Experimental study of rainwater grate blocking and submergence of outfall on drainage network capacity.

Accurately evaluating the performance of urban underground drainage network and its influencing factors is a challenging problem, as this process is affected by many complex factors. In this study, based on an overland flow experiment considering drainage process of pipe network, a series of physical model experiments were conducted to investigate the influences of different surface slopes, rainwater grate blockage and the submergence of outfall on the performance of the drainage pipe network system. The hydrographs of surface runoff and pipe network flow were recorded in collection tanks by precise digital pressure sensors to provide comprehensive information about the characteristics of drainage performance in the pipe network. Through a series of experimental data collection and analysis, the following conclusions are drawn from this study: (1) The longitudinal slope of the road decreases the pipe drainage capacity by 1.68%-8.94%, and this reduction effect is more significant with the increase of slope. (2) The blockage of rainwater grate at different locations has different impacts on the road drainage system, the downstream rainwater grate blockage has the most obvious impact on the performance of the drainage system, which reduces the drainage capacity by 22.59%-25.38%. (3) Different submergence degrees of rainwater outlet have different impacts on the drainage system. Under different slopes, the drainage capacity of the pipe network decreases by 1.88%-23.46% with the increase of the submergence degree of the outfall. These experimental results are helpful in understanding the working conditions of urban road drainage system and the influencing factors of the system's drainage capacity, and also provide measured data for verification of relevant numerical models and coefficient calibration.

Modeling spatiotemporal carbon emissions for two mega-urban regions in China using urban form and panel data analysis.

Spatiotemporal monitoring of urban CO2 emissions is crucial for developing strategies and actions to mitigate climate change. However, most spatiotemporal inventories do not adopt urban form data and have a coarse resolution of over 1 km, which limits their implications in intra-city planning. This study aims to model the spatiotemporal carbon emissions of the two largest mega-urban regions in China, the Yangtze River Delta and the Pearl River Delta, using urban form data from the Local Climate Zone scheme and landscape metrics, nighttime light images, and a year-fixed effects model at a fine resolution from 2012 to 2016. The panel data model has an R2 value of 0.98. This study identifies an overall fall in carbon emissions in both regions since 2012 and a slight elevation of emissions from 2015 to 2016. In addition, urban compaction and integrated natural landscapes are found to be related to low emissions, whereas scattered low-rise buildings are associated with rising carbon emissions. Furthermore, this study more accurately extracts urban areas and can more clearly identify intra-urban variations in carbon emissions than other datasets. The open data supported methodology, regression models, and results can provide accurate and quantifiable evidence at the community level for achieving a carbon-neutral built environment.

The impacts of spatial resolutions on global urban-related change analyses and modeling.

Various global land use/land cover (LULC) products have been developed to drive land-relevant climate and hydrological models for environmental assessments. However, systematic studies remain scarce that assess the uncertainties of using these products. By using a total of 16 commonly used global LULC products, we find a logarithm law of upscaling with the spatial resolution. The law reveals spatial details of urban features will be majorly distorted when using LULC products with coarser resolutions. A tipping point of the law around the 30-m resolution was identified by additional analysis of the 1-m and 10-m local land use dataset. Through the example of assessing crop production loss, we further find that most of these products will yield a significant underestimation of crop production losses, globally and locally. We conclude that the underestimated urban land rooted in most of these products would cause vital impacts on global change analyses and modeling.

Albedo changes caused by future urbanization contribute to global warming.

The replacement of natural lands with urban structures has multiple environmental consequences, yet little is known about the magnitude and extent of albedo-induced warming contributions from urbanization at the global scale in the past and future. Here, we apply an empirical approach to quantify the climate effects of past urbanization and future urbanization projected under different shared socioeconomic pathways (SSPs). We find an albedo-induced warming effect of urbanization for both the past and the projected futures under three illustrative scenarios. The albedo decease from urbanization in 2018 relative to 2001 has yielded a 100-year average annual global warming of 0.00014 [0.00008, 0.00021] °C. Without proper mitigation, future urbanization in 2050 relative to 2018 and that in 2100 relative to 2018 under the intermediate emission scenario (SSP2-4.5) would yield a 100-year average warming effect of 0.00107 [0.00057,0.00179] °C and 0.00152 [0.00078,0.00259] °C, respectively, through altering the Earth's albedo.

