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Assessment of the NOх integral emission from the St.Petersburg megacity by means of mobile DOAS measurements combined with dispersion modelling
Abstract
Megacities are strong sources of environmental pollution. Accurate estimates of the corresponding emissions are important to assess environmental impact and to ensure reliable operation of numerical atmospheric models. One of the most important factors of air pollution in large cities and industrial centers is anthropogenic emission of nitrogen oxides, NOx (= NO + NO2). St. Petersburg is the second largest industrial city in Russia and one of the largest northern megacities in the world. This study aims to experimentally derive the total NOx emission from the metropolitan area of St. Petersburg, based on data from mobile DOAS measurements of NO2 amount. We use data from a series of mobile experiments performed around the city in March and April 2019 and combine them with NO2 field calculations based on HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) simulations. As an initial approximation to a priori information on the spatial distribution of NOx emission sources in the St. Petersburg area, we consider ODIAC (Open-source Data Inventory for Anthropogenic CO2) data. Based on fitting the HYSPLIT simulation results to our mobile DOAS (Differential Optical Absorption Spectroscopy) measurements, and applying some assumptions about the daily, weekly and seasonal cycles of urban anthropogenic pollution, we obtained an experimental estimate of total NOx emissions of 77 ± 27 kilotons in 2019. Moreover, we managed to obtain an estimate of the contribution of urban thermal power plants to the total NOx emissions, which amounting to ∼28%.
... Our results were additionally compared with emissions inventories with the objective of validation. Mobile mini-DOAS measurements have been successfully conducted at different urban areas such as St. Petersburg (Ionov et al., 2022), Beijing (Huang et al., 2020), Montevideo (Osorio et al., 2018), and Tijuana (Rivera et al., 2015). To our knowledge, we are reporting the first mobile mini-DOAS measurements conducted in the TVMA. ...
... However, they were found to be smaller than Beijing, China (2.01-2.19 kg/s, Johansson et al., 2008) and St. Petersburg, Rusia (2.44 kg/s, Ionov et al., 2022). ...
NO2 outflow fluxes were calculated from the City of Toluca, Mexico during January-February 2017 using a mobile zenith viewing mini-DOAS instrument. Measurements were performed in a cross-section of the outflow plume from the Toluca Valley Metropolitan Area with an instrument operating in the visible wavelength region (356 to 510 nm). NO2 retrievals were conducted in the 405 to 465 nm wavelength range. From these measurements, differential vertical columns of NO2 along the measurement route were derived. Using mass-averaged wind speed and wind direction from the Weather Research & Forecasting model, with a 1 km resolution, outflow fluxes of NO2 were calculated from each of the transect measurements. The average NO2 outflow flux for the entire campaign was 1.81 kg/s (156.24 ton/day). To our knowledge, this is the first time that mobile mini-DOAS measurements have been conducted around the City of Toluca and therefore the first time that NO2 outflow fluxes have been reported for this metropolitan area. Comparing our results with emissions inventories we found large variabilities of NOx emissions reported in emissions inventories and between emissions inventories and our measurements. From these discrepancies we infer that emissions inventories underestimate NOx emissions from the TVMA. This study contributes to our understanding of outflow fluxes from metropolitan areas and their possible exchange, being the Toluca Valley Metropolitan Area a few kilometers away from the Mexico City Metropolitan Area, one of the largest megacities of the world.
... St. Petersburg is one of the largest industrial Russian cities, with a population of more than 5 million [17], several large power plants [18], industries [19], and more than one million private motor vehicles [20]. According to [21], the NO x (NO 2 + NO) anthropogenic emissions of St. Petersburg constitute 77 ± 22 kt y −1 . According to independent estimates, the NOx emissions of London, Paris, and Shanghai constitute~113 [22], 132 [23], and 155 kt y −1 [24], respectively. ...
Ozone in the troposphere is a pollutant and greenhouse gas. Atmospheric models can add valuable information to observations for studying the spatial and temporal variations in tropospheric ozone content. The present study is intended to evaluate the variability in tropospheric ozone and its precursors near the Gulf of Finland with a focus on St. Petersburg (Russia) and Helsinki (Finland) in 2016–2019, using the WRF-Chem 3-D numerical model with a spatial resolution of 10 km, together with observations. The diurnal cycle of the near-surface ozone concentrations (NSOCs) in both cities is caused by the variability in NO2 emissions, planetary boundary layer height, and local meteorological conditions. The seasonal variations in NSOCs and tropospheric ozone content (TrOC) are caused by the variability in total ozone content and in ozone formation in the troposphere. The model reveals a VOC-limited regime in the ~0–1 km layer around St. Petersburg, Helsinki, and the Gulf of Finland and a pronounced NOx-limited regime in the 0–2 km layer in the forests of southern Finland, Karelia, some Russian regions, and the Baltic countries in July. The WRF-Chem model overestimates the measured NSOCs by 10.7–43.5% and the TrOC by 7–10.4%. The observed differences are mainly caused by the errors in chemical boundary conditions and emissions of ozone precursors and by the coarse spatial resolution of the modeling.
... The need to control odor impacts involves the development and application of specific methods to assess ground-level odor concentration [4,[10][11][12][13]. In this sense, dispersion modeling is a valuable technique for replicating the spatial-temporal distribution of contaminants released by a single source to estimate areas of population exposure as well as ground-level concentrations of contaminants [14][15][16][17][18][19][20][21][22]. defining short-term peak concentration, a constant factor of 2.3, to be multiplied by the hourly average concentration returned from the model, is suggested [33]. ...
Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.
... Through the superposition of direct and indirect carbon emission results, we analyzed the temporal and spatial changes of LCEs in the GZA at a grid scale. The method used in this study mainly calculated indirect carbon emissions using 1 km high-resolution grid data; this simple process is highly accurate [40]. ...
