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- W4378417760 abstract "Despite the growing importance of particulate matter (PM) air pollution its assessment still remains often limited to only a few point measurements, making it difficult to assess the spatial extent. This research shows that under lower pollution conditions (<50 ppm) the accuracy of low cost sensors is too low to show meaningful spatial differences. However under high pollution conditions, when air quality information is also most needed, they perform quite well, after calibration (precision 10 ppm). With a gradient boosting model (GBM) the relationship between PM2.5 as response variable and relative humidity (%), temperature (°C), windspeed (km/h), precipitation (mm) and Local Climate Zones (LCZ's) as predictor variables was explored. The last variable, LCZ's being a method to classify areas based on their land cover and present building types. If the LCZ's variable would show to be a good predictor variable this could make mapping PM easier as there is less data available on the spatial distribution of PM. And its direct link to urban morphology could prove to be helpful in the process of urban planning. The GBM showed that relative humidity (RH) had the highest relative influence (34.8%.) on PM2.5 concentrations followed by windspeed (23.6%), LCZ's (21.4%) and temperature (20.1%). Especially the so-called “compact built” LCZ's appeared to have a buffering impact on the observed PM2.5 concentrations, most likely because of their narrower roads and associated lower traffic load. These LCZ's are marked by a dense arrangement of buildings with not many, if any, natural elements present." @default.
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- W4378417760 date "2023-08-01" @default.
- W4378417760 modified "2023-09-26" @default.
- W4378417760 title "Assessment of the impact of local climate zones on fine dust concentrations: A case study from Hanoi, Vietnam" @default.
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- W4378417760 doi "https://doi.org/10.1016/j.buildenv.2023.110430" @default.
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