Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates

Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates Meng Gao, LIUHUA ZHU, Fan Wang, Chris Nielsen, Gregory Carmichael This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7271946/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract As cities expand, urban heat islands and air pollution have emerged, threatening health, ecosystems, and infrastructure. Yet a dual solution remains lacking to simultaneously mitigate these two greatly concerned issues due to complex urban climate-chemistry interactions, particularly for humid subtropical cities that suffer more from elevated heat stress. On the basis of coupled urban climate-chemistry modeling and non-dominated sorting genetic algorithm (NSGA-II), we developed optimized spatial planning that considers the intricate interplay between urban heat and atmospheric chemistry. Optimized urban plans suggest a development pattern characterized by medium density and expanded urban green spaces. Both regression analysis and numerical modeling assessments demonstrate that our method could effectively mitigate both urban heat and air pollution. This study offers a new perspective to tackle the negative environmental impacts of urbanization in the context of ongoing climate change, and the developed framework can be easily applied to other urban areas and has potential to improve living conditions, decrease energy consumption and reduce health risks globally. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Environmental sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Urban areas are currently home to over half of the world’s population, with projections indicating that the share could rise to 70% in 2050 1 . The shift of populations from rural to urban areas, known as urbanization, leads to replacement of areas of natural vegetation with artificial, impervious surfaces that decrease albedo and increase solar absorption 2 . The resulting temperature gradient between urban and rural areas is known as the urban heat island (UHI) effect 3 , 4 . Higher temperatures in urban areas can contribute to heat-related illness and mortality, and affect local ecosystems 5 , 6 . In addition, the UHI effect often amplifies air pollution by increasing energy demand for cooling, enhancing temperature inversions, and changing local wind patterns 7 . Human activities in urban areas account for 80% of carbon emissions and most emissions of air pollutants 8 , deteriorating air quality. The UHI effect and air pollution are interconnected, often coinciding and intensifying health risks for city dwellers 5 , 9 , 10 . Furthermore, the unique urban thermal environment and underlying surfaces, coupled with climate change, have led to more extreme events such as heatwaves 11 and heavy precipitation 12 , which damage urban infrastructure, increase mortality rates, and cause economic loss. Despite the negative impacts, urbanization remains an inevitable part of societal progress in developing countries, especially China. To enhance urban resilience against climate change and pollution, various green (vegetation-based), blue (water-based) and grey (engineered) infrastructures (GBGI) have been established globally 13 – 17 . Prashant et al. 18 suggested that green and blue components such as botanical gardens, wetlands, green walls and street trees could potentially reduce urban warming by more than 3 K. Although effective, these measures are commonly implemented in existing urban areas and require significant financial investment. An alternative strategy to mitigate the negative effects of urbanization involves optimizing future urban expansion. Lo et al. 19 emphasized that the spatial location arrangement of urban land use influences the development of the UHI effect, based on satellite data. Additionally, different urban forms could effectively combat urban heat under climate change from an urban planning perspective 20 , 21 . Liu et al. 22 suggested that a moderately dispersed and decentralized urban area is optimal for UHI mitigation in China. Optimizing urban development hinges on refining urban land use locations, building density, location of green spaces, and other factors to minimize adverse effects while maximizing benefits. This optimization process encompasses a variety of decision variables, objectives, and constraints 23 . Optimization of urban spatial layouts has been used widely to try to achieve long-term balanced development 23 . Optimizing individual urban buildings can yield valuable insights on building heights and siting to reduce energy consumption, enhance thermal comfort, and improve air quality 24 – 26 . Strategic placement of green spaces of different types can result in substantial reductions in land surface temperatures (LST) locally and regionally 27 – 30 , and significant improvements in runoff mangement 31 . Land use optimization includes allocating the location of urban areas 21 , 32 as well as other land use types under future scenarios, prioritizing decision makers’ preference to realize urban cooling 33 , economic benefits, and ecological diversity 34 . A few existing studies seeking to optimize spatially urban land use have focused on mitigating either urban heat or air pollution, but not simultaneously 35 – 37 . However, climate and atmospheric air pollution are highly interdependent 38 , 39 at both local and regional scales. Diverse atmospheric circulation patterns, driven by temperature differences, influence distributions of air pollutants 7 . Lower temperatures tend to stabilize the boundary layer, limiting diffusion of air pollutants 40 – 43 . Accumulated aerosols in the boundary layer also influence surface temperature through aerosol radiative effects 44 – 48 . Elevated shortwave radiation and temperatures favor emissions of gaseous precursors 49 , 50 and formation of ozone 51 . For these reasons, balancing thermal comfort and air quality can be challenging due to conflicting interactive processes. To the best of our knowledge, no prior research has thoroughly analyzed the intricate interactions between urban climate and atmospheric chemistry to formulate effective and realistic co-mitigation strategies. Given these challenges, we aim here to design an urban development optimization framework that considers the complex interplay between urban climate and atmospheric chemistry and can achieve improvement of thermal comfort and air quality simultaneously. The optimization is demonstrated by considering a compound heatwave and ozone episode in the Great Bay Area (GBA) of South China, one of the most densely populated and developed urban clusters in China. We divided the study area into grid cells. Optimization involves designating the non-urban grid cells as urban land types, and assigning the fraction of urban as well as other land use type within these newly developed urban grid cells. Based on coupled urban climate-chemistry modeling and multi-objective optimization, we demonstrate that co-mitigation of urban heat and air pollution can be achieved despite conflicting effects of associated geophysical mechanisms. The newly developed framework can be easily applied to other urban areas and has potential to improve living conditions, decrease energy consumption and reduce health risks in other cities around the world. Results Robustness of estimated relationships between land use types and environmental variables Robust relationships between environmental variables (LST, PM 2.5 concentrations, ozone concentrations) and land use types are essential for building objective functions for optimization. We first grouped the detailed land use types used in urban climate-chemistry model into eight categories, namely tree, shrub, grass, wetland, crop, urban, barren and water (Supplementary Table 4). The particulars for developing estimated relationships can be found in the Methods section. Evaluation metrics including the coefficient of determination (R 2 ), mean absolute error (MAE) and root square mean error (RMSE) were used. As shown in Fig. 1 , geographically weighted regression (GWR) models demonstrate reliable predictability due to their consideration of spatial variability, with all models exhibiting mean biases smaller than 0.5%. The LST and land use relationship exhibits good fitting, with an R 2 value of 0.76, comparable to the findings of Duan et al. 52 (R 2 = 0.73). Although there is an underestimation of more than 2 K for LST values exceeding 306 