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Driven by China’s carbon peaking and carbon neutrality goals, this study constructs a carbon emission inversion model for 57 county-level in the core city cluster of the Huaihai Economic Zone. It analyzes the spatial-temporal characteristics of carbon emissions in these counties between 2001 and 2021. It also explores the impacts of different socio-economic factors on carbon emissions by adopting geographically and temporally weighted regression methods. The results showed that: (1) From 2001 to 2021, the total carbon emissions of counties increased from 63.42 Mt to 279.89 Mt, and the distribution pattern of the region is “high in the middle and low in the east and west.” (2) Carbon emissions have significant positive spatial correlations, with the global Moran’s I index showing a wave-like downward trend from 0.233 to 0.0954, indicating that the spatial concentration of carbon emissions in different counties is gradually decreasing, and the differences show a trend of narrowing. (3) Significant spatial and temporal heterogeneity is among the influencing factors. It is mainly manifested in population size and industrial structure, while economic scale and technological progress are the dominant factors in carbon emissions. The research results will inform county-level low-carbon development.

Land use, as one of the major sources of carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national carbon emission management and control. Consequently, it is essential to explore the spatiotemporal effects and optimization strategies of land-use carbon emissions at the county scale to promote the achievement of regional dual carbon targets. This study, focusing on Shaanxi Province, analyzed the spatiotemporal characteristics of land use from 2000 to 2020. By establishing a carbon emission evaluation model, the spatiotemporal effects of county-level carbon emissions were clarified. Utilizing Geodetector and K-means clustering methods, the driving mechanisms and clustering characteristics of county-level carbon emissions were elucidated, and optimization strategies for land use carbon emission were explored. The results showed that during 2000–2020, land use in Shaanxi Province underwent significant spatiotemporal changes, with constructed land increasing by 97.62%, while cultivated land and grassland were substantially reduced. The overall county-level carbon emissions exhibited a pattern of North > Central > South. The total carbon emissions within the province increased nearly fourfold over 20 years, reaching 1.00 × 108 tons. Constructed land was the primary source of emissions, while forest land contributed significantly to the carbon sink of the study area. Interactions among factors had significant impacts on the spatial differentiation of total county-level carbon emissions. For counties with different types of carbon emissions, differentiated optimization strategies were recommended. Low-carbon emission counties should intensify ecological protection and rational utilization, medium-carbon emission counties need to strike a balance between economic development and environmental protection, while high-carbon emission counties should prioritize profound emission reduction and structural transformation. [ABSTRACT FROM AUTHOR]

Global warming caused by extensive carbon emissions is a critical global issue. However, the lack of county-level carbon emissions data in China hampers comprehensive research. To bridge this gap, we employ a deep learning method on nighttime light data sets to estimate county-level carbon emissions in mainland China from 1997 to 2019. Our key contributions include the successful derivation of more reliable data, revealing the evolution of spatial dynamics and emissions epicenters. Moreover, we identify a novel inverted N-shaped relationship between gross domestic product per capita and carbon emissions in the eastern and western regions, as well as an N-shaped relationship in the central region, challenging mainstream wisdom. Additionally, we highlight the significant impacts of population density, industrial structure, and carbon intensity on carbon emissions. Our study also unveils the nuanced effects of government spending, which exhibits both inhibitory and region-specific influences. These findings serve to enhance our understanding of the factors influencing carbon emissions and contribute to informed decision-making in addressing climate change-related challenges.

BackgroundThere were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates.ObjectiveThe purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data.MethodsInternational Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output.ResultsModeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality.ConclusionsSocial media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs' performance in county-level maize yield simulations. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignant tumor among women in China. In 2016, there were about 306 000 new cases of breast cancer in Chinese females. Of these, about 33% (100 400) occurred in rural areas. County-level (counties or county-level cities) hospitals are the first diagnosis units for most rural breast cancer patients and play an important role in cancer prevention, screening, maintenance treatment, rehabilitation, follow-up, and referral. Due to economic and geographical constraints, county-level hospitals are relatively deficient in medical equipment, health human resources, and drug accessibility. There is an imperative need for breast cancer prevention and management guidelines that are suitable for the actual situation in China's counties. Therefore, under the policy background of hierarchical diagnosis and treatment, the Chinese expert group formulated the“Guideline for the management path and quality control of breast cancer prevention and treatment in China's counties (2023 edition)”, aiming to expand the availability of quality medical resources and ensure they are better distributed among regions, enhance the capacity for breast cancer prevention and treatment, so as to improve the prognosis and quality of life of patients in China's counties. This guideline provides path diagrams which are concise, unambiguous and easy to translate into clinical practice, as reference for clinicians in county-level hospitals [ABSTRACT FROM AUTHOR]

Abstract Background COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic. Methods Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC. Results As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (β = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (β = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (β: -1.19 and -0.42; p-value is

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning. Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data, it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China (Northeast China and the Huang–Huai region), covering 34 years. Three effective machine learning algorithms (K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model’s generalizability was further improved through 5-fold cross-validation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error (MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Background: COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic. Methods: Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC. Results: As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (β = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (β = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (β: -1.19 and -0.42; p-value is < 0.05 for both). Conclusions: County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises. [ABSTRACT FROM AUTHOR]