Integrating weather observations and local-climate-zone-based landscape patterns for regional hourly air temperature mapping using machine learning.

Air temperature is a crucial variable of urban meteorology and is essential to many urban environments, urban climate and climate-change-related studies. However, due to the limited observational records of air temperature and the complex urban morphology and environment, it might not be easy to map the hourly air temperature with a fine resolution at the surface level within and around cities via conventional methods. Thus, this study employed machine learning (ML) algorithms and meteorological and landscape data to develop hourly air temperature mapping techniques and methods at the 1-km resolution over a multi-year warm seasons period. Guangdong Province, China was selected for the case study. Random forest algorithm was employed for the hourly air temperature mapping. The validation results showed that the hourly air temperature maps exhibit good accuracy from 2008 to 2019, with mean R2, root mean square error (RMSE) and mean absolute error (MAE) values of 0.8001, 1.4821 °C and 1.0872 °C, respectively. The importance assessment of the driving factors showed that meteorological factors, especially relative humidity, contributed the most to the air temperature mapping. Simultaneously, landscape factors also played a non-negligible role. Further analysis revealed that the maps steadily maintained high accuracy at nighttime (20:00-7:00), which is essential for investigating nighttime urban climate conditions, especially the urban heat island effect. Moreover, a correlation existed between the nighttime air temperature changes and urban morphology represented by the local climate zones. Air temperatures tended to fall more slowly in the core of metropolitan areas than in the urban fringe. Using ML, this study reliably improves the spatial refinement of hourly air temperature mapping and reveals the spatially explicit air temperature patterns in and around cities at different times in a day during the warm seasons. Moreover, it provides a novel valuable and reliable dataset for air-temperature-related implementation and studies.

Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England.

The spatiotemporal inventory of carbon dioxide (CO2) emissions from the building sector is significant for formulating regional and global warming mitigation policies. Previous studies have attempted to use energy consumption models associated with field investigations to estimate CO2 emissions from buildings at local scales, or they used spatial proxies to downscale emission sources from large geographic units to grid cells for larger scales. However, mapping the spatiotemporal distributions of CO2 emissions on a large scale based on buildings remains challenging. Hence, we conducted a case study in England in 2015, wherein we developed linear regression models to analyze monthly CO2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite night-time lights images. The results showed that the proposed model that considered building data and night-time light imagery achieved the best fit. Fine-scale spatial heterogeneity was observed in the distributions of building-based CO2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO2 emissions. Specifically, buildings emitted significantly more CO2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.

Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios.

This study presents a global land projection dataset with a 1-km resolution that comprises 20 land types for 2015-2100, adopting the latest IPCC coupling socioeconomic and climate change scenarios, SSP-RCP. This dataset was produced by combining the top-down land demand constraints afforded by the CMIP6 official dataset and a bottom-up spatial simulation executed via cellular automata. Based on the climate data, we further subdivided the simulation products' land types into 20 plant functional types (PFTs), which well meets the needs of climate models for input data. The results show that our global land simulation yields a satisfactory accuracy (Kappa = 0.864, OA = 0.929 and FoM = 0.102). Furthermore, our dataset well fits the latest climate research based on the SSP-RCP scenarios. Particularly, due to the advantages of fine resolution, latest scenarios and numerous land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.

1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020-2100.

In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China's future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020-2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at https://doi.org/10.6084/m9.figshare.14776128.v1 ) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality.

Global simulation of fine resolution land use/cover change and estimation of aboveground biomass carbon under the shared socioeconomic pathways.

