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While Earth observation (EO) increasingly provides a multitude of solutions to address environmental issues and sustainability from the city to global scale, their operational integration into the Sustainable Development Goals (SDG) framework is still falling behind. Within this framework, SDG Indicator 11.6.2 asks countries to report the “annual mean levels of fine particulate matter (PM2.5) in cities (population-weighted)”. The official United Nations (UN) methodology entails aggregation into a single, national level value derived from regulatory air quality monitoring networks, which are non-existent or sparse in many countries. EO, including, but not limited to remote sensing, brings forth novel monitoring methods to estimate SDG Indicator 11.6.2 alongside more traditional ones, and allows for comparability and scalability in the face of varying city definitions and monitoring capacities which impact the validity and usefulness of such an indicator. Pursuing a more harmonised global approach, the H2020 SMURBS/ERA-PLANET project provides two EO-driven approaches to deliver the indicator on a more granular level across Europe. The first approach provides both city and national values for SDG Indicator 11.6.2 through exploiting the Copernicus Atmospheric Monitoring Service reanalysis data (0.1° resolution and incorporating in situ and remote sensing data) for PM2.5 values. The SDG Indicator 11.6.2 values are calculated using two objective city definitions—“functional urban area” and “urban centre”—that follow the UN sanctioned Degree of Urbanization concept, and then compared with official indicator values. In the second approach, a high-resolution city-scale chemical transport model ingests satellite-derived data and calculates SDG Indicator 11.6.2 at intra-urban scales. Both novel approaches to calculating SDG Indicator 11.6.2 using EO enable exploration of air pollution hotspots that drive the indicator as well as actual population exposure within cities, which can influence funding allocation and intervention implementation. The approaches are introduced, and their results frame a discussion around interesting policy implications, all with the aim to help move the dial beyond solely reporting on SDGs to designing the pathways to achieve the overarching targets.

The lockdown measures taken to prevent a rapid spreading of the coronavirus in Europe in spring 2020 led to large emission reductions, particularly in road traffic and aviation. Atmospheric concentrations of NO2 and PM2.5 were mostly reduced when compared to observations taken for the same time period in previous years; however, concentration reductions may not only be caused by emission reductions but also by specific weather situations. In order to identify the role of emission reductions and the meteorological situation for air quality improvements in central Europe, the meteorology chemistry transport model system COSMO-CLM/CMAQ was applied to Europe for the period 1 January to 30 June 2020. Emission data for 2020 were extrapolated from most recent reported emission data, and lockdown adjustment factors were computed from reported activity data changes, e.g. Google mobility reports. Meteorological factors were investigated through additional simulations with meteorological data from previous years. The results showed that lockdown effects varied significantly among countries and were most prominent for NO2 concentrations in urban areas with 2-week-average reductions up to 55 % in the second half of March. Ozone concentrations were less strongly influenced (up to ±15 %) and showed both increasing and decreasing concentrations due to lockdown measures. This depended strongly on the meteorological situation and on the NOx / VOC emission ratio. PM2.5 revealed 2 %–12 % reductions of 2-week-average concentrations in March and April, which is much less than a different weather situation could cause. Unusually low PM2.5 concentrations as observed in northern central Europe were only marginally caused by lockdown effects. The lockdown can be seen as a big experiment about air quality improvements that can be achieved through drastic traffic emission reductions. From this investigation, it can be concluded that NO2 concentrations can be largely reduced, but effects on annual average values are small when the measures last only a few weeks. Secondary pollutants like ozone and PM2.5 depend more strongly on weather conditions and show a limited response to emission changes in single sectors.

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen’s health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from −12% to +7% for NO2 and PM2.5. The developed “dynamic transport approach” is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