K, these deviations are rare (~ 0.9%) and exert negligible impacts on subsequent analyses. PM 2.5 and ozone predictions display superior performances, with R 2 = 0.84 and R 2 = 0.91, respectively, outperforming the models proposed by Huang et al. 53 (R 2 = 0.72 for PM 2.5 and R 2 = 0.65 for ozone). In summary, the established relationships demonstrate high robustness and can effectively serve as objective functions in the optimization process. Major features of optimized urban plans We carried out spatial optimization with multi-objectives based on an initial assumption of 10% urban share of all land use (details in the Methods section). We then generated competitive urban plans through optimization (details are listed in Supplementary Note 1). These urban plans were all considered optimal, meaning here that no single urban plan outperforms the others across all objective functions (Supplementary Fig. 1). This flexibility empowers decision-makers to customize their selections to specific requirements. Figure 2 delineates the spatial characteristics of optimized versus unplanned urban development. Figure 2 a shows the frequency among all 400 urban plans that a given grid cell is developed as urban. Unplanned urban expansion exhibits a dispersed pattern across the area, with grid cells sharing similar probabilities for development. Conversely, optimal urban plans demonstrate an uneven likelihood of urbanization, particularly with higher development potential observed in areas along urban boundaries of the GBA. Urban density, representing the average urban land use fraction of an altered grid cell categorized as urban, was categorized into low ( 0.8) density following the WRF-Noah urban canopy model (Fig. 2 b). The average urban fractions for unplanned and optimized urban development are 0.77 and 0.71, respectively. Although the urban fractions are similar, optimized urban land use plans favor medium-density urban areas, making up 62.37% of newly developed urban grid cells. In contrast, unplanned urban development tends to produce medium and high-density areas equally, accounting for 52.13% and 46.64%, respectively. Green spaces play crucial roles in urban areas due to their cooling and air quality benefits. We also allocated urban green spaces in newly developed urban grid cells. Figure 2 c displays unplanned urban development exhibits low vegetation cover with a 0.15 areal share, whereas optimized urban development plans exhibit more green spaces with a share of 0.26. Overall, optimized urban development, featuring a medium density urban form with expanded green spaces, is able to improve urban thermal comfort and air quality. Urban climate and air quality benefits of the optimized urban plans We used regression to evaluate the effectiveness of the urban development optimization framework we have established by analyzing the relationships between environmental factors and land use patterns. As shown in Fig. 3 , unplanned urban development leads to a 0.07 K rise in LST over the whole region, whereas optimized urban development yields a 0.03 K increase. Although the 0.04 K temperature reduction due to optimization is modest, it is statistically significant as confirmed by a t-test analysis. When regional urban warming effects were averaged across altered urban grid cells, the mitigated warming due to optimization amounts to 0.17 K. Unplanned urban development is associated with increased PM 2.5 levels. However, the trend is effectively counteracted through optimization strategies. All urban plans demonstrate that urbanization does not exacerbate ozone pollution in urban areas. This is associated with stronger NOx titration in urban areas and elevated BVOC emissions in suburban and rural areas (Supplementary Fig. 2) 50 , indicating ozone concentrations in the GBA are less sensitive to the urban land type. We performed the simulations based on the WRF-GC model to further assess the effectiveness of optimization strategies. Given that simulations for all generated urban land use plans are resources-intensive, we selected four representative urban plans. Two of these urban plans were derived from unplanned urban development scenarios, while the other two were taken from optimized urban development scenarios, chosen based on objective values (see Supplementary Table 5) and urban form (supplementary Fig. 3). The Unplanned-187 shows a dispersed urban layout, contrasting with compact urban patterns of other plans (Supplementary Fig. 3). When comparing Unplanned-392 with Optimized-325, both featuring compact urban designs, optimization leads to a 60% reduction in urban warming, a 175% decrease in PM 2.5 levels and a 114% decline of ozone concentrations (Fig. 4 ). Similarly, comparing Unplanned-392 with Optimized-266, both of which also exhibit compact urban forms, we also find 80% lower warming, 77% decrease in PM 2.5 and 109% decline of ozone. When comparing Unplanned-187 with Unplanned-392, it is notable that the dispersed urban development form suffers fewer side effects related to urban warming and air pollution. The newly developed urban grid cells of Unplanned-187 are more scattered, which enhance the mix of various land use types, potentially mitigating urban heat and air pollution 54 , 55 . However, dispersed urban form may have transportation-related pollution 56 , which is difficult to accurately measure in numerical models. In contrast, when comparing Unplanned-187 with Optimized-325, the optimization effectively addresses environmental issues associated with centralized urban forms, leading to reduced urban warming and improved air quality. This suggests that urban form is not a critical factor affecting the urban thermal and pollution environment when urban land use is optimized. Discussion The expansion of urban areas has led to significant environmental challenges that require urgent mitigation efforts. In this study, we introduced a viable method for optimizing urban development with a focus on environmentally conscious planning. By acknowledging the conflicts between urban warming and the deterioration of air quality, our proposed framework integrated four trade-off objectives within the optimization process. We utilized the NSGA-II algorithm to produce a diverse array of urban plans for stakeholders to evaluate. When applied to a case study in the GBA, this approach revealed several important insights. Both regression analyses and numerical modeling simulations highlighted the effectiveness of optimizing urban development. The advantages of optimization are especially evident in urban regions. By incorporating environmental considerations into urban development optimization, it is possible to co-mitigate urban heat and air pollution, both of which are associated with dense population and rising energy consumption in urban areas. Unlike traditional land use optimization strategies, which are typically applied at the street or block level as indicated by existing research 25 , 29 , 31 , 34 , our method takes a different path by determining the location, urban and green space fractions of changed urban grid cells on a regional scale, with a spatial resolution of 3 km. In China, urbanization can take the form of so-called “mega-urban agglomeration,” characterized by clusters of cities 57 . Understanding the regional impact of urbanization and finding ways to mitigate its negative effects is crucial. Furthermore, climate and air quality issues extend beyond local boundaries, requiring consideration of regional-level attributes such as atmospheric transportation. Conducting urban land use optimization at this larger scale not only highlights the regional impacts of urbanization but also deepens understanding of the fundamental physical mechanisms involved. Setting objectives is a crucial step in the optimization process. Climate and air quality issues are major concerns for urban development and public health, so we used LST, PM 2.5 , and ozone concentration as indicators of these adverse effects, as commonly utilized in other studies. Accurately characterizing how environmental variables respond to land use changes is essential for influencing optimization outcomes. To ensure precision in the regression relationships, we employed the Least Squares method and Random Forest regression for comparison with the GWR method. The results indicated that GWR provided superior accuracy (Supplementary Figs. 4 and 5). Although LST and PM 2.5 variations show diurnal patterns 21 , our analysis