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

2018 ◽  
Vol 18 (5) ◽  
pp. 3433-3456 ◽  
Author(s):  
Meng Li ◽  
Zbigniew Klimont ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Power Plants ◽  
Comprehensive Evaluation ◽  
National Level ◽  
Model Performance ◽  
Chemical Transport ◽  
Emission Factors ◽  
Emission Inventories ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up

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

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

2017 ◽  
Author(s):  
Meng Li ◽  
Zbigniew Klimont ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Model Performance ◽  
Chemical Transport ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up ◽  
Essential Input ◽  
Inventory Development

Abstract. Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely-used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find the clues in improving emission inventories. We first compared the activity rates and emission factors used in two inventories, and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. Discrepancies at sectorial and provincial level are much higher. We then examined the impacts of different inventories on model performance, by using the nested GEOS-Chem model. We finally derived top-down NOx emissions by using the NO2 columns from the Ozone Monitoring Instrument (OMI) and compared with the bottom-up estimates. To our knowledge, this is the first work where source-sector comparisons are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we summarized the potential directions for further improvement in inventory development.

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

scholarly journals Improvement from the satellite-derived NO<sub>x</sub> emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in Yangtze River Delta, China

2020 ◽  
Author(s):  
Yang Yang ◽  
Yu Zhao ◽  
Lei Zhang ◽  
Jie Zhang ◽  
Xin Huang ◽  
...  
Keyword(s):  
Air Quality ◽  
Model Performance ◽  
Yangtze River Delta ◽  
Nox Emissions ◽  
Top Down ◽  
Bottom Up ◽  
Inorganic Aerosols ◽  
Quality Modeling ◽  
Emission Estimation ◽  
Reduced Rate

Abstract. We developed a top-down methodology combining the inversed chemistry transport modeling and satellite-derived tropospheric vertical column of NO2, and estimated the NOx emissions of Yangtze River Delta (YRD) region at a horizontal resolution of 9 km for January, April, July and October 2016. The effect of the top-down emission estimation on air quality modeling, and the response of ambient ozone (O3) and secondary inorganic aerosols (SO42−, NO3−, and NH4+, SNA) to the changed precursor emissions were evaluated with the Community Multi-scale Air Quality (CMAQ) system. The top-down estimates of NOx emissions were smaller than those in a national emission inventory, MEIC (i.e., the bottom-up estimates), for all the four months, and the monthly mean was calculated at 260.0 Gg/month, 24 % less than the bottom-up one. The NO2 concentrations simulated with the bottom-up estimate of NOx emissions were clearly higher than the ground observation, indicating the possible overestimation in current emission inventory attributed to its insufficient consideration of recent emission control in the region. The model performance based on top-down estimate was much better, and the biggest change was found for July with the normalized mean bias (NMB) and normalized mean error (NME) reduced from 111 % to −0.4 % and from 111 % to 33 %, respectively. The results demonstrate the improvement of NOx emission estimation with the nonlinear inversed modeling and satellite observation constraint. With the smaller NOx emissions in the top-down estimate than the bottom-up one, the elevated concentrations of ambient O3 were simulated for most YRD and they were closer to observation except for July, implying the VOC (volatile organic compound)-limit regime of O3 formation. With available ground observations of SNA in the YRD, moreover, better model performance of NO3− and NH4+ were achieved for most seasons, implying the effectiveness of precursor emission estimation on the simulation of secondary inorganic aerosols. Through the sensitivity analysis of O3 formation for April 2016, the decreased O3 concentrations were found for most YRD region when only VOCs emissions were reduced or the reduced rate of VOCs emissions was two times of that of NOx, implying the crucial role of VOCs control on O3 pollution abatement. The SNA level for January 2016 was simulated to decline 12 % when 30 % of NH3 emissions were reduced, while the change was much smaller with the same reduced rate for SO2 or NOx. The result suggests that reducing NH3 emissions was the most effective way to alleviate SNA pollution for YRD in winter.

