Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India.

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong.

OBJECTIVE: This paper examines the relationship between bicycle collisions and the amount of cycling at the local level. Most previous research has focused on national and city comparisons, little is known about differences within a city (the mesoscale). METHODS: This study mainly used three types of data sets relating to bicycle collisions, use of bicycles and local neighbourhood characteristics in Hong Kong. In particular, bicycle usage, measured as bicycle-kilometres travelled, was estimated from travel surveys following the activity-based approach. Negative binomial regression models were established to model the relationship between the amount of cycling and the occurrence of bicycle collisions at the spatial scale of the Tertiary Planning Unit, which is the smallest planning unit of the city. RESULTS: The numbers of bicycle collisions went up with the increasing use of bicycles, but the increase in the number of collisions in a given community was less than a linear proportion of the bicycle flow. When other local neighbourhood variables are controlled, the amount of cycling is a statistically significant variable in accounting for the number of collisions. CONCLUSIONS: Even in a highly motorised city where bicycles are a minor transport mode, cyclists are less likely to be involved in road collisions in communities with higher cycling volume. Since cycling activities are likely to vary within a city, a more local-based approach in promoting cycling is needed. In particular, the higher safety risks in neighbourhoods of low bicycle usage, especially at an initial stage of promoting cycling, need to be addressed properly.

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus, including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically.

Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity.

Foodborne diseases are a critical public health problem worldwide and significantly impact human health, economic losses, and social dynamics. Understanding the dynamic relationship between the detection rate of bacterial foodborne diseases and a variety of meteorological factors is crucial for predicting outbreaks of bacterial foodborne diseases. This study analyzed the spatio-temporal patterns of vibriosis in Zhejiang Province from 2014 to 2018 at regional and weekly scales, investigating the dynamic effects of various meteorological factors. Vibriosis had a significant temporal and spatial pattern of aggregation, and a high incidence period occurred in the summer seasons from June to August. The detection rate of Vibrio parahaemolyticus in foodborne diseases was relatively high in the eastern coastal areas and northwestern Zhejiang Plain. Meteorological factors had lagging effects on the detection rate of V. parahaemolyticus (3 weeks for temperature, 8 weeks for relative humidity, 8 weeks for precipitation, and 2 weeks for sunlight hours), and the lag period varied in different spatial agglomeration regions. Therefore, disease control departments should launch vibriosis prevention and response programs that are two to eight weeks in advance of the current climate characteristics at different spatio-temporal clustering regions.

Spatial and temporal analyses of the influences of meteorological and environmental factors on Aedes albopictus (Diptera: Culicidae) population dynamics during the peak abundance period at a city scale.

Aedes albopictus (Diptera: Culicidae) is a major vector of multiple diseases. While vaccines have been developed, preventing these Aedes-borne diseases continues to primarily depend on monitoring and controlling the vector population. Despite increasing research on the impacts of various factors on Ae. albopictus population dynamics, there is still no consensus on how meteorological or environmental factors affect vector distribution. In this study, the relationships between mosquito abundance and meteorological and environmental indicators were examined at the town level based on data collected from July to September, the peak abundance period of 2019 in Shanghai. In addition to performing Poisson regression, we employed the geographically weighted Poisson regression model to account for spatial dependency and heterogeneity. The result showed that the environmental factors (notably human population density, the Normalized Difference Vegetation Index (NDVI), socioeconomic deprivation, and road density) had more significant impacts than the meteorological variables in accounting for the spatial variation of mosquito abundance at a city scale. The dominant environmental variable differed in urban and rural places. Furthermore, our findings indicated that deprived townships are more susceptible to higher vector densities compared to non-deprived townships. Therefore, it is crucial not only to allocate more resources but also to increase attention towards controlling the vectors responsible for their transmission in these townships.

Study on reactions of long-lived phenoxathiin radical cation with aliphatic alcohols, phenol and phenyl halides in ambient condition by fused-droplet electrospray ionization mass spectrometry.

he reactions of phenoxathiin radical cations with diverse organic compounds in ambient conditions were realized by using fused-droplet electrospray ionization mass spectrometry. In the investigation, the phenoxathiin radical cation was prepared by electrospray ionization. The reactants included aliphatic alcohols, phenol and phenyl halides and the reaction studies showed the unique reactivity the of phenoxathiin radical cation towards neutral organic compounds in ambient conditions, which has not been revealed in previous studies.

Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level.

As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas without congested environments. Separations need to be installed between roadways and sidewalks to improve elderly road safety.

Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China.

