Abstract
It is known, that the polluted air influences straightforwardly on human wellbeing. Along these lines, the air quality checking surveys the nature of air and recognize defiled territories. Geographic information systems (GIS) provides appropriate tools for the purpose of creating models and describing spatial relationships. This study aims to develop an AQI prediction algorithm based on some meteorological parameters collected using an inverse distance weighted geostatistical technique analysis results, from measurements of three meteorological stations adjacent to the study area Kuala Lumpur of the period June to August 2018. A GIS spatial statistical analysis approach was used. An ordinary least squares (OLS) process was adopted for the 3 months data separately and three models have been obtained. An accuracy value of model performance has been computed were set as (97, 99, and 97%) respectively, specified thru the analysis. So as to test the model, validation applied again using predicted AQI and compared them with observed AQI data, the accuracy was set as (96, 99, and 93%), respectively. The result indicated a very good fit of the OLS model to the observed points, verified that the consequences of these analyses are able to monitor and predict AQI with high accuracy.
Highlights
There is an important necessity to determine the changing levels of air contaminants in cities because of their negative effects on health and to take future precautionary measures (Jumaah et al 2018)
We proposed an alternate strategy to measure Air Quality Index (AQI) utilizing regression models dependent on data attained by Geographic information systems (GIS) toolbox of geostatistical technique analysis
The resultant inverse distance weighted (IDW) map included AQI, T, H, P, and Wind speed (Ws) values classified as classes ranged by colors from low to high value
Summary
There is an important necessity to determine the changing levels of air contaminants in cities because of their negative effects on health and to take future precautionary measures (Jumaah et al 2018). Cities air quality can be improved with a number of interferences, on various sectorial (industrial, transportation, and residential, etc.), as. There is widespread concern about increasing the number of vehicles on the roads and its relation to public health. Where the recently published medical articles have identified this concern over the increase in respiratory diseases and their relationship with air pollution. It is important to ascertain areas where contamination levels exceed the standards in order to derive a reliable air quality prediction if can (Gattrell and Loytonen 1998). Given serious air contamination problems, monitoring of the AQI has currently received more attention (Yang et al 2018)
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