Land use change stands as the primary factor influencing habitat quality (HQ). Clarifying the spatiotemporal change and the obstacle factors of the coupling relationship between HQ and urbanization level (UL) can provide imperative references for achieving sustainability in the Yellow River Basin (YRB). This study is based on the InVEST model, spatial autocorrelation, and obstacle factor analysis to measure the spatiotemporal dynamics and impediments of the coupling relationship between HQ and UL from 2000 to 2020 in the YRB. The findings were as follows: (1) From 2000 to 2020, the HQ showed a tendency of rise first and then fall, with the pattern of \"High in the middle and west, low in the east\"; (2) from 2000 to 2020, the UL had an upward trend, with the pattern of \"Low in the west, high in the middle and east\"; (3) the coupling and coordination level of HQ and UL in the YRB changed from extreme incoordination to verge of coordination, and it had a distribution pattern of \"High in the east, low in the west\", with the high-value area expanding to the east and the low-value area shrinking to the west. (4) Location condition, climate, proportion of construction land, vegetation index, and proportion of non-agricultural employment are the main obstacle factors that determined the coupling and coordination of the HQ and UL.

Urban sprawl threatens biodiversity and is responsible for significant changes in the species that live in these environments. Given the high cost of comprehensive surveillance, monitoring disease indirectly, such as detecting skin lesions in birds, may help us better understand the prevalence of diseases affecting wild populations. We assessed the frequency of leg skin lesions, as a proxy of disease presence, in 1,565 individuals of 25 species, along the urban matrix of a large Neotropical city, Brasília, Federal District, Brazil. We tested the hypothesis that there is an increase in the frequency of skin lesions in birds due to urban intensification. We observed an increasing trend in some bird species between the frequency of occurrence of lesions and the intensity of urbanization. Species with a higher number of captures had an increase in the percentage of lesions, indicating that the occurrence of lesions may be linked to higher population density or that detection of the effect occurs only when sample sizes are high and controlled among urbanization categories. Our study highlights how the intensity of urbanization may increase the risk of disease transmission for these species. Unfortunately, studies on this topic are scarce in Neotropical regions, despite the region\'s high biodiversity and urban expansion.

Urban expansion has been rapidly increasing and is projected to be tripled in 2030 in worldwide. The impact of urbanization has adverse effects on the environment and economic development. Residential lands consist of almost one-third of the urban area and heavily affect the city\'s inhabitants. The capital of Mongolia, Ulaanbaatar, has been significantly expanded, particularly in the urban periphery where poor living conditions and a lack of essential urban services were identified. The paper aims to conduct a suitability analysis of residential areas in Ulaanbaatar city based on three main categories (livability, affordability, and accessibility) of fifteen criteria using the fuzzy logic. Through the study, we have identified some potential suitable residential areas for further development, such as apartment residential area located in the southern part of the city and four low-rise ger areas were distributed along major transport corridors. Moreover, the results indicated that the spatial structure of the whole town might be in transition to a polycentric pattern. However, a concentric ring pattern in the \'city\'s periphery displayed a concerning uncontrolled ger area expansion, which may increase low living conditions in the area. This study recommends better urban sprawl control policies and more property market investment in the ger area to ensure sustainable development goals in Ulaanbaatar.

