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Review

Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis

by
Bruno Augusto
1,2,*,
Sandra Rafael
2,3,
Margarida C. Coelho
4,5 and
Joana Ferreira
1,2
1
Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
2
CESAM-Centre for Environmental and Marine Studies, University of Aveiro, 3810-193 Aveiro, Portugal
3
Institute of Environment and Development (IDAD), University of Aveiro, 3810-193 Aveiro, Portugal
4
Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
5
Centre for Mechanical Technology and Automation (TEMA), University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(1), 18; https://doi.org/10.3390/su16010018
Submission received: 27 September 2023 / Revised: 1 December 2023 / Accepted: 14 December 2023 / Published: 19 December 2023
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Abstract

:
The expected increase in urbanization changes the morphology of urban areas. These changes affect the urban environment and impact several aspects, such as climate, energy, air quality, and socioeconomic factors, among others. Therefore, it is important to lead cities towards sustainable development. The goal of this paper is to understand which domains should be considered to assess the sustainability of cities, at an environmental level and with a focus on air quality, and how those domains are connected, based on a comprehensive literature review, which resulted in 1479 articles. The results show that “Urban Climate”, “Air Quality”, “Urban Morphology”, “Health”, “Energy” and “Mobility” are the most common domains in research, and that 39% of articles only assess one domain. It is possible to understand that though 96% of articles contain up to three domains, the great majority do not assess those domains in an integrated way. There are many studies that evaluate important domains of urban areas; however, they mostly evaluate these effects in isolation, and rarely in tandem. Moving forward, it is important to understand how to best connect the most relevant domains, under an integrated multicriteria approach, thus allowing a more complete assessment of the sustainability of urban areas.

1. Introduction

Some of the major challenges that befall cities are population growth, climate change and its effects, and economic development. These challenges threaten the sustainable development of urban areas, especially since 73% of European population inhabits cities, and this percentage is expected to reach 82% by 2050 [1,2].
Urban areas are responsible for close to 80% of greenhouse gas (GHG) emissions, mainly due to transport and energy use in buildings [3]; they consume about 78% of the world’s energy, making them major contributors to climate change [3,4]. Moreover, in addition to transport and energy use, the increase in other human activities in cities contributes to intensifying air pollution, with impacts on citizens’ health and wellbeing. The main critical pollutants are still nitrogen dioxide (NO2), particulate matter (PM10 and PM2.5), and ozone (O3). Population growth worsens climate change, and climate change exacerbates air pollution problems by affecting the atmospheric processes that determine pollutant concentrations, (i.e., emissions of pollutants, chemical production, and loss rate, amongst other factors [5]. These effects lead to premature deaths linked to air pollution and climate change. Thus, air pollution poses a significant threat to urban areas [6,7].
The expected population growth implies the enlargement of cities and thus an increase in urbanization. The problem with growing urbanization is that it causes changes in the morphology of urban areas. The natural response for a cityscape when faced with population growth is urban sprawl. This term refers to an extensive, low-density form of land use conversion in the city’s periphery, reducing agricultural productivity, which leads to environmental degradation [8], and air quality deterioration [9,10]. One alternative to urban sprawl is compact cities. Through densification and compact building, these aim to counteract some of the negative effects of urban sprawl, such as inefficient land use and related environmental problems. Compact cities have both benefits [11,12,13] and drawbacks [12,14]. They present higher population exposure to urban heat and to air pollutants, because compact areas with higher population density are more affected, since more people (in smaller areas) are exposed to air pollution and higher temperatures [10,14,15,16,17].
Regardless of a city being sprawled or compact, it is evident that its morphology is a big factor and affects its environment, which means that changes to its morphology and diverse urban adaptation strategies can have an impact on several aspects [18], such as climate, energy, air quality, socioeconomic, among others [8,9,10,19,20]. Thus, a comprehensive and quantitative assessment is necessary to understand the impacts of these urban planning strategies (both sprawling or compact).
The urban environment and its processes are complex subjects. To understand how the effects of changes to urban morphology under climate change are studied and assessed, it is paramount to identify the existing connections between the different domains and to pinpoint the gaps in research that need to be filled. In the scope of this work, domains are meant as the parameters and factors that affect the urban sustainability, from an environmental perspective. In this context, this paper aims to identify and understand which domains are considered to assess the sustainability of cities, at an environmental level (focused on environmental variables and not social, economic or political) and with a focus on air quality; how those domains connect with each other; and their temporal trends, based on a comprehensive literature review. In doing so, this work will not only be of interest to the scientific community, as its outcomes will recognize the current research gaps in the environmental sciences field, but will also be relevant to planners and policymakers, as it will provide insight into the domains that should be considered to assess and promote cities’ sustainability, leading to better air quality. Given the diversity of domains related to urban sustainability, at an environmental level, this review is inter- and multi-disciplinary. The complementary assessment with VOSviewer allows for an evaluation of how authors use keywords, and how those keywords correlate with the domains.
This work is distinguishable from previous studies because, to the best of the authors’ knowledge, it is the first study that identifies the different domains that are evaluated when assessing the sustainability of cities, with a focus on air quality under a context of changing climate, as well as how those domains connect with each other. By understanding the domains that are and have been studied, as well as their connections, it is possible to identify the gaps in research that will inform future studies. This kind of study is particularly important, since one of the seventeen Sustainable Development Goals adopted by the United Nations highlights the urgent need to make cities inclusive, safe, resilient, and sustainable [21]. This can be achieved by providing stakeholders and decision makers with more tools to help prioritize policies that deal with urban morphology. To develop such tools, it is mandatory to know the main domains that drive cities’ sustainability and ultimately affect air quality and human health.
This paper is structured as follows: Section 2 presents the methodology used for the review and the tools applied to analyze the results. Section 3 pertains to the meta-analysis of the research results. Section 4 focuses on the analysis of the role of urban morphology, on an environmental level, and adds research needs and recommendations. Finally, conclusions are drawn in Section 5.

