TY - JOUR

T1 - Analysis of wintertime O3 variability using a random forest model and high-frequency observations in Zhangjiakou—an area with background pollution level of the North China Plain

AU - Liu, Huazhen

AU - Liu, Junfeng

AU - Liu, Ying

AU - Ouyang, Bin

AU - Xiang, Songlin

AU - Yi, Kan

AU - Tao, Shu

PY - 2020/7/1

Y1 - 2020/7/1

N2 - The short-term health effects of ozone (O3) have highlighted the need for high-temporal-resolution O3 observations to accurately assess human exposure to O3. Here, we performed 20-s resolution observations of O3 precursors and meteorological factors to train a random forest model capable of accurately predicting O3 concentrations. Our model performed well with an average validated R2 of 0.997. Unlike in typical linear model frameworks, variable dependencies are not clearly modelled by random forest model. Thus, we conducted additional studies to provide insight into the photochemical and atmospheric dynamic processes driving variations in O3 concentrations. At nitrogen oxides (NOx) concentrations of 10–20 ppb, all the other O3 precursors were in states that increased the production of O3. Over a short timescale, nitrogen dioxide (NO2) can almost track each high-frequency variation in O3. Meteorological factors play a more important role than O3 precursors do in predicting O3 concentrations at a high temporal resolution; however, individual meteorological factors are not sufficient to track every high-frequency change in O3. Nevertheless, the sharp variations in O3 related to flow dynamics are often accompanied by steep temperature changes. Our results suggest that high-temporal-resolution observations, both ground-based and vertical profiles, are necessary for the accurate assessment of human exposure to O3 and the success and accountability of the emission control strategies for improving air quality.

AB - The short-term health effects of ozone (O3) have highlighted the need for high-temporal-resolution O3 observations to accurately assess human exposure to O3. Here, we performed 20-s resolution observations of O3 precursors and meteorological factors to train a random forest model capable of accurately predicting O3 concentrations. Our model performed well with an average validated R2 of 0.997. Unlike in typical linear model frameworks, variable dependencies are not clearly modelled by random forest model. Thus, we conducted additional studies to provide insight into the photochemical and atmospheric dynamic processes driving variations in O3 concentrations. At nitrogen oxides (NOx) concentrations of 10–20 ppb, all the other O3 precursors were in states that increased the production of O3. Over a short timescale, nitrogen dioxide (NO2) can almost track each high-frequency variation in O3. Meteorological factors play a more important role than O3 precursors do in predicting O3 concentrations at a high temporal resolution; however, individual meteorological factors are not sufficient to track every high-frequency change in O3. Nevertheless, the sharp variations in O3 related to flow dynamics are often accompanied by steep temperature changes. Our results suggest that high-temporal-resolution observations, both ground-based and vertical profiles, are necessary for the accurate assessment of human exposure to O3 and the success and accountability of the emission control strategies for improving air quality.

KW - Atmospheric dynamics

KW - High frequency

KW - Ozone variability

KW - Photochemistry process

U2 - 10.1016/j.envpol.2020.114191

DO - 10.1016/j.envpol.2020.114191

M3 - Journal article

C2 - 32126436

AN - SCOPUS:85080090502

VL - 262

JO - Environmental Pollution

JF - Environmental Pollution

SN - 0269-7491

M1 - 114191

ER -