scholarly journals Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH4 Emissions in China

2019 ◽  
Vol 11 (7) ◽  
pp. 2054 ◽  
Author(s):  
Penwadee Cheewaphongphan ◽  
Satoru Chatani ◽  
Nobuko Saigusa
Keyword(s):  
Coal Mining ◽  
Rice Cultivation ◽  
Emission Inventories ◽  
Ch4 Emission ◽  
Top Down ◽  
Activity Data ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
Model Studies ◽  
Livestock Sector

Bottom-up CH4 emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH4 bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH4 emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH4 emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.

scholarly journals Estimating European volatile organic compound emissions using satellite observations of formaldehyde from the Ozone Monitoring Instrument

2010 ◽  
Vol 10 (23) ◽  
pp. 11501-11517 ◽  
Author(s):  
G. Curci ◽  
P. I. Palmer ◽  
T. P. Kurosu ◽  
K. Chance ◽  
G. Visconti
Keyword(s):  
Satellite Observations ◽  
Atmospheric Composition ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up ◽  
Voc Emissions ◽  
The Balkans ◽  
Monitoring Instrument ◽  
Volatile Organic ◽  
Ozone Monitoring

Abstract. Emission of non-methane Volatile Organic Compounds (VOCs) to the atmosphere stems from biogenic and human activities, and their estimation is difficult because of the many and not fully understood processes involved. In order to narrow down the uncertainty related to VOC emissions, which negatively reflects on our ability to simulate the atmospheric composition, we exploit satellite observations of formaldehyde (HCHO), an ubiquitous oxidation product of most VOCs, focusing on Europe. HCHO column observations from the Ozone Monitoring Instrument (OMI) reveal a marked seasonal cycle with a summer maximum and winter minimum. In summer, the oxidation of methane and other long-lived VOCs supply a slowly varying background HCHO column, while HCHO variability is dominated by most reactive VOC, primarily biogenic isoprene followed in importance by biogenic terpenes and anthropogenic VOCs. The chemistry-transport model CHIMERE qualitatively reproduces the temporal and spatial features of the observed HCHO column, but display regional biases which are attributed mainly to incorrect biogenic VOC emissions, calculated with the Model of Emissions of Gases and Aerosol from Nature (MEGAN) algorithm. These "bottom-up" or a-priori emissions are corrected through a Bayesian inversion of the OMI HCHO observations. Resulting "top-down" or a-posteriori isoprene emissions are lower than "bottom-up" by 40% over the Balkans and by 20% over Southern Germany, and higher by 20% over Iberian Peninsula, Greece and Italy. We conclude that OMI satellite observations of HCHO can provide a quantitative "top-down" constraint on the European "bottom-up" VOC inventories.

scholarly journals Evaluation of mobile emissions contributions to Mexico City's emissions inventory using on-road and cross-road emission measurements and ambient data

2009 ◽  
Vol 9 (17) ◽  
pp. 6305-6317 ◽  
Author(s):  
M. Zavala ◽  
S. C. Herndon ◽  
E. C. Wood ◽  
T. B. Onasch ◽  
W. B. Knighton ◽  
...  
Keyword(s):  
Measurement Techniques ◽  
Early Morning ◽  
Emission Factors ◽  
Top Down ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Mobile Sources ◽  
Mobile Emissions ◽  
The City ◽  
Road Emission

