Land-use Model Research Articles

Understanding future water-carbon-land coupled systems in the era of COP 27: The case of the Hanjiang river Basin, China

The 27th Conference of the Parties (COP 27) emphasized addressing this century's interconnected challenges of food, water, and energy. This study proposes a coupled ecosystem service and multi-scenario land-use change simulation model designed to investigate the future dynamics of water-carbon-land coupled systems in the Hanjiang River Basin (HJRB), the primary objective is to provide valuable insights that can inform strategic spatial management decisions, aligning with the ambitious objectives outlined in COP 27. Specifically, this study utilized the PLUS model, InVEST model, and redundancy analysis to comprehensively analyze the interplay between ecosystem services and land-use changes, with a specific focus on water, carbon, and land dynamics. The results showed that regardless of the simulated scenarios, there was a consistent pattern observed in the changes of land use types within the HJRB, with a decrease in farmland and an increase in forest. However, the water area showed an increasing trend in all scenarios, especially in the ecological land protection (ELP) and sustainable development scenarios. Furthermore, the ELP scenario effectively suppressed the expansion of building land and the erosion of ecological land. Ecosystem services under different scenarios showed similar spatial distribution patterns but presented varying degrees of change related to the impact of future land use and urban development on ecosystem services. The water yield (WY), carbon storage, and soil conservation in the upstream areas increased to varying degrees, while those in the downstream areas decreased. In conclusion, precipitation, land use/land cover change, DEM (Digital Elevation Model), and NDVI (Normalized Difference Vegetation Index) were the main driving factors affecting ecosystem services, with precipitation having the most significant and enduring impact on WY. This study supports the adoption of targeted spatial management measures to promote sustainable development and enhance human well-being.

Association between Long-Term Exposure to Traffic-Related Air Pollution and Cardio-Metabolic Phenotypes: An MRI Data-Based Analysis.

Long-term exposure to traffic-related air pollution (TRAP) is associated with cardiometabolic disease; however, its role in subclinical stages of disease development is unclear. Thus, we aimed to explore this association in a cross-sectional analysis, with cardiometabolic phenotypes derived from magnetic resonance imaging (MRI). Phenotypes of the left (LV) and right cardiac ventricle, whole-body adipose tissue (AT), and organ-specific AT were obtained by MRI in 400 participants of the KORA cohort. Land-use regression models were used to estimate residential long-term exposures to TRAP, e.g., nitrogen dioxides (NO2) or particle number concentration (PNC). Associations between TRAP and MRI phenotypes were modeled using linear regression. Participants' mean age was 56 ± 9 years, and 42% were female. Long-term exposure to TRAP was associated with decreased LV wall thickness; a 6.0 μg/m3 increase in NO2 was associated with a -1.9% [95% confidence interval: -3.7%; -0.1%] decrease in mean global LV wall thickness. Furthermore, we found associations between TRAP and increased cardiac AT. A 2,242 n/cm3 increase in PNC was associated with a 4.3% [-1.7%; 10.4%] increase in mean total cardiac AT. Associations were more pronounced in women and in participants with diabetes. Our exploratory study indicates that long-term exposure to TRAP is associated with subclinical cardiometabolic disease states, particularly in metabolically vulnerable subgroups.

Can regionalization enhance the performance of land-use change models in rapidly urbanizing areas?

The performance of Land Use Change (LUC) models is influenced by the regional spatial characteristics that trigger the changes. However, the literature on LUC models generally reports validation results for entire regions without considering subregions that differ significantly in their LUC drivers. This research explores how the LUC driving forces differ among subregions and whether regionalization can improve the performance of LUC models in areas undergoing rapid urbanization. We analyzed the Geomod, Cellular Automata-Markov, and Land Change Modeler models across rural and urbanized subregions on the western edge of Mexico City. Regionalization significantly enhanced the overall accuracy of the models and the concordance of spatial patterns with the reference data in rural regions but was of limited benefit in urbanized regions. This shows the need to consider regionalized modeling to improve the performance of LUC models when there are noticeable differences in LUC drivers between subregions. These findings will enhance the usefulness of LUC models for urban planning and land management policies, promoting more precise and effective decision-making.