BACKGROUND: Aedes albopictus is a vector of major arboviral diseases and a primary pest in tropical and temperate regions of China. In most cities of China, the current monitoring system for the spread of Ae. albopictus is based on the subdistrict scale and does not consider spatial distribution for analysis of species density. Thus, the system is not sufficiently accurate for epidemic investigations, especially in large cities. METHODS: This study used an improved surveillance program, with the mosquito oviposition trap (MOT) method, integrating the actual monitoring locations to investigate the temporal and spatial distribution of Ae. albopictus abundance in an urban area of Shanghai, China from 2018 to 2019. A total of 133 monitoring units were selected for surveillance of Ae. albopictus density in the study area, which was composed of 14 subdistricts. The vector abundance and spatial structure of Ae. albopictus were predicted using a binomial areal kriging model based on eight MOTs in each unit. Results were compared to the light trap (LT) method of the traditional monitoring scheme. RESULTS: A total of 8,192 MOTs were placed in the study area in 2018, and 7917 (96.6%) were retrieved, with a positive rate of 6.45%. In 2019, 22,715 (97.0%) of 23,408 MOTs were recovered, with a positive rate of 5.44%. Using the LT method, 273 (93.5%) and 312 (94.5%) adult female Ae. albopictus were gathered in 2018 and 2019, respectively. The Ae. albopictus populations increased slowly from May, reached a peak in July, and declined gradually from September. The MOT positivity index (MPI) showed significant positive spatial autocorrelation across the study area, whereas LT collections indicated a nonsignificant spatial autocorrelation. The MPI was suitable for spatial interpolation using the binomial areal kriging model and showed different hot spots in different years. CONCLUSIONS: The improved surveillance system integrated with a geographical information system (GIS) can improve our understanding of the spatial and temporal distribution of Ae. albopictus in urban areas and provide a practical method for decision-makers to implement vector control and mosquito management.

Transmission center and driving factors of hand, foot, and mouth disease in China: A combined analysis.

Hand, foot, and mouth disease (HFMD) has become a major public health issue in China. The disease incidence varies substantially over time and across space. To understand the heterogeneity of HFMD transmission, we compare the spatiotemporal dynamics of HFMD in Qinghai and Shanghai by conducting combined analysis of epidemiological, wavelet time series, and mathematical methods to county-level data from 2009 to 2016. We observe hierarchical epidemic waves in Qinghai, emanating from Huangzhong and in Shanghai from Fengxian. Besides population, we also find that the traveling waves are significantly associated with socio-economic and geographical factors. The population mobility also varies between the two regions: long-distance movement in Qinghai and between-neighbor commuting in Shanghai. Our findings provide important evidence for characterizing the heterogeneity of HFMD transmission and for the design and implementation of interventions, such as deploying optimal vaccine and changing local driving factors in the transmission center, to prevent or limit disease spread in these areas.

Pre-evacuation Time Estimation Based Emergency Evacuation Simulation in Urban Residential Communities.

The timely and secure evacuation of an urban residential community is crucial to residents' safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recognized for approximately 40 years. However, pre-evacuation time is rarely discussed in previous community-scale emergency evacuation studies. This paper proposes a new method that estimates the pre-evacuation time, which makes the evacuation simulation in urban residential communities more realistic. This method integrates the residents' pre-evacuation behavior data obtained by surveys to explore the influencing factors of pre-evacuation time and builds a predictive model to forecast pre-evacuation times based on the Random Forest algorithm. A sensitivity analysis is also conducted to find the critical parameters in evacuation simulations. The results of evacuation simulations in different scenarios can be compared to identify potential evacuation problems. A case study in Luoshanqicun Community, Pudong New District, Shanghai, China, was conducted to demonstrate the feasibility of the proposed method. The simulation results showed that the pre-evacuation times have significant impacts on the simulation procedure, including the total evacuation time, the congestion time and the congestion degree. This study can help to gain a deeper understanding of residents' behaviors under emergencies and improve emergency managements of urban communities.

Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: space-time path vs. potential path tree methods.

Research on the extent to which pedestrians are exposed to road collision risk is important to the improvement of pedestrian safety. As precise geographical information is often difficult and costly to collect, this study proposes a potential path tree method derived from time geography concepts in measuring pedestrian exposure. With negative binomial regression (NBR) and geographically weighted Poisson regression (GWPR) models, the proposed probabilistic two-anchor-point potential path tree (PPT) approach (including the equal and weighted PPT methods) are compared with the deterministic space-time path (STP) method. The results indicate that both STP and PPT methods are useful tools in measuring pedestrian exposure. While the STP method can save much time, the PPT methods outperform the STP method in explaining the underlying vehicle-pedestrian collision pattern. Further research efforts are needed to investigate the influence of walking speed and route choice.