Land use change is one of the key elements leading to carbon emission changes, and is of great significance to the process of achieving the goals of carbon peaking and carbon neutrality. In this study, we calculated the land-use carbon emissions (LCE) in the Guanzhong area (GZA) of Shaanxi province from 2000 to 2019 by using an improved LCE measurement model. Meanwhile, the spatial and temporal changes of LCE were analyzed and the driving forces were investigated based on the correlation analysis and multi-scale geographical weighting regression (MGWR). The results showed that the total amount of LCE showed a significant increasing trend from 2000 to 2019. Regions where the LCE significantly increased occupied 71.20% of the total area; these regions were distributed in the central and eastern parts of the study area. The LCE showed a significant positive spatial correlation and had a remarkable aggregation state. The H-H agglomeration area of LCE was distributed in the central urban agglomeration. The L-L agglomeration areas were always distributed in the southwest part of the GZA with low carbon emissions. The average correlation coefficients between LCE and nighttime light (NTL), population density (PD), and gross primary productivity (GPP) were 0.13, 0.21, and −0.05, respectively. The NLT and PD had obvious positive effects on LCE, while GPP has obvious negative effects on carbon emissions, which can be ascribed to the carbon sink effect of forests and grasslands. The results of this study have important reference value regarding the formulation of carbon emission reduction policies and the development of a low-carbon social economy.
The results of a series of spectroscopic measurements of NO2 content in the troposphere, carried out on a closed route of the ring road (KAD) of St. Petersburg in different seasons of 2012, 2014, 2015 and 2016, are considered. A unified approach to the interpretation of all experimental data using numerical modeling of the dispersion of air pollution and a priori information on the spatial distribution of its anthropogenic sources made it possible to significantly reduce the error in determining the integral NOx emission. The total amount, converted into gross annual anthropogenic emission from the territory of St. Petersburg, constitutes to 81 ± 17 thousand tons of NOx. This value exceeds the official data of the city inventory of air pollution which is 61...63 thousand tons, but is consistent (within the error limits) with the estimate previously obtained on the basis of similar mobile measurements in the spring of 2019 (75 ± 26 thousand tons).
The main results of scientific research and development in the field of atmospheric radiation in Russia for the period 2019–2022 are considered The main attention is paid to research in the field of radiative transfer theory, atmospheric spectroscopy, radiation climatology, aerosol and radiation forcing, remote sensing of the atmosphere and surface, interpretation of satellite measurements Lists of major publications in these areas for this period are provided.
The anthropogenic impact is a major factor of climate change, which is highest in industrial regions and modern megacities. Megacities are a significant source of emissions of various substances into the atmosphere, including CO2 which is the most important anthropogenic greenhouse gas. In 2019 and 2020, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was carried out on the territory of St Petersburg which is the second-largest industrial city in Russia with a population of more than 5 million people. In 2020, several measurement data sets were obtained during the lockdown period caused by the COVID-19 (COronaVIrus Disease of 2019) pandemic. One of the goals of EMME was to evaluate the CO2 emission from the St Petersburg agglomeration. Previously, the CO2 area flux has been obtained from the data of the EMME-2019 experiment using the mass balance approach. The value of the CO2 area flux for St Petersburg has been estimated as being 89±28 kt km-2 yr-1, which is 3 times higher than the corresponding value reported in the official municipal inventory. The present study is focused on the derivation of the integral CO2 emission from St Petersburg by coupling the results of the EMME observational campaigns of 2019 and 2020 and the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model. The ODIAC (Open-Data Inventory for Anthropogenic CO2) database is used as the source of the a priori information on the CO2 emissions for the territory of St Petersburg. The most important finding of the present study, based on the analysis of two observational campaigns, is a significantly higher CO2 emission from the megacity of St Petersburg compared to the data of municipal inventory, i.e. ∼75800±5400 kt yr-1 for 2019 and ∼68400±7100 kt yr-1 for 2020 versus ∼30000 kt yr-1 reported by official inventory. The comparison of the CO2 emissions obtained during the COVID-19 lockdown period in 2020 to the results obtained during the same period of 2019 demonstrated the decrease in emissions of 10 % or 7400 kt yr-1.
Global climate change is one of the most important scientific, societal and economic contemporary challenges. Fundamental understanding of the major processes driving climate change is the key problem which is to be solved not only on a global but also on a regional scale. The accuracy of regional climate modelling depends on a number of factors. One of these factors is the adequate and comprehensive information on the anthropogenic impact which is highest in industrial regions and areas with dense population – modern megacities. Megacities are not only “heat islands”, but also significant sources of emissions of various substances into the atmosphere, including greenhouse and reactive gases. In 2019, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was conducted within the St. Petersburg agglomeration (Russia) aiming to estimate the emission intensity of greenhouse (CO2, CH4) and reactive (CO, NOx) gases for St. Petersburg, which is the largest northern megacity. St. Petersburg State University (Russia), Karlsruhe Institute of Technology (Germany) and the University of Bremen (Germany) jointly ran this experiment. The core instruments of the campaign were two portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometers which were used for ground-based remote sensing measurements of the total column amount of CO2, CH4 and CO at upwind and downwind locations on opposite sides of the city. The NO2 tropospheric column amount was observed along a circular highway around the city by continuous mobile measurements of scattered solar visible radiation with an OceanOptics HR4000 spectrometer using the differential optical absorption spectroscopy (DOAS) technique. Simultaneously, air samples were collected in air bags for subsequent laboratory analysis. The air samples were taken at the locations of FTIR observations at the ground level and also at altitudes of about 100 m when air bags were lifted by a kite (in case of suitable landscape and favourable wind conditions). The entire campaign consisted of 11 mostly cloudless days of measurements in March–April 2019. Planning of measurements for each day included the determination of optimal location for FTIR spectrometers based on weather forecasts, combined with the numerical modelling of the pollution transport in the megacity area. The real-time corrections of the FTIR operation sites were performed depending on the actual evolution of the megacity NOx plume as detected by the mobile DOAS observations. The estimates of the St. Petersburg emission intensities for the considered greenhouse and reactive gases were obtained by coupling a box model and the results of the EMME observational campaign using the mass balance approach. The CO2 emission flux for St. Petersburg as an area source was estimated to be 89 ± 28 ktkm-2yr-1, which is 2 times higher than the corresponding value in the EDGAR database. The experiment revealed the CH4 emission flux of 135 ± 68 tkm-2yr-1, which is about 1 order of magnitude greater than the value reported by the official inventories of St. Petersburg emissions (∼ 25 tkm-2yr-1 for 2017). At the same time, for the urban territory of St. Petersburg, both the EMME experiment and the official inventories for 2017 give similar results for the CO anthropogenic flux (251 ± 104 tkm-2yr-1 vs. 410 tkm-2yr-1) and for the NOx anthropogenic flux (66 ± 28 tkm-2yr-1 vs. 69 tkm-2yr-1).