Land use change driven by human activities plays a critical role in the terrestrial carbon budget through habitat loss and vegetation change. Despite the projections of the global population and economic growth under the framework of the Shared Socioeconomic Pathways (SSPs), little is known of land use/cover change (LUCC) at a fine spatial resolution and how carbon pools respond to LUCC under different SSPs. This study projected the future global LUCC with 1 km spatial resolution and a 10-year time step from 2010 to 2100 and then explored its direct impacts on aboveground biomass carbon (AGB) under SSPs. Scenario SSP3 yields the highest global cropland expansion, among which approximately 48% and 46% is expected to be located in the current forest land and grassland, respectively. Scenario SSP1 has the largest forest expansion and is mainly converted from grassland (54%) and cropland (30%). Due to the spatial change in land use/cover, global AGB loss is expected to reach approximately 3.422 Pg C in 2100 under scenario SSP3 and increases by approximately 0.587 Pg C under scenario SSP1. Africa is expected to lose 30% of AGB under the scenario SSP3. Aboveground biomass in Asia will fix 0.774 Pg C to reverse the AGB loss in 2100 under scenario SSP1. The global carbon loss estimated by the land use products with 10 km and 25 km resolution are less than that with 1 km by 1.5% (ranging from -11.2% in Africa to +34.0% in Oceania) and 2.9% (ranging from -11.8% in Africa to +24.0% in Oceania), respectively. These findings suggest that sufficient spatial details in the existing SSP scenario projections could reduce the uncertainties of AGB assessment, and reasonable land use development and management is a key measure to mitigate the negative impacts of LUCC on the biomass carbon pool.

Global projections of future urban land expansion under shared socioeconomic pathways.

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50-63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1-4%, corresponding to the annual food needs for a certain crop of 122-1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China.

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth's system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Chromium supplementation for adjuvant treatment of type 2 diabetes mellitus: Results from a pooled analysis.

SCOPE: We conducted a pooled analysis of randomized controlled trials to evaluate the effects of chromium supplementation on clinically relevant metabolic biomarkers in type 2 diabetes mellitus (T2DM) patients. METHODS AND RESULTS: Electronic searches were conducted and the bibliographies of located articles were searched, and 28 studies were suitable for statistical pooling. Endpoints were calculated as weighted mean differences (WMDs) with 95% confidence intervals (CIs) by using fixed-effects or random-effects models. Statistical heterogeneity, publication bias, subgroup analyses, sensitivity analysis and meta-regression assessments were also assessed. Chromium reduced levels of fasting plasma glucose (WMD, -0.99 mmol/L; 95% CI, -1.72 to -0.25; p = 0.008), hemoglobin A1c (WMD, -0.54 %; 95% CI, -0.82 to -0.25; p = 0.0002), triglycerides (WMD, -11.71 mg/dL; 95% CI, -18.38 to -5.04; p = 0.0006). Chromium also increased levels of high-density lipoprotein cholesterol (WMD, 1.73 mg/dL; 95% CI, 0.50 to 2.96; p = 0.006). These results were robust in sensitivity analysis and were not dependent on the chromium dose and duration of supplementation. Subgroup analyses indicated that these notably favorable effects were presented in T2DM subjects ingesting chromium chloride and chromium picolinate formulations. CONCLUSION: Our pooled analysis suggested that chromium supplementation might be a candidate as an adjunct to pharmacological management in patients with T2DM.

Lack of efficacy of pomegranate supplementation for glucose management, insulin levels and sensitivity: evidence from a systematic review and meta-analysis.

