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Proceeding Paper

Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing †

by
Paloma C. Toscan
1,*,
Alcindo Neckel
1,
Emanuelle Goellner
1,
Marcos L. S. Oliveira
2 and
Eduardo N. B. Pereira
1
1
ISISE, ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, Portugal
2
Department of Civil Engineering, Universidad De La Costa, CUC, Calle 58 #55-66, Barranquilla 080002, Atlántico, Colombia
*
Author to whom correspondence should be addressed.
†
Presented at the 1st International Conference on Advanced Remote Sensing (ICARS 2025), Barcelona, Spain, 26–28 March 2025; Available online: https://sciforum.net/event/ICARS2025.
Eng. Proc. 2025, 94(1), 15; https://doi.org/10.3390/engproc2025094015
Published: 5 August 2025
(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))
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Abstract

Urban air pollution poses a significant challenge to public health and sustainable development, particularly in mid-sized cities with limited monitoring capabilities. This study investigates the impact of Nature-Based Solutions (NBS) on air quality and Land Surface Temperature (LST) in Guimarães, Portugal. The first phase involves mapping pollutants and assessing European guidelines, traditional monitoring methods, and emerging tools such as sensors and satellite data. The findings indicate gaps in spatial coverage, emphasizing the importance of integrating data from Sentinel-3, Sentinel-5P, local sensors, and drones. These insights establish a foundation for the next phase, which involves predictive modeling of NBS, LST, and pollutants using machine learning techniques to support data-driven policy-making.

1. Introduction

Cities are increasingly facing challenges related to air pollution, identified by the World Health Organization (WHO) as one of the leading environmental risks to health and urban quality of life. Air pollution contributes to millions of premature deaths due to exposure to harmful pollutants such as particulate matter (PM2.5 and PM10), nitrogen oxides (NOx), ozone (O3), and other toxic compounds [1]. This issue is particularly severe in densely populated areas where industrial emissions, traffic, and climate change converge. Despite growing awareness, many cities continue to struggle with accurately assessing the environmental and social impacts of air pollution. This difficulty stems partly from the limited coverage of monitoring stations, which hampers comprehensive spatial and temporal analyses of pollution levels [2].
The absence of high-resolution spatial and temporal data complicates our understanding of the environmental and social impacts of pollution, making it challenging to implement effective strategies such as low-emission zones, green spaces, and sustainable urban infrastructure. However, technologies like remote sensing, machine learning, and predictive modeling have emerged as promising solutions to address these limitations [2].
Simultaneously, Nature-Based Solutions (NBS) have been researched for their beneficial effects on air quality and urban health. Green infrastructure—such as parks, vegetated corridors, and green roofs—enhances biodiversity, thermal regulation, and the reduction of pollutants, including particulate matter and greenhouse gases [3,4]. Numerous studies have demonstrated that NBS are effective at decreasing Land Surface Temperature (LST), particularly in densely built urban areas, using spatial analyses based on remote sensing data [5,6,7]. Vegetation, through shading and evapotranspiration, significantly lowers LST, which can positively influence the dispersion and concentration of air pollutants [8]. However, there remains a shortage of studies that spatially quantify the impact of NBS on air quality while also considering LST as a mediating or moderating variable.
This study aims to assess how NBS affect urban air quality and LST in Guimarães, Portugal, by utilizing remote sensing data and machine learning. This integrated approach intends to enhance our understanding of the environmental impacts of NBS in urban contexts, thereby supporting the planning of more resilient and sustainable cities while also encouraging the use of complementary technologies to measure, record, and report air pollution data.
The research is structured in two phases. The first phase, presented in this paper, focuses on the regulatory frameworks and air quality monitoring methods in Guimarães, and also identifies suitable mapping approaches and remote sensors for pollutant detection, particularly given the small spatial scale of local NBS. To guide this initial phase, we have defined the following research questions:
(1)
Which air pollutants are mandatorily monitored in Guimarães, and what regulations govern this process?
(2)
What approaches and sensors, including those based on remote sensing, are available to measure air pollutants in urban areas, and which are most suitable for small-scale analysis?
The second phase of the study will involve applying multitemporal analyses and machine learning algorithms to model patterns of LST and air pollution, evaluating their correlation with the presence and development of NBS in the region. The guiding research question for this upcoming phase is as follows:
(1)
What is the impact of small-scale NBS on urban air quality, and how is it related to variations in LST?