This paper describes the Eulerian urban dispersion model EPISODE. EPISODE was developed to address a need for an urban air quality model in support of policy, planning, and air quality management in the Nordic, specifically Norwegian, setting. It can be used for the calculation of a variety of airborne pollutant concentrations, but we focus here on the implementation and application of the model for NO2 pollution. EPISODE consists of an Eulerian 3D grid model with embedded sub-grid dispersion models (e.g. a Gaussian plume model) for dispersion of pollution from line (i.e. roads) and point sources (e.g. chimney stacks). It considers the atmospheric processes advection, diffusion, and an NO2 photochemistry represented using the photostationary steady-state approximation for NO2. EPISODE calculates hourly air concentrations representative of the grids and at receptor points. The latter allow EPISODE to estimate concentrations representative of the levels experienced by the population and to estimate their exposure. This methodological framework makes it suitable for simulating NO2 concentrations at fine-scale resolution ( m) in Nordic environments. The model can be run in an offline nested mode using output concentrations from a global or regional chemical transport model and forced by meteorology from an external numerical weather prediction model; it also can be driven by meteorological observations. We give a full description of the overall model function and its individual components. We then present a case study for six Norwegian cities whereby we simulate NO2 pollution for the entire year of 2015. The model is evaluated against in situ observations for the entire year and for specific episodes of enhanced pollution during winter. We evaluate the model performance using the FAIRMODE DELTA Tool that utilises traditional statistical metrics, e.g. root mean square error (RMSE), Pearson correlation R, and bias, along with some specialised tests for air quality model evaluation. We find that EPISODE attains the DELTA Tool model quality objective in all of the stations we evaluate against. Further, the other statistical evaluations show adequate model performance but that the model scores greatly improved correlations during winter and autumn compared to the summer. We attribute this to the use of the photostationary steady-state scheme for NO2, which should perform best in the absence of local ozone photochemical production. Oslo does not comply with the NO2 annual limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes with the highest NO2 concentrations, which lead to the occurrence of exceedances of the AQD hourly limit for NO2, occur primarily in the winter and autumn in Oslo, so this strongly supports the use of EPISODE for application to these wintertime events. Overall, we conclude that the model is suitable for an assessment of annual mean NO2 concentrations and also for the study of hourly NO2 concentrations in the Nordic winter and autumn environment. Further, in this work we conclude that it is suitable for a range of policy applications specific to NO2 that include pollution episode analysis, evaluation of seasonal statistics, policy and planning support, and air quality management. Lastly, we identify a series of model developments specifically designed to address the limitations of the current model assumptions. Part 2 of this two-part paper discusses the CityChem extension to EPISODE, which includes a number of implementations such as a more comprehensive photochemical scheme suitable for describing more chemical species and a more diverse range of photochemical environments, as well as a more advanced treatment of the sub-grid dispersion.

Marine traffic in harbors can be responsible for significant atmospheric concentrations of ultrafine particles (UFPs), which have widely recognized negative effects on human health. It is therefore essential to model and measure the time evolution of the number size distributions and chemical composition of UFPs in ship exhaust to assess the resulting exposure in the vicinity of shipping routes. In this study, a sequential modelling chain was developed and applied, in combination with the data measured and collected in major harbor areas in the cities of Helsinki and Turku in Finland, during winter and summer in 2010&ndash, 2011. The models described ship emissions, atmospheric dispersion, and aerosol dynamics, complemented with a time&ndash, microenvironment&ndash, activity model to estimate the short-term UFP exposure. We estimated the dilution ratio during the initial fast expansion of the exhaust plume to be approximately equal to eight. This dispersion regime resulted in a fully formed nucleation mode (denoted as Nuc2). Different selected modelling assumptions about the chemical composition of Nuc2 did not have an effect on the formation of nucleation mode particles. Aerosol model simulations of the dispersing ship plume also revealed a partially formed nucleation mode (Nuc1, peaking at 1.5 nm), consisting of freshly nucleated sulfate particles and condensed organics that were produced within the first few seconds. However, subsequent growth of the new particles was limited, due to efficient scavenging by the larger particles originating from the ship exhaust. The transport of UFPs downwind of the ship track increased the hourly mean UFP concentrations in the neighboring residential areas by a factor of two or more up to a distance of 3600 m, compared with the corresponding UFP concentrations in the urban background. The substantially increased UFP concentrations due to ship traffic significantly affected the daily mean exposures in residential areas located in the vicinity of the harbors.

This paper describes the CityChem extension of the Eulerian urban dispersion model EPISODE. The development of the CityChem extension was driven by the need to apply the model in largely populated urban areas with highly complex pollution sources of particulate matter and various gaseous pollutants. The CityChem extension offers a more advanced treatment of the photochemistry in urban areas and entails specific developments within the sub-grid components for a more accurate representation of dispersion in proximity to urban emission sources. Photochemistry on the Eulerian grid is computed using a numerical chemistry solver. Photochemistry in the sub-grid components is solved with a compact reaction scheme, replacing the photo-stationary-state assumption. The simplified street canyon model (SSCM) is used in the line source sub-grid model to calculate pollutant dispersion in street canyons. The WMPP (WORM Meteorological Pre-Processor) is used in the point source sub-grid model to calculate the wind speed at plume height. The EPISODE–CityChem model integrates the CityChem extension in EPISODE, with the capability of simulating the photochemistry and dispersion of multiple reactive pollutants within urban areas. The main focus of the model is the simulation of the complex atmospheric chemistry involved in the photochemical production of ozone in urban areas. The ability of EPISODE–CityChem to reproduce the temporal variation of major regulated pollutants at air quality monitoring stations in Hamburg, Germany, was compared to that of the standard EPISODE model and the TAPM (The Air Pollution Model) air quality model using identical meteorological fields and emissions. EPISODE–CityChem performs better than EPISODE and TAPM for the prediction of hourly NO2 concentrations at the traffic stations, which is attributable to the street canyon model. Observed levels of annual mean ozone at the five urban background stations in Hamburg are captured by the model within ±15 %. A performance analysis with the FAIRMODE DELTA tool for air quality in Hamburg showed that EPISODE–CityChem fulfils the model performance objectives for NO2 (hourly), O3 (daily max. of the 8 h running mean) and PM10 (daily mean) set forth in the Air Quality Directive, qualifying the model for use in policy applications. Envisaged applications of the EPISODE–CityChem model are urban air quality studies, emission control scenarios in relation to traffic restrictions and the source attribution of sector-specific emissions to observed levels of air pollutants at urban monitoring stations.

