Existing Emission Inventories Research Articles

Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines.

Anthropogenic activities emit ~2,000 Mg y-1 of the toxic pollutant mercury (Hg) into the atmosphere, leading to long-range transport and deposition to remote ecosystems. Global anthropogenic emission inventories report increases in Northern Hemispheric (NH) Hg emissions during the last three decades, in contradiction with the observed decline in atmospheric Hg concentrations at NH measurement stations. Many factors can obscure the link between anthropogenic emissions and atmospheric Hg concentrations, including trends in the reemissions of previously released anthropogenic ("legacy") Hg, atmospheric sink variability, and spatial heterogeneity of monitoring data. Here, we assess the observed trends in gaseous elemental mercury (Hg0) in the NH and apply biogeochemical box modeling and chemical transport modeling to understand the trend drivers. Using linear mixed effects modeling of observational data from 51 stations, we find negative Hg0 trends in most NH regions, with an overall trend for 2005 to 2020 of -0.011 ± 0.006 ng m-3 y-1 (±2 SD). In contrast to existing emission inventories, our modeling analysis suggests that annual NH anthropogenic emissions must have declined by at least 140 Mg between the years 2005 and 2020 to be consistent with observed trends. Faster declines in 95th percentile Hg0 values than median values in Europe, North America, and East Asian measurement stations corroborate that the likely cause is a decline in nearby anthropogenic emissions rather than background legacy reemissions. Our results are relevant for evaluating the effectiveness of the Minamata Convention on Mercury, demonstrating that existing emission inventories are incompatible with the observed Hg0 declines.

Source apportionment of measured volatile organic compounds in Maricopa County, Arizona

ABSTRACT With the goal of corroborating existing emissions inventories of volatile organic compounds (VOCs), a statistical analysis was undertaken on measured ambient VOC concentrations in Maricopa County, Arizona. The Chemical Mass Balance (CMB) model was used to generate emissions source contribution estimates based on ambient VOC concentrations collected at the JLG Supersite in Phoenix, Arizona, and emissions source profiles obtained from EPA’s SPECIATE database. With trial-and-error, optimal model performance using a combination of emissions source profiles yielded source contribution estimates which could be compared to existing regulatory engineering-based emissions inventories. The ultimate objective of this study is to offer a comparison to the “top-down” emissions modeling via CMB and the “bottom-up” modeling traditionally used in preparing emission inventories to identify possible discrepancies and help direct future investigations to better understand local air quality. The methods used to develop the “bottom-up” inventory rely upon sound modeling developed to accurately capture emissions from various source categories. The results show discrepancies between the “bottom-up” and “top-down” emission inventory for VOC emissions from biogenic and natural gas combustion sources, suggesting that the emission strength from these source categories should be further investigated. Implications: The following implication statement has been prepared for the manuscript titled Source Apportionment of Measured Volatile Organic Compounds in Maricopa County, Arizona. The purpose of preparing such a study was to independently corroborate the findings of Maricopa County Air Quality Department (MCAQD) on source contribution estimates of VOC emissions as published in their 2020 Periodic Emissions Inventory for Ozone Precursors. The goal of preparing the findings in the study was to provide additional commentary on the significance of various VOC emissions sources to tropospheric ozone formation in Maricopa County through an alternate air quality modeling approach. The findings from this study are significant to the environment and health of Maricopa County as they offer additional insights into the pathways by which tropospheric ozone may form.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

ABSTRACT Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NOx), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4–69%, 14–65%, and 21–97% of BC, NOx, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26–46%, 17–63%, and −7–18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NOx to CO and BC to NOx emission ratios, which were between 0.21–0.32 μg m−3 NOx (μg m−3 CO)−1 and 0.013–0.018 μg m−3 BC (μg m−3 NOx)−1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes. Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Evaluation of the WRF and CHIMERE models for the simulation of PM2.5 in large East African urban conurbations