2. Materials and Methods

2.1. Study Area

Guimarães, located in northern Portugal within the Braga district, was chosen as the study area due to its national and European leadership in sustainable urban planning. With a population of approximately 156,800 inhabitants and an average population density of 868 people per square kilometer, the city has been a model for implementing NBS since 2013, focusing on climate impact mitigation, environmental restoration, and enhancing urban quality of life (Figure 1) [9].
Recognized as the European Green Capital for 2026 and 1 of the 112 cities selected by the European Union to achieve carbon neutrality by 2030 [10], Guimarães possesses unique environmental characteristics influenced by its proximity to the Atlantic Ocean (about 40 km away) and the surrounding mountainous areas. These features make the city vulnerable to rising temperatures and extreme weather events, as reported by the Portuguese Institute for Sea and Atmosphere (IPMA) [11]. Additionally, Guimarães has a well-established urban network and multiple NBS initiatives at both the local and global scales (Figure 2). However, the small spatial extent of these initiatives poses challenges for detection using low-resolution remote sensors.
Thus, Guimarães provides a real-world urban context with concrete environmental initiatives and accessible data, making it an ideal setting for applied analyses that can support adaptive urban planning, environmental public policies, and accurate reporting of pollution and climate data—factors that are essential for achieving climate goals.

2.2. Regulatory, Technical, and Methodological Survey of Applicable Sensors and Approaches

A document analysis was conducted to identify the primary European guidelines and regulations used to assess air quality in Guimarães, based on the Green City Accord (GCA) program, to which the city is a signatory and responsible for submitting reports [12]. This review allowed for the analysis of applicable directives, mandatory monitoring pollutants, reference standards, and recognized measurement methods. Additionally, a systematic review was undertaken to examine the primary approaches currently employed to measure air pollution in urban areas, encompassing both traditional methods and emerging technologies, based on scientific articles and the gray literature. The goal was to identify measurement methods that could support future validation efforts or complement the use of remote sensing data. Furthermore, strategic mapping was conducted, focusing on the main platforms and spaceborne sensors capable of detecting pollutants or correlated variables, such as Aerosol Optical Depth (AOD), which is widely used as an indirect indicator of airborne particulate matter. The aim was to determine which variables need to be measured and which available approaches and sensors are most applicable for use in the second phase of this study.

