Air Pollution Mapping Using Nonlinear Land Use Regression Models

Champendal, Alexandre; Kanevski, Mikhaïl; Huguenot, Pierre-Emmanuel

doi:10.1007/978-3-319-09150-1_50

Cited by 11 publications

(16 citation statements)

References 11 publications

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“…Random forests may be more accurate predictors of pollutant concentrations if they can indeed capture more patterns based on land use data. A random forest has been empirically shown to estimate concentrations of nitrogen dioxide based on land use data in the urban area of Geneva with a lower error when compared to regression (Champendal et al, 2014), although the authors did not compare the model’s cross validated performance with a traditional land use regression model. We hypothesize that land use random forest (LURF) models, as compared to LUR models, will result in more accurate and precise estimates of PM2.5 elemental component concentrations.…”

Section: Introductionmentioning

confidence: 99%

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp

Jandarov

Rao

et al. 2017

Atmospheric Environment

199

124

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Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

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Section: Introductionmentioning

confidence: 99%

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp

Jandarov

Rao

et al. 2017

Atmospheric Environment

199

124

View full text Add to dashboard Cite

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“…They reported that this algorithm has the advantage of using complex datasets from different data sources and can discover hidden patterns in the data. Ensembling the decision tree regression algorithm (such as the widely used random forest regression [19]) was used for predicting yearly averages of N O 2 by Champendal et al [8]. They reported good prediction accuracy against standard linear regression methods.…”

Section: Related Workmentioning

confidence: 99%

“…weather data) would result in a complex regression problem [6]. The complex non-linear correlation relationship in the data [8] makes the predictions hard for the traditionally used linear regression and it results in low prediction accuracy using the state-of-the-art regression algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies [10], [9], [11], [8] use the fundamentals of land use regression methods to improve the prediction accuracy of the standard land use regression model for monthly and yearly concentration level predictions. To achieve the desired accuracy improvement, they use different regression algorithms such as neural network regression [12] or support vector machine regression [13] however, these studies focused only on prediction at lower temporal resolution (monthly and yearly) than hourly concentration level modelling.…”

Section: Introductionmentioning

confidence: 99%

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Signal Selection in a Complex Environmental Distributed Sensing Problem

Makrai¹,

Bate

2017

2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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Supporting sustainable development for the urban environment is crucial in the age of rapid urbanisation. Air pollution modelling is one of the key tools for researchers, scientists, and urban planners to understand pollution behaviour. Recent updates in air quality regulations are challenging the state-of-the-art air pollution modelling techniques by requiring accurate predictions on a high temporal level, i.e. predictions at the hourly level rather than the annual level. Current stateof-the-art models designed to have good prediction accuracy on the low temporal resolution by assuming that the pollution is in steady state. Making predictions on higher temporal resolution violates this assumption and causing inaccurate predictions. We introduce a novel statistical regression based air pollution model which produces accurate hourly predictions by using data with high temporal resolution and advanced regression algorithms. We conducted an analysis which shows that the state-of-the-art evaluation techniques (e.g. RMSE) do not describe the nature of the mispredictions of the models built on different data subsets. We carried out an extensive input data evaluation experiment where we concluded that our approach could achieve further accuracy improvement by training the models on a carefully selected subset of the input data.

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“…They have regressed observed SO 2 concentrations against a comprehensive set of land use and transportation variables. Champendal et al (2014) deal with a new development of nonlinear LUR models based on machine learning algorithms. They assessed the Multi-Layer Perceptron and Random Forest algorithms and their abilities to model the NO 2 pollutant in the urban zone of Geneva.…”

Section: Literature Reviewmentioning

confidence: 99%

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

2015

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Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting cooccurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance. Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

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Air Pollution Mapping Using Nonlinear Land Use Regression Models

Cited by 11 publications

References 11 publications

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Signal Selection in a Complex Environmental Distributed Sensing Problem

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

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