2. Methodology

To identify the connections between the different parameters that are studied regarding the urban morphology, at an environmental level and with a focus on air quality, and to identify the gaps in research that need to be filled, a literature review was conducted using academic indexing and a search engine (Scopus) for published articles (original and review articles) and book chapters. The keywords and search terms used to identify literature were “climate change”, “urban”, “air”, “impact” or “effect”, to refine the review to assessments at an environmental level, more specifically, atmospheric sciences with a focus on air quality, as previously mentioned. Some study areas that did not relate with the research aim were excluded to refine the search (e.g., “Business” and “Pharmacy”, among others.). The publishing period was not restricted. The language was restricted to English. Papers that were not available through Scopus were not reviewed. The literature review resulted in 1479 documents for 45 years, ranging from 1970 to 2021. Figure 1 shows the systematic research methodology used, where the dashed lines serve to differentiate the analysis from the results. “VOSviewer”, “Domains” (i.e., the classification of the studies), and “Research Gaps and Recommendations for future work” are considered as outcomes, and not part of the analysis process.
The methodology followed two distinct steps: (i) the meta-analysis; and (ii) the systematic review of the impact assessment of urban morphologies, focusing on air quality. The meta-analysis aims to identify the domains and the connections between them, while the systematic review of the impact assessment of urban morphologies aims to understand how changes to the urban forms are assessed for the different domains. For the meta-analysis, all of the 1479 documents from the literature review were analyzed and classified according to the topic addressed, i.e., their domain (parameters and factors that affect the urban sustainability, from an environmental perspective), and sometimes with multiple domains. The domains were derived from the papers, and whenever there was a need, a new domain was added. In the end, the domains included were as follows:
  • “Socioeconomic”, which included cost–benefit studies or any study that considered the cost or other economic parameters;
  • “Urban Morphology”, when dealing with urban form, or the effects of urban land use (this excludes papers that focus on changes to, for example, forest areas, or non-urban water bodies); “Urban Climate”, when focused on the urban microclimate, ranging from the urban heat island effect to thermal comfort. This also includes studies wherein precipitation is assessed;
  • “Energy”, which included heat fluxes, energy sources, and air conditioning analysis;
  • “Air quality”, which included pollutant emissions and concentrations. Even though “air quality” is a search term, there are studies that do not include it as a domain;
  • “Mobility”, referring to both pedestrian and land transport;
  • “Health”, comprising thermal comfort, allergens, and morbidity, among other factors;
  • “Population”, which dealt with population demographics;
  • “Urban metabolism”, which comprised ecological footprint studies as well as food supply chains;
  • “Water resources”, which included wastewater treatments and water pollutants;
  • “Sustainability”, which included nature-based solutions and ecological topics,
  • “General”, which included the general effects and impacts of climate change, as well as papers from conferences wherein multiple topics were addressed;
  • “Other”, when the scope was too alien for the research interest, resulting from a search engine error.
The papers were also classified in terms of the tools used, into “Modelling”, which included the use of modelling tools, and “Monitoring”, where techniques such as remote sensing or in situ measurements were used. This classification is important to understand which tools are used to assess certain domains.
Still during the Meta-analysis phase, the results of the literature review were uploaded to the VOSviewer software (version 1.6.16) to create visual maps based on the extracted data. VOSviewer is a freely available computer program developed to construct and visualize bibliometric maps, and is adequate for large volumes of scientific literature [22]. Other computer software to help with bibliographic mapping are available; however, VOSviewer focuses on the graphical representation, allowing for an easier understanding of the bibliometric maps [23]. VOSviewer was also used to construct and visualize the time periods of the publication. The software has been widely used to analyze different articles, and assess and visualize the data networks [23,24,25].
VOSviewer represents two-dimensional distance-based maps, meaning that the distance between two keywords (in this particular case) reflects the strength of their relationship, where a smaller distance reflects a stronger relation. The size of nodes and words represents their weight, or frequency, meaning that the bigger the node/word, the more common the keyword. The lines represent a connection between the two keywords, where their thickness shows the frequency of that connection. Finally, the different colors represent different clusters, as classified by VOSviewer.
The analysis from VOSviewer was based on the co-occurrence of author keywords and filtered through a thesaurus to homogenize some of the terms. Some examples of homogenized terms are: “urban areas”, and “urban area”, and “urbanization”; “human” and “humans”; “air pollution” and “air pollutant”; and “atmospheric temperature” and “temperature”.
In the second phase of the analysis of the literature review—the systematic review of the impact assessment of urban morphologies—which aims to focus on modelling studies, articles were restricted according to the following criteria: (i) studies that assessed the effects of changes to the urban morphology; and (ii) studies that used modelling tools at a regional scale, so as to model the entire urban area. The selected studies (32 out of 1479) were evaluated pertaining to their domains (as defined above), the case study, the models used, if they used a multicriteria analysis, and their scenarization (if they considered climate change, land use changes, and/or emissions changes).

