time that BC source apportionment results were used to evaluate traffic emissions calculated using the bottom-up modeling approach. We demonstrate that the two independent approaches yield similar results. We use thus validated emission inventory for evaluating different emission reduction scenarios. We show that excluding 10% of vehicles that are highest BC or NOx emitters would reduce the total BC or NOx emissions from traffic in Maribor by 39% and 33% respectively.

Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations

Abstract. The main aim of this paper is to update existing sulfur dioxide (SO2) emission inventories over China using modern inversion techniques, state-of-the-art chemistry transport modelling (CTM) and satellite observations of SO2. Within the framework of the EU Seventh Framework Programme (FP7) MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) project, a new SO2 emission inventory over China was calculated using the CHIMERE v2013b CTM simulations, 10 years of Ozone Monitoring Instrument (OMI)/Aura total SO2 columns and the pre-existing Multi-resolution Emission Inventory for China (MEIC v1.2). It is shown that including satellite observations in the calculations increases the current bottom-up MEIC inventory emissions for the entire domain studied (15–55° N, 102–132° E) from 26.30 to 32.60 Tg annum−1, with positive updates which are stronger in winter ( ∼ 36 % increase). New source areas were identified in the southwest (25–35° N, 100–110° E) as well as in the northeast (40–50° N, 120–130° E) of the domain studied as high SO2 levels were observed by OMI, resulting in increased emissions in the a posteriori inventory that do not appear in the original MEIC v1.2 dataset. Comparisons with the independent Emissions Database for Global Atmospheric Research, EDGAR v4.3.1, show a satisfying agreement since the EDGAR 2010 bottom-up database provides 33.30 Tg annum−1 of SO2 emissions. When studying the entire OMI/Aura time period (2005 to 2015), it was shown that the SO2 emissions remain nearly constant before the year 2010, with a drift of −0.51 ± 0.38 Tg annum−1, and show a statistically significant decline after the year 2010 of −1.64 ± 0.37 Tg annum−1 for the entire domain. Similar findings were obtained when focusing on the greater Beijing area (30–40° N, 110–120° E) with pre-2010 drifts of −0.17 ± 0.14 and post-2010 drifts of −0.47 ± 0.12 Tg annum−1. The new SO2 emission inventory is publicly available and forms part of the official EU MarcoPolo emission inventory over China, which also includes updated NOx, volatile organic compounds and particulate matter emissions.

Evaluation of modeling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope = 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Abundance and sources of atmospheric halocarbons in the Eastern Mediterranean

Abstract. A wide range of anthropogenic halocarbons is released to the atmosphere, contributing to stratospheric ozone depletion and global warming. Using measurements of atmospheric abundances for the estimation of halocarbon emissions on the global and regional scale has become an important top-down tool for emission validation in the recent past, but many populated and developing areas of the world are only poorly covered by the existing atmospheric halocarbon measurement network. Here we present 6 months of continuous halocarbon observations from Finokalia on the island of Crete in the Eastern Mediterranean. The gases measured are the hydrofluorocarbons (HFCs), HFC-134a (CH2FCF3), HFC-125 (CHF2CF3), HFC-152a (CH3CHF2) and HFC-143a (CH3CF3) and the hydrochlorofluorocarbons (HCFCs), HCFC-22 (CHClF2) and HCFC-142b (CH3CClF2). The Eastern Mediterranean is home to 250 million inhabitants, consisting of a number of developed and developing countries, for which different emission regulations exist under the Kyoto and Montreal protocols. Regional emissions of halocarbons were estimated with Lagrangian atmospheric transport simulations and a Bayesian inverse modeling system, using measurements at Finokalia in conjunction with those from Advanced Global Atmospheric Gases Experiment (AGAGE) sites at Mace Head (Ireland), Jungfraujoch (Switzerland) and Monte Cimone (Italy). Measured peak mole fractions at Finokalia showed generally smaller amplitudes for HFCs than at the European AGAGE sites except for periodic peaks of HFC-152a, indicating strong upwind sources. Higher peak mole fractions were observed for HCFCs, suggesting continued emissions from nearby developing regions such as Egypt and the Middle East. For 2013, the Eastern Mediterranean inverse emission estimates for the four analyzed HFCs and the two HCFCs were 13.9 (11.3–19.3) and 9.5 (6.8–15.1) Tg CO2eq yr−1, respectively. These emissions contributed 16.8 % (13.6–23.3 %) and 53.2 % (38.1–84.2 %) to the total inversion domain, which covers the Eastern Mediterranean as well as central and western Europe. Greek bottom-up HFC emissions reported to the UNFCCC were higher than our top-down estimates, whereas for Turkey our estimates agreed with UNFCCC-reported values for HFC-125 and HFC-143a, but were much and slightly smaller for HFC-134a and HFC-152a, respectively. Sensitivity estimates suggest an improvement of the a posteriori emission estimates, i.e., a reduction of the uncertainties by 40–80 % in the entire inversion domain, compared to an inversion using only the existing central European AGAGE observations.

Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> over China

Abstract. Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find clues to improve emission inventories. We first compared the activity rates and emission factors used in two inventories and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. The FGD (flue-gas desulfurization) device penetration rate and removal efficiency, LNB (low-NOx burner) application rate and abatement efficiency in power plants, emission factors of industrial boilers and various vehicle types, and vehicle fleet need further verification. Diesel consumptions are quite uncertain in current inventories. Discrepancies at the sectorial and provincial levels are much higher than those of the national total. We then examined the impacts of different inventories on model performance by using the nested GEOS-Chem model. We finally derived top-down emissions by using the retrieved columns from the Ozone Monitoring Instrument (OMI) compared with the bottom-up estimates. High correlations were observed for SO2 between model results and OMI columns. For NOx, negative biases in bottom-up gridded emission inventories (−21 % for MIX, −39 % for ECLIPSE) were found compared to the satellite-based emissions. The emission trends from 2005 to 2010 estimated by two inventories were both consistent with satellite observations. The inventories appear to be fit for evaluation of the policies at an aggregated or national level; more work is needed in specific areas in order to improve the accuracy and robustness of outcomes at finer spatial and also technological levels. To our knowledge, this is the first work in which source comparisons detailed to technology-level parameters are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we identified potential directions for further improvement in inventory development.

Identifying Ammonia Hotspots in China Using a National Observation Network.

The limited availability of ammonia (NH3) measurements is currently a barrier to understanding the vital role of NH3 in secondary aerosol formation during haze pollution events and prevents a full assessment of the atmospheric deposition of reactive nitrogen. The observational gaps motivated us to design this study to investigate the spatial distributions and seasonal variations in atmospheric NH3 on a national scale in China. On the basis of a 1-year observational campaign at 53 sites with uniform protocols, we confirm that abundant concentrations of NH3 [1 to 23.9 μg m-3] were identified in typical agricultural regions, especially over the North China Plain (NCP). The spatial pattern of the NH3 surface concentration was generally similar to those of the satellite column concentrations as well as a bottom-up agriculture NH3 emission inventory. However, the observed NH3 concentrations at urban and desert sites were comparable with those from agricultural sites and 2-3 times those of mountainous/forest/grassland/waterbody sites. We also found that NH3 deposition fluxes at urban sites account for only half of the emissions in the NCP, suggesting the transport of urban NH3 emissions to downwind areas. This finding provides policy makers with insights into the potential mitigation of nonagricultural NH3 sources in developed regions.

중국 대기오염물질 배출의 시공간적 변화 분석

Fast economic growth and urbanization of China have been causing air pollution not only over its domestic but transboundary atmosphere. Recent high fine particle pollution episodes in China made the government move toward more stringent air pollution control policies - which are mostly fuel switching and emissions control. In this research, we tried to understand characteristics of Chinese emissions and their change by analyzing its emissions inventory by year, sector, and region. From the inter-comparison of existing bottom-up emission inventories, we found relatively good agreements (<20% difference) for SO₂ and NOx, but 30% or more discrepancies for some pollutants. Inter-comparison with top-down NOx emissions estimates also showed 20~50% differences by year. The regional distribution and inter-annual changes of emissions revealed different stages of energy/fuel mix and policy penetration. Early increase of pollutants emissions in the eastern part of China might give strong influences to the Korean peninsular in early 2000s but, more stringent control in that region would help improving air pollution in Korea in near future.

Emission of intermediate, semi and low volatile organic compounds from traffic and their impact on secondary organic aerosol concentrations over Greater Paris

