Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches

Ramos, Yuddy; Réquia, Weeberb J.; St-Onge, Benoît; Blanchet, Jean‐Pierre; Kestens, Yan; Smargiassi, Audrey

doi:10.1016/j.envres.2018.06.036

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…Our model had relatively good performance in areas with high population density. Differences in performance for highly populated areas and less urban regions were also reported previously . Regarding the temporal variation, model performance was best in the summer season, whereas performance was weakest in winter.…”

Section: Results and Discussionsupporting

confidence: 82%

“…Besides the variation of O 3 precursors, rates of O 3 formation are also sensitive to meteorological conditions, such as the temperature and solar radiation. Previous studies have shown that variations in O 3 trends are associated with differences in characteristic local weather patterns. , Low precipitation, high temperature, and low wind speed favor O 3 formation and buildup. , Relative humidity is negatively correlated with O 3 because cloudy days with precipitation tend to have lower actinic flux than clear sky days and therefore less photochemical activity. In addition, dry atmospheric conditions can cause drought stress and suppress stomatal O 3 uptake and contribute to the high warm-season O 3 level …”

Section: Introductionmentioning

confidence: 99%

“…Modeling approaches to estimate O 3 concentrations over space and time have been developed to improve exposure characterization for health studies. These O 3 exposure models fall into several classes, including chemical transport model simulations, geostatistical interpolation approaches, ,, land use regression models, , source dispersion models, models based on remote sensing technology, , ensemble-based forecast, and, most recently, machine learning models. − These various modeling approaches have different strengths and limitations that result in varying levels of exposure misclassification. The great advantage of machine learning is that these models can represent any kind of nonlinear relationship in which the variables from different data sources have complex interactions.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

Réquia

Silvern

et al. 2020

Environ. Sci. Technol.

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126

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In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O 3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R 2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R 2 , 0.93, and performance was weakest for the western mountainous regions (R 2 of 0.86) and New England (R 2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R 2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O 3 over space and time, especially in health studies at an intra-urban scale.

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Section: Results and Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

Réquia

Silvern

et al. 2020

Environ. Sci. Technol.

Self Cite

126

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“…As a powerful oxidizing and phytotoxic air pollutant, groundlevel ozone (O 3 ) is widely known with detrimental effects on the human health, forest trees and crops [1,2]. Global background tropospheric O 3 level has doubled during the twentieth century following the increased production of its precursors (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike fine particulate matter, ambient O 3 air pollution is invisible and may be exist when the sky is blue and clear [6]. As O 3 concentration incredibly increases within spring and summer seasons, this secondary major air pollutant is pronounced an important environmental issue in urban areas [2,[6][7][8]. In 2015, the Global Burden of Diseases (GBD) study estimated that ambient O 3 caused 254,000 deaths globally, making it the 33rd ranking risk factor for premature deaths [7,9].…”

Section: Introductionmentioning

confidence: 99%

Human, Forest and vegetation health metrics of ground-level ozone (SOMO35, AOT40f and AOT40v) in Tehran

Faridi

Akbari

Faridi

et al. 2020

J Environ Health Sci Engineer

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Purpose We aimed to investigate the spatial O 3 indices (SOMO35: annual sum of maximum daily 8-h ozone means over 35 ppb, AOT40: the accumulated exposure over an hourly threshold of 40 ppb during daylight hours between 8:00 and 20:00 in the growing seasons of plants) in Tehran (2019-2020). Methods The data of ambient O 3 concentrations, measured at twenty-three regulatory ambient air quality monitoring stations (AQMSs) in Tehran, were obtained. Results The annual mean O 3 concentrations were found to be 15.8-25.7 ppb; the highest and lowest annual mean concentration of ambient O 3 were observed in Shahrdari 22 and Shahr-e-Rey stations, respectively. Spatial distribution of exposure to O 3 across Tehran was in the range of 1.36-1.64; the highest O 3 concentrations were observed in the northern, west and south-western parts of Tehran, while the central and south areas of Tehran city experienced low to moderate concentrations. The indices of SOMO35, AOT40f and AOT40v across AQMSs in Tehran was in the range of 1830-6437 ppb. Days, 10,613-39,505 ppb.h and 4979-16,804 ppb.h, respectively. For Tehran city, the indices of SOMO35 and AOT40f were 4138 ppb. days and 27,556 ppb.h respectively.Our results revealed that the value of SOMO35 across AQMSs of Tehran was higher than the recommended target value of 3000 ppb. days. Conclusions To reduce O 3 pollution and its effects on both human and plants health, the governmental organizations should take appropriate sustainable control policies.

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Tropospheric Ozone in Tehran, Iran, during the last 20 years

Borhani

Motlagh

Stohl

et al. 2021

Environ Geochem Health

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Air pollution and its effects on human health and the environment are one of the main concerns in urban areas. This study focuses on the distribution and changes in the concentrations of ozone and its precursors (i.e., NO, NO 2 and CO) in Tehran for the 20-year period from 2001 to 2020. The effects of precursors and meteorological conditions (temperature, wind speed, dew point, humidity and rainfall) on ozone were investigated using data from 22 stations of the Air Quality Control Company (AQCC) and meteorological stations. Regression models were applied to evaluate the dependence of ozone concentration on its precursors and meteorological parameters based on monthly average values. Finally, the monthly and annual levels of surface ozone and total column ozone were compared during the study period. The results show that the average ozone concentration in Tehran varied substantially between 2001 and 2008, and decreased after 2008 when stringent air quality control measures were implemented. The highest average concentration of ozone occurred in the southwest of Tehran. Although mobile and resident sources play an important role in the release of precursors, the results also indicate a significant effect of meteorological conditions on the changes in ozone concentration. This study is an effective step toward a better understanding of ozone changes in Tehran under the changing influence of precursors and meteorological conditions.

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Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches

Cited by 21 publications

References 34 publications

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

Human, Forest and vegetation health metrics of ground-level ozone (SOMO35, AOT40f and AOT40v) in Tehran

Tropospheric Ozone in Tehran, Iran, during the last 20 years

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