3. Meta-Analysis

In this section, the results from a meta-analysis are assessed to understand the connections between the different domains in research, and how they have evolved over time. The results are presented in two subsections. Section 3.1 focuses on the relations between the different domains and their occurrence in the literature. Section 3.2 presents the development of research over time.

3.1. Relationships between Domains

The articles resulting from the literature review were analyzed and catalogued into their respective domains. Table 1 summarizes these results. “Modelling” and “Monitoring” were not included in Table 1, because they are considered tools and not domains; however, they are further analyzed separately in Table 2. “Urban Climate”, “Air Quality”, and “Urban Morphology” are the most common domains, as expected. From the 1479 articles, 574 articles (38.8%) assess 1 domain, 605 articles (40.9%) assess 2 domains, 250 articles (16.9%) assess 3 domains, 41 articles (2.7%) assess 4 domains, 6 articles (0.4%) assess 5 domains, and 3 articles (0.2%) assess 6 domains.
All the articles with six domains include “Urban Climate”, “Air Quality”, and “Sustainability”, and refer to studies regarding the implementation of nature-based solutions. They are mostly based on modelling tools, where the implementation of, for example, a green space is simulated in an urban area to assess its effects on temperature and air quality. The articles with five domains are mostly focused on “Urban Morphology”, “Urban Climate”, and “Air Quality”, and are also based on modelling tools. The articles with four domains mostly include the same topics, with a new emphasis on “Health”. The co-occurrence of “Urban Climate”, “Urban Morphology”, and “Health” indicates that the article is focused on thermal comfort. “Energy” and “Sustainability” are also very prevalent. The articles with three domains and two domains are very diverse, but “Urban Morphology” and “Urban Climate” were still the most common intersections.
Despite “Air Quality” and “Urban Morphology” being keywords in the literature review, some articles do not contain either of them as their focus, and “Air Quality” is not even the most frequent domain.
Regarding the tools “Modelling” and “Monitoring”, the former is the more common tool, with 305 occurrences, versus 53 monitoring applications. “Modelling” tools were used in 37 out of 50 (74%) articles that contain over 4 domains (included).
The distribution of keywords, and their relation, was analyzed with VOSviewer. The co-occurrence of keywords reflects research hotspots and provides auxiliary support for scientific research [26]. Before the use of the thesaurus, 3700 keywords were obtained. These were reduced to 3317 keywords with the use of the thesaurus, wherein 2708 appeared only once, accounting for 81.6%. For the visualization, only the 50 most commonly occurring keywords were displayed to facilitate visualization and interpretation. Figure 2 shows the results built by VOSviewer.
In Figure 2, it is possible to see the relations between the different domains. The larger words/nodes indicate the domains with higher frequencies, most prominently in the center of the figure, and the thicker lines that connect them show the most common co-occurrences. The relations of “climate change” with “air quality”, “urban area”, “climate”, “urban heat island” and “health” are clear. The keywords “climate change”, “air quality” and “urban heat island” present the most occurrences with 389, 248 and 159, respectively. VOSviewer divided the results into five clusters.
The total link strength refers to the sum of the link strengths of one node over all the other nodes. The greater the frequency of the co-occurrence, the higher the link strength. In Table 2, the 10 highest total link strengths are represented.
Due to the goals of this study, as well as the research terms, it was expected that the domains “climate change”, “air quality”, and “urban area” would be the most prevalent, as they are the main research interest and part of the research terms. Figure 2 confirms those expectations, adding that “urban heat island” is also very relevant. The other relevant keywords clearly show a large focus on temperature and urban climate, as well as on “health”, which, in most of these studies, is related to thermal comfort, as is also evidenced by Table 2. It makes sense that “climate change” has the highest total link strength, since it is normally used as a very general keyword, which is clear when comparing it with the results in Table 1, wherein “Climate change” does not appear as one of the more common domains.