of GWR regression coefficients for different land use types during the day and night revealed consistent characteristics (Supplementary Fig. 6). Therefore, we chose not to include diurnal variations in the LST and PM 2.5 regression models to simplify the number of objective functions. Following the development pattern in the GBA—i.e., mega-urban agglomeration—we used compactness as a key indicator. Economic costs were not included in our objectives because we limited development to 10% of urban grid cells and assumed uniform renovation costs across all grid cells to simplify cost considerations. Current quantitative assessments for multi-objective optimization largely rely on regression relationships. This research underscores the importance of both regression-based evaluation methods and numerical model simulations. Regression estimations provide a simple and quick way to assess all optimization results, but they lack the physical significance inherent in their relationships. On the other hand, numerical simulations incorporate atmospheric thermodynamics, transportation, and the interactions between climate and chemistry. Although they come with inherent uncertainties, numerical simulations offer plausible explanations and reveal underlying mechanisms, thereby enhancing understanding. Our study offers a new perspective for stakeholders to tackle the negative impacts of urbanization in the context of climate change. Urban planning presents a complex challenge that requires considerations beyond the objectives we have outlined. However, our proposed method offers a collection of equally optimal urban plans that emphasize minimizing environmental impacts, helping policymakers to understand the comparative effects of different land use planning strategies based on their specific criteria. Additionally, policymakers can incorporate various targets, such as ecological benefits and economic costs, into the urban development optimization tool, demonstrating its adaptability. Looking forward, future research should aim to further explore numerical modeling techniques to improve the accuracy of models and emissions estimates based on altered land use patterns. Methods Coupled urban climate-chemistry modelling We used WRF-GC v2.0 58 to perform the urban climate and air quality simulations. WRF-GC is an online coupling of the Weather and Research and Forecasting meteorological (WRF) model and the GEOS-Chem atmospheric chemistry model. Supplementary Fig. 7 shows the characteristics of meteorological and air pollution variables during the selected event. We configured 3 domains with grid resolutions of 27 km, 9 km, and 3 km, respectively (Supplementary Fig. 8). The primary parameterization options used for physical schemes and domain settings are listed in Supplementary Table 1. The meteorological initial and boundary conditions were obtained from the National Center for Environmental Prediction Final Operational Global Analysis (NCEP FNL, 1° × 1°) ( https://rda.ucar.edu/datasets/d083002/ ) with 6-hour intervals. The chemical boundary and initial conditions were taken from a standard full-chemistry simulation from GEOS-Chem v12.8.1. The monthly mean anthropogenic emissions covering China with 0.25° × 0.25° grid resolution in 2020 were obtained from the Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org ) 59 . Anthropogenic emissions for the areas outside China were obtained from a mosaic Asian anthropogenic emission inventory 60 , 61 . Validations against observations show that although biases exist, the accuracy is comparable to other studies 50 , 58 , 62 , 63 . More details about the validation can be found in Supplementary Note 2, Supplementary Fig. 9, Supplementary Table 2 and Supplementary Table 3. Multi-objective optimization framework for urban land use development Supplementary Fig. 10 outlines the structure of multi-objective optimization for urban development. Initially, we identified the key objectives and represented them with mathematical functions. Subsequently, optimization was conducted to yield anticipated results. The effectiveness of optimization was evaluated through regression analysis and numerical simulations. We mainly considered urban heat and urban air quality, with urban heat indicated by LST while air quality is indicated by hourly concentrations of PM 2.5 and maximum daily 8-hour average ozone (MDA8 O 3 ). Our primary objective was to promote sustainable urban development by mitigating urban warming and enhancing air quality simultaneously. Considering the fact that highly urbanized areas in the GBA are densely located around the Pearl River estuary, we included compactness 29 as an additional objective. These objectives were summarized in the blue box in Supplementary Fig. 10 and represented by different functions. Urban compactness is expressed as follows: $$\:Compactness=\sum\:_{j=1}^{J}N,\:\forall\:\:j=\text{1,2},3\dots\:.,j$$ 1 Where j denotes each developed urban area, and N represents the total number of urban grid cells within a fixed distance centered on j . This formula is further elucidated in Supplementary Fig. 11. Our hypothesis assumed 10% urban land use based on the current level. Zhou et al. 21 showed that percentage does not have a significant effect on the results of optimization. For consistency across experiments, all data, including land use, land use fractions, meteorological data, and air quality variables, were extracted from the simulations of the innermost domain (Supplementary Fig. 8). This domain comprises 1105 urban-type grid cells, each with a horizontal spatial resolution of 3 km x 3 km. Notably, a cell is classified as urban if at least one third of its area has urban land use, while the remainder can have other land uses such as water bodies, forests, shrubs, and crops. Several constraints were applied to limit urban development consistent with regulatory and geographical realities in the region: Urban development should not encroach upon croplands or water bodies, to protect these vital resources. The elevation must not exceed 95m, which corresponds to the 95th percentile of current urban elevations. Selected urban cells should have water on a maximum of 50% of their borders, to exclude isolated islands. Application of these constraints yielded 110 grid cells we define as urban, and further yielded fractions of urban, green, and other land use types for each such cell. The distribution of potential urban grid cells and urban land use grid cells is illustrated in Supplementary Fig. 12. Given the potential conflicts among different objectives, no singular optimal solution is attainable and choosing from a set of solutions requires expert judgement of the trade-offs for society. Therefore, we introduced the widely utilized non-dominated sorting genetic algorithm (NSGA-II) 64 – 66 to generate a range of urban plans considered equally optimal, enabling decision-makers to make the final decisions. Detailed technical particulars for NSGA-II are available in Supplementary Note 1. Relationship between land use types and LST, ozone, and PM 2.5 Considering the spatial heterogeneity across the GBA area, we utilized the GWR model to estimate the environmental variables. This approach allowed us to determine the autocorrelation between land use types and environmental factors. The formulation is represented as follows: $$\:{y}_{i}=\:{\beta\:}_{0}\left({u}_{i},{v}_{i}\right)+\sum\:_{k=1}^{N}{\beta\:}_{k}\left({u}_{i},{v}_{i}\:\right){x}_{ik}+{\epsilon\:}_{i}$$ 2 where N is the number of predictor variables \(\:;\:\left({u}_{i},{v}_{i}\right)\:\) denotes the coordinates pair for location i ; \(\:{\beta\:}_{k}\) signifies the varying weight; and \(\:{\beta\:}_{0}\) represents the changing intercept based on locations. Thus, GWR extends traditional global regression techniques to accommodate non-stationary spatial relationships 67 . Finally, y is the dependent environmental variable, in particular LST to represent climate and hourly PM 2.5 and ozone concentrations to represent air quality. We took both the effects of each land use type at cell i and all adjacent cells into account to predict the dependent variables. Land use types in WRF-GC simulations were compiled into a smaller number of classes (Supplementary Table 4). The effects from adjacent cells were calculated by summing the inversed distance weighted (IDW) with a window size of 5 × 5. Assessment of spatially-optimized planning strategies Spatially optimized urban planning strategies