3. Results and Discussion

Due to the exploratory nature of this study, the results primarily focus on the regulatory and technical landscape that will support the upcoming modeling stage. Guimarães is a signatory of the GCA, a European Union initiative that unites cities committed to improving air and water quality, enhancing biodiversity, and reducing noise pollution and urban waste generation [12]. This institutional commitment strengthens the adoption of integrated environmental policies; however, its effectiveness is contingent on the local technical capacity and the diversity of the monitoring methods available.
In terms of air quality, the GCA mandates that cities monitor pollutant levels, keeping them close to or below the thresholds established by the Ambient Air Quality Directives (AAQD) 2008/50/EC and 2004/107/EC (effective until 2029), as well as the new Directive (EU) 2024/2881 (effective from 2026), along with the 2021 WHO guideline values (Table 1). The pollutants that are mandatory for monitoring include fine PM2.5, PM10, and NO2 [12]. These pollutants have direct health impacts: PM2.5 can penetrate deeply into the lungs and bloodstream, contributing to respiratory and cardiovascular diseases; PM10 affects the upper airways; and NO2, primarily produced by fuel combustion, exacerbates lung conditions and aids in the formation of ozone and secondary particulates [13].
Although these pollutants are some of the most harmful to human health, the effectiveness of mitigation strategies relies on precise measurements with high spatial resolution—this is particularly challenging for mid-sized cities like Guimarães.
The new AAQD Directive (2024/2881) requires pollutants to be monitored by supersites located in urban or rural areas using standardized methods. For PM2.5 and PM10, gravimetric measurement is applied in accordance with the EN 12341:2023 standard [14], while NO2 is monitored using chemiluminescence, following the EN 14211:2012 standard [15,16]. However, Guimarães has only one monitoring supersite, which is located in an urban area (see Figure 3). This limitation restricts the ability to capture the spatial variability of pollution, particularly in regions with distinct microclimates. This situation supports findings from studies that point out the inadequacy of traditional monitoring networks in mid-sized European cities [17,18].
Although gravimetric and chemiluminescence methods are accurate [14,16], they have limitations regarding territorial coverage, high operational and maintenance costs, and low flexibility for network expansion [19,20]. These drawbacks hinder the identification of pollution hotspots and prevent a comprehensive understanding of intra-urban environmental conditions. As an alternative, studies suggest that integrating data from multiple sources—such as low-cost sensors, satellites, and modeling—provides a more resilient and effective solution [21,22,23].
In light of this, the following review presents the main tools currently available for monitoring air pollutants (Table 2). The analysis revealed a wide range of possible approaches. It underscored the importance of integrated strategies in overcoming the limitations of traditional monitoring stations, especially in contexts like Guimarães, where many NBS are implemented on a small spatial scale and require higher-resolution sensors.
Each method has its advantages and limitations: fixed stations are accurate but offer limited coverage; low-cost sensors are affordable but less reliable; passive samplers provide good accuracy but have low temporal resolution; satellites offer broad coverage but face limitations related to observation frequency and cloud cover; drones enhance spatial coverage with good precision but have limited autonomy and are sensitive to weather conditions; and air quality modeling enables large-scale projections and analysis but requires high-quality data and continuous validation [22,24].
To support the interpretation of the monitoring approaches presented in Table 2, it is also important to highlight the main sensors and technologies associated with each method for assessing air quality in urban areas. Fixed monitoring stations operate with high-precision instruments, such as gravimetric and optical analyzers for particulate matter (PM2.5 and PM10), as well as specific detectors for gases like CO, NO2, SO2, O3, and volatile organic compounds (VOCs), following standards established by agencies such as the EPA and WHO [25,26,27,28]. Low-cost sensor and microsensor networks have gained prominence, using technologies such as electrochemical sensors (e.g., Alphasense NO2-B43F, SPEC Sensors 110-601), metal oxide sensors (e.g., MQ-135), and optical particle sensors (e.g., Plantower PMS5003, Alphasense OPC-N2), although they present challenges such as environmental interference and the need for frequent calibration [29,30]. Recent advances include photoionization detectors (PIDs), which are highly sensitive to VOCs, and MEMS sensors such as the SGP30, which enable multi-gas measurements through portable devices [31,32]. Passive samplers are still widely used for NO2, SO2, and O3, standing out for their cost effectiveness and operational simplicity, although they require laboratory analysis and do not provide real-time data [33]. In remote monitoring, satellite-based sensors such as MODIS and TROPOMI allow the detection of trace gases and aerosols on a large scale, with good spatial resolution [34]. UAVs have been successfully used for measurements in hard-to-reach areas, integrating multi-gas sensors (CO2, SO2, CH4, PM), as demonstrated by systems like little-RAVEN [35]. Finally, air quality modeling has integrated sensor data with statistical techniques and machine learning algorithms, such as linear regression, Random Forest, and neural networks, to predict pollutant dispersion and support decision-making [36].