3.2. Research over Time

Figure 3 shows the contribution, in percentage, of the different domains, per decade of publication (e.g., in the decade of 1970–1980, 50% of papers related to “Urban Morphology”, and the same domain accounted for 10% of papers in the decade 2001–2010). The year of 2021 was excluded from the time period, because at the time of performing the literature review and writing this study, the year was not yet complete.
It is possible to observe from Figure 3 that “Urban Morphology”, “Urban Climate” and “Air Quality” are the most prevalent domains throughout the decades, and that “Urban Morphology” suffered a great decline in research over time. It is also possible to perceive that “Sustainability”, “Energy” and “Health” have been growing in relevance in recent decades—despite a drop from earlier decades—showing that researchers are more focused on the future and human wellbeing. As mentioned above, the “Modelling” and “Monitoring” tools are not included with the domains in Figure 3. Figure 4 shows the contribution, in percentages, of both tools per decade of publication.
“Modelling” tools are the most commonly used tools; however, there has been an increase in “Monitoring” tools in the last two decades, likely due to the rapid development of satellite-based technology [27]. Both “Modelling” and “Monitoring” tools only began being utilized (in the studies included in this paper) in 1991. As technology evolves and becomes more accessible, so do the different tools that are available to researchers, and both tools show an increase in use over time. The call for the use of modelling and monitoring tools by the European Commission will also have the effect of increasing the use of these tools in the future [28]. Both tools have advantages and disadvantages. Monitoring tools are more suitable for certain pollutants, and they are also site-dependent, meaning that results are usually only valid near the monitoring stations. Modelling tools, on the other hand, are suited for larger areas and for studying future scenarios, but their results vary due to the accuracy, and spatial and temporal resolution of the input data [29].
VOSviewer also allows us to represent the timeline of the keywords, as shown in in Figure 5. It can be observed that, over time, some keywords have become more frequent, for example “climate variability”, “land use change”, “drought”, “transportation” and “ecosystem services”. In contrast, keywords such as “air quality”, “health”, and “energy” have been less prevalent in more recent years, which contrasts with Figure 3, which shows them growing as domains. As previously mentioned, this trend clearly shows a preference for the study of urban climate, especially temperature, with a rise in studies regarding services. Figure 5 only shows results starting at 2013, since before that date, the keywords do not link enough times to be represented.
The analysis from Figure 3, Figure 4 and Figure 5 provides an insight into how research has occurred in the past five decades, but it is important to try and predict how it will evolve. However, in the recent COP26 report [30], there is a large focus on targets regarding temperature rise. Taking this into consideration, it is expected that “Urban Climate” studies, especially those focused on temperature, will increase in the future, despite this already being one of the more common domains/keywords. The same report also focuses on “Air Quality”, “Energy”, and “Mobility”, and presents intent to transition to a more sustainable mobility [30]. Those domains are also expected to occur more frequently in future studies, despite the decline in the use of keywords for both “Air Quality” and “Energy” in recent years.

