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The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. However, less than 1% of users geotag their messages, leaving geolocating users through user profiles or message content addresses increasingly crucial. This paper evaluates and visualizes the margin of error incurred when using user profiles or message-mentioned addresses to geolocate social media data for disaster research. Using Twitter data during the 2017 Hurricane Harvey as an example, this research assessed the inconsistencies in predicting users' locations in various administrative units during each disaster phase using three geolocating strategies. The results reveal that the similarities between geotags, and user profile locations decrease from 94.07% to 64.56%, 43.9%, 31.82%, 27.05%, and 26.7% as the geographical scale changes from country to state, county, block group, 1-kilometer, and 30-meter levels. These similarities are overall higher than the agreements between locations derived from geotags and tweet content. The geolocation consistencies among the three methods remain stable across disaster phases. The impacts of uncertainties in geolocating Twitter data for disaster management applications were further unraveled. The findings offer valuable insights into the trade-off between spatial scale and geolocation accuracy and inform the selection of appropriate scales when applying different geolocating strategies in future social media-based investigations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase would inevitably bring unexpected challenges to vulnerable populations. There is a need to identify the emergent needs of underprepared, vulnerable populations under lockdowns to inform future pandemic management. In this study, the messages posted on an online mutual help-requesting platform during the 2022 Shanghai lockdown were leveraged as the near real-time agent to represent the side effects of the lockdown policy. This work explores the spatiotemporal correlation of the posts to their potential influencing factors and mines knowledge from the textual content of the posts. The results indicate that the help requests were clustered in downtown Shanghai. The access to profuse medical resources and public services may be hindered in districts with more stringent lockdown policies. The help requests' content unveils a need for medication and groceries under lockdown. It also underscores that the elderly population was affected most by the lockdown. Based on these insights, policymakers can better anticipate and address the urgent residential needs that may arise in future lockdowns. This knowledge can contribute to more effective planning and preparedness efforts to mitigate the impact on vulnerable populations and ensure their well-being during similar crisis situations. [ABSTRACT FROM AUTHOR]

This paper investigates the chemical composition of Particulate Matter, Organic Carbon (OC), and Elemental Carbon (EC) in residential and traffic sites in Mumbai. The average PM2.5 and PM10 concentrations at the traffic site (Sakinaka) were 240 µg/m3 and 424 µg/m3, respectively. The observed levels of OC were 35 µg/m3, 22 µg/m3, and 15.5 µg/m3 at Sakinaka junction (high-density traffic), YP-Gate (low-density traffic), and Hostel Premise (Residential), respectively. The average OC/EC ratio value was high (4.5) at the residential site, indicating contributions from stationary combustion sources and secondary production of carbonaceous species to OC. The residential site has a higher percentage of low volatile OC fraction (57%) in total OC than the traffic sites. On the other hand, Sakinaka has a higher percentage of highly volatile OC fractions (36%) in total OC. The crustal-originated metals were dominating in all areas, but the concentration of metals from anthropogenic sources was highest at Sakinaka, i.e., As (381 ng/m3), Pb (352 ng/m3), Zn (679 ng/m3). The K/Al, Ca/Al, Mg/Al, and Fe/Al ratios were high in all the samples compared to the crustal ratio indicating biomass burning and traffic emission sources of these metals. PM originating from traffic was more enriched with heavy metals that are toxic to human health, increasing cancer risks (CR) through inhalation. The hazard quotient was above 1 at all the locations, and CR was above 1 × 10− 4, causing health risks. According to the dosimetry model, more PM was deposited in the lungs of traffic location occupants through inhalation, increasing the cancerous risk. [ABSTRACT FROM AUTHOR]