Abstract. Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of the capacity to forecast air quality make difficult to quantify the real level of air pollution in this area. The CHIMERE chemistry transport model has been used along with the Weather Research and Forecasting (WRF) meteorological model to run high-spatial-resolution (2 × 2 km) simulations of hourly concentrations of particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5) for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya, and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality model for a target monthly period in 2017, and the results were compared against observed data from urban, roadside, and rural sites. The results show that the model is able to reproduce hourly and daily temporal variabilities in aerosol concentrations that are close to observed values from urban, roadside, and rural environments. CHIMERE's performance as a tool for managing air quality was also assessed. The analysis demonstrated that, despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66 %–99 % of the daily PM2.5 exceedances above the World Health Organization (WHO) 24 h mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24 h average PM2.5 levels was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a poor Air Quality Index for PM2.5 that was in the unhealthy category for human health, thereby exposing between 10 000 and 30 000 people per square kilometre to harmful levels of air contamination.

County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China

Emissions from animal dung burning in the Qinghai–Tibetan Plateau (QTP) influenced heavily on the regional and global climate and environment, from which the emission inventory of multiple pollutants is not yet available. This study developed a county-level emission inventory of particles and gases pollution based on field measured emission factors and detailed activity data for the QTP in 2019. The results revealed that the total emissions of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), organic carbon (OC), elemental carbon (EC), eight ions, fifty-one polycyclic aromatic hydrocarbons (PAHs) in gaseous and particulate phases, 108 volatile organic compounds (VOCs), CO, and CO2 were 41.1, 23.1, 2.36, 3.15, 1.13, 6.55, 135, and 4963 Gg, respectively. Gonghe, Qilian, Nyima, and Gangcha Counties in northeast of Qinghai and northwestern Tibet were the major contributors of all pollutants contributed (accounting for 11.9%–14.6% of the total emission) due to their higher livestock numbers. Sheep dung burning contributed a little more (average of 67.1%) than yak dung burning (32.9%) due to the high EFs and consumptions. For temporal distribution, the heating season contributed majority of the total emissions (70%–82%). Although with much less population, the QTP showed even higher emission densities than those in plain areas due to less oxygen and poor combustion technology. Compared with existing emission inventories, the uncertainties of all pollutant emission inventories were considerably reduced (42.7–163%), with EC as an exception because of its high coefficients of variation of EFs than the other pollutants. The developed emission inventory can help formulate targeted air pollution control measures for the QTP region by providing detailed information on the spatiotemporal distribution of multitype air pollutants. Meanwhile, the relative high resolution and accuracy inventory could serve a better basis for atmospheric modeling such as pollution transportation and source apportionment in both regional and global scales.

Highly anomalous fire emissions from the 2019–2020 Australian bushfires

While it is widely recognized that extreme fires have been increasing under warming and drying climate, knowledge regarding the magnitude and intensity of extreme fires is very limited. Moreover, fire emissions reported by existing emissions inventories show large discrepancies due to different approaches and parameters. In this study, we analyzed the fire intensity and emissions magnitude of the 2019–2020 Australian bushfires using fire observations from multiple satellites. The results show that the bushfires were extreme in both their number and intensity, which were higher by a factor of 25 and 19, respectively, compared to the past two-decade seasonal mean. The 2019–2020 bushfires burned a total of 112.3 Tg biomass and released 178.6 ± 13.6 Tg CO2 (carbon dioxide), 1.71 ± 1.28 Tg PM2.5 (particulate matter with a diameter <2.5 μm), and 0.061 ± 0.04 Tg BC (black carbon) across eastern and southern Australia. The CO2 emissions are 35% of Australia’s greenhouse emissions from all sectors combined in 2020. Furthermore, the extreme fires in the most severe day and hour released 10% and 1.4% of the entire seasonal emissions, respectively. Our findings provide quantitative information for investigating the impacts of smoke emissions on air quality, ecosystem, and climate.

