A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas

A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas

Mean Inflection Point Distance: Artificial Intelligence Mapping Accuracy Evaluation Index—An Experimental Case Study of Building Extraction

Mapping is a fundamental application of remote sensing images, and the accurate evaluation of remote sensing image information extraction using artificial intelligence is critical. However, the existing evaluation method, based on Intersection over Union (IoU), is limited in evaluating the extracted information’s boundary accuracy. It is insufficient for determining mapping accuracy. Furthermore, traditional remote sensing mapping methods struggle to match the inflection points encountered in artificial intelligence contour extraction. In order to address these issues, we propose the mean inflection point distance (MPD) as a new segmentation evaluation method. MPD can accurately calculate error values and solve the problem of multiple inflection points, which traditional remote sensing mapping cannot match. We tested three algorithms on the Vaihingen dataset: Mask R-CNN, Swin Transformer, and PointRend. The results show that MPD is highly sensitive to mapping accuracy, can calculate error values accurately, and is applicable for different scales of mapping accuracy while maintaining high visual consistency. This study helps to assess the accuracy of automatic mapping using remote sensing artificial intelligence

An Integrated Air Quality Improvement Path of Energy-Environment Policies in the Guangdong-Hong Kong-Macao Greater Bay Area

Energy-related clean air measures in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) can yield substantial air quality improvement benefits and promote energy structure optimization. Here, we first evaluate the reduction effect of the stringent energy-related clean air measures in the GBA during the 13th Five-Year Plan period. First, a reduction of 19.3% emission in air pollutant equivalent was measured in 2020 compared to 2015. Second, we compare the energy structure development and air quality benefits of energy-environment policy scenarios by 2025 (SBAU, SA, SO) geared towards proposing integrated energy-environment development paths of air quality improvement. Under SBUA, SA and SO, the annual average PM2.5 concentration will be 21.7, 19.9 and 18.1 μg/m3, respectively, and the total energy demand would be controlled within 318.9, 300.6 and 282.3 Mtce in the GBA in 2025, reaching 7.5%, 8.4% and 9.4% of SO2, 23.5%, 29.3% and 35.4% of NOX, 18.2%, 19.6% and 22.7% of primary PM2.5, and 25.1%, 29.9% and 34.7% of VOCs emission reductions compared to 2020, respectively. Our study proposes that it is necessary for the GBA to jointly set up regional air quality improvement targets and issue integrated regional energy-environment policies in the process of building an “Air Quality Improvement Pioneering Demonstration Area”

Housing environment and early childhood development in sub-Saharan Africa : A cross-sectional analysis

Author summary Why was this study done? Sub-Saharan Africa (SSA) had the largest number of children (29.4 million) with low cognitive and/or social-emotional development worldwide in 2010. Housing might be linked to early childhood development (ECD) by affecting a child's health and a parent's ability to provide adequate care but is neglected in public health, especially in low- and middle-income countries (LMIC). What did the researchers do and find? We tested the hypothesis that improved housing is associated with on-track development in 2 ECD domains (cognitive and social-emotional). We analyzed data from 88,271 children aged 36 to 59 months in 20 SSA countries collected from nationally representative surveys conducted between 2010 and 2019. Improved housing was associated with 15% higher odds of being developmentally on track in the cognitive domain. Overall, improved housing was associated with on-track social-emotional development in girls but not boys. What do these findings mean? Housing improvement in SSA may be associated not only with benefits for children's physical health but also with broader aspects of healthy child development. Future work to establish a causal link between housing and ECD and to assess its underlying mechanisms is crucial for the designing of cost-effective ECD interventions in SSA. Background The influence of the safety and security of environments on early childhood development (ECD) has been under-explored. Although housing might be linked to ECD by affecting a child's health and a parent's ability to provide adequate care, only a few studies have examined this factor. We hypothesized that housing environment is associated with ECD in sub-Saharan Africa (SSA). Methods and findings From 92,433 children aged 36 to 59 months who participated in Multiple Indicator Cluster Survey (MICS) in 20 SSA countries, 88,271 were tested for cognitive and social-emotional development using the Early Childhood Development Index (ECDI) questionnaire and were thus included in this cross-sectional analysis. Children's mean age was 47.2 months, and 49.8% were girls. Children were considered developmentally on track in a certain domain if they failed no more than 1 ECDI item in that domain. In each country, we used conditional logistic regression models to estimate the association between improved housing (housing with finished building materials, improved drinking water, improved sanitation facilities, and sufficient living area) and children's cognitive and social-emotional development, accounting for contextual effects and socioeconomic factors. Estimates from each country were pooled using random-effects meta-analyses. Subgroup analyses were conducted by the child's gender, maternal education, and household wealth quintiles. On-track cognitive development was associated with improved housing (odds ratio [OR] = 1.15, 95% CI 1.06 to 1.24, p < 0.001), improved drinking water (OR = 1.07, 95% CI 1.00 to 1.14, p = 0.046), improved sanitation facilities (OR = 1.15, 95% CI 1.03 to 1.28, p = 0.014), and sufficient living area (OR = 1.06, 95% CI 1.01 to 1.10, p = 0.018). On-track social-emotional development was associated with improved housing only in girls (OR = 1.14, 95% CI 1.04 to 1.25, p = 0.006). The main limitations of this study included the cross-sectional nature of the datasets and the use of the ECDI, which lacks sensitivity to measure ECD outcomes. Conclusions In this study, we observed that improved housing was associated with on-track cognitive development and with on-track social-emotional development in girls. These findings suggest that housing improvement in SSA may be associated not only with benefits for children's physical health but also with broader aspects of healthy child development