Long-term stability in protected-areas? A vision from American/New World amphibians

Protected areas (PA) have proven to be one of the best ways to conserve biodiversity against environmental changes. Amphibians are considered the most threatened group, with habitat loss due to deforestation identified as their major threat. Here, we assessed for each PA of the American continent: 1) amphibian’s occurrence (Global Biodiversity Information Facility (GBIF) vs. International Union for Conservation of Nature (IUCN) data); 2) temperature velocity and estimated the climate residence time, and using the latest models of the land future use; 3) we estimated the changes of natural vs. modified cover in three future scenarios. Amphibian occurrence showed differences between databases, while GBIF data shows that 52 % of the amphibian species occurring in the continent are in PA, based on IUCN data, 85 % are protected. Results from climate change show a low pace of climate velocity during the last century that is maintained in the green scenario (SSP126). However, change in temperature increases in rate in the rest of the scenarios, with scenario SSP58 showing the highest velocity of temperature change. Future estimates of residence times in PA show that lower levels as emission scenarios tend to be higher. These results are worrisome since climate lag, specifically temperature increase over the PA will probably affect amphibian communities as shown in previous studies. Changes in climate patterns have a direct—mostly negative—impact on amphibians’ ability to disperse and reproduce. The results of land use change were unexpected, since the categories showed minimal changes. However, the data on urbanization changes do not seem to be reflecting the trends of other databases, which may be causing artifacts in the comparisons in the future models of land use. Further research will be necessary to evaluate the extent of similarities and differences in future projections of land use including urbanization and human population between different databases.

Association of fine particulate matter and residential green space with rheumatoid arthritis

BackgroundFine particulate matter (PM2.5) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM2.5 and rheumatoid arthritis (RA) is still controversial. Additionally, evidence on the relationship of green space with RA is scarce. This study aimed to investigate the separate and combined associations of PM2.5 and green space with risk of RA. MethodsOur study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM2.5 concentrations were determined using a land-use regression model. Residential green space was assessed using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from satellite images. We employed the Cox proportional hazard model to evaluate the relationships of PM2.5 and green space with RA. ResultsDuring the 176,894 person-years of follow-up period, 354 cases of RA were identified. Hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) for every interquartile range (IQR) increase in PM2.5 were 1.23 (95% CI: 1.02, 1.49). Compared with lower exposure to residential green space, individuals living in areas with more green space had a decreased risk of RA (HR was 0.80 (95% CI: 0.70, 0.92), 0.80 (95% CI: 0.70, 0.92), and 0.79 (95% CI: 0.70, 0.89) for 250m, 500m, and 1000m NDVI buffers, respectively). Similar results were observed for the association between EVI and RA. Furthermore, a significant multiplicative interaction was observed between PM2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM2.5 on the relationship between green space and RA was observed. ConclusionOur findings indicated that living in areas with higher green space was linked to a reduced risk of RA, whereas living in areas with higher PM2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM2.5.

Ambient air pollution, low-grade inflammation, and lung function: Evidences from the UK Biobank

The associations of ambient air pollution exposure and low-grade inflammation with lung function remain uncertain. In this study, 276,289 subjects were enrolled in the UK Biobank. Individual exposure to ambient air pollution (including nitrogen dioxide [NO2], nitrogen oxides [NOx]), and particulate matter [PM2.5, PM10, PMcoarse]) were estimated by using the land-use regression model. Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were tested, and low-grade inflammation score (INFLA score) was calculated for each subject. In this cross-sectional study, the median concentrations of air pollution were 9.89 µg/m3 for PM2.5, 15.98 µg/m3 for PM10, 6.09 µg/m3 for PMcoarse, 25.60 µg/m3 for NO2, and 41.46 µg/m3 for NOx, respectively. We observed that PM2.5, PM10, PMcoarse, NO2, NOx was negatively associated with lung function. Besides, significant positive associations between PM exposure and low-grade inflammation were noted. Per interquartile range (IQR) increase in PM2.5, PM10, and PMcoarse was related to higher INFLA score, and the β (95 % CI) was 0.06 (0.03, 0.08), 0.03 (0.02, 0.05), and 0.03 (0.01, 0.04), respectively. Additionally, we found significant negative associations between INFLA scores and lung function. One-unit increase in INFLA score was linked with 12.41- and 11.31-ml decreases in FVC and FEV1, respectively. Compared with individuals with low air pollution exposure and low INFLA scores, participants with high air pollution and high INFLA scores had the lowest FVC and FEV1. Additionally, we observed that INFLA scores could modify the relationships of PM2.5, NO2, and NOx with FVC and FEV1 (Pinteraction <0.05). The negative impact of air pollutants on lung function was more pronounced in subjects with high INFLA scores in comparison to those with low INFLA scores. In conclusion, we demonstrated negative associations between ambient air pollution and lung function, and the observed associations were strengthened and modified by low-grade inflammation.

