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Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas. [ABSTRACT FROM AUTHOR]

The rapid recognition of rainstorm-induced emergencies (e.g., building and road inundation, facility damage, and trapped citizens) is vital to timely disaster response. One big challenge that limits the performance of emergency recognition is the data imbalance between different emergency domains. The present study aims to develop an effective cross-domain transfer learning framework for rainstorm-induced emergency recognition based on the text reports provided by citizens. The critical component of the framework is the use of joint distribution adaption (JDA) analysis embedded in a discriminative feature mapping procedure, which transfers rich knowledge learned from large-scale datasets to the learning task from small emergence data. Considering the feature incompleteness that is caused by short text length, a basic probability assignment function is constructed and applied to extract important spatial features for rainstorm emergency recognition, with an improved marginal Fisher analysis being adopted to optimize cross-domain text feature representation. The proposed scheme is validated using the empirical data of ten emergency classes from Wuhan City, China. Our experimental results show that the proposed method could significantly address data imbalance and thus help achieve high recognition performance through domain knowledge complementation. Meanwhile, the use of various spatial features is proved to be effective in tackling missing features. This scheme can be further developed into smart systems for rainstorm disaster response with reasonable performance and imbalanced sample sizes. [ABSTRACT FROM AUTHOR]

This study aims to examine and explain the differences at city level in cumulative COVID-19 cases and time from first to last infection during the first 6 weeks of the epidemic in China. A quantitative study is conducted in China based on the multisource spatial data of 315 Chinese cities. Firstly, the spatial discrepancy of COVID-19 transmission was examined based on spatial autocorrelation analysis and hot pot analysis. Next, a comprehensive indicator framework was established by including a wide range of factors such as human mobility, geographical features, public health measures, and residents' awareness. Finally, multivariate regression models using these variables were constructed to identify the determinants of COVID-19 transmission. Significant spatial discrepancy of transmission was proved, and 10 determinants were identified. The transmission consequence (measured by the number of cumulative cases) was mostly correlated with the migration scale from Wuhan, followed by socioeconomic factors. Transmission duration (measured as the time from the first to last case within the city) was mostly determined by total migration scale and lockdown speed, which suggests that timely implementation of public health measures facilitated fast control of transmission. Residents' attention to COVID-19 was proved to be not only helpful for reducing confirmed cases, but also in favor of rapid transmission control. Altitude produced slight but significant effect on transmission duration. These conclusions are expected to provide decision support for the local governments of China and other jurisdictions. [ABSTRACT FROM AUTHOR]

• Medical staff experience mental health disturb during the COVID-19 pandemic. • Direct and indirect exposure to COVID-19 affects the mental health profoundly. • Psychological materials and resources provide some protection. • Interventions with appropriate level are urgent. The severe 2019 outbreak of novel coronavirus disease (COVID-19), which was first reported in Wuhan, would be expected to impact the mental health of local medical and nursing staff and thus lead them to seek help. However, those outcomes have yet to be established using epidemiological data. To explore the mental health status of medical and nursing staff and the efficacy, or lack thereof, of critically connecting psychological needs to receiving psychological care, we conducted a quantitative study. This is the first paper on the mental health of medical and nursing staff in Wuhan. Notably, among 994 medical and nursing staff working in Wuhan, 36.9% had subthreshold mental health disturbances (mean PHQ-9: 2.4), 34.4% had mild disturbances (mean PHQ-9: 5.4), 22.4% had moderate disturbances (mean PHQ-9: 9.0), and 6.2% had severe disturbance (mean PHQ-9: 15.1) in the immediate wake of the viral epidemic. The noted burden fell particularly heavily on young women. Of all participants, 36.3% had accessed psychological materials (such as books on mental health), 50.4% had accessed psychological resources available through media (such as online push messages on mental health self-help coping methods), and 17.5% had participated in counseling or psychotherapy. Trends in levels of psychological distress and factors such as exposure to infected people and psychological assistance were identified. Although staff accessed limited mental healthcare services, distressed staff nonetheless saw these services as important resources to alleviate acute mental health disturbances and improve their physical health perceptions. These findings emphasize the importance of being prepared to support frontline workers through mental health interventions at times of widespread crisis. [ABSTRACT FROM AUTHOR]

Changes in vegetation traits influence the particulate pollution mitigating effects of trees in street canyons; however, it remains unclear whether tree canopy density (i.e. the proportion of the street floor covered by the vertical projection of the tree canopy) promotes or reduces this effect. A 12-day field experiment was conducted in four representative street canyons to examine the mitigating effects of street trees on particulate matter (PM) for PM 1 , PM 2.5 , PM 4 , PM 7 , PM 10 , and total suspended particles (TSP) among four canopy density treatments, including (1) open spaces and areas with (2) sparse (≤35%), (3) medium (35–70%) and (4) dense (≥70%) canopy densities. The results showed that canopy density is the dominant vegetation trait that affects PM dispersion, with peak decreases occurring at a canopy density of ~30%. The particulate matter attenuation coefficient (PMAC) indicates the PM reduction capability of trees. The PMAC of each particle size class correlated negatively with canopy density and TSP (<100 μm) showed the greatest attenuation. In relation to open space treatment, a canopy density range 30–36% showed the largest reductions in the PM 10 and TSP concentrations of 26.75% and 27.49%, respectively. And for the PM 2.5 concentration, a canopy density range 24–36% exhibited the largest reduction (7.44%). It was also concluded that sparse canopy density is optimal for trees in areas with high PM concentration. Medium canopy density also promotes pollutant dispersion (especially PM 2.5), while dense canopy density causes air quality deterioration. This study will provide new insights into the response of atmospheric PM spatial dispersion to the characteristics of tree cover in street canyons, as well as the regulation mechanism of this response. By investigating this issue under different scenarios, this study aims to contribute to the quantitative tree planting design in urban planning. Image 1 • Trees morphological features affected the dispersion of particulate matter in street canyons. [ABSTRACT FROM AUTHOR]