BACKGROUND: The potential glucose-lowering effects of pomegranate have been reported in animal and observational studies, but intervention studies in humans have generated mixed results. In this paper, we aimed to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the precise effects of pomegranate supplementation on measures of glucose control, insulin levels and insulin sensitivity in humans. METHODS: Comprehensive electronic searches were conducted in PubMed, Embase, and the Cochrane Library. Studies included were RCTs that evaluated the changes in diabetes biomarkers among adults (≥18 years) following pomegranate interventions. The predefined outcomes included fasting blood glucose (FBG), fasting blood insulin (FBI), glycated haemoglobin (HbA1c), and homeostatic model assessment of insulin resistance (HOMA-IR). Endpoints were calculated as weighted mean differences (WMDs) with 95% confidence intervals (CIs) by using a random-effects model. Publication bias, subgroup analyses, sensitivity analysis and random-effects meta-regression were also performed to explore the influence of covariates on the net changes in fasting glucose and insulin concentrations. RESULTS: Sixteen eligible trials with 538 subjects were included. The pooled estimates suggested that pomegranate did not significantly affect the measures of FBG (WMD, -0.6 mg/dL; 95% CI, -2.79 to 1.58; P=0.59), FBI (WMD, 0.29 µIU/mL; 95% CI, -1.16 to 1.75; P=0.70), HOMA-IR (WMD, -0.04; 95% CI, -0.53 to 0.46; P=0.88) or HbA1c (WMD, -0.11%; 95% CI, -0.39 to -0.18; P=0.46). Overall, significant heterogeneity was detected for FBI and HOMA-IR, but subgroup analysis could not identify factors significantly influencing these parameters. These results were robust in sensitivity analysis, and no significant publication bias was found in the current meta-analysis. CONCLUSION: Pomegranate intake did not show a notably favourable effect on improvements in glucose and insulin metabolism. The current evidence suggests that daily pomegranate supplementation is not recommended as a potential therapeutic strategy in glycemic management. Further large-scale RCTs with longer duration are required to confirm these results.

The effects of resveratrol intervention on risk markers of cardiovascular health in overweight and obese subjects: a pooled analysis of randomized controlled trials.

BACKGROUND: Potential effects of resveratrol consumption on cardiovascular disease risk factors and body weight in overweight/obese adults have not been fully elucidated. Our present analysis was to evaluate the effects of resveratrol consumption on risk markers related to cardiovascular health in overweight/obese Individuals. METHODS: Multiple literature databases were systematically searched, and 21 studies were included. Effect sizes were expressed as weighted mean difference (WMD) and 95% confidence interval (CI), and heterogeneity was assessed with the I2 test. Publication bias and subgroup analyses were also performed. RESULTS: There were variations in reporting quality of included studies. Resveratrol intervention significantly lowered total cholesterol (WMD, -0.19 mmol/L; 95% CI, -0.32 to -0.06; P = 0.004), systolic blood pressure (WMD, -2.26 mmHg; 95% CI, -4.82 to -0.49; P = 0.02), and fasting glucose (WMD, -0.22 mmol/L; 95% CI, -0.42 to -0.03; P = 0.03). Heterogeneity was noted for these outcomes (35.6%, 38.7% and 71.4%, respectively). Our subgroup analysis showed significant reductions in total cholesterol, systolic blood pressure, diastolic blood pressure, glucose, and insulin in subjects ingesting higher dose of resveratrol (≥300 mg/day). CONCLUSION: Our finding provides evidence that daily resveratrol consumption might be a candidate as an adjunct to pharmacological management to better prevent and control cardiovascular disease in overweight/obese individuals.

Effects of Berries Consumption on Cardiovascular Risk Factors: A Meta-analysis with Trial Sequential Analysis of Randomized Controlled Trials.

