Spatial interpolation of ambient ozone concentrations from sparse monitoring points in Belgium

Hooyberghs, Jef; Mensink, Clemens; Dumont, Gerwin; Fierens, Frans

doi:10.1039/b612607n

Cited by 58 publications

(29 citation statements)

References 10 publications

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“…First, model performance surpasses existing studies. Some previous studies adopted land-use regression, Kriging or other methods and achieved RMSE > 10 ppb in Belgium (Hooyberghs et al 2006); RMSE > 10 ppb in Italy (Carnevale et al 2008); daily R 2 = 0.653 in Quebec (Adam-Poupart et al 2014). Our hybrid model outperformed land-use regression results, with averaged cross-validated annual R 2 = 0.76 and RMSE = 7.36 ppb.…”

Section: Discussionmentioning

confidence: 99%

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Rowland

Koutrakis

et al. 2016

Journal of the Air & Waste Management Association

118

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Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

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Section: Discussionmentioning

confidence: 99%

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Rowland

Koutrakis

et al. 2016

Journal of the Air & Waste Management Association

118

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“…Numerous studies have applied the IDW technique for spatial mapping of pollutants such as polycyclic aromatic hydrocarbons (PAHs) in Pennsylvania, USA (Witter et al, 2014), O 3 in northern Georgia, China, and Belgium (Bell, 2006; Hooyberghs et al, 2006; Wang et al, 2014), PM 10 and PM 2.5 in USA and China (Wang et al, 2014; Yanosky et al, 2008), and CO, SO 2 , and NO 2 in China (Wang et al, 2014). The IDW technique has been expanded in the environmental field to forecast ambient air pollutants’ concentration.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, B/T ratios lower than 0.5 suggest that transportation is the predominant source of BTEX (Elbir et al, 2007; Hartmann et al, 1997; Zhang et al, 2008). For instance, Hartmann et al (1997) and Elbir et al (2006) showed that the B/T ratio in a suburban area was about 0.16–1, while next to congested roadways, the B/T ratio was about 0.5 (Elbir et al, 2007; Hartmann et al, 1997). In addition to the B/T ratio, the xylenes/benzene (X/B) and ethylbenzene/benzene (Ebz/B) ratios are generally applied as indices of photochemical, reactivity.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and health effects of BTEX in a hot spot for urban pollution

Dehghani

Fazlzadeh

Sorooshian

et al. 2018

Ecotoxicology and Environmental Safety

193

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This study reports a spatiotemporal characterization of toluene, benzene, ethylbenzene, and xylenes concentrations (BTEX) in an urban hot spot in Iran, specifically at an bus terminal region in Shiraz. Sampling was carried out according to NIOSH Compendium Method 1501. The inverse distance weighting (IDW) method was applied for spatial mapping. The Monte Carlo simulation technique was applied to evaluate carcinogenic and non-carcinogenic risk owing to BTEX exposure. The highest average BTEX concentrations were observed for benzene in the morning (at 7:00–9:00A.M. local time) (26.15±17.65μg/m3) and evening (at 6:00–8:00P.M. local time) (34.44±15.63μg/m3). The benzene to toluene ratios in the morning and evening were 2.02 and 3.07, respectively. The main sources of BTEX were gas stations and a municipal solid waste transfer station. The inhalation lifetime cancer risk (LTCR) for benzene in the morning and evening were 1.96×10−4 and 2.49×10−4, respectively, which exceeds the recommended value by US EPA and WHO. The hazard quotient (HQ) of all these pollutants was less than 1. The results of this work have implications for public health near ‘hot spots’ such as IKBT where large populations are exposed to carcinogenic emissions.

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“…Data assimilation methods are used since several decades in atmospheric sciences [46][47][48][49][50]. More recently, they have also been applied in air quality studies [51][52][53][54][55], mainly adopting chemical transport models [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67], and more rarely with urban dispersion models [68]. Here, we present the results of a data assimilation method named Source Apportionment Least Square (SALS), developed for the SIRANE urban air quality model [69,70].…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

2018

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Abstract:Developing effective strategies for reducing the atmospheric pollutant concentrations below regulatory threshold levels requires identifying the main origins/sources of air pollution. This can be achieved by implementing so called source apportionment methods in atmospheric dispersion models. This study presents the results of a source apportionment module implemented in the SIRANE urban air-quality model. This module uses the tagged species approach and includes two methods, named SA-NO and SA-NOX, in order to evaluate the sources' contributions to the NO 2 concentrations in air. We also present results of a data assimilation method, named SALS, that uses the source apportionment estimates to improve the accuracy of the SIRANE model results. The source apportionment module and the assimilation method have been tested on a real case study (the urban agglomeration of Lyon, France, for the year 2008) focusing on the NO 2 emissions and concentrations. Results of the source apportionment with the SA-NO and SA-NOX models are similar. Both models show that traffic is the main cause of NO 2 air pollution in the studied area. Results of the SALS data assimilation method highlights its ability in improving the predictions of an urban atmospheric models.

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Spatial interpolation of ambient ozone concentrations from sparse monitoring points in Belgium

Cited by 58 publications

References 10 publications

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Characteristics and health effects of BTEX in a hot spot for urban pollution

Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

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