While China has seen an economic boom in recent decades, ecological issues are becoming increasingly challenging, with biodiversity seriously threatened. Establishing nature reserves (NRs) is deemed a feasible measure to ease ecological pressure, but few studies have evaluated the impact of the establishment of NRs on the urban–rural income gap. Therefore, based on the county-level panel data from 2010 to 2018, this study analyzed in depth the effects of the establishment of NRs on the urban–rural income gap in China. The results suggested that establishing NRs is beneficial to bridging the income gap between urban and rural areas, especially in the western and the northeastern regions. In contrast to other levels (provincial level, city level, and county level), the establishment of national-level NRs demonstrated a more prominent effect on narrowing the urban–rural income gap. It is thus proposed to improve corresponding laws to strengthen the protection of NRs, especially for those located in the western and northeastern regions, where the increase in eco-economic compensation and development of tourism brands for the ecological tourism industry are of necessity. Taken together, this study provides evidence that the establishment of NRs alleviates the urban–rural income gap in China. [ABSTRACT FROM AUTHOR]

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

The climate and ecological problems caused by excessive carbon dioxide emissions are attracting more and more attention, and the need for carbon reduction has reached a consensus. Carbon peak and carbon neutrality is a solemn commitment made by China to the world and a strategic arrangement to achieve economic and social transformation. This study focused on Shanxi Province, a base of power source and heavy chemical industry in China. Based on energy consumption data, carbon emission data set at the county level, land use data and socioeconomic data, we built a carbon ecological pressure index in order to analyze the spatiotemporal evolution characteristics of the carbon footprint, carbon carrying capacity and carbon ecological security status of each county in Shanxi Province from 2000 to 2020. The results showed that: (1) the total carbon footprint of Shanxi Province increased, and the number of high carbon emission counties showed an increasing trend. The largest part of carbon footprint was coal. (2) The vegetation carbon-carrying capacity showed an increasing trend in general, and forest land was the main contributor to the carbon-carrying capacity. (3) The carbon deficit of Shanxi Province was greater than 0 and behaved as a carbon source. The carbon ecological security decreased from a relatively safe level to a general safe level. (4) The carbon ecological pressure index gradually increased. It was predicted that the carbon ecological security level of each county will remain basically unchanged by 2025 and some will still be at a carbon ecological insecurity level. In general, the carbon ecological pressure index of some counties was still large. It is necessary to strengthen the use of clean energy, optimize the industrial structure and increase the carbon sink of forest land in order to reduce carbon emissions and increase the carbon sink, so as to ensure carbon ecological security and realize the goal of a low-carbon economy. [ABSTRACT FROM AUTHOR]

【】The temporal and spatial change of crop planting structure reflects the change of regional agricultural supply and demand patterns, and is an important basis for the government to formulate regional agricultural development strategy in the future.【Objective】The study aims to explore the temporal and spatial changes of crop planting structures in Guangdong-Hong Kong-Macao Greater Bay Area(GBA) in the past 30 years, and provide a basis for the reasonable adjustment of crop planting structure and agricultural layout next step.【Method】With the regional and county-level crop planting data, the temporal and spatial characteristics of the planting structures in the GBA from 1990 to 2020 were analyzed from the types of planting structure and the change trend of planting proportion by using the methods of time series change trend and spatial agglomeration analysis.【Result】From 1990 to 2020, 82.35% of counties and cities in the GBA adjusted the planting structures. The proportions of grain, oil plants, sugarcane and other important agricultural products decreased gradually, while those of the high-income crops such as vegetables and fruits increased significantly, and the richness of the main planting types of crops increased. In terms of planting structure type, with 2010 as the boundary, the GBA has changed from a rice-dominated type to a vegetable-dominated type accounting for half, supplemented by a rice-vegetable-fruit combination type, showing a balanced layout of rice-dominated type, fruit-dominated type and vegetable-fruit combination type. In terms of spatial pattern, different planting structure types have different spatial aggregation patterns, among which vegetable and rice planting show spatial aggregation characteristics in Guangzhou and Foshan areas with relatively higher urbanization development level.【Conclusion】In the past 30 years, the planting structure of the GBA has changed from food production to diversified planting, and further concentrated on cash crops such as vegetables and fruits. Under the background of increasing resource and environmental constraints, keeping the red line of cultivated land improving comprehensive agricultural service level, and further meetin the demand of urban residents for high-quality agricultural products are the key points to the adjustment of planting structures in the GBA next step.

The Sequential Intercept Model has helped conceptualize interventions for people with serious mental illness in the criminal/legal system. This paper operationalizes the Sequential Intercept Model into a 35-item scorecard of behavioral health and legal practices. Using interviews, survey, and observational methods, the scorecard assesses an exploratory sample of 19 counties over 27 independent data collections. A series of ordinary least squares regression models assessed the predictor scores on four jail outcomes: prevalence of serious mental illness, length of stay, connections to treatment, and recidivism. Increases in pre-booking scores showed significant decreases in jail prevalence of serious mental illness at the p < 0.05 level, and post-booking scores and overall scores showed significant positive associations with connections to treatment at the p < 0.05 level, though these were non-significant after correcting for multiple comparisons. Preliminary findings suggest a combination of practices across the Sequential Intercept Model could have synergistic impacts on key jail diversion outcomes. [ABSTRACT FROM AUTHOR]

