A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa

Tularam, Hasheel; Ramsay, Lisa; Muttoo, Sheena; Brunekreef, Bert; Meliefste, Kees; Hoogh, Kees de; Naidoo, Rajen N.

doi:10.1016/j.envpol.2021.116513

Cited by 26 publications

(12 citation statements)

References 30 publications

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“…Based on land use information, the atmospheric pollutant concentration in unmonitored areas is anticipated by regression [16,17]. By establishing land use information, traffic conditions, and population distribution around the site as predictors, the LUR model can be applied to estimate the pollutant concentration in any region at multiple spatial scales when the change of the existing pollutant concentration is fully explained [18][19][20]. At a small spatial scale, such as urban areas, the concept of using the same set of predictor variables to forecast the concentration of pollutants in all areas of the research area is evidently flawed because of the differences in major pollution sources in distinct areas.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Land Use Regression Modelling of PM2.5 Spatial Variations in Different Seasons across China

et al. 2022

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Fine particulate matter (PM2.5), one of the main components of haze, is of wide concern for its potential negative health effects. In order to further improve ambient air quality, it is essential to conclude the spatial variability of pollutants by investigating air pollution exposure. We divide China into two parts, north and south, and use a Land Use Regression (LUR) model to extract data including meteorological data, land use factors, and AOD retrievals, and use the machine learning algorithm to optimize the model to achieve predictions of the spatial distribution of near-surface PM2.5 mass concentrations in southern and northern China. We evaluated the seasonal consistency of the models in southern and northern China, and in northern China, we found a better fit with better seasonal consistency for the heating season and annual average model, while in southern China, we did not find a more fitted seasonal phase. The study illustrates that it is feasible to simulate the spatial distribution of PM2.5 mass concentration in large-scale areas based on the LUR model, and the seasonal consistency of the LUR model has been done to some extent.

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Section: Introductionmentioning

confidence: 99%

Optimization of Land Use Regression Modelling of PM2.5 Spatial Variations in Different Seasons across China

et al. 2022

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“…On the other hand, PM 10 concentrations levels were higher in Durban North than South and the authors attributed this to vehicular emissions, bush fires and domestic solid fuel burning. ( Tuluram et al., 2021 ) Comparing Methods to Impute Missing Daily Ground-Level PM 10 Concentrations between 2010-2017 in South Africa The study developed innovative modelling approaches to impute missing air quality data through the use of spatiotemporal predictor variables supported by random forest machine learning method to explore the relationship between average daily PM 10 concentrations and spatiotemporal predictors for example meteorological elements, land use and source-related variables. The findings were that this approach is a feasible approach to compensate for missing data.…”

Section: Resultsmentioning

confidence: 99%

Research trends in the field of ambient air quality monitoring and management in South Africa: A bibliometric review

Matandirotya

2021

Environmental Challenges

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“…Among these fields, a primary category is to predict urban air pollution, including PM 2.5 , PM 10 (particulate matters with a diameter of 10 micrometres or less), NO x , CO, SO 2 , O 3 , black carbon, etc. [28,[34][35][36]. In LUR models, the relationship between an air pollutant and potential explanatory variables is estimated using regression models and the mean values or ratios of explanatory variables within a series of buffers of air pollutant monitoring locations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The potential explanatory variables usually consist of land use and land cover, road networks, traffic intensity, population, vegetation coverage, water areas, elevation, etc. [28,[34][35][36]. For instance, in the European Study of Cohorts for Air Pollution Effects (ESCAPE, www.escapeproject.eu) project, spatial distributions of PM 2.5 , PM 10 , and other particulate matters were predicted using LUR models for 20 European study areas using observations at 20 sites per area [37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, LUR models can effectively use categorical variables, such as land use, in models [35,36], which are difficult to be added in kriging-based models. The common categorical variables of geospatial data include land cover and land use, soil types, geological strata, river catchment zones, climate zones, ecological zones, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

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Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa

Cited by 26 publications

References 30 publications

Optimization of Land Use Regression Modelling of PM2.5 Spatial Variations in Different Seasons across China

Optimization of Land Use Regression Modelling of PM2.5 Spatial Variations in Different Seasons across China

Research trends in the field of ambient air quality monitoring and management in South Africa: A bibliometric review

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

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