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In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications.

The U.S. Geological Survey (USGS) land use (LU) data employed in the Weather Research and Forecasting (WRF) model classify most LU types in Taiwan as mixtures of irrigated cropland and forest, which is not an accurate representation of current conditions. The WRF model released after version 3.1 provides an alternative LU dataset retrieved from 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. The MODIS data correctly identify most LU-type distributions, except that they represent western Taiwan as being extremely urbanized. A new LU dataset, obtained using 2007 Système Probatoire d’Observation de la Terre (SPOT) satellite imagery [from the National Central University of Taiwan (NCU)], accurately shows the major metropolitan cities as well as other land types. Three WRF simulations were performed, each with a different LU dataset. Owing to the overestimation of urban area in the MODIS data, WRF-MODIS overpredicts daytime temperatures in western Taiwan. Conversely, WRF-USGS underpredicts daytime temperatures. The temperature variation estimated by WRF-NCU falls between those estimated by the other two simulations. Over the ocean, WRF-MODIS predicts the strongest onshore sea breezes, owing to the enhanced temperature gradient between land and sea, while WRF-USGS predicts the weakest onshore flow. The intensity of the onshore breeze predicted by WRF-NCU is between those predicted by WRF-MODIS and WRF-USGS. Over Taiwan, roughness length is the key parameter influencing wind speed. WRF-USGS significantly overpredicts the surface wind speed owing to the shorter roughness length of its elements, while the surface wind speeds estimated by WRF-NCU and WRF-MODIS are in better agreement with the observed data.

Abstract. On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20 • and 40 • . In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m 3 with an average depth of 40 m.

Exploring the effects of different types of PM2.5 is necessary to reduce associated deaths, especially in low- and middle-income countries (LMICs). Hence we determined types of ambient PM2.5 before exploring their effects on under-five and maternal mortality in Africa. The spectral derivate of aerosol optical depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) products from 2000 to 2015 were employed to determine the aerosol types before using Generalized Linear and Additive Mixed-Effect models with Poisson link function to explore the associations and penalized spline for dose-response relationships. Four types of PM2.5 were identified in terms of mineral dust, anthropogenic pollutant, biomass burning and mixture aerosols. The results demonstrate that biomass PM2.5 increased the rate of under-five mortality in Western and Central Africa, each by 2%, and maternal mortality in Central Africa by 19%. Anthropogenic PM2.5 increased under-five and maternal deaths in Northern Africa by 5% and 10%, respectively, and maternal deaths by 4% in Eastern Africa. Dust PM2.5 increased under-five deaths in Northern, Western, and Central Africa by 3%, 1%, and 10%, respectively. Mixture PM2.5 only increased under-five deaths and maternal deaths in Western (incidence rate ratio = 1.01, p < 0.10) and Eastern Africa (incidence rate ratio = 1.06, p < 0.01), respectively. The findings indicate the types of ambient PM2.5 are significantly associated with under-five and maternal mortality in Africa where the exposure level usually exceeds the World Health Organization’s (WHO) standards. Appropriate policy actions on protective and control measures are therefore suggested and should be developed and implemented accordingly.