Chemistry, transport, emission, and shading effects on NO2 and Ox distributions within urban canyons

Dai, Yuqing; Cai, Xiaoming; Zhong, Jian; Mazzeo, Andrea; MacKenzie, A. Rob

doi:10.1016/j.envpol.2022.120347

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…The tracer for factor 2 is O x , with a coefficient of 0.75, the highest among all factors. O x is commonly used as a typical secondary source tracer in other studies (Dai et al., 2022; Yu et al., 2020; Yuan et al., 2012). Regarding tracer selection for Factor 3, ethane, with exhibited a relatively high loading of 0.47 and had the least linear relationship with toluene tracer in Factor 1, was chosen as the tracer for Factor 3 to represent the transport emissions.…”

Section: Resultsmentioning

confidence: 99%

Revealing the Influencing Factors of an Oxygenated Volatile Organic Compounds (OVOCs) Source Apportionment Model: A Case Study of a Dense Urban Agglomeration in the Winter

Zou,

Guan,

Flores

et al. 2024

JGR Atmospheres

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Understanding sources of oxygenated volatile organic compounds (OVOCs) in the atmosphere still has large uncertainties. In this study, an improved OVOC source apportionment model was developed by principal component analysis and multiple linear regression based on the online monitoring of nonmethane hydrocarbons (NMHCs) and OVOCs in a dense urban agglomeration in the winter. The modeled concentrations were in good agreement with the measured concentrations (R2 = 0.56–0.97). The concentrations of major OVOCs, except for 2‐methylacrolein, were greatly affected by anthropogenic sources (15.8%–76.8%) and secondary generation (0.0%–51.7%), while transport and natural sources contributed to 0.0%–26.8% and 0.0%–32.0%, respectively. The selection of isoprene as the natural tracer led to an underestimation of the OVOC species from primary emission and an overestimation from natural sources. In addition, photochemical reactions significantly reduced the simulation accuracy of the model for NMHCs in the afternoon, with the R2 of 0.60 ± 0.23, which was lower than the overall value of 0.82 ± 0.11. However, the R2 for OVOCs (0.83 ± 0.14) did not decrease significantly in the afternoon due to the compensation of secondary oxidation. Furthermore, the concentration gradient distribution of the species gradually changes from a normal distribution to an exponential normal distribution with a decrease in concentration, the accuracy of the model was influenced by the degree of matching between tracer and species concentration gradient as species concentration change. Developing models with additional tracers at different concentration levels may enhance the robustness of the OVOC source apportionment model without increasing its complexity.

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Section: Resultsmentioning

confidence: 99%

Revealing the Influencing Factors of an Oxygenated Volatile Organic Compounds (OVOCs) Source Apportionment Model: A Case Study of a Dense Urban Agglomeration in the Winter

Zou,

Guan,

Flores

et al. 2024

JGR Atmospheres

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“…These rates were found to be significantly higher than those observed in Naples in winter [43]. Moreover, in this extremely deep urban canyon, shading effects could have an important impact on the concentrations of reactive species [26,[44][45][46]. Therefore, the photochemical rates (Table S3) were scaled through a comparison of daytime surface solar radiation, which was obtained from the ERA5 global atmospheric reanalysis dataset, between the first of August in Birmingham and the monitoring period in January in Naples.…”

Section: Model Configuration and Data Pre-processingmentioning

confidence: 99%

“…Atmosphere 2023, 14, x FOR PEER REVIEW 9 of important impact on the concentrations of reactive species [26,[44][45][46]. Therefore, photochemical rates (Table S3) were scaled through a comparison of daytime surface so radiation, which was obtained from the ERA5 global atmospheric reanalysis datas between the first of August in Birmingham and the monitoring period in January Naples.…”

Section: Model Configuration and Data Pre-processingmentioning

confidence: 99%

Modelling of Deep Street Canyon Air Pollution Chemistry and Transport: A Wintertime Naples Case Study

Dai,

Mazzeo,

Zhong

et al. 2023

Atmosphere

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The impact of urban morphology on air quality, particularly within deep canyons with longer residence times for complex chemical processes, remains insufficiently addressed. A flexible multi-box framework was used to simulate air quality at different canyon heights (3 m and 12 m). This approach incorporated essential parameters, including ventilation rates, background concentrations, photochemical schemes, and reaction coefficients. A field campaign within a deep canyon with an aspect ratio of 3.7, in Naples, Italy was conducted and used for the model evaluation. The model performance demonstrated good agreement, especially at the street level, when employing a realistic light intensity profile and incorporating volatile organic compound (VOC) chemistry. Our findings indicate that peroxyl radical production affects NO2 and O3 levels by up to 9.5% in deep canyons and underscore the significance of vertical distribution (approximately 5% variance) in health assessments and urban air quality strategy development. The model response was sensitive to changes in emissions as expected, but also, somewhat more surprisingly, to background conditions, emphasizing that policies to remove pollution hotspots must include local and broader citywide action. This work advances the understanding of air quality dynamics in deep urban canyons and presents a valuable tool for effective air quality management in intricate urban environments.

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“…Some research used the XGBoost model to explain the nonlinear relationship between near-surface NO 2 measurements and influencing factors in the retrieval of NO 2 concentrations [15]. Several studies developed numerical models based on transmission and photochemical processes to assess high-resolution NO 2 distributions at the urban scale [16,17] and used the 3D-CTMS model to construct a framework for summer NO 2 emissions in China [18]. One study determined the driving factors of NO 2 column concentrations using Geo-detector [19], and another study used a stable isotope and Bayesian-based SIAR model to identify the source of nitrate in rivers [20].…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis

Chen,

Hu,

Song

et al. 2023

Remote Sensing

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With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.

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Chemistry, transport, emission, and shading effects on NO2 and Ox distributions within urban canyons

Cited by 7 publications

References 60 publications

Revealing the Influencing Factors of an Oxygenated Volatile Organic Compounds (OVOCs) Source Apportionment Model: A Case Study of a Dense Urban Agglomeration in the Winter

Revealing the Influencing Factors of an Oxygenated Volatile Organic Compounds (OVOCs) Source Apportionment Model: A Case Study of a Dense Urban Agglomeration in the Winter

Modelling of Deep Street Canyon Air Pollution Chemistry and Transport: A Wintertime Naples Case Study

The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis

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