Assessment of urban CO2 budget: Anthropogenic and biogenic inputs

Understanding combinations of biogenic and anthropogenic inputs, and dynamics of carbon dioxide (CO2) fluxes in the urban environment is critical to investigate feedback between carbon cycle and climate system. The main objectives of this study are to analyse the role and interactions of anthropogenic and biogenic CO2 emission sources and sinks in the total carbon cycle within city limits, determine processes controlling urban ecosystem-atmosphere gas exchange under varying environmental conditions with a high temporal and spatial resolution.The research addressing this issue was carried out in Wroclaw, the fourth largest city in Poland, throughout the year from July 2017 to August 2018. Chamber measurements of surface-atmosphere CO2 and CO2 net fluxes across various types of land cover (grassland, city park and arable land) provided the basis for assessing biogenic component of the urban C budget. In particular, the approach applied Picarro CRDS (cavity ring-down spectroscopy) technique with model G2201-I. Anthropogenic C fluxes were reported based on local statistical datasets and existing emission inventories, as well as the Low Emission Economy Plan from the Wroclaw urban area.The findings showed that biogenic fluxes, over the course of one year, accounted for about 16.3% (RESP = 0.68 Mton CO2·yr−1) of the total city carbon emissions for Wroclaw, being entirely compensated by local vegetation C uptake (GEE = −0.89 Mt. CO2·yr−1). Compared to fluxes given by the previously reported emission inventory, the net biogenic CO2 sink (NEE) of 0.201 Mt. CO2·yr−1 was substantially greater, which indicates that the biosphere in Wroclaw is a net biogenic C sink. The average annual C budget in Wroclaw, obtained from the investigation of sectoral emission trends including those related to energy, road traffic, industrial processes, and other fluxes (human respiration), reaches about 3.87 kg C·m−2·yr−1.

Evolution of formaldehyde (HCHO) in a plume originating from a petrochemical industry and its volatile organic compounds (VOCs) emission rate estimation

Large industrial facilities, such as petrochemical complexes, have decisive effects on regional air quality: directly due to their own hazardous volatile organic compounds (VOCs) emissions and indirectly due to their contribution to secondary air pollution. In South Korea, pronounced ozone and particulate matter issues have been reported in industrial areas. In this study, we develop a new top-down VOC emission rate estimation method using in situ airborne formaldehyde (HCHO) observations in the downwind plume of the Daesan Petrochemical Complex (DPC) in South Korea during the 2016 Korea–United States Air Quality (KORUS-AQ) mission. On May 22, we observed a peak HCHO mole fraction of 12 ppb after a transport time of 2.5 h (distance approximately 36 km) under conditions where the HCHO photochemical lifetime was 1.8 h. Box model calculations indicate that this elevated HCHO is mainly due to secondary production (more than 90% after 2 h of plume aging) from various VOC precursors including ethene, propene, and 1,3-butadiene. We estimate a lower limit for yearly DPC VOC emissions of 31 (±8.7) × 103 MT/year for HCHO precursors and 53 (±15) × 103 MT/year for all measured primary VOCs. These estimates are 1.5–2.5 times higher than the latest Korean emission inventories, KORUSv5. This method is beneficial not only by tracking the sources, sinks, and evolution of HCHO but also by validating existing emission inventories.

CO2 emission accounts of Russia\u2019s constituent entities 2005\u20132019

Constituent entities which make up Russia have wide-ranging powers and are considered as important policymakers and implementers of climate change mitigation. Formulation of CO2 emission inventories for Russia’s constituent entities is the priority step in achieving emission reduction. Russia is the world’s largest exporter of oil and gas combined and the fourth biggest CO2 emitter, so it’s efforts in mitigating CO2 emissions are globally significant in curbing climate change. However, the existing emission inventories only present national CO2 emissions; the subnational emission details are missing. In addition, the emission factors are not country-specific and energy activity data by fossil energy types and sectors are not sufficiently detailed. In this study, the CO2 emission inventories of Russia and its 82 constituent entities from 2005 to 2019 are constructed. The emission inventories include energy-related emissions with 89 socio-economic sectors and 17 energy types and process-related emissions. The uniformly formatted emission inventories can be a reference for in-depth analysis of emission characteristics and emission-related studies of Russia.