were evaluated using the estimated relationships and the WRF-GC model. To assess the estimates, environmental variables were calculated based on optimized and unplanned urban development respectively. Given the computational limitations of assessing all urban plans using the WRF-GC model, we selected four planning cases from the unplanned (two) and optimized (two) sets respectively. Supplementary Table 5 presents the selected four plans alongside their objective function values (termed Unplanned-187, Unplanned-392, Optimized-266, Unplanned-325). Updated land use, land use fraction, leaf area index and vegetation type fraction data were obtained to align with expanded urban land use scenarios before been integrated into the model. Adjustments to anthropogenic emissions were also necessary in response to changes in land use, given the distinctive characteristics of various land types. Emission reallocations were implemented following the procedure detailed in Supplementary Note 3. The total emissions after adjustment were consistent with that during case study. Supplementary Fig. 13 illustrates the differences of PM 2.5 emissions for 266th urban plan in the optimized urban plan sets. Increased PM 2.5 emissions from power, industry, residential and transport can be seen in the newly developed urban areas. Declarations Acknowledgments This study was supported by the grants from National Natural Science Foundation of China (no. 42375095), the Research Grants Council of the Hong Kong Special Administrative Region, China (project nos. C2002-22Y, 12202021 and 12201023), Environmental and Conservation Fund (121/2022), and the Center for Ocean Research in Hong Kong and Macau (CORE), a joint research center between the Laoshan Laboratory and HKUST. Data Availability Statement All data are available in the main text, methods or the supplementary materials. References Elmqvist T et al (2013) Urbanization, biodiversity and ecosystem services: challenges and opportunities: a global assessment. Springer Nature Hu Y et al (2015) The cumulative effects of urban expansion on land surface temperatures in metropolitan JingjinTang, China. 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Atmos Chem Phys 24:3925–3952. https://doi.org/10.5194/acp-24-3925-2024 . https://doi.org: Li M et al (2017) MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos Chem Phys 17:935–963. https://doi.org:10.5194/acp-17-935-2017 Fan Q et al (2013) Effect of different meteorological fields on the regional air quality modelling over Pearl River Delta, China. Int J Environ Pollut 53:3–23. https://doi.org:https://doi.org/10.1504/IJEP.2013.058816 Zhang J, Zhai S, Tai AP in EGU General Assembly Conference Abstracts. 15216 Gao P et al (2021) Sustainable land-use optimization using NSGA-II: Theoretical and experimental comparisons of improved algorithms. Landscape Ecol 36:1877–1892 Shaygan M, Alimohammadi A, Mansourian A, Govara ZS, Kalami SM (2013) Spatial multi-objective optimization approach for land use allocation using NSGA-II. IEEE J Sel Top Appl Earth Observations Remote Sens 7:906–916 Cao K et al (2011) Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II. Int J Geogr Inf Sci 25:1949–1969 Zhao C, Jensen J, Weng Q, Weaver R (2018) A geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon. Remote Sens 10:1428 Additional Declarations There is NO Competing Interest. Supplementary Files 355330supp226479sxp2pk.docx Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates nrreportingsummary.pdf Article File - Reporting Summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7271946","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495595960,"identity":"78eab67b-7b8e-4652-a37c-9d90069d1e1e","order_by":0,"name":"Meng Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLCCDxVgig3KTWBg4CGgg3HGGVK1MPO2kaJFt3/xwwe88+rk+PsPsD34ueewvHl7AuODt224tZjdeGZsILntsLHEjQR2w55nhw3nnHnAbDgXr5YDZhKG2w4kNtxgYJPgOZDGOEMigU2aF6+W498kEufU1c8/f4BN8s+BNHugFvbfeLWc7zGTONjAnGBwAGg4zwGbRJAtzPht4Sk2bDh22HDjjcQ2aZkDNskzeB42S845h8+W4xsf/6mpk5c7f/iY5JsDErYz2JMPfnhThlsLg0QCjMXYgM7AAfgP4JcfBaNgFIyCUcAAAL/CVc3w+m4JAAAAAElFTkSuQmCC","orcid":"","institution":"Harvard University","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Gao","suffix":""},{"id":495595962,"identity":"791f7c6c-4b46-4c4c-bb5f-7f2d664c03c1","order_by":1,"name":"LIUHUA ZHU","email":"","orcid":"https://orcid.org/0000-0002-1645-4619","institution":"Hong Kong Baptist University","correspondingAuthor":false,"prefix":"","firstName":"LIUHUA","middleName":"","lastName":"ZHU","suffix":""},{"id":495595964,"identity":"015e486e-d5de-47d1-babe-e9a900848b63","order_by":2,"name":"Fan Wang","email":"","orcid":"","institution":"Hong Kong Baptist University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wang","suffix":""},{"id":495595965,"identity":"13c5629c-ec3b-45cf-8c5e-b9f17f3e36a4","order_by":3,"name":"Chris Nielsen","email":"","orcid":"https://orcid.org/0000-0001-8043-2409","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Nielsen","suffix":""},{"id":495595966,"identity":"3ec04531-c30d-42e3-9fe3-14bdce767932","order_by":4,"name":"Gregory Carmichael","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Carmichael","suffix":""}],"badges":[],"createdAt":"2025-08-01 13:55:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7271946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7271946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88898097,"identity":"8ebd101a-405d-4817-9696-961b8775d93d","added_by":"auto","created_at":"2025-08-12 13:19:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58875,"visible":true,"origin":"","legend":"\u003cp\u003eDensity scatter plot of predictions and observed values. (a) LST; (b) PM\u003csub\u003e2.5\u003c/sub\u003e; and (c) ozone.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/f69de995cc4d1d3c0a32a38c.jpg"},{"id":88898101,"identity":"a649d229-de55-4bf4-a902-25b3585b493d","added_by":"auto","created_at":"2025-08-12 13:19:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153518,"visible":true,"origin":"","legend":"\u003cp\u003eUrban spatial comparisons of unplanned (left) and optimized (right) urban development. (a) the frequency that grid cells are developed as urban; (b) average density in a developed urban grid cell; and (c) fraction of a developed urban grid cell that is green space. The left column presents the results from unplanned urban development and the right column presents the results from optimized urban development.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/b124919e27df78c54c256d11.jpg"},{"id":88898100,"identity":"ff6c26f3-e0cf-4821-8698-9824ab2304e2","added_by":"auto","created_at":"2025-08-12 13:19:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37326,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of environment-inclusive optimization of urban development based on estimated relationship of environmental variables and land use. (a) the averaged impacts on all grid cells; (b) the average impacts on grid cells with changed land use. For each categorial pair, the left box is from unplanned urban development and the right box is from optimized urban development.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/3dee1fb3068db2aa7e6a108c.jpg"},{"id":88898099,"identity":"6732b573-6b48-4aad-9e8b-eb2cd26fe61a","added_by":"auto","created_at":"2025-08-12 13:19:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38475,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of urbanization environmental impacts between unplanned urban development and optimized urban development. (a) the averaged impacts on all cells; (b) the average impacts on urban cells; and (c) the average impacts on cells becoming urban.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/adf016ae4fe3ed42f46ea185.jpg"},{"id":88900990,"identity":"4b015577-a98a-4f9d-84ca-73428439cb2b","added_by":"auto","created_at":"2025-08-12 13:44:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":900925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/a87d2284-e512-4806-b383-da1b722235c9.pdf"},{"id":88898110,"identity":"0486c3fb-c5b5-481b-9861-cbe1a36f748b","added_by":"auto","created_at":"2025-08-12 13:19:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2952514,"visible":true,"origin":"","legend":"Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates","description":"","filename":"355330supp226479sxp2pk.docx","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/25d7f1d2991cc00a5dd10eb3.docx"},{"id":88899252,"identity":"00c7f4cc-5430-4894-844f-ba61ec350a2c","added_by":"auto","created_at":"2025-08-12 13:27:59","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665663,"visible":true,"origin":"","legend":"Article File - Reporting Summary","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7271946/v1/59a51257e72cf69c27a32644.