Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

Abstract

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

“…Population density (an indication of domestic fuel burning) was present in the winter NO 2 , summer PM 10 , as well as annual PM 2.5 LUR models. This is consistent with the findings reported in other studies [ 4 , 16 , 33 ], in which the population density variable has been related to these sources of PM. While it was expected for a traffic variable to have appeared in the winter NO 2 model, the population variable appeared instead, indicating that areas with higher population density are related to higher NO 2 levels during winter.…”

“…Each individual has their own unique personal exposure to air pollution during their daily life, occurring both in indoor and outdoor environments, and therefore the quantifying process is complex [ 1 ]. To determine the effect of these exposures on health, many of these studies have estimated individual air pollution exposure by making use of air quality monitoring datasets that are representative of the study area and have also made use of more complex approaches such as spatial interpolation [ 2 , 3 , 4 ].…”

Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

“…Previous LUR prediction models for PM2.5 in SSA have been shown to have relatively poorer performance (Saucy et al, 2018;Tularam, 2019) compared to gaseous pollutant models for SSA or PM2.5 models developed in higher income regions. Given our results, as well as other air pollution research conducted in urban SSA that also show strong correlations between neighborhood-level solid fuel use and outdoor PM concentrations (Zhou et al, 2011), we suggest future modeling efforts in this region should incorporate solid fuel use data to improve PM2.5 modeling predictions.…”

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

“…MLR is easy to implement. However, one of the main problems could be the multicollinearity, because MLR does not analyze the correlation between predictors [57]. On the other hand, the linear PLS helps to avoid the multicollinearity creating new latent variables with few observations [34].…”