• Attention-deficit/hyperactivity disorder (ADHD) is a childhood disorder characterized by impairments in family, academic, and social settings. Most U.S. studies of childhood ADHD prevalence focus on national or subpopulation rates, but no recent studies provide U.S.-wide county-level prevalence estimates. • We applied small-area estimation to national survey data to estimate area-level ADHD rates in U.S. children. Findings showed elevated ADHD prevalence in the New England, South Atlantic, East South Central, and West South Central census divisions. • Study results can be used by policymakers and public health officials to determine which geographic areas and sociodemographic groups need additional programming, funding, or other resources (e.g., strategic plans, trainings, or toolkits) to systematically assess and treat children with ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood disorder often characterized by long-term impairments in family, academic, and social settings. Measuring the prevalence of ADHD is important as treatment options increase around the U.S. Prevalence data helps inform decisions by care providers, policymakers, and public health officials about allocating resources for ADHD. In addition, measuring geographic variation in prevalence estimates can facilitate hypothesis generation for future analytic work. Most U.S. studies of ADHD prevalence among children focus on national or demographic group rates. Using a small area estimation approach and data from the 2016 to 2018 National Survey of Children's Health, we estimated childhood ADHD prevalence estimates at the census regional division, state, and county levels. The sample included approximately 70,000 children aged 5–17 years. The national ADHD rate was estimated to be 12.9% (95% Confidence Interval: 11.5%, 14.4%). Counties in the West South Central, East South Central, New England, and South Atlantic divisions had higher estimated rates of childhood ADHD (55.1%, 53.6%, 49.3%, and 46.2% of the counties had rates of 16% or greater, respectively) compared to counties in the Mountain, Mid Atlantic, West North Central, Pacific, and East North Central divisions (2.1%, 4%, 5.8%, 6.9%, and 11.7% of the counties had rates of 16% or greater, respectively). These local-level rates are useful for decision-makers to target programs and direct sufficient ADHD resources based on communities' needs. [ABSTRACT FROM AUTHOR]

Introduction: HIV incidence remains high in the U.S. as do disparities in new HIV diagnosis between White and Black populations and access to preventive therapies like pre-exposure prophylaxis (PrEP). The federal Ending the HIV Epidemic (EHE) initiative was developed to prioritize resources to 50 jurisdictions with high HIV incidence. Methods: We conducted secondary analyses of data (2013–2019) from the CDC, Census Bureau, and AIDSVu to evaluate the correlation between PrEP use, HIV incidence, and HIV incidence disparities. We compared the PrEP-to-need ratio (PnR) with the ratio of Black and White HIV incidence rates in 46 EHE counties. Subsequent analyses were performed for the seven states that contained multiple EHE counties. Results: These 46 counties represented 25.9% of the U.S. population in 2019. HIV incidence ranged from 10.5 in Sacramento County, CA, to 59.6 in Fulton County, GA (per 100,000). HIV incidence disparity ranged from 1.5 in Orleans Parish, LA, to 12.1 in Montgomery County, MD. PnR ranged from 26.8 in New York County, NY, to 1.46 in Shelby County, TN. Change in HIV incidence disparities and percent change in PnR were not significantly correlated (ρ = 0.06, p = 0.69). Change in overall HIV incidence was significantly correlated with increase in PnR (ρ = −0.42, p = 0.004). Conclusions: PrEP has the potential to significantly decrease HIV incidence; however, this benefit has not been conferred equally. Within EHE priority counties, we found significant HIV incidence disparities between White and Black populations. PrEP has decreased overall HIV incidence, but does not appear to have decreased HIV incidence disparity. [ABSTRACT FROM AUTHOR]

Carbon emissions from land use (LUCE) is an increasingly serious problem in China, and the direction of carbon reduction in urban agglomerations is still unclear. This study aimed to identify the key factors that influence county-level LUCE and their response to LUCE in Beijing-Tianjin-Hebei (BTH) region. Based on multi-source data, our study calculated county-level LUCE and its spatiotemporal variations in BTH from 1992 to 2018. Random forest (RF) and geographically weighted regression (GWR) models were applied to discuss influential factors' impact and spatial response on LUCE by quantifying relative importance and spatial heterogeneity. The results show that countylevel LUCE in BTH increased by 1300.04×104t, with an average annual growth rate of 46.11% during 1992-2018, and exhibited prominent spatial autocorrelation, with higher-value counties mostly in the core areas and lower-value counties in the suburbs within BTH. The RF estimates were better than the multiple linear regression (MLR) estimates with an improvement in R2 from 0.56 to 0.98. Population, urbanization, energy intensity, industry proportion, road network density and technical progress had effective explanatory power for county-level LUCE. In addition, GWR estimation shows that the main influencing factors demonstrated significant spatial heterogeneity, and the impacts of population, urbanization, economic, energy structure and road factors in the central core counties were significantly higher than in the northwestern and southern regions. Industrial structure and technology have the greatest impact in the southern and northeast counties. This study explores different causes of LUCE in different counties of urban agglomerations and the continuation of low-carbon control measures in BTH is required. [ABSTRACT FROM AUTHOR]