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban areas are currently home to over half of the world\u0026rsquo;s population, with projections indicating that the share could rise to 70% in 2050\u003csup\u003e1\u003c/sup\u003e. The shift of populations from rural to urban areas, known as urbanization, leads to replacement of areas of natural vegetation with artificial, impervious surfaces that decrease albedo and increase solar absorption\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The resulting temperature gradient between urban and rural areas is known as the urban heat island (UHI) effect\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Higher temperatures in urban areas can contribute to heat-related illness and mortality, and affect local ecosystems\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, the UHI effect often amplifies air pollution by increasing energy demand for cooling, enhancing temperature inversions, and changing local wind patterns\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Human activities in urban areas account for 80% of carbon emissions and most emissions of air pollutants\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, deteriorating air quality. The UHI effect and air pollution are interconnected, often coinciding and intensifying health risks for city dwellers\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, the unique urban thermal environment and underlying surfaces, coupled with climate change, have led to more extreme events such as heatwaves\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and heavy precipitation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, which damage urban infrastructure, increase mortality rates, and cause economic loss.\u003c/p\u003e\u003cp\u003eDespite the negative impacts, urbanization remains an inevitable part of societal progress in developing countries, especially China. To enhance urban resilience against climate change and pollution, various green (vegetation-based), blue (water-based) and grey (engineered) infrastructures (GBGI) have been established globally\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Prashant et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e suggested that green and blue components such as botanical gardens, wetlands, green walls and street trees could potentially reduce urban warming by more than 3 K. Although effective, these measures are commonly implemented in existing urban areas and require significant financial investment. An alternative strategy to mitigate the negative effects of urbanization involves optimizing future urban expansion. Lo et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e emphasized that the spatial location arrangement of urban land use influences the development of the UHI effect, based on satellite data. Additionally, different urban forms could effectively combat urban heat under climate change from an urban planning perspective\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Liu et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e suggested that a moderately dispersed and decentralized urban area is optimal for UHI mitigation in China.\u003c/p\u003e\u003cp\u003eOptimizing urban development hinges on refining urban land use locations, building density, location of green spaces, and other factors to minimize adverse effects while maximizing benefits. This optimization process encompasses a variety of decision variables, objectives, and constraints\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Optimization of urban spatial layouts has been used widely to try to achieve long-term balanced development\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Optimizing individual urban buildings can yield valuable insights on building heights and siting to reduce energy consumption, enhance thermal comfort, and improve air quality\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Strategic placement of green spaces of different types can result in substantial reductions in land surface temperatures (LST) locally and regionally\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and significant improvements in runoff mangement\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Land use optimization includes allocating the location of urban areas\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e as well as other land use types under future scenarios, prioritizing decision makers\u0026rsquo; preference to realize urban cooling\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, economic benefits, and ecological diversity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA few existing studies seeking to optimize spatially urban land use have focused on mitigating either urban heat or air pollution, but not simultaneously\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, climate and atmospheric air pollution are highly interdependent \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e at both local and regional scales. Diverse atmospheric circulation patterns, driven by temperature differences, influence distributions of air pollutants\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Lower temperatures tend to stabilize the boundary layer, limiting diffusion of air pollutants\u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Accumulated aerosols in the boundary layer also influence surface temperature through aerosol radiative effects\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Elevated shortwave radiation and temperatures favor emissions of gaseous precursors\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and formation of ozone\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. For these reasons, balancing thermal comfort and air quality can be challenging due to conflicting interactive processes. To the best of our knowledge, no prior research has thoroughly analyzed the intricate interactions between urban climate and atmospheric chemistry to formulate effective and realistic co-mitigation strategies.\u003c/p\u003e\u003cp\u003eGiven these challenges, we aim here to design an urban development optimization framework that considers the complex interplay between urban climate and atmospheric chemistry and can achieve improvement of thermal comfort and air quality simultaneously. The optimization is demonstrated by considering a compound heatwave and ozone episode in the Great Bay Area (GBA) of South China, one of the most densely populated and developed urban clusters in China. We divided the study area into grid cells. Optimization involves designating the non-urban grid cells as urban land types, and assigning the fraction of urban as well as other land use type within these newly developed urban grid cells. Based on coupled urban climate-chemistry modeling and multi-objective optimization, we demonstrate that co-mitigation of urban heat and air pollution can be achieved despite conflicting effects of associated geophysical mechanisms. The newly developed framework can be easily applied to other urban areas and has potential to improve living conditions, decrease energy consumption and reduce health risks in other cities around the world.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eRobustness of estimated relationships between land use types and environmental variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRobust relationships between environmental variables (LST, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, ozone concentrations) and land use types are essential for building objective functions for optimization. We first grouped the detailed land use types used in urban climate-chemistry model into eight categories, namely tree, shrub, grass, wetland, crop, urban, barren and water (Supplementary Table\u0026nbsp;4). The particulars for developing estimated relationships can be found in the Methods section. Evaluation metrics including the coefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), mean absolute error (MAE) and root square mean error (RMSE) were used. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, geographically weighted regression (GWR) models demonstrate reliable predictability due to their consideration of spatial variability, with all models exhibiting mean biases smaller than 0.5%. The LST and land use relationship exhibits good fitting, with an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.76, comparable to the findings of Duan et al.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.73). Although there is an underestimation of more than 2 K for LST values exceeding 306 K, these deviations are rare (~\u0026thinsp;0.9%) and exert negligible impacts on subsequent analyses. PM\u003csub\u003e2.5\u003c/sub\u003e and ozone predictions display superior performances, with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.84 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91, respectively, outperforming the models proposed by Huang et al.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.72 for PM\u003csub\u003e2.5\u003c/sub\u003e and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.65 for ozone). In summary, the established relationships demonstrate high robustness and can effectively serve as objective functions in the optimization process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMajor features of optimized urban plans\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe carried out spatial optimization with multi-objectives based on an initial assumption of 10% urban share of all land use (details in the Methods section). We then generated competitive urban plans through optimization (details are listed in Supplementary Note 1). These urban plans were all considered optimal, meaning here that no single urban plan outperforms the others across all objective functions (Supplementary Fig.\u0026nbsp;1). This flexibility empowers decision-makers to customize their selections to specific requirements. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e delineates the spatial characteristics of optimized versus unplanned urban development. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the frequency among all 400 urban plans that a given grid cell is developed as urban. Unplanned urban expansion exhibits a dispersed pattern across the area, with grid cells sharing similar probabilities for development. Conversely, optimal urban plans demonstrate an uneven likelihood of urbanization, particularly with higher development potential observed in areas along urban boundaries of the GBA. Urban density, representing the average urban land use fraction of an altered grid cell categorized as urban, was categorized into low (\u0026lt;\u0026thinsp;0.5), medium (0.5\u0026ndash;0.8), and high (\u0026gt;\u0026thinsp;0.8) density following the WRF-Noah urban canopy model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The average urban fractions for unplanned and optimized urban development are 0.77 and 0.71, respectively. Although the urban fractions are similar, optimized urban land use plans favor medium-density urban areas, making up 62.37% of newly developed urban grid cells. In contrast, unplanned urban development tends to produce medium and high-density areas equally, accounting for 52.13% and 46.64%, respectively. Green spaces play crucial roles in urban areas due to their cooling and air quality benefits. We also allocated urban green spaces in newly developed urban grid cells. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec displays unplanned urban development exhibits low vegetation cover with a 0.15 areal share, whereas optimized urban development plans exhibit more green spaces with a share of 0.26. Overall, optimized urban development, featuring a medium density urban form with expanded green spaces, is able to improve urban thermal comfort and air quality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUrban climate and air quality benefits of the optimized urban plans\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used regression to evaluate the effectiveness of the urban development optimization framework we have established by analyzing the relationships between environmental factors and land use patterns. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, unplanned urban development leads to a 0.07 K rise in LST over the whole region, whereas optimized urban development yields a 0.03 K increase. Although the 0.04 K temperature reduction due to optimization is modest, it is statistically significant as confirmed by a t-test analysis. When regional urban warming effects were averaged across altered urban grid cells, the mitigated warming due to optimization amounts to 0.17 K. Unplanned urban development is associated with increased PM\u003csub\u003e2.5\u003c/sub\u003e levels. However, the trend is effectively counteracted through optimization strategies. All urban plans demonstrate that urbanization does not exacerbate ozone pollution in urban areas. This is associated with stronger NOx titration in urban areas and elevated BVOC emissions in suburban and rural areas (Supplementary Fig.\u0026nbsp;2)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, indicating ozone concentrations in the GBA are less sensitive to the urban land type.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe performed the simulations based on the WRF-GC model to further assess the effectiveness of optimization strategies. Given that simulations for all generated urban land use plans are resources-intensive, we selected four representative urban plans. Two of these urban plans were derived from unplanned urban development scenarios, while the other two were taken from optimized urban development scenarios, chosen based on objective values (see Supplementary Table\u0026nbsp;5) and urban form (supplementary Fig.\u0026nbsp;3). The Unplanned-187 shows a dispersed urban layout, contrasting with compact urban patterns of other plans (Supplementary Fig.\u0026nbsp;3). When comparing Unplanned-392 with Optimized-325, both featuring compact urban designs, optimization leads to a 60% reduction in urban warming, a 175% decrease in PM\u003csub\u003e2.5\u003c/sub\u003e levels and a 114% decline of ozone concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, comparing Unplanned-392 with Optimized-266, both of which also exhibit compact urban forms, we also find 80% lower warming, 77% decrease in PM\u003csub\u003e2.5\u003c/sub\u003e and 109% decline of ozone. When comparing Unplanned-187 with Unplanned-392, it is notable that the dispersed urban development form suffers fewer side effects related to urban warming and air pollution. The newly developed urban grid cells of Unplanned-187 are more scattered, which enhance the mix of various land use types, potentially mitigating urban heat and air pollution\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. However, dispersed urban form may have transportation-related pollution\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, which is difficult to accurately measure in numerical models. In contrast, when comparing Unplanned-187 with Optimized-325, the optimization effectively addresses environmental issues associated with centralized urban forms, leading to reduced urban warming and improved air quality. This suggests that urban form is not a critical factor affecting the urban thermal and pollution environment when urban land use is optimized.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe expansion of urban areas has led to significant environmental challenges that require urgent mitigation efforts. In this study, we introduced a viable method for optimizing urban development with a focus on environmentally conscious planning. By acknowledging the conflicts between urban warming and the deterioration of air quality, our proposed framework integrated four trade-off objectives within the optimization process. We utilized the NSGA-II algorithm to produce a diverse array of urban plans for stakeholders to evaluate. When applied to a case study in the GBA, this approach revealed several important insights. Both regression analyses and numerical modeling simulations highlighted the effectiveness of optimizing urban development. The advantages of optimization are especially evident in urban regions. By incorporating environmental considerations into urban development optimization, it is possible to co-mitigate urban heat and air pollution, both of which are associated with dense population and rising energy consumption in urban areas.