Ship emissions in ports can have a significant impact on local air quality (AQ), population exposure, and therefore human health in harbour cities. We determined the impact of shipping emissions on local AQ and population exposure in the Baltic Sea harbour cities Rostock (Germany), Riga (Latvia) and the urban agglomeration of Gdansk-Gdynia (Poland) for 2012. An urban AQ study was performed using a global-to-local Chemistry Transport Model chain with the EPISODE-CityChem model for the urban scale. We simulated NO2, O3 and PM concentrations in 2012 with the aim to determine the impact of local shipping activities to outdoor population exposure in Baltic Sea harbour cities. Based on simulated concentrations, dynamic population exposure on outdoor NO2 concentrations for all urban domains was calculated. We developed and used a novel generic approach to model dynamic population activity in different microenvironments based on publicly available data. The results of the new approach are hourly microenvironment-specific population grids with a spatial resolution of 100 × 100 m2. We multiplied these grids with surface pollutant concentration fields of the same resolution to calculate total population exposure. We found that the local shipping impact on NO2 concentrations is significant, contributing with 22 %, 11 %, and 16 % to the total annually averaged grid mean concentration for Rostock, Riga and Gdansk-Gdynia, respectively. For PM2.5, the contribution of shipping is substantially lower with 1–3 %. When it comes to microenvironment-specific exposure to annual NO2, the highest exposure to NO2 from all emission sources was found in the home environment (54–59 %). Emissions from shipping have a high impact on NO2 exposure in the port area (50–80 %) while the influence in home, work and other environments is lower on average (3–14 %), but still with high impacts close to the port areas and downwind of them. Besides this, the newly developed generic approach allows for dynamic population exposure calculations in European cities without the necessity of individually measured data or large-scale surveys on population data.

In the German project Traffic Development and the Environment an advanced model chain was built up that includes traffic models, fleet composition developments, new driving technologies, and emission factors in order to produce spatio-temporal emission distributions for use in atmospheric chemistry transport models. This novel model chain was first used to calculate current day traffic emissions in Germany and then to develop consistent future scenarios for 2040. In all scenarios, NOx emissions from traffic decrease by approximately 80% while PM emissions show a lower reduction. The scenarios Free Play, which is based on a free market economics logic, and Regulated Shift, which considers stricter environmental regulations, represent large differences in traffic emissions. NOx emissions will be 32% lower and PM emissions 13% lower in the Regulated Shift scenario compared to the Free Play. The data can be combined with other anthropogenic emissions for investigating air quality with chemistry transport models.

We investigated the contribution of road traffic and shipping related emissions of NO2 and PM2.5 to total air quality and annual mean population exposure in Hamburg 2012. For this purpose, we compiled a detailed emission inventory following SNAP categories focusing on the detailed representations of road traffic and shipping emissions. The emission inventory was applied to a global-to-local Chemistry Transport Model (CTM) system to simulate hourly NO2 and PM2.5 concentrations with a horizontal grid resolution of 500 m. To simulate urban-scale pollutant concentrations we used the coupled prognostic meteorological and chemistry transport model TAPM. The comparison of modelled to measured hourly values gives high correlation and small bias at urban and background stations but large underestimations of NO2 and PM2.5 at measurements stations near roads. Simulated contributions of road traffic emissions to annual mean concentrations of NO2 and PM2.5 is highest close to highways with relative contributions of 50% for NO2 and 40% for PM2.5. Nevertheless, the urban domain is widely affected by road traffic, especially in the city centre. Shipping impact focuses on the port and nearby industrial areas with contributions of up to 60% for NO2 and 40% for PM2.5. In residential areas in the north of the port, shipping contributes with up to 20–30% for NO2 and PM2.5. Our simulation resulted in 14% of the population of Hamburg being exposed to hourly NO2 concentration above the hourly limit of 200 μg/m³