4. The Role of Urban Morphology at an Environmental Level

In this section, the literature review is further analyzed by focusing on how the identified domains assess urban morphology, and recommendations for future work are detailed. Section 4.1 analyses studies that focus on modelling the effects of changes in urban morphology. Section 4.2 refers to the identified research gaps and presents a path for future research.

4.1. Systematic Review on the Impact of Urban Morphology

Studies that focus on modelling the effects of changes to urban morphology at a regional scale are compiled and analyzed in Table 3. The analysis includes the domains, as explained previously; the details of the case study (the city and country); the models used, and the modelled resolution; if the study pertains to a multicriteria analysis or not; and the scenarization, that is, if the study considers the effects of climate change, if it considers changes in land use, and if it considers changes in greenhouse gases and/or pollutant emissions.
From Table 3, it is possible to identify certain common occurrences amongst the studies. In total, there were 32 studies that focused on modelling the effects of changes to the urban morphology at a regional scale.
The great majority of the articles (26 out of 32) focus on modelling the effects of urbanization on the “Urban Climate” [15,19,27,28,29,30,31,32,33,34,35,42,43,44,46,50,51,52,53,54,55,56,57,58,59], with a greater focus on temperature, especially the urban heat island effect [33,36,42,51,54,55]. Most of these studies have China as a case study [31,32,33,34,35,40,43,44,50,51,56,58]. Of the 32 studies in this analysis, 20 used the Weather Research Forecast (WRF) model, with horizontal resolutions ranging from 25 km to 0.3 km. The use of other models was less frequent, with the MM5 model being the second most used, in 4 out of 32 studies [33,40,46,52]. “Urban Climate” was also analyzed using REMO/WETTREG [59], CAM5.1/CLM4 [56], HARMONIE/AROME/SURFEX [39], LL/LM [37], and MM5 [33].
The five articles that contain the domain “Energy” [14,32,35,44,54] focus on energy heat fluxes, with one focusing only on anthropogenic heat flux [32]. All of the articles use the WRF model.
For the “Air Quality” domain, there were nine articles [19,40,41,43,45,47,48,49,50,52,56], that include studies about dust [48], studies that focus on air pollutant concentrations [19,47], one study that assessed the effects of urbanization on air stagnation [40], and studies that focused on emissions, where [41,45,52] regard air pollutant emissions (and [40,47] focus on biogenic emissions; meanwhile, [45] focuses on biogenic emissions and vehicle emissions), [53] focuses on GHG emissions, and [49] deals with both. Regarding emissions, only 6 out of 32 studies apply some change to the emissions in their scenarios. Three studies focus on air pollutant emissions [41,45,52], two on GHG emissions [36,56], and one study refers to both [49], and also includes emission desegregation by sector. Furthermore, from the three studies that focus on air pollutant emissions, two focus on biogenic emissions, with [45,52] focusing only on biogenic emissions, and [49] including both biogenic and anthropogenic emissions. From the nine articles that studied “Air Quality”, five of them also related to “Urban Climate” [19,40,43,50,52]. To analyze “Air Quality” other models than WRF were used (WRF-Chem [43,50], PCM/MOZART-2/MM5/CMAQ [52], MM5 [45,47], STIRPAT [36], MCCM [48], or models were coupled with WRF (WRF-Chimere [19], WRF-CAMx [49] to allow the authors to analyze air quality, since WRF is a meteorological model.
Most of the studies include a multicriteria analysis (28 out of 32); however, this analysis is mostly based on assessing different parameters separately, but not relating them with each other. Thus, it does not qualify as an integrated analysis. For example, these studies analyze climate and assess temperature and precipitation individually, but do not assess how one relates to the other, and what this relationship means to the urban area. This separation between the domains is made more obvious by the fact that 17 out of 32 studies contain only two domains.
As for Scenarization, 12 out of the 32 studies take into account the effects of climate change, usually considering either RCP4.5 or RCP8.5 [19,37,39,49,51,54] (scenarios that stabilize radiative forcing at 4.5 and 8.5 W/m2, respectively, for the year 2100, and include long-term global emissions of greenhouse gases [60]), even though most studies refer to climate change in some way. As for land use changes, 27 out of 32 studies apply some form of land use change in their scenarios, and take that change into account for their results. The changes to land use mostly involve changing land use categories, such as “urban”, to a non-built-up category, such as “croplands”, “grassland”, or “mixed forest”, when accounting for sustainability, and the reverse when representing urban expansion. Most of the studies refer to the urban expansion, either that has already occurred or that it is expected to occur, and its effects on the “Urban Climate” [15,31,32,33,34,35,39,42,44,46,47,50,51,53,54,56,57,58,59]. By changing the land use category to urban, these studies conclude that there has been an increase in surface temperature. By adding blue areas, the temperature of the water will dictate if it cools or warms the surrounding area. Overall, the studies that focus on energy show that urban expansion results in increase in heat fluxes (anthropogenic and sensible), which leads to increases in temperature [14,32,35,40,44,54]. Still, for “Urban Climate”, studies show that an increase in urban areas leads to varied effects when it comes to precipitation [34,56,58], but mostly it increases.
Other studies focus on changing the land use to assess air quality. With an increase in urban land use, there is an increase in air stagnation [40], in CO2 emissions [41], and lower VOC emissions [52]. In varying the land use there are also other effects, such as variations in dust [48]. In adding green areas, there is a decrease in PM10 and NO2 [19], and temperatures vary. Studies that assess the effects of urban expansion on O3 exist as well, but their results vary [19,47,50,52].