Exhaust particle emissions are mostly made of black carbon and/or organic compounds, with some of these organic compounds existing in both the gas and particle phases. Although emissions of volatile organic compounds (VOC) are usually measured at the exhaust, emissions in the gas phase of lower volatility compounds (POAvapor) are not. However, these gas-phase emissions may be oxidised after emission and enhance the formation of secondary organic aerosols (SOA). They are shown here to contribute to most of the SOA formation in Central Paris. POAvapor emissions are usually estimated from primary organic aerosol emissions in the particle phase (POA). However, they could also be estimated from VOC emissions for both gasoline and diesel vehicles using previously published measurements from chamber measurements. Estimating POAvapor from VOC emissions and ageing exhaust emissions with a simple model included in the Polyphemus air-quality platform compare well to measurements of SOA formation performed in chamber experiments. Over Greater Paris, POAvapor emissions estimated using POA and VOC emissions are compared using the HEAVEN bottom-up traffic emissions model. The impact on the simulated atmospheric concentrations is then assessed using the Polyphemus/Polair3D chemistry-transport model. Estimating POAvapor emissions from VOC emissions rather than POA emissions lead to lower emissions along motorway axes (between −50% and −70%) and larger emissions in urban areas (up to between +120% and +140% in Central Paris). The impact on total organic aerosol concentrations (gas plus particle) is lower than the impact on emissions: between −8% and 25% along motorway axes and in urban areas respectively. Particle-phase organic concentrations are lower when POAvapor emissions are estimated from VOC than POA emissions, even in Central Paris where the total organic aerosol concentration is higher, because of different assumptions on the emission volatility distribution, stressing the importance of characterizing not only the emission strength, but also the emission volatility distribution.

Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

Abstract. Current estimates of agricultural ammonia (NH3) emissions in China differ by more than a factor of 2, hindering our understanding of their environmental consequences. Here we apply both bottom-up statistical and top-down inversion methods to quantify NH3 emissions from agriculture in China for the year 2008. We first assimilate satellite observations of NH3 column concentration from the Tropospheric Emission Spectrometer (TES) using the GEOS-Chem adjoint model to optimize Chinese anthropogenic NH3 emissions at the 1∕2° × 2∕3° horizontal resolution for March–October 2008. Optimized emissions show a strong summer peak, with emissions about 50 % higher in summer than spring and fall, which is underestimated in current bottom-up NH3 emission estimates. To reconcile the latter with the top-down results, we revisit the processes of agricultural NH3 emissions and develop an improved bottom-up inventory of Chinese NH3 emissions from fertilizer application and livestock waste at the 1∕2° × 2∕3° resolution. Our bottom-up emission inventory includes more detailed information on crop-specific fertilizer application practices and better accounts for meteorological modulation of NH3 emission factors in China. We find that annual anthropogenic NH3 emissions are 11.7 Tg for 2008, with 5.05 Tg from fertilizer application and 5.31 Tg from livestock waste. The two sources together account for 88 % of total anthropogenic NH3 emissions in China. Our bottom-up emission estimates also show a distinct seasonality peaking in summer, consistent with top-down results from the satellite-based inversion. Further evaluations using surface network measurements show that the model driven by our bottom-up emissions reproduces the observed spatial and seasonal variations of NH3 gas concentrations and ammonium (NH4+) wet deposition fluxes over China well, providing additional credibility to the improvements we have made to our agricultural NH3 emission inventory.

Particulate matter emissions over the oil sands regions in Alberta, Canada

Particulate matter (PM) emissions from the expanded oil sands development in Alberta are becoming a focus among the aerosol science community because of its significant negative impact on the regional air quality and climate change. Open-pit mining, petroleum coke (petcoke) dust, and the transportation of oil sands and waste materials by heavy-duty trucks on unpaved roads could release PM into the air. Incomplete combustion of fossil fuels by engines and stationary boilers leads to the formation of carbonaceous aerosols. In addition, wildfire and biogenic emissions surrounding the oil sands regions also have the potential to contribute primary PM to the ambient air. Secondary organic aerosol formation has been revealed as an important source of PM over nearby and distant areas from the oil sands regions. This review summarizes the primary PM sources and some secondary aerosol formation mechanisms that are linked to oil sands development. It also reviews the approaches that can be applied in aerosol source apportionment. Meteorological condition is an important factor that may influence the primary PM emission and secondary aerosol formation in Alberta’s oil sands regions. Current concern should not be limited to the primary emission of atmospheric PM. Secondary formation of aerosols, especially secondary organic aerosol originating from photochemical reaction, should also be taken into consideration. To obtain a more comprehensive understanding of the sources and amount of PM emissions based on the bottom-up emission inventory approach, investigations on how to reduce the uncertainty in determination of real-world PM emission factors for the variable sources are needed. Long-range transport trajectories of fine PM from Alberta’s oil sands regions remain unknown.