Since 1978, China\'s rapid urbanization and industrialization have significantly increased carbon emissions. This study employs spatial autocorrelation, kernel density estimation, and spatiotemporal geographically weighted regression (GTWR) methods to analyze the spatiotemporal evolution characteristics of carbon emissions across 336 Chinese cities from 1978 to 2020. It also explores the dominant influencing factors for different cities at various stages of development. The findings reveal that carbon emissions in Chinese cities exhibit a stepwise growth pattern: \"slow growth (1978-1995) - low-level stability (1996-2000) - rapid growth (2001-2012) - high-level stability (2013-2020).\" The gap between cities has widened rapidly, and spatially, the distribution follows a \"core-periphery\" pattern. The increase in carbon emissions in core cities has transformed the urban hierarchy from a \"generally low-carbon\" structure to a \"pyramid\" structure. Compared to 1995, the influence of population size on carbon emissions decreased in 2020 (0.54-0.38), while the impact of infrastructure development and technological advances increased (0.02-0.25, 0.09 to 0.19). Due to the varying stages of urban development across regions, the influencing factors of carbon emissions exhibit spatial heterogeneity. Specifically, population size has a stronger positive impact on carbon emissions in the Southeast, technological advances in East and North China, and industrial structure in the Yangtze River Basin region. Infrastructure construction and investment levels show a dampening effect on carbon emissions in the Yangtze River Basin. Finally, the study proposes policy recommendations focusing on implementing regional \"gradient\" carbon reduction and promoting regional collaborative carbon reduction driven by core cities.

UNASSIGNED: Urban-rural differences in schizophrenia risk have been widely evidenced across Western countries. However, explanation of these differences is lacking. We aimed to identify contextual risk factors for schizophrenia that explain urban-rural differences in schizophrenia risk.
UNASSIGNED: Utilizing Danish population-based registers, we partitioned Denmark into 1885 geographic \"neighborhoods\" homogeneously sized in terms of population. Information on the entire Danish population from 1981 to 2016 was used to quantify a spectrum of neighborhood-level domains. We subsequently conducted multilevel survival analyses following persons born in Denmark from 1971 to 1982 for the development of schizophrenia allowing for clustering of people within neighborhoods. We used this method to tease apart the effects of individual, specific, and general contextual risk factors for schizophrenia.
UNASSIGNED: A significant general contextual effect in schizophrenia risk across neighborhoods was estimated (Medium Incidence Rate Ratio (MRR):1.41; 95% CI:1.35-1.48). Most of the specific contextual factors examined were associated with schizophrenia risk. For instance, neighborhood-level proportion of lone adult households (Incidence Rate Ratios (IRR):1.53; 95% CI:1.44-1.63) had largest risk estimate. Adjustment for all individual-level and specific contextual constructs reduced the IRR for urbanicity from 1.98 (95% CI:1.77-2.22) to 1.30 (95% CI:1.11-1.51).
UNASSIGNED: In the largest prospective multilevel survival analyses of schizophrenia risk conducted to date, multiple neighborhood-level characteristics were associated with raised schizophrenia risk, with these contextual factors explaining most of the elevated risk linked with urbanicity. However, the unexplained heterogeneity that was evident in our multilevel models indicates that our understanding of the role of urbanicity in schizophrenia\'s etiology remains incomplete.

Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.

Human activities are altering the existing patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) on a global scale. However, long-term trends of LULC and LST are largely unknown in many remote mountain areas such as the Karakorum. . The objective of our study therefore was to evaluate the historical changes in land use and land cover (LULC) in an alpine environment located in Islamabad Capital Territory, Pakistan. We used Landsat satellite pictures (namely Landsat 5 TM and Landsat 8 OLI) from the years 1988, 2002, and 2016 and applied the Maximum Likelihood Classification (MLC) approach to categorize land use classes. Land Surface Temperatures (LST) were calculated using the thermal bands (6, 10, and 11) of Landsat series data. The correlation between the Human Modification Index (HMI) and LULC as well as LST was evaluated by utilizing data from Google Earth Engine (GEE). Over the study period, the urbanized area increased by 9.94%, whilst the agricultural and bare soil areas decreased by 3.81% and 3.94%, respectively. The findings revealed a significant change in the LULC with a decrease of 1.99% in vegetation. The highest LST class exhibited a progressive trend, with an increase from 12.27% to 48.48%. Based on the LST analysis, the built-up area shows the highest temperature, followed by the barren, agricultural, and vegetation categories. Similarly, the HMI for different LST categories indicates that higher LST categories have higher levels of human alteration compared to lower LST categories, with a strong correlation (R-value = 0.61) between HMI and LST. The findings can be utilized to promote sustainable urban management and for biodiversity conservation efforts. The work also has the potential of utilizing it to protect delicate ecosystems from human interference and to formulate strategies and regulations for sustainable urban growth, including aspects of land utilization and zoning, reduction of urban heat stress, and urban infrastructure.

Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures.

BACKGROUND: Mild cognitive impairment (MCI) is a significant public health concern and a potential precursor to Alzheimer\'s disease (AD). This study leverages electronic health record (EHR) data to explore rural-urban differences in MCI incidence, risk factors, and healthcare navigation in West Michigan.
METHODS: Analysis was conducted on 1,528,464 patients from Corewell Health West, using face-to-face encounters between 1/1/2015 and 7/31/2022. MCI cases were identified using International Classification of Diseases (ICD) codes, focusing on patients aged 45+ without prior MCI, dementia, or AD diagnoses. Incidence rates, cumulative incidences, primary care physicians (PCPs), and neuropsychology referral outcomes were examined across rural and urban areas. Risk factors were evaluated through univariate and multivariate Cox regression analyses. The geographic distribution of patient counts, hospital locations, and neurology department referrals were examined.
RESULTS: Among 423,592 patients, a higher MCI incidence rate was observed in urban settings compared to rural settings (3.83 vs. 3.22 per 1,000 person-years). However, sensitivity analysis revealed higher incidence rates in rural areas when including patients who progressed directly to dementia. Urban patients demonstrated higher rates of referrals to and completion of neurological services. While the risk factors for MCI were largely similar across urban and rural populations, urban-specific factors for incident MCI are hearing loss, inflammatory bowel disease, obstructive sleep apnea, insomnia, being African American, and being underweight. Common risk factors include diabetes, intracranial injury, cerebrovascular disease, coronary artery disease, stroke, Parkinson\'s disease, epilepsy, chronic obstructive pulmonary disease, depression, and increased age. Lower risk was associated with being female, having a higher body mass index, and having a higher diastolic blood pressure.
CONCLUSIONS: This study highlights rural-urban differences in MCI incidence and access to care, suggesting potential underdiagnosis in rural areas likely due to reduced access to specialists. Future research should explore socioeconomic, environmental, and lifestyle determinants of MCI to refine prevention and management strategies across geographic settings.
UNASSIGNED: Leveraged EHRs to explore rural-urban differences in MCI in West Michigan.Revealed a significant underdiagnosis of MCI, especially in rural areas.Observed lower rates of neurological referrals and completions for rural patients.Identified risk factors specific to rural and urban populations.

Evaluating the differences in evapotranspiration between urban and surrounding non-urban areas (i.e., ∆ET) has critical implications for urban ecological planning and water resources management. However, it is unclear how the magnitude of changes in ∆ET caused by urbanization varies under different climatic conditions in China. Here, using the remotely ET estimates at 1 km spatial resolution, we firstly estimated the magnitude of changes in ∆ET and then quantified the main driving factors influencing variations in ∆ET of 7 national-level urban agglomerations (UAs) across China during 2003-2020. Results showed that all annual ETurban values were smaller than ETnon-urban of 7 UAs, and the absolute ∆ET values of cities in South China were generally higher than those in North China. There is an apparent effect of urbanization on ∆ET increase in Guanzhong Plain City Group, Central Plain UA and Guangdong-Hong Kong-Macao Greater Bay Area (GHKMGBA), while ∆ET decrease in Chengdu-Chongqing City Group and Yangtze River Delta (YRDUA) were primarily due to the climate change. The suppressing effects of temperature and NDVI on ∆ET decrease in YRDUA were enhanced, and the promoting effect of GDP on ∆ET increase in GHKMGBA was weakened. Considering nonstationary features, urbanization appears to heighten extreme ∆ET by 0.83 %, 4.83 % and 10.39 % under 5-year, 20-year, and 50-year return periods over all the 7 UAs, respectively. Collectively, our findings confirm that urbanization is a significant factor that leads to ∆ET increase, and the factors affecting the response of urban water circulation system need to be deeply decomposed.