Statistical modelling of roadside and urban background ultrafine and accumulation mode particle number concentrations using generalized additive models

Gerling, Lars; Löschau, G.; Wiedensohler, Alfred; Weber, Stephan

doi:10.1016/j.scitotenv.2019.134570

Cited by 17 publications

(8 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this manuscript, we have applied a generalised additive model (GAM) to explore the association between daily reported infected due to COVID-19, and various factors including air pollutants and meteorological conditions in India (Gerling et al, 2020;X. Lin et al, 2018;Ma et al, 2020;Prata et al, 2020;Ravindra et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

View full text Add to dashboard Cite

SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS and WD) with COVID-19 infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

show abstract

Section: Introductionmentioning

confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…GAM with spline function (SGAM) was one of the 5 most accurate models for wind erosion hazard mapping. The spline functions allow the flexible representation of non-linear marginal relationships of the explanatory and response variables without the necessity to define a specific function 18 . Cforest, as a random forest (RF) model, uses conditional inference trees for prediction 19 .…”

Section: Discussionmentioning

confidence: 99%

“…g(µ i ) is the link function. We applied two GAMs, comprising boosting (BGAM) and spline (SGAM) 18 .…”

Section: Generalized Linear Models (Glms)mentioning

confidence: 99%

Mapping wind erosion hazard with regression-based machine learning algorithms

Gholami

Mohammadifar

Bui

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.

show abstract

“…Airborne particles, especially ultrafine particles (UFPs) with diameters of D p < 100 nm, may enter the circulatory system and are associated with acute and chronic effects on human health (Oberdörster et al, 1995;Nemmar et al, 2002;HEI Review Panel on Ultrafine Particles, 2013;Schraufnagel, 2020). UFPs are abundant in cities due to a number of different anthropogenic emission sources such as private and public traffic or commercial and industrial combustion (Tsang et al, 2008;Tie et al, 2009;Bäfver et al, 2011;Weber et al, 2013;Kumar et al, 2014;Gerling et al, 2020). In order to understand the spatiotemporal variation of population exposure to ultrafine particles, information on the strength and location of sources and sinks and the turbulent exchange between the surface and atmosphere are essential (Buzorius et al, 2000;Longley et al, 2003;Mårtensson et al, 2006;Weber et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Measurement report: Three years of size-resolved eddy-covariance particle number flux measurements in an urban environment

Straaten

Weber

2021

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. Size-resolved particle number fluxes in the size range of 10 nm < particle diameter (Dp) < 200 nm were measured over a 3-year period (April 2017–March 2020) using the eddy-covariance technique at an urban site in Berlin, Germany. The observations indicated the site as a net source of particles with a median total particle number flux of FTNC=0.86 × 108 m−2 s−1. The turbulent surface–atmosphere exchange of particles was clearly dominated by ultrafine particles (Dp < 100 nm) with a share of 96 % of total particle number flux (FUFP=0.83 × 108 m−2 s−1). Annual estimates of median FTNC and FUFP slightly decreased by −9.6 % (−8.9 % for FUFP) from the first to the second observation year and a further −5.9 % (−6.1 % for FUFP) from the second to the third year. The annual variation might be due to different reasons such as the variation of flux footprints in the individual years, a slight reduction of traffic intensity in the third year, or a progressive transition of the vehicle fleet towards a higher share of low-emission standards or electric drive. Size-resolved measurements illustrated events of bidirectional fluxes, i.e. simultaneous emission and deposition fluxes within the size spectrum, which occurred more often in spring, late summer, and autumn than in winter. Multi-year observations of size-resolved particle fluxes proved to be important for a deeper understanding of particle exchange processes with the urban surface and the pronounced influence of traffic at this urban site.

show abstract

Statistical modelling of roadside and urban background ultrafine and accumulation mode particle number concentrations using generalized additive models

Cited by 17 publications

References 65 publications

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Mapping wind erosion hazard with regression-based machine learning algorithms

Measurement report: Three years of size-resolved eddy-covariance particle number flux measurements in an urban environment

Contact Info

Product

Resources

About