The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns took place in Romania in September 2014 and August 2015. They focused on two sites: the Bucharest urban area and large power plants in the Jiu Valley. The main objectives of the campaigns were to test recently developed airborne observation systems dedicated to air quality studies and to verify their applicability for the validation of space-borne atmospheric missions such as the TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P). We present the AROMAT campaigns from the perspective of findings related to the validation of tropospheric NO2, SO2, and H2CO. We also quantify the emissions of NOx and SO2 at both measurement sites.
We show that tropospheric NO2 vertical column density (VCD) measurements using airborne mapping instruments are well suited for satellite validation in principle. The signal-to-noise ratio of the airborne NO2 measurements is an order of magnitude higher than its space-borne counterpart when the airborne measurements are averaged at the TROPOMI pixel scale. However, we show that the temporal variation of the NO2 VCDs during a flight might be a significant source of comparison error. Considering the random error of the TROPOMI tropospheric NO2 VCD (σ), the dynamic range of the NO2 VCDs field extends from detection limit up to 37 σ (2.6×1016 molec. cm-2) and 29 σ (2×1016 molec. cm-2) for Bucharest and the Jiu Valley, respectively. For both areas, we simulate validation exercises applied to the TROPOMI tropospheric NO2 product. These simulations indicate that a comparison error budget closely matching the TROPOMI optimal target accuracy of 25 % can be obtained by adding NO2 and aerosol profile information to the airborne mapping observations, which constrains the investigated accuracy to within 28 %. In addition to NO2, our study also addresses the measurements of SO2 emissions from power plants in the Jiu Valley and an urban hotspot of H2CO in the centre of Bucharest. For these two species, we conclude that the best validation strategy would consist of deploying ground-based measurement systems at well-identified locations.
We carried out 19 city-circle-around car multi-axis differential optical absorption spectroscopy (MAX-DOAS) experiments on the 6th Ring Road of Beijing in January, September, and October 2014. The tropospheric vertical column densities (VCDs) of NO2 were retrieved from measured spectra by the MAX-DOAS technique and used to estimate the emissions of NOx (≡NO+NO2) from urban Beijing during the experimental periods. The offline LAPS-WRF-CMAQ model system was used to simulate the wind fields by assimilation of observational data and calculate the NO2-to-NOx concentration ratios, both of which are also needed for the estimation of NOx emissions. The NOx emissions in urban Beijing for the different months derived from the car MAX-DOAS measurements in this study were compared to the multi-resolution emission inventory in China for 2012 (MEIC 2012). Our car MAX-DOAS measurements showed higher NO2 VCD in January than in the other two months. The wind field had obvious impacts on the spatial distribution of NO2 VCD, with the mean NO2 VCD along the 6th Ring Road typically being higher under the southerly wind than under the northerly wind. In addition to the seasonal difference, the journey-to-journey variations of estimated NOx emission rates (ENOx) were large even within the same month, mainly due to uncertainties in the calculations of wind speed, the ratio of NO2 and NOx concentration, and the decay rate of NOx from the emission sources to the measured positions under different meteorological conditions. The ranges of ENOx during the heating and non-heating periods were 22.6×1025 to 31.3×1025 and 9.6×1025 to 12.0×1025 molec. s-1, respectively. The average ENOx values in the heating and non-heating periods were 26.9±6.1×1025 molec. s-1 and 11.0±1.2×1025 molec. s-1, respectively. The uncertainty range of ENOx was 20 %–52 %. The monthly emission rates from MEIC 2012 are found to be lower than the estimated ENOx, particularly in January. Our results provide important information and datasets for the validation of satellite products and also show how car MAX-DOAS measurements can be used effectively for dynamic monitoring and updating of the NOx emissions from megacities such as Beijing.
NOX (NOX = NO + NO2) emissions measurements in Beijing are of great significance because they can aid in understanding how NOX pollution develops in mega-cities throughout China. However, NOX emissions in mega-cities are difficult to measure due to changes in wind patterns and moving sources on roads during measurement. To obtain good spatial coverage on different ring roads in Beijing over a short amount of time, two mobile differential optical absorption spectroscopy (DOAS) instruments were used to measure NOX emission flux from April 18th to 26th, 2018. In addition, a wind profile radar provided simultaneous wind field measurements for altitudes between 50 m and 1 km for each ring road measurement. We first determined NOX emission flux of different ring roads using wind field averages from measured wind data. The results showed that the NOX emission flux of Beijing’s fifth ring road, which represented the urban part, varied from (19.29 ± 5.26) × 1024 molec./s to (36.46 ± 12.86) × 1024 molec./s. On April 20th, NOX emission flux for the third ring was slightly higher than the fourth ring because the two ring roads were measured at different time periods. We then analyzed the NOX emission flux error budget and error sensitivity. The main error source was the wind field uncertainty. For some measurements, the main emission flux error source was either wind speed uncertainty or wind direction uncertainty, but not both. As Beijing’s NOX emissions came from road vehicle exhaust, we found that emission flux error had a more diverse sensitivity to wind direction uncertainty, which improved our knowledge on this topic. The NOX emission flux error sensitivity study indicated that more accurate measurements of the wind field are crucial for effective NOX emission flux measurements in Chinese mega-cities. Obtaining actual time and high resolved wind measurements is an advantage for mega-cities’ NOX emission flux measurements. The emission flux errors caused by wind direction and wind speed uncertainties were clearly distinguished. Other sensitivity studies indicated that NOX/NO2 ratio uncertainty dominated flux errors when the NOX/NO2 ratio uncertainty was >0.4. Using two mobile-DOAS and wind profile radars to measure NOx emission flux improved the quality of the emission flux measuring results. This approach could be applied to many other mega-cities in China and in others countries.