Air Quality Scenario Analysis Application of Multi-Domain Linkage Development in the Pearl River Delta

In the background of constructing a wold-class Guangdong-Hong Kong-Macao Great Bay Area (GBA), the growing demand for industrial, transportation and energy development in the Pearl River Delta (PRD) will put considerable pressure on improvement of regional air quality. It is important to choose a scientific development path to achieve both economic goal and air quality improvement target. This study uses scenario analysis method to construct three “industry-transport-energy” development scenarios within the region while the improvement level of air quality is simulated and analyzed. The results show that: (1) Considering the mutual constraints and influence relations between industry, transportation and energy in scenario analysis, the “industry-transport-energy” development scenario can be established to meets the same economic goal but has different development paths. (2) Along the historical track and established policy path, concentration of fine particulate matter (PM2.5) in the PRD can be reduced to 16.2 µg/m3 by 2035 as regional gross domestic product (GDP) reaching about 23.5 trillion. (3) Under the same economic goals, raising the proportion of emerging industries, freight by rail, public transport travel and non-fossil power to 95%, 10%, 73%, and 46% respectively leads to 29.6~49.2% reductions in the emissions of sulphur dioxide (SO2), nitrous oxides (NOx), primary PM2.5 and volatile organic compounds (VOCs) compared with those in 2017 that the regional PM2.5 concentration will further drop to 14.1 µg/m3. The results show that, under the constraints of economic development objectives, deepening structural adjustment can improve air quality, which gives advice for the PRD to choose its development path. Furthermore, this study can provide reference for the PRD to promote the transformation of industrial, transportation and energy development modes and structural adjustment under the dual objective of promoting the world-class bay area economic level and high-quality air level

Structural characteristics of corncob and eucalyptus contributed to sugar release during hydrothermal pretreatment and enzymatic hydrolysis

Explicitly understanding biomass recalcitrance through the characterization of biomass physicochemical properties may help to develop efficient pretreatment and enzymatic hydrolysis strategies. The lignin of corncob and eucalyptus contain the same main linkage bonds: beta-O-4 aryl ether bonds, beta-beta and beta-5 structures, but the lignin of eucalyptus was of the syringyl (S)-guaiacy (G) type, while that of corncob was SG-p-hydroxyphenyl (H) type, corresponding to lignin S/G ratios of 1.6 and 1.1 respectively. Under the optimum microwave-hydrothermal pretreatment condition of 180 degrees C for 30 min at a 12.5% substrate concentration, the maximum total xylose yield of corncob (64.7%) was lower than that of eucalyptus (79.2%). In contrast, corncob resulted in a greater increase in enzymatic digestibility, from 59.6 to 82.4%, after pretreatments, compared with 16.7 to 74.9% for eucalyptus. There was a positive correlation between the xylose yield and lignin S/G ratio, but the lignin content was negatively correlated with enzymatic digestibility. Furthermore, based on the non-destructive characterization of three-dimensional X-ray microscopy, not only was the increase in the number and size of surface pores beneficial to the accessibility of cellulose to cellulosic enzymes, but the swelling of cell wall could reduce the recalcitrance of sugar release

IS26 mediated blaCTX-M-65 amplification in Escherichia coli increase the antibiotic resistance to cephalosporin in vivo

ABSTRACT: Objectives: To characterize two Escherichia coli strains isolated from a patient pre- and post-treatment, using β-lactams and β-lactam/β-lactamase inhibitor combinations (BLBLIs). Methods: A combination of antibiotic susceptibility testing (AST) with whole genome sequencing using Illumina and Oxford Nanopore platforms. Long-read sequencing and reverse transcription-quantitative PCR were performed to determine the copy numbers and expression levels of antibiotic resistance genes (ARGs), respectively. Effect on fitness costs were assessed by growth rate determination. Results: The strain obtained from the patient after the antibiotic treatment (XH989) exhibited higher resistance to cefepime, BLBLIs and quinolones compared with the pre-treatment strain (XH987). Sequencing revealed IS26-mediated duplications of a IS26-fosA3-blaCTX-M-65 plasmid-embedded element in strain XH989. Long-read sequencing (7.4 G data volume) indicated a variation in copy numbers of blaCTX-M-65 within one single culture of strain XH989. Increased copy numbers of the IS26-fosA3-blaCTX-M-65 element were correlated with higher CTX-M-65 expression level and did not impose fitness costs, while facilitating faster growth under high antibiotic concentrations. Conclusion: Our study is an example from the clinic how BLBLIs and β-lactams exposure in vivo possibly promoted the amplification of an IS26-multiple drug resistance (MDR) region. The observation of a copy number variation seen with the blaCTX-M-65 gene in the plasmid of the post-treatment strain expands our knowledge of insertion sequence dynamics and evolution during treatment