Gridded 1 km × 1 km emission inventory for paddy stubble burning emissions over north-west India constrained by measured emission factors of 77 VOCs and district-wise crop yield data

Every year in the post-monsoon season, ~1.7 billion tons of paddy stubble is burnt openly in the Indo-Gangetic Plain (IGP) producing persistent smog and air quality deterioration that affects the entire IGP. Information concerning the identity, amounts and spatial distribution of volatile organic compounds (VOCs) which drive ozone and aerosol formation is still largely unknown as existing global emission inventories have poor VOC speciation and rely on limited satellite overpasses for mapping burnt areas. Here, emission factors (EFs) of 77 VOCs were measured from paddy fire smoke and combined with 1 km × 1 km stubble burning activity constrained by annual crop production yields and detected fires to compile a new gridded emission inventory for 2017. Our results reveal a large source of acetaldehyde (37.5 ± 9.6 Ggy−1), 2-furaldehyde (37.1 ± 12.5 Ggy−1), acetone (34.7 ± 13.6 Ggy−1), benzene (9.9 ± 2.8 Ggy−1) and isocyanic acid (0.4 ± 0.2 Ggy−1) that are not accounted for by existing emission inventories (GFED, GFAS, FINv2.1). During October–November, these emissions (346 ± 65 Ggy−1 NMVOC; 38 ± 8 Ggy−1 NOx; 16 ± 4 Ggy−1 NH3; 129 ± 9 Ggy−1 PM2.5; 22,125 ± 3674 Ggy−1 GHG CO2 equivalents) are more than 20 times larger than corresponding emissions from traffic and municipal waste burning over north-west India. Mitigation of this source alone can therefore yield massive air-quality climate co-benefits for more than 500 million people.

Deriving emission fluxes of volatile organic compounds from tower observation in the Pearl River Delta, China

Accurate estimation of speciated emissions of volatile organic compounds (VOCs) is challenging due to the complexity of both species and sources. Evaluation of the bottom-up emission inventory (EI) by atmospheric observation is needed to better understand the VOC emissions and then to control air pollutions caused by VOCs. This study conducts vertical measurements of VOCs between November 3 and 11, 2018 at the Canton Tower in the urban core of Pearl River Delta (PRD), China. A mixed layer gradient (MLG) technique is applied to the tower observation data to derive emission fluxes for individual VOC. The results show that the measured VOCs concentrations at ground level were always higher than those at the heights of 118 m and 488 m. Obvious vertical gradients of concentrations were found for VOC species, such as benzene, toluene and isoprene. The emission flux was estimated to be largest for propane (3.29 mg m−2 h−1), followed by toluene (2.55 mg m−2 h−1), isoprene (2.24 mg m−2 h−1), n-butane (2.10 mg m−2 h−1) and iso-pentane (1.73 mg m−2 h−1). The total VOC emission fluxes were around 3 times larger than those in the EI, suggesting 1.5–2 times underestimations of ozone formation potential (OFP) and secondary organic aerosol potential (SOAP) by current EI. Substantial underestimations (3–20 times) were found for C2-C5 alkanes by current EI. Due to unmeasured input parameters, limited sample size and short sampling period, there are still large uncertainties (40%–117%) in the estimated emission fluxes for individual species. Whereas, this study shows that the tower observation and emission estimation using MLG method could provide useful information for better understanding vertical distributions and emission fluxes of VOCs, and pioneer in assessing the existing emission inventories at species-level and hour-level.

