Citizen Science (CS) projects vary greatly. The aims and goals of a CS project determine the type of citizen involvement and the tools to be used, which in most cases also entail information and communication technology (ICT) that facilitates public participation in scientific research. Resource limitations in CS projects often require adopting suboptimal tools, which, however, may come with hidden costs stemming from poor usability and underwhelming functionality, thus reducing volunteers’ motivation. Meeting the volunteers’ expectations by designing or using existing tools with functional features which fulfill and nurture their motivations, will foster long-term participation and contribute to project sustainability. This paper reviews the types of CS projects, volunteer motivation and retention strategies from the literature and classifies them thematically. This is distilled into guidance that can help CS practitioners to design and implement CS tools and plan and manage CS projects, which better serve their scientific and volunteer-related goals.

As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO₂ predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites.

High-resolution measurements of sulfur dioxide (SO₂), nitric acid (HNO₃), and hydrochloric acid (HCl) were conducted in Athens, Greece, from 2014 to 2016 via a wet rotating annular denuder system paired with an ion chromatograph. Decreased mean annual levels of SO₂ and HNO₃ (equal to 3.3 ± 4.8 μg m⁻³ and 0.7 ± 0.6 μg m⁻³, respectively) were observed relative to the past, whereas for HCl (mean of 0.4 μg m⁻³ ) no such comparison was possible as the past measurements are very scarce. Regional and local emission sources regulated the SO₂ levels and contributed to both the December and the July maxima of 6.6 μg m⁻³ and 5.5 μg m⁻³, respectively. Similarly, the significant enhancement at noon and during the winter nighttime was due to transported SO₂ and residential heating, respectively. The oxidation of NO₂ by OH radicals and the heterogeneous reactions of HNO₃ on sea salt seemed to drive the HNO₃ and HCl formation, respectively, whereas nighttime biomass burning affected only the former by almost 50%. During summer, the sulfate anions dominated over the SO₂, in contrast to the chloride and nitrate ions that prevailed during the winter and were linked to the aerosol acidity that influences their lifetime as well as their impact on ecosystems.

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 (PM_2.5) 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 PM_2.5 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.

A historically large percentage of the world’s population has moved to urban areas in the past few decades, causing various negative effects for the environment, such as air, noise, water, and light pollution; land degradation; and biodiversity loss. Under the current climate crisis, cities are anticipated to play an essential part in adaptation strategies to extreme atmospheric events. This study aims at developing indicators at an urban scale that can highlight adaptation progress by investigating relevant data (especially in situ) and statistics at a pan-European level in support of the EU’s strategy for adapting to the impacts of climate change. The proposed indicator, Urban Biodiversity Indicator for Trees (UBI4T), which can be derived from city tree inventories, assesses one essential component of urban biodiversity by computing the proportion of native, alien, invasive, and toxic tree species spatially across a city. According to our findings (applying the UBI4T for Amsterdam and exploring its policy potential for Barcelona), the UBI4T can offer crucial information for decision and policy makers, as well as stakeholders of a city, with the aim of conducting dedicated and effective strategic initiatives to restore, improve, and protect nature in the urban environment, thus contributing to adaptation and resilience to extreme atmospheric events in cities.

Cities face a wide variety of pressures, many of which negatively impact the health of both the environment and citizens and require an integrated smart city approach to address. By infusing state-of-the-art Earth observation (EO) into tools for cities, the SMart URBan Solutions for air quality, disasters and city growth (SMURBS) project produced a portfolio of smart solutions based off of and refined by continued engagement and co-design with stakeholders and cities to address real needs, gaps, and issues. This portfolio of EO-driven solutions serves as an openly available archive and depository of city solutions for environmental pressures and aims to enhance urban resilience and sustainability, and therefore, make cities smarter. Within this paper, we describe the SMURBS experience of bringing together city stakeholders and EO experts and building the portfolio of smart urban solutions, concluding with the six foundational aspects distilled from this experience to serve as a playbook for solution builders to ensure the production of fit-for-purpose and effective city solutions.