\u003c/p\u003e\u003cp\u003eUnlike traditional land use optimization strategies, which are typically applied at the street or block level as indicated by existing research\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, our method takes a different path by determining the location, urban and green space fractions of changed urban grid cells on a regional scale, with a spatial resolution of 3 km. In China, urbanization can take the form of so-called “mega-urban agglomeration,” characterized by clusters of cities\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Understanding the regional impact of urbanization and finding ways to mitigate its negative effects is crucial. Furthermore, climate and air quality issues extend beyond local boundaries, requiring consideration of regional-level attributes such as atmospheric transportation. Conducting urban land use optimization at this larger scale not only highlights the regional impacts of urbanization but also deepens understanding of the fundamental physical mechanisms involved.\u003c/p\u003e\u003cp\u003eSetting objectives is a crucial step in the optimization process. Climate and air quality issues are major concerns for urban development and public health, so we used LST, PM\u003csub\u003e2.5\u003c/sub\u003e, and ozone concentration as indicators of these adverse effects, as commonly utilized in other studies. Accurately characterizing how environmental variables respond to land use changes is essential for influencing optimization outcomes. To ensure precision in the regression relationships, we employed the Least Squares method and Random Forest regression for comparison with the GWR method. The results indicated that GWR provided superior accuracy (Supplementary Figs.\u0026nbsp;4 and 5). Although LST and PM\u003csub\u003e2.5\u003c/sub\u003e variations show diurnal patterns\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, our analysis of GWR regression coefficients for different land use types during the day and night revealed consistent characteristics (Supplementary Fig.\u0026nbsp;6). Therefore, we chose not to include diurnal variations in the LST and PM\u003csub\u003e2.5\u003c/sub\u003e regression models to simplify the number of objective functions. Following the development pattern in the GBA—i.e., mega-urban agglomeration—we used compactness as a key indicator. Economic costs were not included in our objectives because we limited development to 10% of urban grid cells and assumed uniform renovation costs across all grid cells to simplify cost considerations. Current quantitative assessments for multi-objective optimization largely rely on regression relationships. This research underscores the importance of both regression-based evaluation methods and numerical model simulations. Regression estimations provide a simple and quick way to assess all optimization results, but they lack the physical significance inherent in their relationships. On the other hand, numerical simulations incorporate atmospheric thermodynamics, transportation, and the interactions between climate and chemistry. Although they come with inherent uncertainties, numerical simulations offer plausible explanations and reveal underlying mechanisms, thereby enhancing understanding.\u003c/p\u003e\u003cp\u003eOur study offers a new perspective for stakeholders to tackle the negative impacts of urbanization in the context of climate change. Urban planning presents a complex challenge that requires considerations beyond the objectives we have outlined. However, our proposed method offers a collection of equally optimal urban plans that emphasize minimizing environmental impacts, helping policymakers to understand the comparative effects of different land use planning strategies based on their specific criteria. Additionally, policymakers can incorporate various targets, such as ecological benefits and economic costs, into the urban development optimization tool, demonstrating its adaptability. Looking forward, future research should aim to further explore numerical modeling techniques to improve the accuracy of models and emissions estimates based on altered land use patterns.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eCoupled urban climate-chemistry modelling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used WRF-GC v2.0\u003csup\u003e58\u003c/sup\u003e to perform the urban climate and air quality simulations. WRF-GC is an online coupling of the Weather and Research and Forecasting meteorological (WRF) model and the GEOS-Chem atmospheric chemistry model. Supplementary Fig.\u0026nbsp;7 shows the characteristics of meteorological and air pollution variables during the selected event. We configured 3 domains with grid resolutions of 27 km, 9 km, and 3 km, respectively (Supplementary Fig.\u0026nbsp;8). The primary parameterization options used for physical schemes and domain settings are listed in Supplementary Table\u0026nbsp;1. The meteorological initial and boundary conditions were obtained from the National Center for Environmental Prediction Final Operational Global Analysis (NCEP FNL, 1° × 1°) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rda.ucar.edu/datasets/d083002/\u003c/span\u003e\u003cspan address=\"https://rda.ucar.edu/datasets/d083002/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with 6-hour intervals. The chemical boundary and initial conditions were taken from a standard full-chemistry simulation from GEOS-Chem v12.8.1. The monthly mean anthropogenic emissions covering China with 0.25° × 0.25° grid resolution in 2020 were obtained from the Multi-resolution Emission Inventory for China (MEIC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.meicmodel.org\u003c/span\u003e\u003cspan address=\"http://www.meicmodel.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e59\u003c/sup\u003e. Anthropogenic emissions for the areas outside China were obtained from a mosaic Asian anthropogenic emission inventory\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Validations against observations show that although biases exist, the accuracy is comparable to other studies\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. More details about the validation can be found in Supplementary Note 2, Supplementary Fig.\u0026nbsp;9, Supplementary Table\u0026nbsp;2 and Supplementary Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-objective optimization framework for urban land use development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSupplementary Fig.\u0026nbsp;10 outlines the structure of multi-objective optimization for urban development. Initially, we identified the key objectives and represented them with mathematical functions. Subsequently, optimization was conducted to yield anticipated results. The effectiveness of optimization was evaluated through regression analysis and numerical simulations.\u003c/p\u003e\u003cp\u003eWe mainly considered urban heat and urban air quality, with urban heat indicated by LST while air quality is indicated by hourly concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and maximum daily 8-hour average ozone (MDA8 O\u003csub\u003e3\u003c/sub\u003e). Our primary objective was to promote sustainable urban development by mitigating urban warming and enhancing air quality simultaneously. Considering the fact that highly urbanized areas in the GBA are densely located around the Pearl River estuary, we included compactness\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e as an additional objective. These objectives were summarized in the blue box in Supplementary Fig.\u0026nbsp;10 and represented by different functions. Urban compactness is expressed as follows:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Compactness=\\sum\\:_{j=1}^{J}N,\\:\\forall\\:\\:j=\\text{1,2},3\\dots\\:.,j$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cem\u003ej\u003c/em\u003e denotes each developed urban area, and \u003cem\u003eN\u003c/em\u003e represents the total number of urban grid cells within a fixed distance centered on \u003cem\u003ej\u003c/em\u003e. This formula is further elucidated in Supplementary Fig.\u0026nbsp;11.\u003c/p\u003e\u003cp\u003eOur hypothesis assumed 10% urban land use based on the current level. Zhou et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e showed that percentage does not have a significant effect on the results of optimization. For consistency across experiments, all data, including land use, land use fractions, meteorological data, and air quality variables, were extracted from the simulations of the innermost domain (Supplementary Fig.