As a complement to Table 2, Table 3 outlines the main spaceborne sensors currently used to monitor atmospheric pollutants in urban areas. Understanding the capabilities and limitations of each sensor—especially in terms of pollutant type, spatial resolution, and revisit frequency—is critical for selecting appropriate tools for urban-scale studies. This comparison helps guide the methodological decisions for the next phase of this research, particularly in terms of identifying hybrid combinations of orbital and local data collection systems that can effectively capture the spatial variability of air quality in cities like Guimarães.
Satellite sensors differ significantly in terms of spatial resolution, revisit frequency, and the parameters they observe. Sentinel-5P (TROPOMI) is notable for its ability to directly monitor atmospheric pollutants, including NO2, SO2, CO, and O3, as well as its ability to provide data on aerosols, including AOD. However, it does not directly measure PM2.5 and PM10 [37]. On the other hand, sensors like MODIS and OMI provide long and comprehensive time series, which are useful for regional or global-scale aerosol analyses, but have lower spatial resolution [38,39]. Sentinel-3, through its OLCI sensor, delivers high-spatial resolution environmental products, including AOD estimates and LST, but it also does not directly measure pollutants [40].
To enhance air quality monitoring capabilities in urban areas, combining complementary sensors like Sentinel-3 and Sentinel-5P is particularly effective. This approach is further strengthened when integrated with local data collected from drones, which improves spatial resolution and representativeness, especially at the microscale. These insights support the next phase of this study, which will focus on integrated modeling of air quality and LST using remote sensing and machine learning techniques.
Building on this, recent studies [41,42,43,44,45] have demonstrated that the TROPOMI sensor (Sentinel-5P), despite its moderate native resolution, can be effectively applied in urban analyses through interpolation techniques, sensor fusion, or integration with local data. For example, in Bangladesh, NO2 levels were mapped at a 1 km2 resolution, identifying industrial hotspots with high spatial accuracy [41]; in Ukraine, kriging techniques refined TROPOMI data to 2 km grids, enabling real-time air quality alerts in cities like Kyiv [42]; and in the Iberian Peninsula, the fusion of Sentinel-3 and Sentinel-5P produced 300 m resolution maps of carbon productivity and pollution [43]. Other studies have shown the feasibility of using satellite data to detect urban air quality patterns, integrate with ground sensors for improved exposure assessment, and support data-driven decision-making in European mid-sized cities [44,45]. These examples confirm the effectiveness of integrated satellite-based approaches in urban pollution assessment and reinforce the methodological basis of this study, supporting the use of Sentinel-3 and Sentinel-5P in urban contexts like Guimarães, especially when complemented by local sensors and predictive modeling. However, when addressing microscale phenomena, especially in areas with complex topography or isolated pollution hotspots, satellite data may not be sufficient on its own.
To address these microscale limitations and expand observational capacity at ground level, recent advances have highlighted the role of Unmanned Aerial Vehicles (UAVs) as a crucial component of hybrid air quality monitoring systems. In this context, Unmanned Aerial Vehicles (UAVs), or drones, have emerged as a powerful complementary tool in urban air quality monitoring. Drones equipped with low-cost sensors can provide high-resolution data at multiple altitudes, allowing for spatially refined assessments of pollutants such as PM2.5, NO2, and O3 in real time [46,47]. In contrast to fixed monitoring stations and orbital sensors, UAVs enable targeted measurements in critical microenvironments and can fill spatial gaps in data collection, particularly in areas with complex topography or heavy traffic [48]. Their operational flexibility allows for their integration into dynamic, real-time mapping systems that inform policy decisions, emergency responses, and environmental planning [49]. This is particularly relevant for cities like Guimarães, where the spatial distribution of NBS is fragmented, and precise, site-specific monitoring is essential to evaluate their localized impacts on air quality. Therefore, integrating satellite observations with UAV-based data collection enhances both the spatial resolution and temporal responsiveness of air quality assessment, which is essential for sustainable urban governance.