Exploration of oxidative chemistry and secondary organic aerosol formation in the Amazon during the wet season: explicit modeling of the Manaus urban plume with GECKO-A

Abstract. The GoAmazon 2014/5 field campaign took place in Manaus, Brazil, and allowed the investigation of the interaction between background-level biogenic air masses and anthropogenic plumes. We present in this work a box model built to simulate the impact of urban chemistry on biogenic secondary organic aerosol (SOA) formation and composition. An organic chemistry mechanism is generated with the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds. A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles. The biogenic emissions estimated from existing emission inventories had to be reduced to match measurements. The model is able to reproduce ozone and NOx for clean and polluted situations. The explicit model is able to reproduce background case SOA mass concentrations but does not capture the enhancement observed in the urban plume. The oxidation of biogenic compounds is the major contributor to SOA mass. A volatility basis set (VBS) parameterization applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model. The explicit mechanism may be missing SOA-formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization.

Updating Chinese SO2 emissions with surface observations for regional air-quality modeling over East Asia

Abstract Anthropogenic emissions in East Asia have changed dramatically in recent years. To measure these changing emissions in support of air-quality modeling, we developed a top-down emission update system using surface observations and a geographical information system spatial-allocation technique. We deploy a data-processing system to construct adjustment factors to prefecture-level SO2 emissions by comparing surface and modeled observations. A case study is conducted over East Asia for 2016 in which we update Chinese SO2 emissions using measurements from around 1500 surface monitoring sites. Model simulations using updated SO2 emissions are improved relative to the existing simulation system (e.g., R = 0.23 to R = 0.8), suggesting that the newly designed system provides an efficient, practical forecast solution. Finally, estimated SO2 emissions are compared with existing emission inventories, agreeing well with recent reports of reduced SO2 emissions from Chinese anthropogenic sources.

A fast forecasting method for PM2.5 concentrations based on footprint modeling and emission optimization

We propose a method for fast forecasting of PM2.5 concentrations in the North China Plain based on footprint (source–receptor relationship) modeling and emission inventory inversion. A backward Lagrangian stochastic particle dispersion model was employed to derive the footprint, using meteorological fields and boundary layer parameters provided by the WRF model. An analytical Bayesian inversion model was used to optimize existing emission inventories using long-term, multi-site PM2.5 monitoring data. The fast simulation of PM2.5 concentrations was based on the optimized inventory and the footprint results. Two-year simulations were carried out for six cities (Baoding, Beijing, Dezhou, Shijiazhuang, Tianjin, and Tangshan), with model establishment and emission inversion in the first year (2015) and test forecasting in the second year (2016). Promising simulation results were obtained even when using the primary emission inventory. For all six cities, the fractions of simulations of measurements within a factor of two ranged from 0.49 to 0.68, and the correlation coefficients ranged from 0.40 to 0.56 in 2015. The model also well reproduced temporal variations in PM2.5 concentrations in Beijing during severe haze episodes in the winter of 2015. Great improvement was achieved for the simulations by using the optimized emission inventory. The proportion of samples that met the PM model criteria increased from 88% to 97%, and the proportion that achieved the modeling goal increased from 25% to 44%. This method maintained its high forecasting skill in 2016, with 92% and 46% of samples meeting the PM model criteria and achieving the modeling goal, respectively. However, the corresponding values were86% and 39% if emission optimization was not applied.

Real world emissions performance of heavy-duty Euro VI diesel vehicles

Despite that Heavy-Duty Vehicles (HDVs) represent a small part of the overall vehicle population they have been identified as one of the most important contributors to air pollution. This is one of the reasons why HDV emissions regulations are becoming more and more stringent worldwide. Following this trend, Europe introduced the Euro VI standard which includes more stringent emission limits for hydrocarbons, PM and NOx, while for the first time a limit for solid PN emissions was set. At the same time increased concern regarding greenhouse gas emissions led to a series of initiatives including HDVs CO2 certification and monitoring with the latest being the submission of a proposal for CO2 emission targets for 2025 and 2030. Despite the general concern, published data regarding Euro VI HDV emissions are scarce, while real-world emission factor measurements of regulated and unregulated pollutants are even more difficult to find. The main objective of this paper is to present real-world diesel Euro VI HDVs emissions of both gaseous pollutants and solid PN. For that reason five HDVs, including four trucks and one bus, were tested on-road under typical driving conditions. A breakdown of the emissions to low, medium, and high speed conditions was also performed with the aim investigating the performance of aftertreatment systems under different speed conditions. All tested vehicles performed better compared to older technology diesel HDVs, thus reflecting the technological improvements introduced over the last years. However, relatively high emissions were observed for some of the pollutants over low speed phases due to reduced effectiveness of the corresponding emission control systems. Calculated emission factors were also compared to existing emission inventories and good correlation was found for NOx, CO2 and solid PN emissions.