Abstract. Mobile emissions represent a significant fraction of the total anthropogenic emissions burden in the Mexico City Metropolitan Area (MCMA) and, therefore, it is crucial to use top-down techniques informed by on-road exhaust measurements to evaluate and improve traditional bottom-up official emissions inventory (EI) for the city. We present the measurements of on-road fleet-average emission factors obtained using the Aerodyne mobile laboratory in the MCMA in March 2006 as part of the MILAGRO/MCMA-2006 field campaign. A comparison of our on-road emission measurements with those obtained in 2003 using essentially the same measurement techniques and analysis methods indicates that, in the three year span, NO emission factors remain within the measured variability ranges whereas emission factors of aldehydes and aromatics species were reduced for all sampled driving conditions. We use a top-down fuel-based approach to evaluate the mobile emissions from the gasoline fleet estimated in the bottom-up official 2006 MCMA mobile sources. Within the range of measurement uncertainties, we found probable slight overpredictions of mean EI estimates on the order of 20–28% for CO and 14–20% for NO. However, we identify a probable EI discrepancy of VOC mobile emissions between 1.4 and 1.9; although estimated benzene and toluene mobile emissions in the inventory seem to be well within the uncertainties of the corresponding emissions estimates. Aldehydes mobile emissions in the inventory, however, seem to be underpredicted by factors of 3 for HCHO and 2 for CH3CHO. Our on-road measurement-based estimate of annual emissions of organic mass from PM1 particles suggests a severe underprediction (larger than a factor of 4) of PM2.5 mobile emissions in the inventory. Analyses of ambient CO, NOx and CO/NOx concentration trends in the MCMA indicate that the early morning ambient CO/NOx ratio has decreased at a rate of about 1.9 ppm/ppm/year over the last two decades due to reductions in CO levels rather than by NOx. These trends, together with the analysis of fuel sales and fleet size, suggest that the relative contribution of diesel vehicles to overall NOx levels has increased over time in the city. Despite the impressive increase in the size of the vehicle fleet between 2000 and 2006, the early morning ambient concentrations of CO and NOx have not increased accordingly, probably due to the reported low removal rates of older vehicles, which do not have emissions control technologies, and partially due to the much lower emissions from newer gasoline vehicles. This indicates that an emission-based air quality improvement strategy targeting large reductions of emissions from mobile sources should be directed towards a significant increase of the removal rate of older, highly-polluting, vehicles.

scholarly journals Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

2017 ◽  
Vol 17 (6) ◽  
pp. 4131-4145 ◽  
Author(s):  
Guannan Geng ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Jintai Lin ◽  
Hong Huo ◽  
...  
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Urban Areas ◽  
Model Performance ◽  
Road Transport ◽  
Industrial Sector ◽  
Transport Sector ◽  
Emission Inventories ◽  
Bottom Up ◽  
The Impact

Abstract. Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope  =  1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

scholarly journals NO<sub><i>x</i></sub> emission trends over Chinese cities estimated from OMI observations during 2005 to 2015

2017 ◽  
Vol 17 (15) ◽  
pp. 9261-9275 ◽  
Author(s):  
Fei Liu ◽  
Steffen Beirle ◽  
Qiang Zhang ◽  
Ronald J. van der A ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Power Plants ◽  
Emission Control ◽  
Control Measures ◽  
Nox Emissions ◽  
Ozone Monitoring Instrument ◽  
Emission Data ◽  
Bottom Up ◽  
Chinese Cities ◽  
Emission Changes ◽  
Urban Emissions

Abstract. Satellite nitrogen dioxide (NO2) observations have been widely used to evaluate emission changes. To determine trends in nitrogen oxides (NOx) emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and seven power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations from 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52 % from 2005 to 2011 and decreased by 21 % from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e., power, industrial, and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r = 0. 8 on average), but not for some cities (r = 0. 4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to city level by using spatial distribution proxies.

scholarly journals Innovating (a Lot) With a Little: High-Tech Innovation in Southeast Europe

2021 ◽  
Author(s):  
Nebojša Radojević
Keyword(s):  
National Level ◽  
High Technology ◽  
Innovation Performance ◽  
Innovation Activity ◽  
Southeast Europe ◽  
Top Down ◽  
Bottom Up ◽  
Technology Industries ◽  
High Technology Industries ◽  
The Eu

