Abstract. Because of the vague distinction between urban and rural areas, the Local Climate Zone (LCZ) scheme was developed to better analyze the effect of Urban Heat Island. To map the LCZs in a city, the World Urban Database and Portal Tool is used as conventional method. However, this requires the assignment of training areas for each LCZ, which entails local knowledge of the area and may introduce errors, as distinction between LCZ types through visual inspection is inconclusive. This paper aims to develop a methodology and GIS tool to enhance and automate the mapping of LCZs using seven LCZ properties (sky view factor, building surface fraction, pervious surface fraction, impervious surface fraction, building height, roughness length, and surface albedo), and apply it in Quezon City, Philippines which comprises varying land use and land cover. Fuzzy Logic was used to determine the membership percentage of 100 m cells to an LCZ type based on these properties. Cellular Automata was implemented using Python to derive the LCZ map from the fuzzy layers. Results show that seven out of ten built-up LCZs and five out of seven land cover LCZs were identified. Through visual inspection on a basemap, the mapped LCZs was confirmed to match with the features of the city. Land Surface Temperature (LST) derived from Landsat 8 showed that each LCZ type displayed temperatures consistent with those observed from literature. The developed methodology and tool is ready to be used in other cities as long as the input layers are generated.

<p><strong>Abstract.</strong> As the unmanned aerial vehicle (UAV) technology has gained popularity over the years, it has been introduced for air quality monitoring. This study demonstrates the feasibility of customized UAV with mobile monitoring devices as an effective, flexible, and alternative means to collect three-dimensional air pollutant concentration data. This also shows the vertical distribution of PM concentration and the relationship between the PM_2.5 vertical distribution and the meteorological parameters within 500<span class="thinspace"></span>m altitude on a single flight in UP Diliman, Quezon City. Measurement and mapping of the vertical distribution of particulate matter (PM)_2.5 concentration is demonstrated in this research using integrated air quality sensors and customized Unmanned Aerial Vehicle. The flight covers an area with a radius of 80 meters, following a cylindrical path with 40-meter interval vertically. The PM_2.5 concentration values are analyzed relative to the meteorological parameters including air speed, pressure, temperature, and relative humidity up to a 500<span class="thinspace"></span>meter-flying height in a single flight in Barangay UP Campus, UP Diliman, Quezon City. The study shows that generally, the PM_2.5 concentration decreases as the height increases with an exception in the 200&amp;ndash;280<span class="thinspace"></span>m above ground height interval due to a sudden change of atmospheric conditions at the time of the flight. Using correlation and regression analysis, the statistics shows that PM_2.5 concentration has a positive relationship with temperature and a negative relationship with relative humidity and wind speed. As relative humidity and wind speed increases, PM_2.5 decreases, while as temperature increases, PM_2.5 also increases.</p>

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Abstract. The Urban Heat Island (UHI) phenomenon is a manifestation of the abnormal amount of heat generated in urban areas and anthropogenic land surface modifications. While urbanization can improve material comfort and be a boon to the economy, the accompanying problems associated with urbanization like the UHI effect has implications on health, demand for water and energy, and impacts the microclimate. Land surface temperature (LST), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Built-up Index (NDBI) were calculated from historical remotely-sensed Landsat data from 2013 to present. The global horizontal irradiance (GHI) was computed from the lidar-derived elevation model of Cebu City using the Geographical Resources Analysis Support System (GRASS). It is shown that annual variation in average temperatures in Cebu is generally less than 5 °C. Mean UHI temperatures in Cebu City do not show a clear trend over time, but categorizing data by season, namely the rainy season (June–November), the cool dry season (December–February), and the hot dry season (March–May), permits the emergence of a pattern. Surface temperatures for the cool dry season and hot dry season show a linearly increasing trend with R2 values of 0.916 and 0.514, respectively. This study further investigates the temporal change in the degree and extent of the UHI in Cebu City by analyzing LST maps. Regression analysis is done to determine how LST is affected by the distribution of vegetation (NDVI) and built-up (NDBI), and the seasonal variation in solar radiation through the GHI.

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