Multi-sensor temporal assessment of tropospheric nitrogen dioxide column densities over Pakistan.

Spatial and temporal distributions of tropospheric NO2 vertical column densities over Pakistan during the period 2002-2014 are discussed. Data products from three satellite instruments SCIAMACHY, OMI, and GOME-2 are used to prepare a database of tropospheric NO2 column densities over Pakistan and temporal evolution is also determined. Plausible NO2 sources in Pakistan are also discussed. The results show a large NO2 growth over all provinces and the major cities of Pakistan except the megacity of Karachi. Decline in industrial activities due to energy crises, worsening law and order situation, terrorist attacks, and political instability was explored as the main factor for lower NO2 VCDs over Karachi City. The overall increase can be attributed to the anthropogenic emissions over the areas with high population, traffic density, and industrial activities. Source identification revealed that use of fossil fuels by various sectors including power generation, vehicles, and residential sectors along with agriculture fires are among significant sources of NO2 emissions in Pakistan. Existing emission inventories such as EDGARv4.2 and MACCity largely underestimate the true anthropogenic NOx emissions in Pakistan. This study may provide vital information to policy makers and regulatory authorities in developing countries, including Pakistan, in order to devise effective air pollution abatement policies.

Development of a new gas-flaring emission dataset for southern West Africa

Abstract. A new gas-flaring emission parameterization has been developed, which combines remote sensing observations using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime data with combustion equations. The parameterization has been applied to southern West Africa, including the Niger Delta as a region that is highly exposed to gas flaring. Two 2-month datasets for June–July 2014 and 2015 were created. The parameterization delivers emissions of CO, CO2, NO, NO2 and SO2. A flaring climatology for both time periods has been derived. The uncertainties owing to cloud cover, parameter selection, natural gas composition and the interannual differences are assessed. The largest uncertainties in the emission estimation are linked to the parameter selection. It can be shown that the flaring emissions in Nigeria have significantly decreased by 25 % from 2014 to 2015. Existing emission inventories were used for validation. CO2 emissions with the estimated uncertainty in parentheses of 2.7 (3. 6∕0. 5) Tg yr−1 for 2014 and 2.0 (2. 7∕0. 4) Tg yr−1 for 2015 were derived. Regarding the uncertainty range, the emission estimate is in the same order of magnitude compared to existing emission inventories with a tendency for underestimation. The deviations might be attributed to a shortage in information about the combustion efficiency within southern West Africa, the decreasing trend in gas flaring or inconsistent emission sector definitions. The parameterization source code is available as a package of R scripts.

Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality

Abstract. We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East Asia at a spatial resolution of 20 km × 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in ASia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences of up to 500 % for CO emissions, 190 % for NO, and 160 % for primary PM10. Such discrepancies in the magnitude and the spatial distribution of emissions for various species lead to a 40–70 % difference in surface PM10 concentrations, 16–20 % in surface O3 mixing ratios, and over 100 % in SO2 and NO2 mixing ratios in the polluted areas of China. WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct additional model simulations using REAS emissions for January, April, July, and October of 2007 and evaluate simulations with available ground-level observations. The model results illustrate clear regional variations in the seasonal cycle of surface PM10 and O3 over East Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB ⩽ ±60 %) and O3 (MFB ⩽ ±15 %) at most of the observation sites, although the model underestimates PM10 over northeastern China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates observed NO2 in all 4 months. Our study highlights the importance of constraining emissions at the provincial level for regional air quality modeling over East Asia. Our results suggest that future work should focus on the improvement of provincial-level emissions especially estimating primary PM, SO2, and NOx.

Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS

Abstract. Community Multiscale Air Quality (CMAQ) model simulations utilizing the traditional organic aerosol (OA) treatment (CMAQ-AE6) and a volatility basis set (VBS) treatment for OA (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of OA in southern California. Traditionally, CMAQ treats primary organic aerosol (POA) as nonvolatile and uses a two-product framework to represent secondary organic aerosol (SOA) formation. CMAQ-VBS instead treats POA as semivolatile and lumps OA using volatility bins spaced an order of magnitude apart. The CMAQ-VBS approach underpredicted organic carbon (OC) at IMPROVE and CSN sites to a greater degree than CMAQ-AE6 due to the semivolatile POA treatment. However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA, indicated that CMAQ-VBS better represented the diurnal profile and primary/secondary split of OA. CMAQ-VBS SOA underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate for SOA) concentration by a factor of 5.2, representing a considerable improvement to CMAQ-AE6 SOA predictions (factor of 24 lower than AMS). We use two new methods, one based on species ratios (SOA/ΔCO and SOA/Ox) and another on a simplified SOA parameterization, to apportion the SOA underprediction for CMAQ-VBS to slow photochemical oxidation (estimated as 1.5 × lower than observed at Pasadena using −log(NOx : NOy)), low intrinsic SOA formation efficiency (low by 1.6 to 2 × for Pasadena), and low emissions or excessive dispersion for the Pasadena site (estimated to be 1.6 to 2.3 × too low/excessive). The first and third factors are common to CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7 × . From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48 %, consistent with a substantial fraction of cooking OA in the observations). This is compared to 18 % from gasoline vehicle emissions, 13 % from biomass burning (in the form of residential wood combustion), and 8 % from diesel vehicle emissions. All "other" inventoried emission sources (e.g., industrial, point, and area sources) comprised the final 13 %. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA) + cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but it did capture the AMS diurnal profile of HOA and CIOA well, with the exception of the midday peak. Overall, the CMAQ-VBS with its semivolatile treatment of POA, SOA from intermediate volatility organic compounds (IVOCs), and aging of SOA improves SOA model performance (though SOA formation efficiency is still 1.6–2 × too low). However, continued efforts are needed to better understand assumptions in the parameterization (e.g., SOA aging) and provide additional certainty to how best to apply existing emission inventories in a framework that treats POA as semivolatile, which currently degrades existing model performance at routine monitoring networks. The VBS and other approaches (e.g., AE6) require additional work to appropriately incorporate IVOC emissions and subsequent SOA formation.

Detection of Trends and Seasonal Variation in Tropospheric Nitrogen Dioxide over Pakistan

In this study, spatial and temporal distributions of tropospheric NO2 vertical column densities over Pakistan during the time period of 2002–2012 are discussed. Data products from the satellite instrument SCIAMACHY are used. The results show a large NO2 growth over major cities of Pakistan, particularly the areas with rapid urbanization. Different seasonal cycles were observed in different regions of Pakistan. In the provinces of Punjab (north east), Khyber Paktunkhwa (north west) and Sindh (south east), NO2 columns are maximum in winter and minimum in summer months while a reversed seasonality was observed in the province of Baluchistan (south west). We compared the observed spatio-temporal patterns to existing emission inventories and found that for the most populated provinces the NOx emissions are clearly dominated by anthropogenic sources. In these areas also the strongest positive trends were observed. NOx released from soils and produced by lightning both together contribute about 20% for the provinces of Punjab, Sindh, and Khyber Paktunkhwa, while its contribution in Baluchistan is much stronger (~50%). NOx emissions from biomass burning are negligible. This finding can also explain the observed summer maximum in Baluchistan, since the highet lightning activity occurs during the Monsoon season. Our comparison also indicates that the inventories of anthropogenic NOx emissions over Pakistan seem to underestimate the true emissions by about a factor of two.