Motivation: According to the European Innovation Scoreboard (EIS, 2020), Southeast-European countries are either modest or moderate innovators because they consistently innovate below the EU average. Given that innovation is a key driver of economic growth (Hasan & Tucci, 2010), this implies that Southeast Europe has been economically falling back while simultaneously politically integrating with the EU. However, the EIS categorizes countries according to the inconclusive arithmetic mean of indicators for firm-level innovation (bottom-up) and indicators for inputs and enablers of innovation at the national level (top-down). Besides, it does not separately assess innovation performance of high-technology industries although these are crucial for international competitiveness (Schwab, 2019). Consequently, this paper answers the following research question: What is innovation performance of high and medium-high technology industries in Southeast Europe in comparison to the EU average? Idea: In contrast to the EIS which merges top-down with bottom-up innovation indicators, the core idea of this paper has been to analyse only bottom-up data on comparative innovation performance of high and medium-high technology industries in Southeast Europe. Method and data: The paper methodologically draws on guidelines for collecting, reporting, and using data on innovation by Oslo Manual (OECD & Eurostat, 2018), and uses secondary data from 2010-2016 Community Innovation Surveys of enterprises to compile an own set of 140 data points. Innovation activity within an industry is defined as the ratio of innovative enterprises to the total population of enterprises while innovation performance is the ratio of innovation activity in the respective country to innovation activity of this industry in the whole EU. Tools: All data points have been arranged country-wise as unbalanced contingency panels and plotted to draw conclusions on innovation performance of high and medium-high technology industries in Southeast Europe. Findings: Although all top-down innovation inputs and enablers at the national level are far below the EU average throughout Southeast Europe, several industries in the region reach or surpass the average EU innovation performance: the pharmaceutical industry in Croatia, all medium-high technology industries in Turkey, manufacture of machinery in North Macedonia and Serbia, as well as manufacture of motor vehicles in all countries except for Romania. Contributions and limitations: This is the first known paper to benchmark innovation performance of high and medium-high technology industries throughout Southeast Europe. In addition, the paper reveals the shortcomings of the whole-country method employed by the EIS since it clearly points out that innovation performance of national industries should be assessed instead. Limitations of the paper are the exclusive focus on innovation as a process and partly restricted data availability.

scholarly journals Exploiting satellite measurements to reduce uncertainties in UK bottom-up NO<sub>x</sub> emission estimates

2021 ◽  
Author(s):  
Richard J. Pope ◽  
Rebecca Kelly ◽  
Eloise A. Marais ◽  
Ailish M. Graham ◽  
Chris Wilson ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Spatiotemporal Variability ◽  
Nox Emissions ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up ◽  
Emissions Inventory ◽  
Monitoring Instrument ◽  
Hazardous Pollutants ◽  
The Uk

Abstract. Nitrogen oxides (NOx, NO+NO2) are potent air pollutants which directly impact on human health and which aid the formation of other hazardous pollutants such as ozone (O3) and particulate matter. In this study, we use satellite tropospheric column nitrogen dioxide (TCNO2) data to evaluate the spatiotemporal variability and magnitude of the United Kingdom (UK) bottom-up National Atmospheric Emissions Inventory (NAEI) NOx emissions. Although emissions and TCNO2 represent different quantities, for UK city sources we find a spatial correlation of ~0.5 between the NAEI NOx emissions and TCNO2 from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI), suggesting a good spatial distribution of emission sources in the inventory. Between 2005 and 2015, the NAEI total UK NOx emissions and long-term TCNO2 record from the Ozone Monitoring Instrument (OMI), averaged over England, show decreasing trends of 4.4 % and 2.2 %, respectively. Top-down NOx emissions were derived in this study by applying a simple mass balance approach to TROPOMI observed downwind NO2 plumes from city sources. Overall, these top-down estimates were consistent with the NAEI, but for larger cities such as London and Manchester the inventory is significantly (> 25 %) less than the top-down emissions. This NAEI NOx emission underestimate is supported by comparing simulations from the GEOS-Chem atmospheric chemistry model, driven by the NAEI emissions, with satellite and surface NO2 observations over the UK. This yields substantial model negative biases, providing further evidence to demonstrate that the NAEI may be underestimating NOx emissions in London and Manchester.

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