Shrinking cities are a class of cities that show different trajectories in the urbanization process. Although many studies have examined shrinking cities from multi-dimensional perspectives, the spatiotemporal evolution of land use structure is still poorly understood. This study constructed an analysis framework for spatiotemporal evolution characteristics of land use structure over two 10-year periods based on 334 county-level administrative units in the context of northeast China, a region undergoing substantial population loss and urban shrinkage. This study analyses quantitatively measured population loss and land use conversion and investigated the rationality of the expansion mode of newly added construction land in research units. The results demonstrated that, first, the total population in northeast China continued to decline, but the total construction area continued to grow, and the various types of construction land in most shrinking units did not decline with the loss of population. Second, 67.09% of new construction land came from cropland, and compared with 2000–2010, the growth of new construction land in 2010–2020 slowed down. Third, during the study period, more than half of the expansion area for newly added construction land came from sprawling expansion, and areas in a state of shrinking were no exception. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The COVID-19 pandemic has influenced regular medical procedures and health-seeking behaviors. In this study, we aimed to investigate the influence of the COVID-19 pandemic on the presentation and prognosis of acute ischemic stroke (AIS) patients in county-level stroke centers. PATIENTS AND METHODS: We retrospectively collected AIS patients during the strict lockdown period (January 24, 2020, to March 27, 2020) and the corresponding "new normal" period (2021) of the COVID-19 pandemic. Patients seen during the same timeframe in 2019 were enrolled as controls. Statistical analysis was conducted to compare the clinical characteristics of AIS patients who presented during the lockdown and new normal periods and those who presented during the pre-COVID-19 pandemic period. RESULTS: A total of 134 AIS patients presented during the lockdown period (the 2020 group), 207 patients in the pre-COVID-19 period (the 2019 group) and 201 patients in the "new normal" period (the 2021 group). Compared to the 2019 group, there was approximately 1/3 reduction in the number of patients who presented during the lockdown period, while the number of patients who received IVT or EVT was similar between the two groups. The number of patients, baseline characteristics, workflow intervals and clinical outcomes presented during the "new normal" period were similar between the 2019 and 2021 groups. Logistic regression showed that lockdown or new normal status were not risk factors associated with a poor outcome at 90 days. CONCLUSIONS: In county-level city stroke centers, the COVID-19 lockdown resulted in a reduction in the number of patients with AIS admitted to the hospital but had no effect on patients treated with IVT or EVT. Lockdown or new normal status did not influence the prognosis of AIS patients. [ABSTRACT FROM AUTHOR]

Global warming caused by extensive carbon emissions is a critical global issue. However, the lack of county-level carbon emissions data in China hampers comprehensive research. To bridge this gap, we employ a deep learning method on nighttime light data sets to estimate county-level carbon emissions in mainland China from 1997 to 2019. Our key contributions include the successful derivation of more reliable data, revealing the evolution of spatial dynamics and emissions epicenters. Moreover, we identify a novel inverted N-shaped relationship between gross domestic product per capita and carbon emissions in the eastern and western regions, as well as an N-shaped relationship in the central region, challenging mainstream wisdom. Additionally, we highlight the significant impacts of population density, industrial structure, and carbon intensity on carbon emissions. Our study also unveils the nuanced effects of government spending, which exhibits both inhibitory and region-specific influences. These findings serve to enhance our understanding of the factors influencing carbon emissions and contribute to informed decision-making in addressing climate change-related challenges., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The climate and ecological problems caused by excessive carbon dioxide emissions are attracting more and more attention, and the need for carbon reduction has reached a consensus. Carbon peak and carbon neutrality is a solemn commitment made by China to the world and a strategic arrangement to achieve economic and social transformation. This study focused on Shanxi Province, a base of power source and heavy chemical industry in China. Based on energy consumption data, carbon emission data set at the county level, land use data and socioeconomic data, we built a carbon ecological pressure index in order to analyze the spatiotemporal evolution characteristics of the carbon footprint, carbon carrying capacity and carbon ecological security status of each county in Shanxi Province from 2000 to 2020. The results showed that: (1) the total carbon footprint of Shanxi Province increased, and the number of high carbon emission counties showed an increasing trend. The largest part of carbon footprint was coal. (2) The vegetation carbon-carrying capacity showed an increasing trend in general, and forest land was the main contributor to the carbon-carrying capacity. (3) The carbon deficit of Shanxi Province was greater than 0 and behaved as a carbon source. The carbon ecological security decreased from a relatively safe level to a general safe level. (4) The carbon ecological pressure index gradually increased. It was predicted that the carbon ecological security level of each county will remain basically unchanged by 2025 and some will still be at a carbon ecological insecurity level. In general, the carbon ecological pressure index of some counties was still large. It is necessary to strengthen the use of clean energy, optimize the industrial structure and increase the carbon sink of forest land in order to reduce carbon emissions and increase the carbon sink, so as to ensure carbon ecological security and realize the goal of a low-carbon economy.

Detailed characteristics of crop planting structure (CPS) evolution can inform the optimization of the crop yield proportion in the black soil region of Northeast China (BSRNC). Choosing Hailun County as an example, this study sought to analyze the geographic characteristics of CPS evolution from 2000 to 2020. Our analysis produced new spatiotemporal information based on the remote-sensing interpretation data, namely, Landsat4-5 TM, Landsat7 ETM+, and Landsat8 OLI images. The study characterized the temporal and spatial dynamics of CPS. Our results showed the following: (1) Soybean and maize were the main crops, with a total land area of 70%; they alternated as the most dominant crop. (2) The distribution breadth and aggregation intensity of soybean and maize were spatially complementary; rice had the smallest distribution range but strong water aggregation. (3) The evolution pattern of CPS was the interconversion between a single type of soybean and maize. Our results indicate that the future CPS adjustment of BSRNC needs to consider the county-level optimization of crop area proportion and crop spatial distribution. This context has excellent implications in geographically informing policymaking to adjust county-level CPS of BSRNC, thus safeguarding food security. [ABSTRACT FROM AUTHOR]