4.2. Research Gaps

The main gap identified in this work is that the research domains that the studies focus on do not include an integrated analysis of the most common parameters used to assess the sustainability of urban areas. As highlighted by the meta-analysis, a wide variety of parameters are required to assess the sustainability of a city, but assessing them separately does not provide a complete picture of the situation. For example, in increasing the urban area, there will likely be an increase in temperature, which will lead to an increase in energy for cooling, which will result in an increase in anthropogenic heat and will impact air quality. Therefore, it is not possible to evaluate a system as complex as an urban area by only analyzing one parameter. To the authors knowledge, the assessment of parameters in an isolated manner is only limited by the scope of the studies, meaning that it is up to the authors and stakeholders to change their approach and analyze multiple parameters in an integrated manner. However, it is also of note that with systems as complex as urban areas, the quantity of parameters and domains required to achieve a complete assessment may be too high to be feasible. Thus, moving forward, it is important to understand how best to study the connections between the parameters and domains.
It is also clear from the research that some parameters are less studied than others. There is a large emphasis on “Urban Climate” (especially temperature), but less emphasis on “Urban Metabolism”, for example. It is not evident if that occurs because “Urban Climate” is more important to an urban area, or because there are fewer tools to assess “Urban Metabolism”, or for other reasons. Thus, more integrated research is needed to better understand the role and the impact of these domains in urban areas.
While the second phase of the analysis focused on the modelling of regional areas, most of the studies worked with low resolutions. To understand how changes to the urban morphology affect the sustainability of cities, there is a need to model these changes in a way that the entire modelling domain (i.e., the urban area) is represented, but also with detail that allows for an accurate depiction of the intricacies of the urban scale. It is therefore necessary to develop the existing mesoscale models so that they can be applied at higher resolutions to better represent the minute urban processes. The WRF model is the most frequent choice for simulating regional areas, especially because it is a meteorological model, the most common domain is “Urban Climate”, and it is capable of reaching higher resolutions.
Throughout this study, the presence of studies in China is very prevalent. Studies in the United States and Europe were also very common. All of these areas have very different geographical and demographical contexts, and the differences in studying land use development and sustainability between those areas should be detailed in future studies, because it will affect how the different domains interact in those specific contexts
In regard to this study, there are a few caveats. First, the keywords used in the search engine influence the outcome of the research greatly, and therefore may skew the results by missing several important studies. The scope of this work is limited to studies that, at an environmental level and with a focus on air quality, assess the sustainability of cities in regard to changes to the urban morphology, as well as studies that used modelling tools at a regional scale to assess that sustainability. Therefore, studies that do not fit into that scope may have not been included. Second, the Meta-analysis, as it was conducted, though it serves this purpose, does not allow for an in-depth analysis of the articles. For example, regarding studies that assessed the UHI effect, the vast majority referred to energy heat fluxes to explain the phenomenon. However, the heat fluxes were not directly assessed in the studies, and therefore those studies were not included in the “Energy” domain.

