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The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city's physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification's highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification. [ABSTRACT FROM AUTHOR]

The local climate zone (LCZ) scheme has been widely utilized in regional climate modeling, urban planning, and thermal comfort investigations. However, existing LCZ classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments. In this study, we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations, i.e. daytime Ziyuan-3 stereo imageries (2.1 m) and Luojia-1 nighttime light (NTL) data (130 m). Firstly, we performed land cover classification using multiple machine learning methods (i.e. random forest (RF) and XGBoost algorithms) and various features (i.e. spectral, textural, multi-view features, 3D urban structure parameters (USPs), and NTL). In addition, we developed a set of new cumulative elevation indexes to improve building roughness assessments. The indexes can estimate building roughness directly from fused point clouds generated by both along- and across-track modes. Finally, based on the land cover and building roughness results, we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping. The results for Beijing, China, show that our method yielded satisfactory accuracy for LCZ mapping, with an overall accuracy (OA) of 90.46%. The overall accuracy of land cover classification using 3D USPs generated from both along- and across-track modes increased by 4.66%, compared to that of using the single along-track mode. Additionally, the OA value of LCZ mapping using 2D and 3D USPs (88.18%) achieved a better result than using only 2D USPs (83.83%). The use of NTL data increased the classification accuracy of LCZs E (bare rock or paved) and F (bare soil or sand) by 6.54% and 3.94%, respectively. The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates. [ABSTRACT FROM AUTHOR]

Our survey found that, during the COVID-19 epidemic in Beijing, the majority of our asthma patients had their disease controlled as defined by GINA, and the percentage of controlled asthma was higher than that reported before the pandemic. Patients with mild-to-moderate or well-controlled asthma were encouraged to use digital medical services.7 Outpatient service should be prioritized for patients who have poorly controlled asthma, worsening asthma symptoms, or who have required dose escalations of asthma medications in the past several months' time.4 Since our survey was cross-sectional, no definite conclusion can be drawn about the causal relationship between risk factors and uncontrolled asthma. Dr Chang, Dr Zhang, Dr Dong, Dr Liang, Dr Chen, Dr Shang, Dr Abulikemu, and Dr Sun have nothing to disclose. [Extracted from the article]

Concentrations of PM 2.5 and PM 2.5 -associated elements were observed hourly using an X-ray fluorescence-based online elemental analyzer at a chosen location within urban Beijing from November 28, 2016, to May 30, 2017, when nearly the lowest atmospheric environmental capacity was recorded after enforcing the Air Pollution Prevention and Control Air Plan (APPCAP). Throughout the study period, the PM 2.5 concentrations were an average of 91.2 μg/m3, with 68 days exceeding the second tier of the Chinese ambient air quality standard (75 μg/m3). The PM 2.5 -associated hourly hazardous airborne element (HAE) concentrations ranged between 56 ng/m3 and 4374 ng/m3, accounting for an average of 0.2 ± 0.1 % of PM 2.5 , which were lower than or comparable with those reported in previous studies. Different from the traditional filter sampling offline analysis, dynamic variations and evolution of particulate HAEs were illustrated based on hourly observations, which showed that the concentrations of most HAEs declined with increasing mixed layer height (MLH) as expected, while slight increases in the Cr and Pb concentrations were observed when the MLH increased from 250 m to 500 m. Compared with the values in spring, As, Cr, Hg, Mn, Ni, Pb and Se increased by 135.0 %, 67.2 %, 110.1 %, 36.4 %, 32.4 %, 185.6 % and 63.9 % in winter, respectively. Based on hourly elemental data, the more robust source analysis showed that industrial emissions contributed substantially to Cr, Hg, Ni and Se (58.5 %, 42.7 %, 65.0 % and 62.0 %, respectively), vehicular sources primarily accounted for Mn (41.1 %) and almost all sources except for dust contributed equally to the Pb loading. A nonparametric wind regression analysis applied to 1-h HAE concentrations pinpointed distinct hot spots (high concentrations) for all HAEs except Cd in the northeast wind sector at wind speeds of approximately 0−6 km/h. Based on a potential source contribution function analysis, the areas in western China with intense coal consumption strongly contributed to the loading of As in Beijing. Cr, Hg, Ni, Pb and Se showed source areas that were similar to but larger than that of As; additionally, these elements exhibited moderate and strong hotspots in Shanxi Province with strong coal consumption and industrial production. Ni clearly originated from the coastal areas where the effect of shipping activities was intense. [ABSTRACT FROM AUTHOR]