Evaluation of Volatile Organic Compounds and Polyaromatic Hydrocarbons in Barker Reservoir in Houston, Texas after the 2017 Hurricane Harvey

Analysis of volatile organic compounds (VOCs) and polyaromatic hydrocarbons (PAHs) in the Barker Reservoir in Houston, Texas, United States is reported. Samples were collected within one week after the August 2017 Hurricane Harvey. Using a gas chromatograph equipped with a mass spectrometer, 4 VOCs and 13 PAHs were found in the Barker Reservoir. Concentrations of acetone, benzene, chloroform, and toluene were 1500, 380, 830, and 290 parts per million (ppm), respectively. Benzene and chloroform are classified as probable human carcinogens by the U.S. Environmental Protection Agency (EPA). Six PAHs including benzo[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, benzo[k]fluoranthene, chrysene, and dibenz[a,h]anthracene are probable human carcinogens. The most concentrated PAH was acenaphthylene at 0.068 ppm, while the least one was fluoranthene at 0.00046 ppm. Results revealed water contaminants in Houston and its vicinities during the flooding season and served as references for water monitoring purposes in the future.

Social and geographical disparities in Twitter use during Hurricane Harvey

Socialmedia such as Twitter is increasingly beingused as an effective platform to observe human behaviors in disastrous events.However,uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience.This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana.We seek to answer a fundamental question:did socialgeographical disparities of Twitter use exist during the three phases of emergency management(preparedness,response,recovery)?We employed a Twitter data mining framework to process the data and calculate two indexes:Ratio and Sentiment.Regression analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane Harvey.Communities with higher disasterrelated Twitter use in Harvey generally were communities having better social and geographical conditions.These results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or decreased.Future research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

Disaster Impacts Surveillance from Social Media with Topic Modeling and Feature Extraction: Case of Hurricane Harvey

Twitter can supply useful information on infrastructure impacts to the emergency managers during major disasters,but it is time consuming to filter through many irrelevant tweets.Previous studies have identified the types of messages that can be found on social media during disasters,but few solutions have been proposed to efficiently extract useful ones.We present a framework that can be applied in a timely manner to provide disaster impact information sourced from social media.The framework is tested on a well-studied and data-rich case of Hurricane Harvey.The procedures consist of filtering the raw Twitter data based on keywords,location,and tweet attributes,and then applying the latent Dirichlet allocation(LDA) to separate the tweets from the disaster affected area into categories(topics) useful to emergency managers.The LDA revealed that out of 24 topics found in the data,nine were directly related to disaster impacts-for example,outages,closures,flooded roads,and damaged infrastructure.Features such as frequent hashtags,mentions,URLs,and useful images were then extracted and analyzed.The relevant tweets,along with useful images,were correlated at the county level with flood depth,distributed disaster aid(damage),and population density.Significant correlations were found between the nine relevant topics and population density but not flood depth and damage,suggesting that more research into the suitability of social media data for disaster impacts modeling is needed.The results from this study provide baseline information for such efforts in the future.

Use of Twitter in disaster rescue: lessons learned from Hurricane Harvey

Despite the increasingly prominent role of social media in disaster events,studies analyzing its use in rescue operations remain scanty.Hurricane Harvey hit Texas with unprecedented rainfall and flooding in 2017 and was marked by widespread use of social media for rescue requests.We conducted a survey of 195 Twitter users in Houston and surrounding communities who had requested for rescue during Harvey.The objective was to investigate our targeted group’s socioeconomic and flood exposure characteristics,report the effectiveness of Twitter,and highlight lessons learnt and suggestions made for its use in future rescue missions.Survey revealed that those requesting rescue on Twitter were better educated,employed(80%),and homeowners(81%).Majority of them were flooded(87%),but remained satisfied with current location and did not consider moving.Calling relatives and friends for rescue was most responsive and yielded higher assistance-provided rate than using Twitter.Our respondents found Twitter helpful,but identified issues such as not knowing when volunteers received their requests or whether they would send help.They suggested promoting Twitter accounts and hashtags that accept emergency requests.This study provides baseline information and actionable suggestions for first responders,community managers,and resilience practitioners to improve future rescue missions.

Improving social media use for disaster resilience: challenges and strategies

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and management.The framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and Harvey.The paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive modeling.However,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of modeling.Then,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.