5. Conclusions

The goal of this paper was to understand which domains, at an environmental level, are required to analyze the sustainability of cities, under climate change and with a focus on air quality; how they have been studied; and how they connect with each other.
Based on the performed literature review, the domains that should be considered paramount to promote the sustainability of cities (with a focus on air quality) are “Urban Climate”, “Air Quality”, “Energy”, “Mobility”, and “Health”; however those domains can change based on the context of the case study. With regard to future studies performed to assess the sustainability of urban areas under climate change, it is important to consider a way to connect the most important parameters, such as an indicator that takes several of them into account, taking an integrated multicriteria approach. This would allow for a more comprehensive understanding of the sustainability of urban areas.
The main research needs identified in this paper call for the inclusion of a truly integrated analysis of the different domains that make up complex urban systems, and the role that each of those domains has when trying to achieve the sustainability of an urban area, also considering that the different domains will have different degrees of impact. It is also necessary to improve how urban areas are represented in modelling tools, in order to represent the urban processes in as much detail as possible, while encompassing the entire urban area, to better assess urban planning scenarios.

Author Contributions

Conceptualization, B.A., S.R., M.C.C. and J.F.; methodology, B.A., S.R., M.C.C. and J.F.; software, B.A.; validation, B.A., S.R., M.C.C. and J.F.; formal analysis, B.A.; investigation, B.A.; resources, B.A., S.R., M.C.C. and J.F.; data curation, B.A.; writing—original draft preparation, B.A.; writing—review and editing, B.A., S.R., M.C.C. and J.F.; visualization, B.A.; supervision, S.R., M.C.C. and J.F. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to thank CESAM by FCT/MCTES (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020); to UIDB/00481/2020, UIDP/00481/2020 and UIDB/ECI/04450/2020—FCT—Foundation for Science and Technology; CENTRO-01-0145-FEDER-022083—Regional Operational Program of Central Portugal (Centro2020), under the Partnership Agreement PORTUGAL 2020, financed by the European Regional Development Fund, and the co funding byalongside the FEDER, within the PT2020 Partnership Agreement and Compete 2020, who provided co-funding. Thanks are also due to FCT/MCTES for their financial support through a PhD grant to B. Augusto (2020.06293.BD) and for the contract granted to Joana Ferreira (2020.00622.CEECIND).

Conflicts of Interest

The authors declare no conflict of interest.

