Effects of landscape on thermal livability at the community scale based on fine-grained geographic information: A case study of Shenzhen.

In the current research, the question of how to modify the microclimate through landscape planning to create a livable thermal environment within a residential community area has not been clarified. Therefore, this study investigated the effects of landscape on thermal livability in 2980 communities in Shenzhen, and obtained the following findings: (1) the proportion of trees and the average building height were key indicators to determine the average land surface temperature (LST) of a community, while the two-dimensional building characteristics, particularly shape, similarity, and patch dominance, were mainly responsible for regulating the spatial distribution of LST within a community; (2) at the community scale, the cooling intensity of buildings was strongest when their average height was around 40-60 m, and cooling effect of trees was most pronounced when their proportion achieved 20 %; and (3) the LST threshold for thermal livability in Shenzhen was around 35 °C. In summer, a higher proportion of trees and grass, as well as buildings with higher average heights, larger volume ratios, and more complex three-dimensional structures were favorable to maintain a livable community thermal environment, while in winter, a lower proportion of trees was more encouraged. In addition, a smaller average sky view factor can achieve a community thermal environment that warm in winter and cool in summer. These results are expected to facilitate urban planners to develop community renewal from the perspective of thermal livability.

High-efficiency Ge-based waveguide photodetector integrated with a grating coupler on silicon-on-insulator.

We propose the design of a composite device structure with germanium-based (Ge-based) waveguide photodetectors integrated with grating couplers on a silicon-on-insulator platform. The finite-difference time-domain method is used to establish simulation models and optimize the design of the waveguide detector and grating coupler. For the grating coupler, by adjusting the size parameters to the optimal value and combining the advantages of the nonuniform grating and the Bragg reflector structure, the peak coupling efficiency reaches 85% at 1550 nm and 75.5% at 2000 nm, which is, respectively, 31.3% and 14.6% higher than that of uniform grating. For the waveguide detector, a germanium-tin (GeSn) alloy was introduced to replace Ge as the active absorption layer at 1550 and 2000 nm, which not only broadened the detection range and significantly improved the light absorption of the detector but also realized the near-complete light absorption of the GeSn alloy when the device length was 10 µm. These results make it possible to miniaturize the device structure of Ge-based waveguide photodetectors.

Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs.

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm's calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.

Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City.

Ozone (O3) pollution is a serious issue in China, posing a significant threat to people's health. Traffic emissions are the main pollutant source in urban areas. NOX and volatile organic compounds (VOCs) from traffic emissions are the main precursors of O3. Thus, it is crucial to investigate the relationship between traffic conditions and O3 pollution. This study focused on the potential relationship between O3 concentration and traffic conditions at a roadside and urban background in Guangzhou, one of the largest cities in China. The results demonstrated that no significant difference in the O3 concentration was observed between roadside and urban background environments. However, the O3 concentration was 2 to 3 times higher on sunny days (above 90 µg/m3) than on cloudy days due to meteorological conditions. The results confirmed that limiting traffic emissions may increase O3 concentrations in Guangzhou. Therefore, the focus should be on industrial, energy, and transportation emission mitigation and the influence of meteorological conditions to minimize O3 pollution. The results in this study provide some theoretical basis for mitigation emission policies in China.

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data.

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8-36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3.

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations.

Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China.

Association between fine particulate matter (PM2.5) and respiratory health has attracted great concern in China. Substantial epidemiological evidences confirm the correlational relationship between PM2.5 and respiratory disease in many Chinese cities. However, the causative impact of PM2.5 on respiratory disease remains uncertain and comparative analysis is limited. This study aims to explore and compare the correlational relationship as well as the causal connection between PM2.5 and upper respiratory tract infection (URTI) in two typical cities (Beijing, Shenzhen) with rather different ambient air environment conditions. The distributed lag nonlinear model (DLNM) was used to detect the correlational relationship between PM2.5 and URTI by revealing the lag effect pattern of PM2.5 on URTI. The convergent cross mapping (CCM) method was applied to explore the causal connection between PM2.5 and URTI. The results from DLNM indicate that an increase of 10 µg/m3 in PM2.5 concentration is associated with an increase of 1.86% (95% confidence interval: 0.74%-2.99%) in URTI at a lag of 13 days in Beijing, compared with 2.68% (95% confidence interval: 0.99-4.39%) at a lag of 1 day in Shenzhen. The causality detection with CCM quantitatively demonstrates the significant causative influence of PM2.5 on URTI in both two cities. Findings from the two methods consistently show that people living in low-concentration areas (Shenzhen) are less tolerant to PM2.5 exposure than those in high-concentration areas (Beijing). In general, our study highlights the adverse health effects of PM2.5 pollution on the general public in cities with various PM2.5 levels and emphasizes the needs for the government to provide appropriate solutions to control PM2.5 pollution, even in cities with low PM2.5 concentration.

A comparison of different GRACE solutions in terrestrial water storage trend estimation over Tibetan Plateau.

The Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies (TWSA) estimations provide valuable information for the monitoring of land water resources changes. Multiple parameters and strategies for inversion of the water storage changes have been explored. The explorations on differences between GRACE solutions in local regions and basins are fundamental and important. This study focuses on comparisons of TWSA trends between different GRACE solutions over Tibetan Plateau (TP), both storage and flux among solutions were compared. Results show that great discrepancies exist in TWSA between GRACE solutions derived from the standard spherical harmonic approach (SSH) and the mascon approach. Three SSH-based GRACE solutions (JPL, CSR, and GFZ) detect no significant TWSA changes for the whole area of Tibetan Plateau, whereas JPL mascon solution (JPL-M) and CSR mascon solution (CSR-M) gave decreasing trends of 3.10 km3/yr and 3.77 km3/yr, respectively. This difference also exists in the Yangtze River-Yellow River basin (YYR basin) in the TP. Although five solutions derived consistent TWSA trends in northwest river basin (NWR basin) and southwest river basin (SWR basin) in the TP, the variations between different solutions are 2.88 km3/yr and 4.75 km3/yr for NWR and SWR basin respectively, which could not be neglected. The JPL-M solution, as a result, would overestimate both TWSA decreasing and increasing trends comparing with other GRACE solutions. The results of this study are expected to provide references for the studies of water resource dynamics over Tibetan Plateau and the surrounding areas based on GRACE TWSA products.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China.

Analyzing the association between fine particulate matter (PM2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 µg/m³ in mainland China, and the highest population exposure to PM2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM2.5 was positively related to GDP. An apparent upward trend of population exposure to PM2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km2 at the 95th percentile, respectively.