Development of Indoor Air Pollution Concentration Prediction by Geospatial Analysis

Abstract

Abstract. People living near busy roads are potentially exposed to trafficinduced air pollutants. The pollutants may intrude into the indoor environment, causing health risks to the occupants. Prediction of pollutant exposure therefore is of great importance for impact assessment and policy making related to environmentally sustainable transport. This study involved the selection of spatial interpolation methods that can be used for prediction of indoor air quality based on outdoor pollutant mapping without indoor measurement data. The research was undertaken in the densely populated area of Karees, Bandung, Indonesia. The air pollutant NO 2 was monitored in this area as a preliminary study. Nitrogen dioxide concentrations were measured by passive diffusion tube. Outdoor NO 2 concentrations were measured at 94 locations, consisting of 30 roadside and 64 outdoor locations. Residential indoor NO 2 concentrations were measured at 64 locations. To obtain a spatially continuous air quality map, the spatial interpolation methods of inverse distance weighting (IDW) and Kriging were applied. Selection of interpolation method was done based on the smallest root mean square error (RMSE) and standard deviation (SD). The most appropriate interpolation method for outdoor NO 2 concentration mapping was Kriging with an SD value of 5.45 µg/m 3 and an RMSE value of 5.45 µg/m 3 , while for indoor NO 2 concentration mapping the IDW was best fitted with an RMSE value of 5.92 µg/m 3 and an SD value of 5.92 µg/m 3 .

Enhancing Indoor Air Quality Estimation: A Spatially Aware Interpolation Scheme

“…The closer a data point is to the new location, the higher its weight will be. IDW is a fast and easy-to-implement method, but it can provide inaccurate results when the data have a strong spatial structure, as it does not take into account the spatial autocorrelation of the data [22][23][24][25][26].…”

“…The weights are determined by the spatial autocorrelation structure of the data, which describes how the values at different locations are related to each other. Kriging is a popular method for spatial interpolation, as it can provide accurate results, especially when the data have a strong spatial correlation [25,32,33].…”

The use of Sansevieria sp. and Cymbopogon nardus extracts on humidifier modification to reduce airborne germs

“…This is how the fungus grows unnoticed and undetected [14]. In this instance, development is required to produce healthy and sustainable building environments by establishing indoor air rules that account for all indoor pollutant sources [15] since the pollutants emerged may intrude into the indoor environment, causing health risks to the occupants [16]. In general, the educational building workshops had the highest elemental contamination [17].…”