Enhanced agricultural carbon sinks provide benefits for farmers and the climate

Carbon sequestration on agricultural land, albeit long-time neglected, offers substantial mitigation potential. Here we project, using an economic land-use model, that these options offer cumulative mitigation potentials comparable to afforestation by 2050 at 160 USD2022 tCO2 equivalent (tCO2e−1), with most of it located in the Global South. Carbon sequestration on agricultural land could provide producers around the world with additional revenues of up to 375 billion USD2022 at 160 USD2022 tCO2e−1 and allow achievement of net-zero emissions in the agriculture, forestry and other land-use sectors by 2050 already at economic costs of around 80–120 USD2022 tCO2e−1. This would, in turn, decrease economy-wide mitigation costs and increase gross domestic product (+0.6%) by the mid-century in 1.5 °C no-overshoot climate stabilization scenarios compared with mitigation scenarios that do not consider these options. Unlocking these potentials requires the deployment of highly efficient institutions and monitoring systems over the next 5 years across the whole world, including sub-Saharan Africa, where the largest mitigation potential exists.

Residential ambient air pollution exposure and the development of white matter microstructure throughout adolescence

BackgroundRecent evidence suggests an association of air pollution exposure with brain development, but evidence on white matter microstructure in children is scarce. We investigated how air pollution exposure during pregnancy and childhood impacts longitudinal development of white matter microstructure throughout adolescence. MethodsOur study population consisted of 4108 participants of Generation R, a large population-based birth cohort from Rotterdam, the Netherlands. Residential air pollution exposure to 14 air pollutants during pregnancy and childhood was estimated with land-use regression models. Diffusion tensor images were obtained around age 10 and 14, resulting in a total of 5422 useable scans (n = 3082 for wave 1 and n = 2340 for wave 2; n = 1314 for participants with data on both waves). We calculated whole-brain fractional anisotropy (FA) and mean diffusivity (MD) and performed single- and multi-pollutant analyses using mixed effects models adjusted for life-style and socioeconomic status variables. ResultsHigher exposure to PM2.5 during pregnancy, and PM10, PM2.5, PM2.5-10, and NOX during childhood was associated with a consistently lower whole-brain FA throughout adolescence (e.g. – 0.07 × 10−2 FA [95%CI -0.12; −0.02] per 1 standard deviation higher PM2.5 exposure during pregnancy). Higher exposure to silicon (Si) in PM2.5 and oxidative potential of PM2.5 during pregnancy, and PM2.5 during childhood was associated with an initial higher MD followed by a faster decrease in MD throughout adolescence (e.g. – 0.02 × 10−5 mm2/s MD [95%CI -0.03; −0.00] per year of age per 1 standard deviation higher Si exposure during pregnancy). Results were comparable when performing the analysis in children with complete data on the outcome for both neuroimaging assessments. ConclusionsExposure to several pollutants was associated with a consistently lower whole-brain FA throughout adolescence. The association of few pollutants with whole-brain MD at baseline attenuated throughout adolescence. These findings suggest both persistent and age-limited associations of air pollution exposure with white matter microstructure.

Towards an Urban Planning Scenario Model System—A Tool for Exploring Urban Uncertainty: A Case Study of Diaozhen, China

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss the assumptions and theoretical basis of the model system. The model system will be driven by scenario planning theory and sustainable urban development principles. It will export land-use planning based on selected urban development scenarios and urban planning theories. This paper takes Diao Town in Jinan as an example. Applying GIS spatial analysis and hierarchical analysis, this paper determines the suitability of land use and the weights of different influencing factors, combined with the land-use conflict identification model, for land-use decision-making. Finally, the assessment module verifies whether the planning scheme complies with laws and regulations to achieve an active, reactive response to uncertainty. The paper discusses the ‘uncertainty’ of urban planning and proposes a creative, flexible, and timely planning platform that allows planners and other participants to model and visualize their scenarios.

