Bioaerosol assessment in indoor and outdoor environments: a case study from India.

Exposure to bioaerosols has been associated with the occurrence of a variety of health impacts, including infectious illnesses, acute toxic effects, allergies, and cancer. This study aimed at evaluating airborne bacteria and fungi populations at different indoor and outdoor sites on a college campus in Bengaluru, India. Bioaerosol samples were collected using a two-stage Andersen air sampler; and isolates were identified using standard procedures. Six air samples and meteorological data were collected in March and April 2014 to examine the effects of temperature and relative humidity on bioaerosol concentration using linear regression modeling. Among all sites, the canteen showed the highest bioaerosol levels both indoors and outdoors. Specific bacterial identification was not possible, but gram staining and microscopic analysis helped to identify gram positive and gram negative bacteria. The most prevalent fungal species in the samples were Cladosporium, Aspergillus niger, Penicillium, Rhizopus, Fusarium, Mucor, and Alternaria. Due to the impact of weather conditions, such as temperature and relative humidity, the bioaerosol concentration varied greatly at each site according to the regression model. The indoor bioaerosol concentrations at all sites exceeded the values established by the American Industrial Hygiene Association (< 250 CFU/m3 for total fungi and < 500 CFU/m3 for total bacteria). Higher concentrations of bioaerosols may be attributed to the transportation of microbes from the ground surface to suspended particles, the release of microbes from the respiratory tract, higher rate of shredding of human skin cells, and many other factors.

Utilization of social media in floods assessment using data mining techniques.

Floods are among the devastating types of disasters in terms of human life, social and financial losses. Authoritative data from flood gauges are scarce in arid regions because of the specific type of dry climate that dysfunctions these measuring devices. Hence, social media data could be a useful tool in this case, where a wealth of information is available online. This study investigates the reliability of flood related data quality collected from social media, particularly for an arid region where the usage of flow gauges is limited. The data (text, images and videos) of social media, related to a flood event, was analyzed using the Machine Learning approach. For this reason, digital data (758 images and 1413 video frames) was converted into numeric values through ResNet50 model using the VGG-16 architecture. Numeric data of images, videos and text was further classified using different Machine Learning algorithms. Receiver operating characteristics (ROC) curve and area under curve (AUC) methods were used to evaluate and compare the performance of the developed machine learning algorithms. This novel approach of studying the quality of social media data could be a reliable alternative in the absence of real-time flow gauges data. A flash flood that occurred in the United Arab Emirates (UAE) from March 7-11, 2016 was selected as the focus of this study. Random forest showed the highest accuracy of 80.18% among the five other classifiers for images and videos. Precipitation/rainfall data were used to validate social media data, which showed a significant relationship between rainfall and the number of posts. The validity of the machine learning models was assessed using the area under the curve, precision-recall curve, root mean square error, and kappa statistics to confirm the validity and accuracy of the model. The data quality of YouTube videos was found to have the highest accuracy followed by Facebook, Flickr, Twitter, and Instagram. These results showed that social media data could be used when gauge data is unavailable.