\u0026nbsp;8). This domain comprises 1105 urban-type grid cells, each with a horizontal spatial resolution of 3 km x 3 km. Notably, a cell is classified as urban if at least one third of its area has urban land use, while the remainder can have other land uses such as water bodies, forests, shrubs, and crops. Several constraints were applied to limit urban development consistent with regulatory and geographical realities in the region:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eUrban development should not encroach upon croplands or water bodies, to protect these vital resources.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe elevation must not exceed 95m, which corresponds to the 95th percentile of current urban elevations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSelected urban cells should have water on a maximum of 50% of their borders, to exclude isolated islands.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003eApplication of these constraints yielded 110 grid cells we define as urban, and further yielded fractions of urban, green, and other land use types for each such cell. The distribution of potential urban grid cells and urban land use grid cells is illustrated in Supplementary Fig.\u0026nbsp;12.\u003c/p\u003e\u003cp\u003eGiven the potential conflicts among different objectives, no singular optimal solution is attainable and choosing from a set of solutions requires expert judgement of the trade-offs for society. Therefore, we introduced the widely utilized non-dominated sorting genetic algorithm (NSGA-II)\u003csup\u003e\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e–\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e to generate a range of urban plans considered equally optimal, enabling decision-makers to make the final decisions. Detailed technical particulars for NSGA-II are available in Supplementary Note 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between land use types and LST, ozone, and PM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eConsidering the spatial heterogeneity across the GBA area, we utilized the GWR model to estimate the environmental variables. This approach allowed us to determine the autocorrelation between land use types and environmental factors. The formulation is represented as follows:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}=\\:{\\beta\\:}_{0}\\left({u}_{i},{v}_{i}\\right)+\\sum\\:_{k=1}^{N}{\\beta\\:}_{k}\\left({u}_{i},{v}_{i}\\:\\right){x}_{ik}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the number of predictor variables\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:;\\:\\left({u}_{i},{v}_{i}\\right)\\:\\)\u003c/span\u003e\u003c/span\u003edenotes the coordinates pair for location \u003cem\u003ei\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003e signifies the varying weight; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the changing intercept based on locations. Thus, GWR extends traditional global regression techniques to accommodate non-stationary spatial relationships\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Finally, \u003cem\u003ey\u003c/em\u003e is the dependent environmental variable, in particular LST to represent climate and hourly PM\u003csub\u003e2.5\u003c/sub\u003e and ozone concentrations to represent air quality.\u003c/p\u003e\u003cp\u003eWe took both the effects of each land use type at cell \u003cem\u003ei\u003c/em\u003e and all adjacent cells into account to predict the dependent variables. Land use types in WRF-GC simulations were compiled into a smaller number of classes (Supplementary Table\u0026nbsp;4). The effects from adjacent cells were calculated by summing the inversed distance weighted (IDW) with a window size of 5 × 5.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of spatially-optimized planning strategies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpatially optimized urban planning strategies were evaluated using the estimated relationships and the WRF-GC model. To assess the estimates, environmental variables were calculated based on optimized and unplanned urban development respectively.\u003c/p\u003e\u003cp\u003eGiven the computational limitations of assessing all urban plans using the WRF-GC model, we selected four planning cases from the unplanned (two) and optimized (two) sets respectively. Supplementary Table\u0026nbsp;5 presents the selected four plans alongside their objective function values (termed Unplanned-187, Unplanned-392, Optimized-266, Unplanned-325). Updated land use, land use fraction, leaf area index and vegetation type fraction data were obtained to align with expanded urban land use scenarios before been integrated into the model. Adjustments to anthropogenic emissions were also necessary in response to changes in land use, given the distinctive characteristics of various land types. Emission reallocations were implemented following the procedure detailed in Supplementary Note 3. The total emissions after adjustment were consistent with that during case study. Supplementary Fig.\u0026nbsp;13 illustrates the differences of PM\u003csub\u003e2.5\u003c/sub\u003e emissions for 266th urban plan in the optimized urban plan sets. Increased PM\u003csub\u003e2.5\u003c/sub\u003e emissions from power, industry, residential and transport can be seen in the newly developed urban areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis study was supported by the grants from National Natural Science Foundation of China (no. 42375095), the Research Grants Council of the Hong Kong Special Administrative Region, China (project nos. C2002-22Y, 12202021 and 12201023), Environmental and Conservation Fund (121/2022), and the Center for Ocean Research in Hong Kong and Macau (CORE), a joint research center between the Laoshan Laboratory and HKUST.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eAll data are available in the main text, methods or the supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eElmqvist T et al (2013) Urbanization, biodiversity and ecosystem services: challenges and opportunities: a global assessment. Springer Nature\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu Y et al (2015) The cumulative effects of urban expansion on land surface temperatures in metropolitan JingjinTang, China. 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Remote Sens 10:1428\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7271946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7271946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs cities expand, urban heat islands and air pollution have emerged, threatening health, ecosystems, and infrastructure. Yet a dual solution remains lacking to simultaneously mitigate these two greatly concerned issues due to complex urban climate-chemistry interactions, particularly for humid subtropical cities that suffer more from elevated heat stress. On the basis of coupled urban climate-chemistry modeling and non-dominated sorting genetic algorithm (NSGA-II), we developed optimized spatial planning that considers the intricate interplay between urban heat and atmospheric chemistry. Optimized urban plans suggest a development pattern characterized by medium density and expanded urban green spaces. Both regression analysis and numerical modeling assessments demonstrate that our method could effectively mitigate both urban heat and air pollution. This study offers a new perspective to tackle the negative environmental impacts of urbanization in the context of ongoing climate change, and the developed framework can be easily applied to other urban areas and has potential to improve living conditions, decrease energy consumption and reduce health risks globally.\u003c/p\u003e","manuscriptTitle":"Optimized spatial planning offers a dual solution for managing urban heat and air pollution in humid subtropical climates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:19:55","doi":"10.21203/rs.3.rs-7271946/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0d769b1f-bf3a-4784-96bb-f47906e2d737","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52634041,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":52634042,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2026-04-02T04:10:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 13:19:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7271946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7271946","identity":"rs-7271946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}