• Spatiotemporal patterns and impact factors of LUCE were studied at the county level. • The center of gravity of China's county LUCE was constantly moving to the northwest. • LUCE showed positive spatial autocorrelation and significant spatial agglomeration. • Stable path dependence and spatial locking were observed in county-level LUCE. • The main factors influencing LUCE in China's counties were GDP and carbon intensity. Counties play a foundational role in China's economic progress and the governance of society. Studying land use carbon emissions (LUCE) at the county level is crucial for meeting China's dual carbon targets, which facilitate the decomposition of national emission reduction targets, thereby enabling more refined management and control and enhancing the precision and effectiveness of local-level emission reduction measures. However, prior research into LUCE has mainly concentrated on national and provincial levels. In this study, we calculated the LUCE of China's counties using data for nighttime light, land use, and energy consumption. Based on this foundation, we studied the spatiotemporal patterns and impact factors of LUCE in Chinese counties from 2000 to 2020. The research findings were as follows: (1) LUCE continued to increase over the study period, and its center of gravity (CGY) continued to move to the northwest. (2) There was a positive spatial autocorrelation of LUCE with an obvious spatial heterogeneity and agglomeration. (3) LUCE's local spatial pattern remained stable, with strong spatial integration and dependence, and its spatial correlation was relatively stable, with obvious path-locking characteristics. (4) LUCE was primarily influenced by GDP and carbon intensity. The study's findings have implications for China's carbon reduction strategy. [ABSTRACT FROM AUTHOR]

Abstract Background The public health workforce (PHW) is a key component of a country’s public health system. Since the outbreak of SARS (severe acute respiratory syndrome) in 2003, the scale of PHW in China has been continuously expanding, but policymakers and researchers still focus on the distribution of public health personnel, especially the regional inequality in such distribution. We aimed to identify the root cause of PHW inequality by decomposing different geographical units in China. Methods This study was based on data from a nationwide survey, which included 2712 county-level data. The distribution of the PHW in geographical units was evaluated by the Gini coefficient and Theil T index, and inequalities at regional, provincial, and municipal levels were decomposed to identify the root causes of inequalities in the PHW. Additionally, the contextual factors affecting the distribution of the PHW were determined through regression analysis. Results The overall inequality results show that health professional and field epidemiological investigators faced worse inequality than the staff. In particular, field epidemiological investigators had a Gini coefficient close to 0.4. Step decomposition showed that within-region inequalities accounted for 98.5% or more of overall inter-county inequality in the distribution of all PHW categories; provincial decomposition showed that at least 74% of inequality is still distributed within provinces; the overall contribution of within-municipal inequality and between-municipal inequality was basically the same. Further, the contextual factor that influenced between-municipality and within-municipality inequality for all three categories of PHWs was the agency building area per employee. Per capita GDP had a similar effect, except for between-municipality inequality of professionals and within-municipality inequality of field epidemiological investigators. Conclusions The successive decomposition showed that inequality is mainly concentrated in counties at the within-province and within-municipal levels. This study clearly suggests that the government, especially the municipal government at the provincial level, should increase financial investment in Centers for Disease Control and Prevention (CDCs) with worse resource allocation in their jurisdiction through various ways of compensation and incentives, enhance their infrastructure, and improve the salary of personnel in these institutions, to attract more public health professionals to these institutions.

The purpose of this work was to assess population vulnerability to heat-related health risks and its relationship with urbanization levels to provide essential information for the future development and policy-making for climate change adaptation. We constructed a heat vulnerability index (HVI), quantified the population heat vulnerability in each county across China by a principal component analysis (PCA) of multiple factors, and assessed urbanization levels in each county using multisource data. Then, the HVI was validated using the heat-attributable fraction (heat-AF) of nonaccidental mortality based on death monitoring data and meteorological data from 95 counties across China. The results showed that our HVI was significantly positively associated with the heat AF of nonaccidental mortality. A negative correlation was observed between the urbanization level and the HVI. The HVI was generally higher in less urbanized western China and lower in the more urbanized eastern regions. The baseline mortality occupies the top position in the importance ranking of the heat-vulnerability indicators at all three urbanization levels, but the other indicators, including the aging rate, agricultural population rate, education, ethnic structure, economic status, air conditioner ownership rate, and number of hospitals, ranked differently among different urbanization levels. This finding indicates that to reduce population heat vulnerability, the most important approach is to improve the health status of the whole population and reduce baseline mortality; additionally, regional-specific measures and emphasis should be adjusted reasonably along with the process of urbanization according to the characteristics and key factors of local heat vulnerability.