References

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Sustainability 16 00018 g001
Figure 1. Systematic research methodology.
Figure 1. Systematic research methodology.
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Figure 2. Keyword distribution and their relations, from VOSviewer.
Figure 2. Keyword distribution and their relations, from VOSviewer.
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Figure 3. Contribution (%) of the different domains, per decade of publication.
Figure 3. Contribution (%) of the different domains, per decade of publication.
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Figure 4. Contribution (%) of the different tools, per decade of publication. The decades 1970–1980 and 1981–1990 were not included in the plot, since no paper from that period uses monitoring or modelling tools.
Figure 4. Contribution (%) of the different tools, per decade of publication. The decades 1970–1980 and 1981–1990 were not included in the plot, since no paper from that period uses monitoring or modelling tools.
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Figure 5. Keyword timeline, from VOSviewer.
Figure 5. Keyword timeline, from VOSviewer.
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Table 1. Number of articles addressing each one of the listed domains found in the 1479 articles.
Table 1. Number of articles addressing each one of the listed domains found in the 1479 articles.
DomainsNo. of Articles
Urban Climate635
Air Quality562
Urban Morphology239
Sustainability219
Health197
Energy173
Mobility128
Water resources87
General73
Socioeconomic39
Other12
Population11
Urban Metabolism9
Table 2. Keywords and total link strength, from VOSviewer.
Table 2. Keywords and total link strength, from VOSviewer.
KeywordTotal Link Strength
Climate change389
Air quality248
Urban heat island159
Temperature128
Health117
Climate104
Urban area89
Urban Climate67
Adaptation50
Thermal Comfort46
Table 3. Studies that assess the sustainability of an urban area, using models at a regional scale, where n refers to no (i.e., not found/considered in the article), and y refers to yes.
Table 3. Studies that assess the sustainability of an urban area, using models at a regional scale, where n refers to no (i.e., not found/considered in the article), and y refers to yes.
DomainsCase StudyModelsMulticriteria Analysis (y/n)Scenarization (y/n)Reference
City, CountryResolutionUrban Climate ChangeLand Use ChangeEmission Change
Urban Climate, Urban MorphologyBeijing, China3.3 kmWRFnnyn[31]
Urban Climate, Energy, Urban MorphologyLas Vegas, Nevada, USA3 kmWRFynyn[32]
Urban Climate, Urban MorphologyPearl River Delta, China1.5 kmMM5ynyn[33]
Urban Climate, Urban MorphologyPearl River Delta, China4 kmWRFynyn[34]
Urban Climate, Energy, Urban MorphologyYangtze River Delta, China30 kmWRFynyn[35]
Urban Climate, Urban MorphologyTokyo, Japan2 kmWRFynyy[36]
Urban ClimateLisbon, Portugal100 mLM/LMMyynn[37]
Urban Climate, Health, SustainabilityToronto, Canada0.3 kmWRF/Envi-Metynnn[38]
Urban Climate, Urban MorphologyVantaa, Finland500 mHARMONIE/AROME/SURFEXyyyn[39]
Air Quality, Urban Climate, Energy, Urban MorphologyShenzhen, China1 kmWRFynyn[40]
Air Quality, Urban MorphologyShenyang, China-STIRPATynyy[41]
Urban Climate, Health, Sustainability, Urban MorphologyWest Midlands, UK1 kmWRFynyn[42]
Urban Climate, Air QualityYangtze River Delta, China9 kmWRF-Chemnnnn[43]
Urban Climate, Energy, Urban MorphologyChina20 kmWRFynyn[44]
Air Quality, MobilityMid-Atlantic12 kmMM5ynny[45]
Urban Climate, Urban MorphologyWestern USA25 kmRSM/RegCM3/MM5-CLM3/DRCMynyn[46]
Air Quality, Urban MorphologyNew York, USA4 kmMM5yyyn[47]
Air Quality, Urban MorphologyZacatecas, Mexico12 kmMCCMnnyn[48]
Air QualityPorto, Portugal9 kmWRF-CAMxyyny[49]
Urban Climate, Air Quality, Urban MorphologyYangtze River Delta, China9 kmWRF-Chemynyn[50]
Urban Climate, Air Quality, Sustainability, Urban MorphologyPorto, Portugal1 kmWRF-CHIMEREyyyn[19]
Urban Climate, Urban MorphologyParis, France1 kmNEDUM-2Dynyn[20]
Urban Climate, Sustainability, Urban MorphologyChina, India, Nigeria25 kmWRFyyyn[51]
Urban Climate, Air Quality, Urban MorphologyUSA36 kmPCM/MOZART-2/MM5/CMAQyyyy[52]
Urban Climate, Urban MorphologyHo Chin Minh, Vietnam1 kmWRFyyyn[53]
Urban Climate, Energy, Urban MorphologyHo Chin Minh, Vietnam1 kmWRFyyyn[54]
Urban Climate, Water, Urban MorphologyFictional European City1 kmWRFynyn[55]
Urban Climate, Urban MorphologyChina22 kmCAM5.1/CLM4yyyy[56]
Urban Climate, Urban MorphologySydney, Australia10 kmWRFyyyn[57]
Urban Climate, Urban MorphologyShandong, China3 kmWRFynyn[58]
Urban Climate, Urban MorphologyRostock, Germany10 kmREMO/WETTREGyyyn[59]
Energy, Urban MorphologyEindhoven, Netherlands1 kmWRF-SUEWSynyn[14]
Notes: WRF—Weather Research Forecast; MM5—Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model; LM/LMM—Linear mixed-effects model; STIRPAT—Stochastic Impacts by Regression on Population, Affluence and Technology; RSM—Regional Spectral Model; RegCM3—Regional Climate Model; CLM3—NCAR Community Land Model version 3; DRCM—Davis Regional Climate Model, MCCM—Multiscale Climate Chemistry Model; PCM—Parallel Climate Model, MOZART-2—Model for Ozone and Related Chemical Tracers; CMAQ—Community Multi-scale Air Quality model; CAM5.1—Community Atmosphere Model version 5.1; CLM4—Community Land Model version 4; REMO—Regional Model; WETTREG—Weather Situation-based Regionalization Method; SUEWS—Surface Urban Energy and Water Balance Scheme.
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Augusto, B.; Rafael, S.; Coelho, M.C.; Ferreira, J. Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis. Sustainability 2024, 16, 18. https://doi.org/10.3390/su16010018

AMA Style

Augusto B, Rafael S, Coelho MC, Ferreira J. Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis. Sustainability. 2024; 16(1):18. https://doi.org/10.3390/su16010018

Chicago/Turabian Style

Augusto, Bruno, Sandra Rafael, Margarida C. Coelho, and Joana Ferreira. 2024. "Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis" Sustainability 16, no. 1: 18. https://doi.org/10.3390/su16010018

APA Style

Augusto, B., Rafael, S., Coelho, M. C., & Ferreira, J. (2024). Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis. Sustainability, 16(1), 18. https://doi.org/10.3390/su16010018

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