The effects of berries consumption on cardiovascular disease (CVD) risk factors have not been systematically examined. Here, we aimed to conduct a meta-analysis with trial sequential analysis to estimate the effect of berries consumption on CVD risk factors. PubMed, Embase, and CENTRAL were searched for randomized controlled trials (RCTs) that regarding the effects of berries consumption in either healthy participants or patients with CVD. Twenty-two eligible RCTs representing 1,251 subjects were enrolled. The pooled result showed that berries consumption significantly lowered the low density lipoprotein (LDL)-cholesterol [weighted mean difference (WMD), -0.21 mmol/L; 95% confidence interval (CI), -0.34 to -0.07; P = 0.003], systolic blood pressure (SBP) (WMD, -2.72 mmHg; 95% CI, -5.32 to -0.12; P = 0.04), fasting glucose (WMD, -0.10 mmol/L; 95% CI, -0.17 to -0.03; P = 0.004), body mass index (BMI) (WMD, -0.36 kg/m(2); 95% CI, -0.54 to -0.18, P < 0.00001), Hemoglobin A1c (HbA1c) (WMD, -0.20%; 95% CI, -0.39 to -0.01; P = 0.04) and tumor necrosis factor-α (TNF-α) (WMD, -0.99 ρg/mL; 95% CI, -1.96 to -0.02; P = 0.04). However, no significant changes were seen in other markers. The current evidence suggests that berries consumption might be utilized as a possible new effective and safe supplementary option to better prevent and control CVD in humans.

Omega-3 Polyunsaturated Fatty Acids Inhibited Tumor Growth via Preventing the Decrease of Genomic DNA Methylation in Colorectal Cancer Rats.

Omge-3 polyunsaturated fatty acids (PUFAs) exhibited significant effect in inhibiting various tumors. However, the mechanisms of its anticancer role have not been fully demonstrated. The declination of 5-methylcytosine (5 mC) was closely associated with poor prognosis of tumors. To explore whether omega-3 PUFAs influences on DNA methylation level in tumors, colorectal cancer (CRC) rat model were constructed using N-methyl phosphite nitrourea and omega-3 PUFAs were fed to part of the rats during tumor induction. The PUFAs contents in the rats of 3 experimental groups were measured using gas chromatography and 5 mC level were detected by liquid chromatography tandem mass spectrometry. The results showed that tumor incidence in omega-3 treated rats was much lower than in CRC model rats, which confirmed significant antitumor role of omega-3 PUFAs. Six PUFA members categorized to omega-3 and omega-6 families were quantified and the ratio of omega-6/omega-3 PUFAs was remarkably lower in omega-3 PUFAs treatment group than in CRC model group. 5 mC content in omega-3 PUFAs treated rats was higher than in CRC model rats, suggesting omega-3 PUFAs promoted 5 mC synthesis. Therefore, omega-3 PUFAs probably inhibited tumor growth via regulating DNA methylation process, which provided a novel anticancer mechanism of omega-3 PUFAs from epigenetic view.

Lack of genetic associations between PPAR-γ gene rs1801282 polymorphism and Alzheimer's disease in general population: a meta-analysis.

Published studies have evaluated the association between PPAR-γ rs1801282 polymorphism and Alzheimer's disease (AD) susceptibility. However, a definitive conclusion remains elusive. The aim of this study was to derive a more precise estimation of this association. We searched PubMed, Embase, Alzgene database, Chinese National Knowledge Infrastructure (CNKI), China Biological Medicine Database and Wanfang Databases for related studies. Twelve case-control studies with a total of 4874 cases and 5439 controls were finally identified to be eligible studies in this meta-analysis. The association was assessed by summarizing the odds ratios (ORs) with the corresponding 95% confidence intervals (CIs). Overall, there was no significant association between PPAR-γ rs1801282 polymorphism and Alzheimer's disease risk in all genetic models (the allele model G vs. C: OR=1.09, 95% CI 0.99-1.19, p=0.07; the homozygous model GG vs. CC: OR=1.04, 95% CI 0.75-1.44, p=0.80; heterozygote model GC vs. CC: OR=1.11, 95% CI 1,00-1.22, p=0.05; the dominant model GG+GC vs. CC: OR=1.10, 95% CI 1.00-1.22, p=0.05; the recessive model GG vs. GC+CC: OR=1.02, 95% CI 0.74-1.41, p=0.90). In subgroup analysis by ethnicity, no significant difference was found in both Asians and Caucasians. In summary, the present meta-analysis suggests that the PPAR-γ rs1801282 polymorphism may not be associated with genetic susceptibility of AD in general population.