Background: Communities with robust recovery ecosystems could reduce negative outcomes associated with substance use disorders (SUDs) and facilitate the recovery process. This cross-sectional study examined the relationship between drug overdose mortality rates in the United States and the strength of county-level recovery ecosystems, as measured by the Recovery Ecosystem Index (REI)., Methods: The REI assesses the strength of county-level recovery ecosystems in the United States. Comprised of 14 indicators across 3 component classes, overall and component scores ranging from "one" (strongest) to "five" (weakest) were calculated for each county using standardized values of the indicators. County-level analyses included: (1) correlational analyses between drug overdose mortality rates (n = 2076) and REI scores (overall score and by component); and (2) quadrant analysis (n = 2076), dividing counties based on their drug overdose mortality rates and overall REI scores., Results: Drug overdose mortality rates were inversely related to REI overall, SUD treatment component, and continuum of SUD support component scores, indicating that lower (stronger) scores corresponded to higher rates. Conversely, REI infrastructure and social component scores were positively related to rates. Counties were relatively evenly distributed across quadrants, with 26% (n = 537) with a strong REI score and high overdose mortality rate, 24% (n = 489) with a strong REI score and low overdose mortality rate, 20% (n = 409) with a weak REI and high overdose mortality rate, and 31% (n = 641) with a weak REI and low overdose mortality rate., Conclusions: REI scores were generally inversely associated with drug overdose mortality rates in US counties, suggesting that communities have stronger recovery systems and services as the burden of SUD increases. Given relative variation in the scale of drug overdose mortality and strength of recovery ecosystems among counties, results could guide the identification of communities where the need for expanded recovery systems and services may be particularly critical., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Shrinking cities are a class of cities that show different trajectories in the urbanization process. Although many studies have examined shrinking cities from multi-dimensional perspectives, the spatiotemporal evolution of land use structure is still poorly understood. This study constructed an analysis framework for spatiotemporal evolution characteristics of land use structure over two 10-year periods based on 334 county-level administrative units in the context of northeast China, a region undergoing substantial population loss and urban shrinkage. This study analyses quantitatively measured population loss and land use conversion and investigated the rationality of the expansion mode of newly added construction land in research units. The results demonstrated that, first, the total population in northeast China continued to decline, but the total construction area continued to grow, and the various types of construction land in most shrinking units did not decline with the loss of population. Second, 67.09% of new construction land came from cropland, and compared with 2000–2010, the growth of new construction land in 2010–2020 slowed down. Third, during the study period, more than half of the expansion area for newly added construction land came from sprawling expansion, and areas in a state of shrinking were no exception.

Detailed characteristics of crop planting structure (CPS) evolution can inform the optimization of the crop yield proportion in the black soil region of Northeast China (BSRNC). Choosing Hailun County as an example, this study sought to analyze the geographic characteristics of CPS evolution from 2000 to 2020. Our analysis produced new spatiotemporal information based on the remote-sensing interpretation data, namely, Landsat4-5 TM, Landsat7 ETM+, and Landsat8 OLI images. The study characterized the temporal and spatial dynamics of CPS. Our results showed the following: (1) Soybean and maize were the main crops, with a total land area of 70%; they alternated as the most dominant crop. (2) The distribution breadth and aggregation intensity of soybean and maize were spatially complementary; rice had the smallest distribution range but strong water aggregation. (3) The evolution pattern of CPS was the interconversion between a single type of soybean and maize. Our results indicate that the future CPS adjustment of BSRNC needs to consider the county-level optimization of crop area proportion and crop spatial distribution. This context has excellent implications in geographically informing policymaking to adjust county-level CPS of BSRNC, thus safeguarding food security.

Abstract Objectives Crop production is an important variable in social, economic and environmental analyses. There is an abundance of crop data available for the United States, but we lack a typology of county-level crop production that accounts for production similarities in counties across the country. We fill this gap with a county-level classification of crop production with ten mutually exclusive categories across the contiguous United States. Data description To create the typology we ran a cluster analysis on acreage data for 21 key crops from the United States Department of Agriculture’s 2012 Agricultural Census. Prior to clustering, we estimated undisclosed county acreage values, controlled for acreage in other crop types, and removed counties with low agricultural production to produce proportional scores for each crop type in each county. We used proportional scores to control for the influence of county size in the cluster analysis and used internal and stability measures to validate the analysis. The final dataset features 2922 counties. Future research can leverage this typology as an input for county- or regional-level analysis.

Volatile organic compounds (VOCs) pollution, which is closely linked to photochemical smog and secondary organic aerosols, has become a severe concern in China. Therefore, we compiled a new high-resolution emission inventory for the industrial non-methane Volatile organic compounds (NMVOCs) using "bottom-up" approaches throughout 2010 and 2016. In this work, the industrial sources were divided into five major categories, and 108 specific sources, as well as an emission factor database, was developed for industrial NMVOCs. Results indicated that the total NMVOCs emissions from industrial sources increased from 16.88 Tg in 2010 to 21.04 Tg in 2016 at an annual average rate of 3.7%. The five major source categories including "production of VOCs", "storage and transportation", "industrial processes using VOCs as raw material", "processes using VOCs-containing products", and "fossil fuel combustion" generated 1.92 Tg, 0.94 Tg, 6.54 Tg, 10.04 Tg, and 1.60 Tg NMVOCs, respectively, in 2016. Coke production, plastic manufacturing, raw medicine industry, and architectural decoration were the primary sources of industrial NMVOCs and emissions of these sources increased by 140 Gg, 190 Gg, 640 Gg, and 700 Gg between 2010 and 2016. The emissions displayed distinct spatial characteristics, with significantly higher emissions in the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Cheng-Yu region than in other areas. Shandong, Guangdong, Jiangsu, Zhejiang, and Henan were the top five provinces with the highest NMVOCs emissions, while the emission hotspots in the county-level were mainly distributed in Guangzhou urban area, Shanghai Pudong New Area, Hangzhou urban area, and Shenzhen urban area. The emissions in Henan province, Hubei province, and Cheng-Yu region increased significantly during the study period. Instead, emissions in some counties of Zhejiang province and Hebei province decreased than in 2010. Image 1 • A county-level emission inventory for industrial NMVOCs was established in China. • Spatiotemporal characteristics of industrial NMVOCs were identified in county-level. • High emissions were estimated in Shandong, Guangdong, and Shanghai. • Significant increases were found in Henan, Hubei, and Cheng-Yu region. [ABSTRACT FROM AUTHOR]