4. Conclusions

This study addresses the challenges and opportunities in monitoring air quality in urban areas, with a focus on Guimarães, Portugal. Despite the municipality’s alignment with major European guidelines and its participation in initiatives such as the GCA, the findings reveal significant limitations in the local monitoring infrastructure, particularly in terms of spatial coverage.
The technical analysis conducted in this phase of the study confirms the necessity for an integrated approach that combines the accuracy of traditional monitoring methods with the enhanced spatial and temporal coverage provided by emerging technologies. To improve the detection and analysis of atmospheric pollutants, we propose the combined use of the Sentinel-3 and Sentinel-5P satellites. Additionally, incorporating drone-collected data will enhance measurement resolution and coverage, especially in critical and microscale urban areas.
The results not only deepen our technical understanding of the available monitoring tools, but also underscore the importance of integrated strategies that involve orbital data, UAV-based measurements, field sensors, and predictive modeling. This integrated multiscale approach lays a solid foundation for the next phase of the research, which will focus on forecasting the relationship between air quality, LST, and NBS using machine learning techniques. Furthermore, the anticipated outcomes may directly support urban planning efforts and public policies aimed at mitigating environmental risks on a local scale.

Author Contributions

Conceptualization, P.C.T., A.N., E.G., M.L.S.O., and E.N.B.P.; methodology, P.C.T., A.N., E.G., M.L.S.O., and E.N.B.P.; formal analysis, P.C.T. and E.N.B.P.; investigation, P.C.T. and E.N.B.P.; resources, P.C.T. and E.N.B.P.; data curation, P.C.T.; writing—original draft preparation, P.C.T.; writing—review and editing, P.C.T., A.N., E.G., M.L.S.O., and E.N.B.P.; visualization, P.C.T., A.N., E.G., M.L.S.O., and E.N.B.P.; supervision, M.L.S.O. and E.N.B.P.; project administration, M.L.S.O. and E.N.B.P.; funding acquisition, M.L.S.O. and E.N.B.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the R&D Project “New Space Portugal” (reference 02/C05i01.01/2022.C644936537-00000046), financed by the PRR—Plano de Recuperação e Resiliência—and the European Union through Next Generation EU funds, under the “Agendas para a Inovação Empresarial” incentive system. It was also supported by the PhD grants PRT/BD/154707/2023 (Paloma Toscan), PRT/BD/154704/2023 (Alcindo Neckel), and PRT/BD/154706/2023 (Emanuelle Goellner), awarded by the Atlantic International Research Center (AIR Centre) through Fundação para a Ciência e a Tecnologia (FCT). Additional funding was provided by FCT/MCTES through national funds (PIDDAC), under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UID/04029/2020, and the Associate Laboratory ARISE—Advanced Production and Intelligent Systems, reference LA/P/0112/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. The data used for the regulatory and technical review are publicly available through the official websites of European Union agencies (such as EEA, ESA, and the European Commission), as well as the scientific literature and technical documentation cited in the manuscript.