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R2 values of 0.81 (95 % CI: 0.75–0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87–0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m–225 m) particles, while 21 % was associated with upper-level (825 m–945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan–22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec–24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Determining sources of air pollution exposure inequity in New York City through land-use regression modeling of PM2.5 constituents

Determining sources of air pollution exposure inequity in New York City through land-use regression modeling of PM2.5 constituents

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Effects of indoor environments and outdoor air pollutants in residential areas on acute exacerbation in patients with severe asthma

ABSTRACT This study aimed to determine the effects of indoor environment (IE) and outdoor air pollutants (OAPs) in residential areas on acute exacerbation (AE) in patients with severe asthma. A total of 115 participants were recruited. To characterize IE, we used structured questionnaires and estimated OAP concentrations using a land-use regression model. Participants who were exposed to passive smoking and lived in houses where the kitchen and living room were not separated showed a significantly higher rate of AE (p = 0.014 and 0.0016, respectively). The mean concentration of PM2.5 in residential areas during the last 3 years was significantly higher in participants with AE than that in those without AE (19.8 ± 3.1 vs. 21.0 ± 2.5 µg/m3, p = 0.033). Moreover, the serum level of 8-hydroxy-2′-deoxyguanosine significantly increased in participants with AE compared to those without AE (56.9 ± 30.0 vs. 94.7 ± 44.5 ng/mL, p = 0.0047) suggesting enhanced oxidative stress in those with AE.

Circulating metabolomics revealed novel associations between multiple ambient air pollutants exposure and chronic obstructive pulmonary disease incidence: Evidence from a prospective cohort study

The mechanisms underlying relationships between ambient air pollution and chronic obstructive pulmonary disease (COPD) risk remained largely uncertain. In this study, we aim to evaluate whether metabolic signature comprising multiple circulating metabolites can characterize metabolic response to the multiple air pollution; and to assess whether the identified metabolic signature contribute to COPD risk. A total of 227,962 participants with complete data were included from the UK biobank study. Concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter (PM2.5 and PM10) were evaluated by land-use regression models. We newly computed an air pollution score to reflect joint exposure to multiple air pollutants. Circulating metabolome was quantified by nuclear magnetic resonance (NMR) spectroscopy. During a median of 12.78 years of follow-up, a total of 8685 incident COPD cases were documented. After multiple correction, the Cox regression models showed that 102 of 143 metabolites were significantly associated with COPD risk. Utilizing elastic net regularized regressions, we identified a metabolic signature comprising 106 metabolites (including lipid, fatty acids, glycolysis and amino acids et al.) were robustly related to air pollution score. In the multivariate-adjusted Cox regression models, the derived metabolic signature showed a positive correlation with incident COPD [HR per SD = 1.20 (95% CI: 1.17–1.22)]. Casual mediation analysis further noted that the constructed metabolic signature mediated 10.5 % (8.3%–13.1%) of the air pollution-COPD associations. Taken together, our findings identified a metabolic signature that captured metabolic response to various air pollutants exposure jointly, and predicted future COPD risk independent of known risk factors.

DCAI-CLUD: a data-centric framework for the construction of land-use datasets

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

Changing with the times: From agricultural potential to spatially explicit reconstructions of past land use

Past land-use reconstructions are a key tool for studying long-term human ecodynamics and addressing pressing questions about the origins and evolutionary dynamics of the Anthropocene. In particular, agricultural landcover reconstructions are vital for understanding long-term human-environment dynamics. Most past agricultural land-use models, however, rely heavily on modelling assumptions, make limited use of known archaeological site locations to constrain or inform their estimates and tend to be limited to general estimates of percentages of plant types within catchments around pollen trapping lakes. The lack of information outside catchment areas and low spatial resolution even within catchments constrain the utility of these models. To address this problem, we propose a new approach that combines archaeological predictive modelling with pollen-based agricultural landcover reconstructions to produce more accurate, spatially explicit past landcover estimates. Here, we present the results of a case study deploying the new approach to produce improved past landcover maps for a region in the Western Taurus Mountains, southwestern Turkey. The study area surrounds Sagalassos, an antique urban centre with a regional settlement history encompassing nine millennia. We produced five archaeological predictive models for the study region using the ‘Locally Adaptive Models of Archaeological Potential’ (LAMAP) method spanning the Hellenistic through Late Ottoman period. We then combined those predictive surfaces with ‘Regional Estimates of VEgetation Abundance from Large Sites’ (REVEALS) pollen landcover reconstructions for the same periods based on pollen from sediment cores extracted from three catchments within the study area. Lastly, we compared the resulting hybrid landcover models to the archaeological record using data not used to make the predictions. We found that the hybrid landcover model aligned very well with the known extents of agricultural land-use from the study area. These results indicate that the proposed approach is a viable way to combine pollen-based landcover models with archaeological data and produce more accurate, empirically-based landcover reconstructions reflecting real human activity in the past.