Abstract Examining the spatial distribution of the renewable energy industries (REIs) at the county-level and their driving factors is critical for appropriate future policy-making. However, there have been very few studies on this issue to date in China. From a geographical perspective, this study uses the data of the total number and generating capacity of renewable energy plants at the county-level to investigate the spatial distribution patterns of China's REIs, and analyzes their driving factors. The results show that the past decade has witnessed unprecedented development in renewable energy technologies in China, and REIs are constantly springing up in the southwestern and northwestern China, exhibiting a "double-core" spatial pattern. Furthermore, this distribution pattern significantly exhibits resource-dependent and policy-led characteristics, namely, environmental conditions, the abundance of renewable resources and supportive policies are the crucial factors in the clustering of the REIs in China. In addition, as a powerful stimulus to the development of renewable energy economy, the effect of governmental policy significantly varies among different REIs, and a spatial mismatch between the abundance of renewable resources and local governmental development policies is identified, especially for the wind and solar power industries. Highlights • The spatial distributions of the renewable energy industries in China at a county-level are investigated. • The renewable energy industries are concentrated in southwestern and northwestern China, with a "double-core" pattern. • The distribution of the renewable energy industries exhibits resource-dependent and policy-led characteristics. • A spatial mismatch between renewable resources and the local government's supportive policies is identified. [ABSTRACT FROM AUTHOR]

Highlights • Investigation about the association between county-level social factors and schizophrenia. • County-level social factors were associated with schizophrenia. • Strategies to improve access to health services for individuals in high urbanization rates areas are needed. • Public policies that seek to promote gender equality in areas with high SEC and divorce rates will improve females' mental health. Abstract There is little evidence on the association between area-level social factors and schizophrenia in China. This study aimed to investigate the relationship between county-level social factors and schizophrenia in Chinese adults aged 18 years old and above. We obtained data from the Second China National Sample Survey on Disability, and selected 1,909,205 adults for analysis. Schizophrenia was ascertained according to the International Statistical Classification of Diseases, Tenth Revision. Multilevel logistic regressions showed that areas with higher urbanization rate was associated with increased risk of schizophrenia (areas with moderate urbanization rate: OR = 1.28, 95% CI: 1.15, 1.44; areas with high urbanization rate: OR = 1.48, 95% CI: 1.26, 1.75). Stratified analyses found that, in female adults, urbanization rates, divorce rates and socioeconomic conditions were positively associated with increased risks for schizophrenia. However, in male adults, low socioeconomic conditions was related to decreased risk of schizophrenia, and there was no significant association between rates of divorce and risks for schizophrenia. In conclusion, county-level social factors, in the form of urbanization rate, divorce rate, and socioeconomic conditions, were associated with the risk of schizophrenia in Chinese adults. Gender differences were found in these associations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agricultural machinery is an important non-road mobile source, which can exhaust multi-pollutants, making primary and secondary contributions to the air pollution. China is a significant agricultural country of the world; however, the agricultural machinery emissions research is at an early stage, and an emission inventory with a high temporal-spatial resolution is still needed. In this study, a comprehensive emission inventory with a high temporal-spatial resolution for agricultural machinery in China was first developed. The results showed that the total emissions in 2014 were 262.69 Gg, 249.25 Gg, 1211.39 Gg, 2192.05 Gg, 1448.16 Gg and 25.14 Gg for PM 10 , PM 2.5 , THC, NOx, CO and SO 2 , respectively. Tractors and farm transport vehicles were the top two greatest contributors, accounting for approximately 39.9%-53.6% and 17.4%-24.6%, respectively, of the total emissions of the five pollutants (except THC). The farm transport vehicles contributed the most (81.8%) to the THC emissions. The county-level emissions were further allocated into 1 km × 1 km grids according to source-specific allocation surrogates. The spatial characteristic analysis indicated that high emissions were distributed in northeast, north and central-south China. To obtain a high temporal resolution emission inventory, a comprehensive investigation on the agricultural practice timing in different provinces was conducted. Then, the annual emissions in the different provinces were distributed to a spatial resolution of ten-day periods (i.e. the early, mid- and late ten-day periods in each month). It was found that higher emissions in China occurred in late April, mid-June and early October. In addition, the emission uncertainty was also analyzed based on the Monte Carlo simulation. The estimated high temporal-spatial resolution emission inventory could provide important basic information for environmental/climate implications research, emission control policy making, and air quality modeling. [ABSTRACT FROM AUTHOR]