Sarnia, Ontario, experiences pollutant emissions disproportionate to its relatively small size. The small size of the city limits traditional top-down emission estimate techniques (e.g., satellite) but a low-cost solution for emission monitoring is the mobile MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy). Measurements were made using this technique from 21 March 2017 to 23 March 2017 along various driving routes to retrieve vertical column densities (VCDs) of NO2 and SO2 and to estimate emissions of NOx and SO2 from the Sarnia region. A novel aspect of the current study was the installation of a NOx analyzer in the vehicle to allow real time measurement and characterization of near-surface NOx/NO2 ratios across the urban plumes, allowing improved accuracy of NOx emission estimates. Confidence in the use of near-surface-measured NOx/NO2 ratios for estimation of NOx emissions was increased by relatively well-mixed boundary layer conditions. These conditions were indicated by similar temporal trends in NO2 VCDs and mixing ratios when measurements were sufficiently distant from the sources. Leighton ratios within transported plumes indicated peroxy radicals were likely disturbing the NO–NO2–O3 photostationary state through VOC (volatile organic compound) oxidation. The average lower-limit emission estimate of NOx from Sarnia was 1.60±0.34 th-1 using local 10 m elevation wind-speed measurements. Our estimates were larger than the downscaled annual 2017 NPRI-reported (National Pollution Release Inventory) industrial emissions of 0.9 tNOxh-1. Our lower-limit estimate of SO2 emissions from Sarnia was 1.81±0.83 tSO2h-1, equal within uncertainty to the 2017 NPRI downscaled value of 1.85 tSO2h-1. Satellite-derived NO2 VCDs over Sarnia from the ozone monitoring instrument (OMI) were lower than mobile MAX-DOAS VCDs, likely due to the large pixel size relative to the city's size. The results of this study support the utility of the mobile MAX-DOAS method for estimating NOx and SO2 emissions in relatively small, highly industrialized regions, especially when supplemented with mobile NOx measurements.
The new version of the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) compiles gaseous and particulate air pollutant emissions, making use of the same anthropogenic sectors, time period (1970–2012), and international activity data that is used for estimating GHG emissions, as described in a companion paper (Janssens-Maenhout et al., 2017). All human activities, except large scale biomass burning and land use, land-use change, and forestry are included in the emissions calculation. The bottom-up compilation methodology of sector-specific emissions was applied consistently for all world countries, providing methodological transparency and comparability between countries. In addition to the activity data used to estimate GHG emissions, air pollutant emissions are determined by the process technology and end-of-pipe emission reduction abatements. Region-specific emission factors and abatement measures were selected from recent available scientific literature and reports. Compared to previous versions of EDGAR, the EDGAR v4.3.2 dataset covers all gaseous and particulate air pollutants, has extended time series (1970–2012), and has been evaluated with quality control and quality assurance (QC and QA) procedures both for the emission time series (e.g. particulate matter – PM – mass balance, gap-filling for missing data, the split-up of countries over time, few updates in the emission factors, etc.) and grid maps (full coverage of the world, complete mapping of EDGAR emissions with sector-specific proxies, etc.). This publication focuses on the gaseous air pollutants of CO, NOx, SO2, total non-methane volatile organic compounds (NMVOCs), NH3, and the aerosols PM10, PM2.5, black carbon (BC), and organic carbon (OC). Considering the 1970–2012 time period, global emissions of SO2 increased from 99 to 103 Mt, CO from 441 to 562 Mt, NOx from 68 to 122 Mt, NMVOC from 119 to 170 Mt, NH3 from 25 to 59 Mt, PM10 from 37 to 65 Mt, PM2.5 from 24 to 41 Mt, BC from 2.7 to 4.5 Mt, and OC from 9 to 11 Mt. We present the country-specific emission totals and analyze the larger emitting countries (including the European Union) to provide insights on major sector contributions. In addition, per capita and per GDP emissions and implied emission factors – the apparent emissions per unit of production or energy consumption – are presented. We find that the implied emission factors (EFs) are higher for low-income countries compared to high-income countries, but in both cases decrease from 1970 to 2012. The comparison with other global inventories, such as the Hemispheric Transport of Air Pollution Inventory (HTAP v2.2) and the Community Emission Data System (CEDS), reveals insights on the uncertainties as well as the impact of data revisions (e.g. activity data, emission factors, etc.). As an additional metric, we analyze the emission ratios of some pollutants to CO2 (e.g. CO/CO2, NOx/CO2, NOx/CO, and SO2/CO2) by sector, region, and time to identify any decoupling of air pollutant emissions from energy production activities and to demonstrate the potential of such ratios to compare to satellite-derived emission data. Gridded emissions are also made available for the 1970–2012 historic time series, disaggregated for 26 anthropogenic sectors using updated spatial proxies. The analysis of the evolution of hot spots over time allowed us to identify areas with growing emissions and where emissions should be constrained to improve global air quality (e.g. China, India, the Middle East, and some South American countries are often characterized by high emitting areas that are changing rapidly compared to Europe or the USA, where stable or decreasing emissions are evaluated). Sector- and component-specific contributions to grid-cell emissions may help the modelling and satellite communities to disaggregate atmospheric column amounts and concentrations into main emitting sectors. This work addresses not only the emission inventory and modelling communities, but also aims to broaden the usefulness of information available in a global emission inventory such as EDGAR to also include the measurement community. Data are publicly available online through the EDGAR website http://edgar.jrc.ec.europa.eu/overview.php?v=432_AP and registered under 10.2904/JRC_DATASET_EDGAR.
Recently, Chinese cities have suffered severe events of haze air pollution, particularly in the North China Plain (NCP). Investigating the temporal and spatial distribution of pollutants, emissions, and pollution transport is necessary to better understand the effect of various sources on air quality. We report on mobile differential optical absorption spectroscopy (mobile DOAS) observations of precursors SO2 and NO2 vertical columns in the NCP in the summer of 2013 (from 11 June to 7 July) in this study. The different temporal and spatial distributions of SO2 and NO2 vertical column density (VCD) over this area are characterized under various wind fields. The results show that transport from the southern NCP strongly affects air quality in Beijing, and the transport route, particularly SO2 transport on the route of Shijiazhuang–Baoding–Beijing, is identified. In addition, the major contributors to SO2 along the route of Shijiazhuang–Baoding–Beijing are elevated sources compared to low area sources for the route of Dezhou–Cangzhou–Tianjin–Beijing; this is found using the interrelated analysis between in situ and mobile DOAS observations during the measurement periods. Furthermore, the discussions on hot spots near the city of JiNan show that average observed width of polluted air mass is 11.83 and 17.23 km associated with air mass diffusion, which is approximately 60 km away from emission sources based on geometrical estimation. Finally, a reasonable agreement exists between the Ozone Monitoring Instrument (OMI) and mobile DOAS observations, with a correlation coefficient (R2) of 0.65 for NO2 VCDs. Both datasets also have a similar spatial pattern. The fitted slope of 0.55 is significantly less than unity, which can reflect the contamination of local sources, and OMI observations are needed to improve the sensitivities to the near-surface emission sources through improvements of the retrieval algorithm or the resolution of satellites.