Acknowledgments

The authors gratefully acknowledge the support and guidance provided by their academic supervisors and research institutions throughout the development of this work. The contributions of the technical staff and the collaborative environment provided by the Atlantic International Research Center (AIR Centre) and ISISE were also essential.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The location of Guimarães, Portugal.
Figure 1. The location of Guimarães, Portugal.
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Figure 2. The location of the NBS located in the urban area of Guimarães, Portugal.
Figure 2. The location of the NBS located in the urban area of Guimarães, Portugal.
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Figure 3. The location of the advanced weather station in Guimarães, Portugal.
Figure 3. The location of the advanced weather station in Guimarães, Portugal.
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Table 1. The limit values for the protection of human health under the Ambient Air Quality Directive(s) followed by the Green City Accord.
Table 1. The limit values for the protection of human health under the Ambient Air Quality Directive(s) followed by the Green City Accord.
PollutantMeasureLimit Value
Current EU Directive (2008/50/EC)Revised EU Directive (2024/2881)WHO Guidelines
PM2.5Highest annual mean25 μg/m310 µg/m35 μg/m3
PM1024 h mean50 μg/m3 not to be exceeded for more than 35 days per calendar year45 μg/m3 not to be exceeded for more than 18 days per calendar year45 μg/m3 not to be exceeded for more than 3–4 days per calendar year
Nitrogen dioxide (NO2)Daily concentration40 μg/m320 μg/m310 μg/m3
Table 2. The approaches used to capture pollution in urban areas.
Table 2. The approaches used to capture pollution in urban areas.
ApproachMain PollutantsAdvantagesDisadvantages
Air quality monitoring stationsCO, NOx, SO2, O3, PM2.5, PM10, VOCs, and heavy metalsCapable of measuring many pollutants with high accuracy and high temporal resolutionLow spatial coverage, high cost, requirement of regular maintenance and advanced technical expertise
Sensors/micro-sensors CO, NO2, O3, PM2.5, PM10High temporal and spatial resolution, simple mechanism, and easy-to-operate training requirements Low accuracy, low sensitivity, and susceptibility to environmental changes
Passive samplers NO2, SO2, O3, VOCs Portability, low cost, flexible placement, energy independence, ease of use, and minimal training requirementsLow temporal resolution, sensitivity to environmental changes, and labor-intensive analysis and deployment
Satellites CO, NO2, SO2, O3, AOD High temporal and spatial coverage Weather conditions affect data quality; frequent validation is required
Drones CO, SO2, NO, NO2, O3, VOCs, CO2, CH4, PM2.5, PM10, TSP/PM100 Capability to measure many pollutants, high spatial coverage, and easy mobility Limited autonomy, weather dependence, and requirement of specialized expertise for operation
Air quality modeling NOx, SO2, O3, PM2.5, PM10 High temporal, spatial, and resolution coverage Depends on accuracy of input data
Table 3. The main satellites that monitor atmospheric pollutants, with information manually collected from technical documents and the official websites of the responsible agencies (ESA, NASA, NOAA, and EUMETSAT, among others).
Table 3. The main satellites that monitor atmospheric pollutants, with information manually collected from technical documents and the official websites of the responsible agencies (ESA, NASA, NOAA, and EUMETSAT, among others).
SourceSensorCoverage PeriodSpatial Coverage and FrequencySpatial ResolutionPollutants
Sentinel-3OLCI2016–presentGlobal, revisit every 2 days300 mAOD
Sentinel-5PTROPOMI 2018–present Global, daily revisit 5.5 km × 3.5 km NO2, SO2, CO, O3, CH4, HCHO, AOD
Terra (EOS AM-1) MODIS 2000–present Global, revisit every 1–2 days 1 km AOD
Aqua (EOS PM-1) MODIS 2002–present Global, revisit every 1–2 days 1 km AOD
Aura OMI 2004–present Global, daily revisit13 km × 24 km NO2, SO2, O3, AOD
MetOp-A GOME-2 2007–2021 Global, revisit every 1–5 days80 km × 40 km NO2, O3, SO2, HCHO, AOD
MetOp-B GOME-22013–present 1.5 days 80 km × 40 kmNO2, O3, SO2, HCHO, AOD
MetOp-C GOME-22019–present 1.5 days 80 km × 40 kmNO2, O3, SO2, HCHO, AOD
Suomi NPP VIIRS 2012–present Global, daily revisit 750 m to 375 m AOD
NOAA-20 (JPSS-1)VIIRS 2018–present Global, daily revisit 750 m to 375 m AOD
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Toscan, P.C.; Neckel, A.; Goellner, E.; Oliveira, M.L.S.; Pereira, E.N.B. Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing. Eng. Proc. 2025, 94, 15. https://doi.org/10.3390/engproc2025094015

AMA Style

Toscan PC, Neckel A, Goellner E, Oliveira MLS, Pereira ENB. Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing. Engineering Proceedings. 2025; 94(1):15. https://doi.org/10.3390/engproc2025094015

Chicago/Turabian Style

Toscan, Paloma C., Alcindo Neckel, Emanuelle Goellner, Marcos L. S. Oliveira, and Eduardo N. B. Pereira. 2025. "Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing" Engineering Proceedings 94, no. 1: 15. https://doi.org/10.3390/engproc2025094015

APA Style

Toscan, P. C., Neckel, A., Goellner, E., Oliveira, M. L. S., & Pereira, E. N. B. (2025). Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing. Engineering Proceedings, 94(1), 15. https://doi.org/10.3390/engproc2025094015

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