Broadening the horizon in land use change modelling: Normative scenarios for nature positive futures in Switzerland

Within scenario-based research of social-ecological systems, there has been a growing recognition of the importance of normative scenarios that define positive outcomes for both nature and society. While several frameworks exist to guide the co-creation of normative scenario narratives, examples of operationalizing these narratives in quantitative simulation modelling are still limited. To address this gap, this paper presents an example of how aspects of normative scenarios can be realized within a spatial model of land use and land cover change. This is achieved through a combination of data-driven approaches to encapsulate scenario-specific differences in local and global scale phenomena, as well as iterative expert elicitation to quantify descriptive trends from narratives. This approach is demonstrated with a case study simulating five scenarios of landscape change (three normative and two exploratory) in Switzerland between 2020 and 2060. The resulting maps of future land use and land cover exhibited distinct variations between the scenarios, notably with regard to the prevalence of areas of heterogeneous semi-natural land, such as alpine pastures and grassland, often considered culturally emblematic of Switzerland. While the simulation results were generally consistent with the outcomes expressed in the scenario narratives, following a process of expert feedback, we reflect that there are clear challenges in leveraging such results to elicit further discussions as to the desirability and plausibility of future scenarios. Specifically, the need to summarize spatial simulations in a manner that is easily interpretable and encourages consideration of the broader patterns of change rather than focusing on fine-scale details.

Air Pollution, Genetic Susceptibility, and Risk of Incident Systemic Lupus Erythematosus: A Prospective Cohort Study.

There are few existing studies that investigate the risk of systemic lupus erythematosus (SLE) associated with long-term exposure to air pollutants. This study aimed to explore associations between long-term exposure to air pollutants and incident SLE and further evaluate interactions and joint effects of genetic risk and air pollutants. A total of 459,815 participants were included from UK Biobank. The concentrations of air pollutants (fine particulate matter with diameter ≤2.5 μm [PM2.5], particulate matter diameter ≤10 μm [PM10], nitrogen dioxide [NO2], and nitrogen oxides [NOx]) were estimated by land-use regression model. We applied Cox proportional hazards model to explore linkages of air pollutants and incident SLE. The polygenic risk score (PRS) was used for further assessing the interactions and joint effects of genetic risk and air pollutants. A total of 399 patients with SLE were identified during a median follow-up of 11.77 years. There were positive associations between air pollutant exposure and incident SLE, as the adjusted hazard ratios were 1.18 (95% confidence interval [95% CI] 1.06-1.32), 1.23 (1.10-1.39), 1.27 (1.14-1.41), and 1.13 (1.03-1.23) for each interquartile range increase in PM2.5, PM10, NO2, and NOx, respectively. Moreover, participants with high genetic risk and high air pollution exposure had the highest risk of incident SLE compared with those with low genetic risk and low air pollution exposure (adjusted hazard ratio: PM2.5, 4.16 [95% CI 2.67-6.49]; PM10, 5.31 [95% CI 3.30,-8.55]; NO2, 5.61 [95% CI 3.45-9.13]; and NOx, 4.80 [95% CI 3.00-7.66]). There was a significant multiplicative interaction between NO2 and PRS. Long-term exposure to air pollutants (PM2.5, PM10, NO2, and NOx) may increase the risk of developing SLE.

The spatial planning of housing and urban green space: A combined stated choice experiment and land-use modeling approach

Urban green space (UGS) is receiving increasing attention as a means to make cities better adapted to climate change and to create high-quality living environments for citizens. The spatial planning of UGS generally asks a trade-off between optimizing land-use and accessibility characteristics which may differ between housing types. In this paper, we use a stated choice experiment to estimate residential location preferences related to neighborhood land-use, UGS and accessibility characteristics in the framework of a land-use model. A national sample of 394 Dutch homeowners participated in the experiments and mixed logit models were estimated by housing type distinguishing detached, row houses and apartments. The estimation results show that trade-offs made between a green neighborhood and accessibility to urban amenities tend to differ between the housing types. The estimates are used as parameters in a housing land-use allocation model. An application of the model to a residential area development problem shows that creating green buffers along road infrastructure is the most beneficial way of allocating UGS considering the housing value for residents. The application further shows that apartments benefit more strongly from UGS in the neighborhood than from high accessibility of urban amenities compared to detached and row houses. Finally, we find that the optimal spatial allocation of UGS depends on whether maximizing housing market-value or residents’ utility is the prime objective.