The Open-source Data Inventory for Anthropogenic CO2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO2) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This paper describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (10.17595/20170411.001).
We determined NOx emissions from Paris in summer 2009 and winter 2009/2010 by applying the closed integral method (CIM) to a large set of car multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements performed within the framework of the MEGAPOLI project (http://megapoli.dmi.dk/). MAX-DOAS measurements of the tropospheric NO2 vertical column density (VCD) were performed in large circles around Paris. From the combination of the observed NO2 VCDs with wind fields, the NO2 influx into and the outflux from the encircled area was determined. The difference between the influx and outflux represents the total emission. Compared to previous applications of the CIM, the large number of measurements during the MEGAPOLI campaign allowed the investigation of important aspects of the CIM. In particular, the applicability of the CIM under various atmospheric conditions could be tested. Another important advantage of the measurements during MEGAPOLI is that simultaneous atmospheric model simulations with a high spatial resolution (3 × 3 km2) are available for all days. Based on these model data, it was possible to test the consistency of the CIM and to derive information about favourable or non-favourable conditions for the application of the CIM. We found that in most situations the uncertainties and the variability in the wind data dominate the total error budget, which typically ranges between 30 and 50 %. Also, measurement gaps and uncertainties in the partitioning ratio between NO and NO2 are important error sources. Based on a consistency check, we deduced a set of criteria on whether measurement conditions are suitable or not for the application of the CIM. We also developed a method for the calculation of the total error budget of the derived NOx emissions. Typical errors are between ± 30 and ±50 % for individual days (with one full circle around Paris). From the application of the CIM to car MAX-DOAS observations we derive daily average NOx emissions for Paris of 4.0 × 1025 molec s-1 for summer and of 6.9 × 1025 molec s-1 in winter. These values are a factor of about 1.4 and 2.0 larger than the corresponding emissions derived from the application of the CIM to the model data, using the Toegepast Natuurwetenschappelijk Onderzoek (TNO) MEGAPOLI emission inventory, in summer and winter, respectively. Similar ratios (1.5 and 2.3 for summer and winter, respectively) were found for the comparison with the Monitoring Atmospheric composition and climate III (MACC-III) emission inventory. The highest NOx emissions were found during some cold days in February. Enhanced domestic heating and a reduced conversion efficiency of catalytic converters might contribute to these enhanced NOx emissions.
Mobile differential optical absorption spectroscopy (mobile DOAS) is an optical remote sensing method that can rapidly measure trace gas emission flux from air pollution sources (such as power plants, industrial areas, and cities) in real time. Generally, mobile DOAS is influenced by wind, drive velocity, and other factors, especially in the usage of wind field when the emission flux in a mobile DOAS system is observed. This paper presents a detailed error analysis and NOx emission with mobile DOAS system from a power plant in Shijiazhuang city, China. Comparison of the SO2 emission flux from mobile DOAS observations with continuous emission monitoring system (CEMS) under different drive speeds and wind fields revealed that the optimal drive velocity is 30–40 km/h, and the wind field at plume height is selected when mobile DOAS observations are performed. In addition, the total errors of SO2 and NO2 emissions with mobile DOAS measurements are 32% and 30%, respectively, combined with the analysis of the uncertainties of column density, wind field, and drive velocity. Furthermore, the NOx emission of 0.15 ± 0.06 kg/s from the power plant is estimated, which is in good agreement with that from CEMS observations of 0.17 ± 0.07 kg/s. This study has significantly contributed to the measurement of the mobile DOAS system on emission from air pollution sources, thus improving estimation accuracy.
Control policies such as “odd-and-even license plate rule” were implemented by the Chinese government to restrict traffic and suspend factory production in Beijing and neighboring cities during the Asia-Pacific Economic Cooperation summit. We use ozone monitoring instrument (OMI), mobile differential optical absorption spectroscopy (DOAS), and multi-axis differential optical absorption spectroscopy (MAX-DOAS) to measure the variation of the spatial and temporal patterns of NO2 column densities from October 24, 2014 to November 22, 2014. It is found that the NO2 column densities during the episode of control policies are significantly lower than those during other periods, and the emission flux of NO2 calculated by mobile DOAS is also lower than the results from other periods. Some daily low NO2 column densities occur with the northwest wind direction. We then compare the relationship between OMI and mobile DOAS NO2 column density observations, and the results of mobile DOAS are approximately 2.7 times larger than the OMI values. The largest discrepancy occurs in the northern part of Beijing city. In other parts, the two instruments have a better correlation coefficient (R
2) of 0.61. The low NO2 column densities that occur during the episode of control policies are affected by the control policies as well as meteorological conditions.
The mobile DOAS technique was used to quantify the NOxvehicle
emission of the central urban area, enclosed by the Inner Ring Viaduct
Road (IRVR), of Shanghai, China. Three field measurement campaigns were
performed during the pre-EXPO period in October 2009, the EXPO opening
ceremony, and the closing ceremony period in 2010. The spatial and
temporal distributions of NO2 Vertical Column Densities were
derived, and the effects of traffic and wind conditions were studied.
The averaged NO2 emissions rates from the IRVR area were
determined to be 2.1 ± 0.9 ton/h, 2.8 ± 1.4 ton/h and 2.7
± 1.4 ton/h for these three campaigns. The annual vehicle
NOx emissions for the central urban area were estimated to be
(2.3 ± 1.0) × 104 ton in 2009 and (3.0 ±
1.5) × 104 ton in 2010, and those for the whole city
were (13.4 ± 5.9) × 104 ton in 2009 and (17.6
± 8.4) × 104 ton in 2010, by considering a
typical NO/NO2 ratio, NOx lifetime, as well as the
traffic emission share of central urban area with respect to the entire
city. The vehicle NOx emissions quantified by mobile DOAS
were found to exhibit an increasing trend in the central urban area of
Shanghai, by 1.6 times and 2.1 times from 2006 to 2009 and 2010. Due to
wind field variations, unknown NOx chemical transformation,
and uncertainties in AMFs, the systematic derivation of the final
NOx emission rates obtained was about 55% for averaged
several consecutive days.