Background Ambient fine particulate matter (PM 2.5 ) pollution is currently a serious environmental problem in China, but evidence of health effects with higher resolution and spatial coverage is insufficient. Objective This study aims to provide a better overall understanding of long-term mortality effects of PM 2.5 pollution in China and a county-level spatial map for estimating PM 2.5 related premature deaths of the entire country. Method Using four sets of satellite-derived PM 2.5 concentration data and the integrated exposure-response model which has been employed by the Global Burden of Disease (GBD) to estimate global mortality of ambient and household air pollution in 2010, we estimated PM 2.5 related premature mortality for five endpoints across China in 2010. Result Premature deaths attributed to PM 2.5 nationwide amounted to 1.27 million in total, and 119,167, 83,976, 390,266, 670,906 for adult chronic obstructive pulmonary disease, lung cancer, ischemic heart disease, and stroke, respectively; 3995 deaths for acute lower respiratory infections were estimated in children under the age of 5. About half of the premature deaths were from counties with annual average PM 2.5 concentrations above 63.61 μg/m 3 , which cover 16.97% of the Chinese territory. These counties were largely located in the Beijing-Tianjin-Hebei region and the North China Plain. High population density and high pollution areas exhibited the highest health risks attributed to air pollution. On a per capita basis, the highest values were mostly located in heavily polluted industrial regions. Conclusion PM 2.5 -attributable health risk is closely associated with high population density and high levels of pollution in China. Further estimates using long-term historical exposure data and concentration-response (C-R) relationships should be completed in the future to investigate longer-term trends in the effects of PM 2.5 . [ABSTRACT FROM AUTHOR]

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

Purpose: We aimed to explore the effect of county-level general surgeon (GS) and colorectal surgeon (CS) density on colorectal cancer (CRC) mortality and to identify additional county characteristics associated with outcomes. Methods: Using data from the Area Resource File, US Census, and National Cancer Institute, we developed multivariate regression models to determine the effect of density of GS and CS on CRC death between 2005 and 2009 while controlling for CRC incidence, county demographics, and other socioeconomic factors. Results: In total, we included 1767 counties: Mean CRC incidence and death rates were 64.9 and 19.9 %, respectively. In this cohort, 45 % were metropolitan areas. Mean GS and CS densities were 7.2 and 0.15 per 100,000 people, respectively. Counties with at least 1 GS had a statistically significant decrease in CRC-specific mortality (beta coefficient −0.18, p < 0.001). Increasing GS density beyond 8 per 100,000 people did not result in any further meaningful reductions in mortality. The presence of at least 1 CS at the county-level was not associated with differences in CRC mortality (beta coefficient −0.021, p = 0.37). Metropolitan counties and a larger percentage of individuals who were <65 years old were associated with decreased CRC mortality. Conversely, a higher proportion below the poverty line was correlated with a significant increase in CRC mortality. Conclusion: Unlike CS, the presence of GS at the county-level was associated with lower CRC mortality. However, increasing GS density beyond a certain point did not result in further meaningful reductions in mortality. A balanced strategy of distributing the surgical workforce across all counties can result in population-based improvements in CRC outcomes. [ABSTRACT FROM AUTHOR]

The rapid development of the urban and rural transport sectors has increased focus on energy and the environment. To support the transport sector in its efforts to achieve carbon neutrality targets, low-carbon road transport planning for specific regions has become a necessity. Using Ruicheng County, Shanxi, China as an example, county-level planning of the road transport sector is conducted in terms of quantifying energy consumption and CO 2 emissions; the applicable forms and economic costs of technologies including rooftop photovoltaics and smart chargers in different regions are also discussed. A bottom-up model is built and a scenario analysis is adopted to predict the effects of different policy measures by 2035. Under an aggressive scenario, the demand for fossil fuel will be reduced, while the electricity demand will increase. 91% of CO 2 emissions can be reduced. The results show that measures including replacing conventional vehicles with electric vehicles and encouraging public transport could effectively facilitate the road transport sector to gradually approach zero carbon emissions. However, the consumption of diesel by agricultural vehicles is the greatest obstacle to achieving carbon neutrality. Electric vehicles and building-related charging infrastructure can potentially have an important role in the future transport sector and electricity systems. • A county-level road transport plan aiming at near zero carbon emissions is studied. • A bottom-up model is built to quantify energy consumption and CO 2 emissions. • Measures including promotion of electric vehicles can effectively reduce emissions. • Building-related charging infrastructure will have an important role. [ABSTRACT FROM AUTHOR]

Background: This systematic review summarized published literature on county-level predictors of drug overdose mortality in the United States (US).Methods: Peer-reviewed studies and doctoral dissertations published in English between 1990 and July 19, 2022 were identified from PubMed, Web of Science, ProQuest Dissertations & Theses, PsycINFO, CINAHL, and EconLit. Eligible studies examined at least one county-level predictor of drug overdose mortality in US counties. Two reviewers independently completed screening, quality assessment (with an adapted National Institutes of Health Quality Assessment Tool), and data extraction. Results were qualitatively summarized and grouped by predictor categories.Results: Of 56 studies included, 42.9% were subnational, and 53.6% were limited to opioid overdose. In multiple studies, measures related to opioid prescribing, illness/disability, economic distress, mining employment, incarceration, family distress, and single-parent families were positively associated with drug overdose mortality outcomes, while measures related to cannabis dispensaries, substance use treatment, social capital, and family households were negatively associated with drug overdose mortality outcomes. Both positive and negative associations were documented for smoking, uninsurance, healthcare professional shortage status, physicians per capita, unemployment, income, poverty, educational attainment, racial composition, and rurality. Findings within studies also differed by subpopulation (by race/ethnicity, gender, age, or rurality) and the type of drugs involved in overdose.Conclusions: The findings of this review provide relatively mixed evidence regarding many county-level predictors of overdose mortality, several of which also vary between subpopulations, supporting the importance of additional research to elucidate pathways through which the county context may shape risk of fatal overdose. [ABSTRACT FROM AUTHOR]