Aerosol emissions factors from traditional biomass cookstoves in India: insights from field measurements

Abstract. Residential solid biomass cookstoves are important sources of aerosol emissions in India. Cookstove emissions rates are largely based on laboratory experiments conducted using the standard water-boiling test, but real-world emissions are often higher owing to different stove designs, fuels, and cooking methods. Constraining mass emissions factors (EFs) for prevalent cookstoves is important because they serve as inputs to bottom-up emissions inventories used to evaluate health and climate impacts. Real-world EFs were measured during winter 2015 for a traditional cookstove (chulha) burning fuel wood, agricultural residue, and dung from different regions of India. Average (±95 % confidence interval) EFs for fuel wood, agricultural residue, and dung were (1) PM2.5 mass: 10.5 (7.7–13.4) g kg−1, 11.1 (7.7–15.5) g kg−1, and 22.6 (14.9–32.9) g kg−1, respectively; (2) elemental carbon (EC): 0.9 (0.6–1.4) g kg−1, 1.6 (0.6–3.0) g kg−1, and 1.0 (0.4–2.0) g kg−1, respectively; and (3) organic carbon (OC): 4.9 (3.2–7.1) g kg−1, 7.0 (3.5–12.5) g kg−1, and 12.9 (4.2–15.01) g kg−1, respectively. The mean (±95 % confidence interval) OC ∕ EC mass ratios were 6.5 (4.5–9.1), 7.6 (4.4–12.2), and 12.7 (6.5–23.3), respectively, with OC and EC quantified by the IMPROVE_A thermal-optical reflectance protocol. These real-world EFs are higher than those from previous laboratory-based measurements. Combustion conditions have larger effects on EFs than the fuel types. We also report the carbon mass fractions of our aerosol samples determined using the thermal-optical reflectance method. The mass fraction profiles are consistent between the three fuel categories but markedly different from those reported in past literature – including the source profiles for wood stove PM2.5 emissions developed as inputs to receptor modeling studies conducted by the Central Pollution Control Board of India. Thermally stable OC (OC3 in the IMPROVE_A protocol) contributed nearly 50 % of the total carbon mass for emissions from all fuels.

An empirical model to predict road dust emissions based on pavement and traffic characteristics

The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables.We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.

Deriving Global OH Abundance and Atmospheric Lifetimes for Long-Lived Gases: A Search for CH3CCl3 Alternatives.

An accurate estimate of global hydroxyl radical (OH) abundance is important for projections of air quality, climate, and stratospheric ozone recovery. As the atmospheric mixing ratios of methyl chloroform (CH3CCl3) (MCF), the commonly used OH reference gas, approaches zero, it is important to find alternative approaches to infer atmospheric OH abundance and variability. The lack of global bottom-up emission inventories is the primary obstacle in choosing a MCF alternative. We illustrate that global emissions of long-lived trace gases can be inferred from their observed mixing ratio differences between the Northern Hemisphere (NH) and Southern Hemisphere (SH), given realistic estimates of their NH-SH exchange time, the emission partitioning between the two hemispheres, and the NH versus SH OH abundance ratio. Using the observed long-term trend and emissions derived from the measured hemispheric gradient, the combination of HFC-32 (CH2F2), HFC-134a (CH2FCF3, HFC-152a (CH3CHF2), and HCFC-22 (CHClF2), instead of a single gas, will be useful as a MCF alternative to infer global and hemispheric OH abundance and trace gas lifetimes. The primary assumption on which this multispecies approach relies is that the OH lifetimes can be estimated by scaling the thermal reaction rates of a reference gas at 272 K on global and hemispheric scales. Thus, the derived hemispheric and global OH estimates are forced to reconcile the observed trends and gradient for all four compounds simultaneously. However, currently, observations of these gases from the surface networks do not provide more accurate OH abundance estimate than that from MCF.

VEIN v0.2.2: an R package for bottom-up Vehicular Emissions Inventories

Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies, but also for urban planning and environmental health protection. We developed an open source model (named VEIN v0.2.2) that provides high resolution vehicular emissions inventories for different fields of studies. We focused on vehicular sources at street and hourly levels % they are the major source of air pollution in megacities. due to the current lack of information about these sources, mainly in developing countries. The type of emissions covered by VEIN are: exhaust (hot and cold) and evaporative considering the deterioration of the factors. VEIN also performs speciation and incorporates functions to generate and spatially allocate emissions databases. It allows users to load their own emissions factors, but it also provides emissions factors from the road transport model (Copert), the United States Environmental Protection Agency (EPA) and Brazilian databases. The VEIN model reads, distributes by age of use and extrapolates hourly traffic data, and estimates hourly and spatially emissions. Based on our knowledge, VEIN is the first bottom-up vehicle emissions software that allows input to the WRF-Chem model. Therefore, the VEIN model provides an important, easy and fast way of elaborating or analyzing vehicular emissions inventories, under different scenarios. The VEIN results can be used as an input for atmospheric models, health studies, air quality standardizations and decision making.