The Mexico City Metropolitan Area (MCMA) has presented severe pollution
problems for many years. There are several point and mobile emission sources
inside and outside the MCMA which are known to affect air quality in the
area. In particular, speculation has risen as to whether the Tula industrial
complex, located 60 km northwest of the MCMA has any influence on high
SO2 levels occurring on the northern part of the city, in the winter
season mainly. As part of the MILAGRO Field Campaign, from 24 March to 17
April 2006, the differential vertical columns of sulfur dioxide (SO2)
and nitrogen dioxide (NO2) were measured during plume transects in the
neighborhood of the Tula industrial complex using mobile mini-DOAS
instruments. Vertical profiles of wind speed and direction obtained from
pilot balloons and radiosondes were used to calculate SO2 and NO2
emissions. According to our measurements, calculated average emissions of
SO2 and NO2 during the field campaign were 384±103 and 24±7 tons day−1,
respectively. The standard deviation of these
estimations is due to actual variations in the observed emissions from the
refinery and power plant, as well as to the uncertainty in the wind fields
at the exact time of the measurements. Reported values in recent inventories
were found to be in good agreement with calculated emissions during the
field campaign. Our measurements were also found to be in good agreement
with simulated plumes.
We here present the results from mobile measurements using two ground-based zenith viewing Differential Optical Absorption Spectroscopy (DOAS) instruments. The measurement was performed in a cross-section of the plume from the Mexico City Metropolitan Area (MCMA) on 10 March 2006 as part of the MILAGRO field campaign. The two instruments operated in the UV and the visible wavelength region respectively and have been used to derive the differential vertical columns of HCHO and NO2 above the measurement route. This is the first time the mobile mini-DOAS instrument has been able to measure HCHO, one of the chemically most important and interesting gases in the polluted urban atmosphere. Using a mass-averaged wind speed and wind direction from the WRF model the instantaneous flux of HCHO and NO2 has been calculated from the measurements and the results are compared to the CAMx chemical model. The calculated flux through the measured cross-section was 1.9 (1.5-2.2) kg/s of HCHO and 4.4 (4.0-5.0) kg/s of NO2 using the UV instrument and 3.66 (3.63-3.73) kg/s of NO2 using the visible light instrument. The modeled values from CAMx for the outflow of both NO2 and HCHO, 1.1 and 3.6 kg/s, respectively, show a reasonable agreement with the measurement derived fluxes.
The HYSPLIT_4 (HYbrid Single-Particle Lagrangian Integrated Trajectory) model is designed for quick response to atmospheric emergencies, diagnostic case studies, or climatological analyses using previously gridded meteorological data. Calculations may be performed sequentially on multiple meteorological grids, going from fine to coarse resolution and using either archive or forecast data fields. Air concentration calculations associate the mass of the pollutant species with the release of either puffs, particles, or a combination of both. The dispersion rate is calculated from the vertical diffusivity profile, wind shear, and horizontal deformation of the wind field. Air concentrations are calculated at a specific grid-point for puffs and as cell-average concentrations for particles. The model results are evaluated against ACE balloon trajectories, air concentrations from the ANATEX tracer experiment, radiological deposition from the Chernobyl accident, and satellite photographs of the Rabaul volcanic eruption. One common feature of the model results was their sensitivity to the vertical atmospheric structure; trajectories in terms of their height when near ground-level due to the strong gradients of wind speed and direction, air concentrations with respect to the rate of vertical mixing, and deposition as a result of the vertical distribution of the pollutant.
Mobile Differential Optical Absorption Spectroscopy measurements of
SO2 and NO2 were performed in the Guangzhou
Eastern Area (GEA) during the Guangzhou Asian Games 2010 from November
2010 to December 2010. Spatial and temporal distributions of
SO2 and NO2 in this area were obtained and
emission sources were determined by using wind field data. The
NO2 vertical column densities were found to agree with OMI
values. The correlation coefficient (R2) was 0.88 after cloud
filtering. During the Guangzhou Asian Games and Asian Paralympics (Para)
Games, the SO2 and NO2 emissions in the area were
quantified using averaged wind speed and wind direction. For times
outside the Games the average SO2 emission was estimated to
be 9.50 ± 0.90 tons per hour and the average NO2
emission was estimated to be 3.50 ± 1.89 tons per hour. During
the phases of the Asian and Asian Para Games, the SO2 and
NO2 emissions were reduced by 53.5 and 46%, respectively,
compared to the usual condition. We also investigated the influence of
GEA on Guangzhou University Town, the main venue located northwest of
the GEA, and found that SO2 concentrations here were about
tripled by emissions from the GEA.
An automatic spectral complex developed at the Institute of Physics, St. Petersburg State University, is described. This complex
is used for regular ground-based spectroscopic measurements of the total NO2 content in the vertical column of the atmosphere during the twilight and daylight hours of the day near St. Petersburg (Petrodvorets).
In 2004–2006, a number of ground-based twilight measurements of the total NO2 content were obtained near St. Petersburg, and variations in the NO2 content in the troposphere were estimated from the results of daytime ground-based measurements. An example of the spatial
annual mean distribution of the NO2 content (central and northern Europe, northwestern Russia) based on the data of satellite measurements over the period 2003–2005
is presented. This example demonstrates the main sources of anthropogenic pollution. An increase in the mean annual contents
of tropospheric NO2 near Moscow and St. Petersburg is preliminarily estimated for the entire period of satellite observations with the GOME instrument
at about 30–40% over ten years.
