A Gradient-Descent Optimization of CO2-CO-NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown.

Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2-CO-NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.

Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model.

Ultrafine particles (UFPs) are respirable particles with a diameter less than 100 nm, which some studies have associated with adverse effects upon health. UFPs are currently not regulated as the health evidence is insufficient and very few observational data are available in most cities. The 2021 WHO Global Air Quality Guidelines highlighted the pressing issue of UFPs and provided a good practice statement for UFPs, which recommends that more measurement and modelling studies are implemented in future. Particle number concentrations (PNC) are the most common metric for UFPs as this fraction normally dominates the total ambient PNC in urban environments. This study simulates the dispersion of particle number concentrations in the West Midlands (a metropolitan area), UK using the local scale ADMS-Urban model, which is an advanced quasi-Gaussian plume dispersion modelling system. ADMS-Urban implements a physics-based approach to represent the characteristics of the atmospheric boundary layer and has been widely used in the dispersion modelling of air pollutants. It can represent a variety of source types (such as road and grid emissions) occurring in urban environments and requires a range of input data. Particle number was used as a passive scalar, with no inclusion of aerosol microphysics within the model, as a first implementation in the ADMS-Urban model for the West Midlands, UK. Evaluation was conducted by comparing the modelled (from a receptor run) and measured data at the Birmingham Air Quality Supersite. Overall, the model performed well although there was a slight underestimation for PNC. Based on the modelling output from a contour run, PNC maps at a variety of spatial scales (i.e. street scale, ward level and local authority level) and temporal resolutions (i.e. annual, 24-hour, and 1-hour) were generated. PNC mapping could be linked to local population and health data for potential epidemiological studies.

From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities.

BACKGROUND: Urban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes. RESULTS: Our study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level. CONCLUSIONS: We conclude that quantified individual cities' climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement.

The CO2 Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO2, CH4 and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO2 Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.

Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations.

Two years of satellite observations were used to quantify methane emissions from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570 ± 98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production while emitting 55 ± 10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations, our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.

Modeling emissions for three-dimensional atmospheric chemistry transport models.

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date. IMPLICATIONS: Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.

Urban air quality: the challenge of traffic non-exhaust emissions.

About 400,000 premature adult deaths attributable to air pollution occur each year in the European Region. Road transport emissions account for a significant share of this burden. While important technological improvements have been made for reducing particulate matter (PM) emissions from motor exhausts, no actions are currently in place to reduce the non-exhaust part of emissions such as those from brake wear, road wear, tyre wear and road dust resuspension. These "non-exhaust" sources contribute easily as much and often more than the tailpipe exhaust to the ambient air PM concentrations in cities, and their relative contribution to ambient PM is destined to increase in the future, posing obvious research and policy challenges. This review highlights the major and more recent research findings in four complementary fields of research and seeks to identify the current gaps in research and policy with regard to non-exhaust emissions. The objective of this article is to encourage and direct future research towards an improved understanding on the relationship between emissions, concentrations, exposure and health impact and on the effectiveness of potential remediation measures in the urban environment.

Non-exhaust emissions of PM and the efficiency of emission reduction by road sweeping and washing in the Netherlands.

From research on PM(2.5) and PM(10) in 2007/2008 in the Netherlands, it was concluded that the coarse fraction (PM(2.5-10)) attributed 60% and 50% respectively, to the urban-regional and street-urban increments of PM(10). Contrary to Scandinavian and Mediterranean countries which exhibit significant seasonal variation in the coarse fraction of particulate matter (PM), in the Netherlands the coarse fraction in PM at a street location is rather constant during the year. Non-exhaust emissions by road traffic are identified as the main source for coarse PM in urban areas. Non-exhaust emissions mainly originate from re-suspension of accumulated deposited PM and road wear related particles, while primary tire and brake wear hardly contribute to the mass of non-exhaust emissions. However, tire and brake wear can clearly be identified in the total mass through the presence of the heavy metals: zinc, a tracer for tire wear and copper, a tracer for brake wear. The efficiency of road sweeping and washing to reduce non-exhaust emissions in a street-canyon in Amsterdam was investigated. The increments of the coarse fraction at a kerbside location and a housing façade location versus the urban background were measured at days with and without sweeping and washing. It was concluded that this measure did not significantly reduce non-exhaust emissions.

Lead emissions from road transport in Europe: a revision of current estimates using various estimation methodologies.

Large-scale use of leaded gasoline was an important source of the neurotoxin lead in the European environment. After a sequence of regulations on the allowed gasoline lead content and, eventually, a ban on the use of lead additives in gasoline, road transport was no longer considered a source of atmospheric lead. Currently a discrepancy exists between measured atmospheric lead concentrations and model-predicted concentrations, suggesting that lead emissions to the atmosphere may be underestimated. Recently it was suggested that lead emission from unleaded gasoline combustion is still an important source and may (partly) fill the gap between modelled and observed atmospheric lead concentrations. In this paper we assess the plausibility of the latter suggestion by following various emission estimation methodologies. The uncertainty of lead emissions from road transport is further reduced by chemical analysis of fuel samples. The result of our assessment is that lead from road transport fuel combustion is not the missing lead source needed to fill the gap between modelled and observed lead concentrations. Road transport is still a source of lead through brake wear and a small contribution from exhaust emissions but this contributes no more than 5-8% of the EU25 total emission.