The Ozone Monitoring Instrument is a trace gas monitoring instrument in the line of GOME (ERS-2) and Sciamachy (ENVISAT). Following these instruments, OMI provides UV-visible spectroscopy with a resolution sufficient to separate out the various absorbing trace gases (using DOAS or `Full' retrieval), but shaped as an imaging spectrometer. This means that a two dimensional detector is used where one dimension records the spectrum and the other images the swath. The scanning mechanism from the GOME and Sciamachy is not required anymore and there are considerable advantages with respect to simultaneous measurement of swath pixels, polarisation and obtainable swath width. The OMI consortium for a phase B is formed by Fokker Space & Systems and TPD in the Netherlands and VTT in Finland. In the presentation UV-visible atmospheric remote sensing will be placed in perspective and the OMI will be explaned.
We present car Multi-Axis (MAX-) DOAS observations of tropospheric NO2 carried out on circles around the cities of Mannheim and Ludwigshafen (Germany) on 24 August 2006. Together with information on wind speed and direction, the total emissions of the encircled source(s) are quantified from these measurements. In contrast to recent similar studies based on zenith scattered sun light (elevation angle of 90°), we use a MAX-DOAS instrument mounted on a car, which observes scattered sun light under different elevation angles (here 45°, and 90°). Compared to simple zenith sky observations, MAX-DOAS observations have higher sensitivity and reduced uncertainty, and avoid systematic offsets in the determination of the vertically integrated trace gas concentration. The determination of the absolute value of the integrated tropospheric trace gas concentrations is especially important for the calculation of absolute trace gas fluxes through arbitrary transects. However, even if emission sources are completely surrounded, systematic offsets in the measured vertically integrated trace gas concentration can lead to errors in the determined total emissions, especially for observations around extended areas. In this study we discuss and quantify different error sources. In most cases, the largest error source is the variability and imperfect knowledge of the wind field. In addition - depending on the trace species observed - also chemical transformations between the emission sources and the measurement location have to be considered. For that purpose we use local observations within the encircled area to quantify and/or correct these errors. From our observations we derive a total NOx emission from the Mannheim/Ludwigshafen area of (7.4±1.8)×1024 molec/sec, which if assumed to be constant throughout the year would correspond to a total emission of 17 830±4340 t/yr (calculated with the mass of NO2 t/yr, consistent with existing emission estimates. From our observations it is also possible to separately determine the average influx into the Mannheim/Ludwigshafen area (5.4±0.9×1024 molec/sec or 13 010±2170 t/yr) and the average outflux (12.8±1.8×1024 molec/sec or 13 010±4340 t/yr).
Emissions of CO2 from fossil fuel combustion are a critical quantity that must be accurately given in established flux inversion frameworks. Work with emerging satellite-based inversions requires spatiotemporally-detailed inventories that permit analysis of regional natural sources and sinks. Conventional approaches for disaggregating national emissions beyond the country and city levels based on population distribution have certain difficulties in their application. We developed a global 1 km×1 km annual fossil fuel CO2 emission inventory for the years 1980–2007 by combining a worldwide point source database and satellite observations of the global nightlight distribution. In addition to estimating the national emissions using global energy consumption statistics, emissions from point sources were estimated separately and were spatially allocated to exact locations indicated by the point source database. Emissions from other sources were distributed using a special nightlight dataset that had fewer saturated pixels compared with regular nightlight datasets. The resulting spatial distributions differed in several ways from those derived using conventional population-based approaches. Because of the inherent characteristics of the nightlight distribution, source regions corresponding to human settlements and land transportation were well articulated. Our distributions showed good agreement with a high-resolution inventory across the US at spatial resolutions that were adequate for regional flux inversions. The inventory can be extended to the future using updated data, and is expected to be incorporated into models for operational flux inversions that use observational data from the Japanese Greenhouse Gases Observing SATellite (GOSAT).
The results of measurements of surface concentrations of nitrogen oxides NOx (NO and NO2) performed at the atmospheric monitoring station near St. Petersburg (Petergof, 59.88 N, 29.83 E) in 2012–2018 are presented. The main patterns of the temporal variability of NOx concentrations typical for the urban atmosphere of a large megacity (seasonal, daily, and weekly variations) are revealed. On average, NOx concentrations in summer are lower than in the cold season. The average daily variations are characterized by two pronounced peaks: in the morning and late at night, which are separated by the periods of relatively low concentrations during the day and early in the morning.
The weekly variations in NOx concentration due to the cyclic pattern of urban economic and business activity are manifested in a noticeable decrease in the average NOx concentration on Sunday relative to its maximum level on weekdays (by 17% for NO2 and by 33% for NO). Based on the distribution of the average NOx concentration depending on the directions of surface wind, a rough estimate of the average NO2 concentration in St. Petersburg was obtained: 41 μg/m3, which is close to the average annual maximum permissible concentration (40 μg/m3).
Mobile DOAS (Differential Optical Absorption Spectroscopy) circular measurements of tropospheric nitrogen dioxide (NO2) were performed on a number of days in 2012 and 2014–2016 around St.Petersburg. These observations figured out an evolution of urban pollution plume, released from the megacity. The HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model was configured to simulate the observed NO2 dispersion, taking into account the municipal inventory database of urban nitrogen oxides (NOx = NO+ NO2) emission. The simulation results were found consistent with the data of mobile measurements, allowing to fit an estimate of integral NOx emission rate from St.Petersburg (about 60 kt year⁻¹ on the average). Coincident satellite measurements the Aura Ozone Monitoring Instrument (OMI) of tropospheric NO2 mostly agree with the data of simulation on the days of mobile observations. In general, presented results demonstrate the capabilities of joint interpretation of disparate tropospheric NO2 data in the vicinity of a megacity.
The results of spectroscopic measurements of tropospheric NO2 content performed on a closed route along the circular road around the city of St. Petersburg in 2012, 2014, and 2015 are presented. A procedure for determining the integral emission of NOx based on the data of measurements on the route enveloping the sources under study is described. An analysis of the experimental data together with the results of a numerical simulation of air pollutant dispersion (the HYSPLIT model) provided an estimate of the total volume of NOx emitted by all sources located inside the circular road. The average emission rate of NOx according to the sources of the megacity of St. Petersburg is 57000 t/yr, which correlates satisfactorily with the official data of a municipal inventory of the sources of air pollution (62000–63 000 t/yr).
HYSPLIT, developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.
We present mobile differential optical absorption spectroscopy (DOAS) zenith-sky observations of tropospheric NO2 from field experiments carried out at St Petersburg (Russia) on a number of days during May–October of 2009–2012. We conducted a detailed analysis of our measurements on a closed route around the entire city that took place on 14 August 2012. We used the data from these circular observations to derive our top–down estimate of total NOx emission from St Petersburg on that day, i.e. (12.6 ± 2.4) ×1024 molecules s−1. This value, if assumed to be constant throughout the year, would correspond to a total emission rate of (31 ± 6) kt/year (calculated on condition that all NOx was NO2). Our estimate is half of that reported in a published official inventory of St Petersburg (63 kt/year). Possible reasons for that discrepancy and an outlook for further improvements are considered and discussed further.
The results of ground-based and satellite spectroscopic measurements of the tropospheric NO2 content near St. Petersburg in January–March 2006 are presented. It is shown that the increased concentrations of NO2 observed in St. Petersburg and its vicinities in this period were caused by NO2 accumulation due to unfavorable weather conditions, which is confirmed by an analysis of meteorological data and the results of a numerical simulation of the dispersion of urban air pollutants. Data from satellite and ground-based measurements agree with each other satisfactorily (a correlation coefficient of 0.5) and with model calculations of tropospheric NO2 conducted for the coordinates of a station of ground-based measurements (a correlation coefficient of 0.6). The HYSPLIT dispersion model also made it possible to estimate the scale of the NO2 spatial-temporal variability in the near-surface layer in the vicinities of St. Petersburg.
NO2 fluxes were measured using a mobile mini-DOAS during Cal-Mex 2010 field study, between May 15 and June 30, 2010, from the urban area of Tijuana, Baja California as well as the Rosarito power plant. The average calculated NO2 fluxes were 328 ± 184 (269 ± 201) g s−1, and 23.4 ± 4.9 (12.9 ± 11.9) g s−1 for Tijuana urban area and Rosarito power plant, respectively, using model based wind fields and onsite measurements (in parenthesis). Wind speed and wind direction data needed to estimate the fluxes were both modeled and obtained from radiosondes launched regularly during the field campaign, whereas the mixing layer height throughout the entire field campaign was measured using a ceilometer. Large variations in the NO2 fluxes from both the Tijuana urban area and Rosarito power plant were observed during Cal-Mex 2010; however, the variability was less when model based wind fields were used. Qualitative comparisons of modeled and measured plumes from the Tijuana urban area and Rosarito power plant showed good agreement.
Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies
This paper presents a novel method for quantification of the total emission of gases from area sources using the mobile mini-DOAS (Differential Optical Absorption Spectroscopy) instrument, a ground based optical remote sensing technique. The presented method has been applied to measure the emission of SO2 and NO2 from the city of Beijing, P.R. China, on a time scale of approximately 2h. The measurements have been performed during two field campaigns, in April and in August 2005. The estimated emission of NO2 is roughly the same during the two field campaigns with an average emission of 189tonsd−1 during April and 174tonsd−1 during August. The estimated emission of SO2 varies greatly between the two periods, with an average emission of 293tonsd−1 during April and 52tonsd−1 during August.
We examine the representation of emissions from megacities in three global anthropogenic emission inventories. Despite the many common sources of data between the inventories, and the similarities in their construction methodologies, there are some very large differences (often a factor of two) between the emissions for individual cities, even when the total global emissions are very similar. We find that the geographical distribution of the emissions within countries plays a larger role in explaining the differences between the inventories than differences in the country total emissions. We also find very large differences between the contribution of various sectors to the total emissions from each city, and relate these differences to the respective methodologies used in the inventory construction. By and large, in OECD countries megacity emissions from the global inventories are dominated by road transport, especially for CO and to a lesser degree for NOx. In non-OECD countries, notably in Asia, megacity CO emissions are dominated by residential biofuel use, while industrial emissions predominate for NOx. Non-methane hydrocarbon emissions in OECD megacities are caused by industry and traffic, whereas in non-OECD countries residential biofuel use makes significant contributions. These emission signatures often result from assumptions about the distribution of emissions according to gridded population density maps rather than according to the actual location of the emitting processes. We recommend the use of an ensemble of inventories, that the geographical distribution of emissions receives increased attention, and that local inventories be integrated into global emission inventories.
Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies
Megacities are immense sources of air pollutants, with large impacts on air quality and climate. However, emission inventories in many of them still are highly uncertain, particularly in developing countries. Satellite observations allow top-down estimates of emissions to be made for nitrogen oxides (NO(x) = NO + NO(2)), but require poorly quantified a priori information on the NO(x) lifetime. We present a method for the simultaneous determination of megacity NO(x) emissions and lifetimes from satellite measurements by analyzing the downwind patterns of NO(2) separately for different wind conditions. Daytime lifetimes are ~4 hours at low and mid-latitudes, but ~8 hours in wintertime for Moscow. The derived NO(x) emissions are generally in good agreement with existing emission inventories, but are higher by a factor of 3 for the Saudi Arabian capital Riyadh.
About half of the world's population now lives in urban areas because of the opportunity for a better quality of life. Many of these urban centers are expanding rapidly, leading to the growth of megacities, which are defined as metropolitan areas with populations exceeding 10 million inhabitants. These concentrations of people and activity are exerting increasing stress on the natural environment, with impacts at urban, regional and global levels. In recent decades, air pollution has become one of the most important problems of megacities. Initially, the main air pollutants of concern were sulfur compounds, which were generated mostly by burning coal. Today, photochemical smog--induced primarily from traffic, but also from industrial activities, power generation, and solvents--has become the main source of concern for air quality, while sulfur is still a major problem in many cities of the developing world. Air pollution has serious impacts on public health, causes urban and regional haze, and has the potential to contribute significantly to climate change. Yet, with appropriate planning, megacities can efficiently address their air quality problems through measures such as application of new emission control technologies and development of mass transit systems. This review is focused on nine urban centers, chosen as case studies to assess air quality from distinct perspectives: from cities in the industrialized nations to cities in the developing world. While each city--its problems, resources, and outlook--is unique, the need for a holistic approach to the complex environmental problems is the same. There is no single strategy in reducing air pollution in megacities; a mix of policy measures will be needed to improve air quality. Experience shows that strong political will coupled with public dialog is essential to effectively implement the regulations required to address air quality problems.
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