Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact

Zhang, Chengxin; Liu, Cheng; Li, Bo; Zhao, Fei; Zhao, Chunhui

doi:10.1016/j.envpol.2022.119510

Cited by 15 publications

(3 citation statements)

References 49 publications

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“…The Chinese government established an air quality monitoring network, the China National Environmental Monitoring Center (CNEMC) (https://air.cnemc.cn:18007/, last access: 18 March 2021), consisting of approximately 1,500 sites in 454 cities by 2018 (Kong et al., 2021; Zhai et al., 2019). At each site, the PM 2.5 and PM 10 are measured using the tapered element oscillating microbalance (TEOM) method, and gas pollutants such as NO 2 , SO 2 , O 3 , CO are measured via chemiluminescence method, the ultraviolet fluorescence method, the ultraviolet spectrophotometry, and the gas filter correlation infrared absorption, respectively (Zhang et al., 2022). In this study, the hourly in‐situ data measured at the Tiyuxi station (ID: 2846A; 23.132°N, 113.322°E) were used.…”

Section: Measurement and Methodologymentioning

confidence: 99%

Diagnosis of Ozone Formation Sensitivities in Different Height Layers via MAX‐DOAS Observations in Guangzhou

et al. 2022

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Diagnosing the vertical distributions of ozone formation sensitivities within the boundary layers is the prerequisite for formulating proper ozone control strategies. From 1 January 2020, to 20 August 2021, a multi‐function MAX‐DOAS instrument was set up in Guangzhou to detect the NO2 and HCHO vertical profiles. The ozone formation sensitivities in different height layers were diagnosed using the FNRsec, which was defined as the ratio of secondary HCHO to NO2 VMRs (HCHOsec/NO2). The secondary sources in ambient HCHO participate in the photochemical reactions directly, and the FNRsec can indicate the ozone formation sensitivities more accurately. Therefore, the secondary source was separated from ambient HCHO via a multi‐linear regression method, and its relative contribution was approximately 66.9 ± 6.3%. The sensitivity thresholds of ozone formation were determined by the slope of the linear regression analysis for O3 versus the normalized secondary HCHO or NO2 mixing ratios for different FNRsec ranges, which were 0.22 and 0.36. For the ground layer, the VOC‐limited, transitional, and NOx‐limited regimes accounted for 40.66%, 32.42%, and 26.92%, respectively. Diurnally, the ground layer was usually in the VOC‐limited regime in morning, but in the transitional regime in noon and afternoon. Vertically, the ozone formation sensitivity changed with altitude from VOC‐limited (0.02–0.22 km) to transitional (0.22–0.42 km) to NOx‐limited (0.42–2.02 km). These findings can help formulate proper ozone control strategies in time according to the varying local conditions.

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Section: Measurement and Methodologymentioning

confidence: 99%

Diagnosis of Ozone Formation Sensitivities in Different Height Layers via MAX‐DOAS Observations in Guangzhou

et al. 2022

Self Cite

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“…Real-time gas pollutants are monitored using the ultraviolet fluorescence method, the chemiluminescence method, and other techniques, all in compliance with the China Environmental Protection Standards [39]. The detailed data include the monitoring time, geographical information, real-time concentrations of gas pollutants and fine particles, as well as the daily maximum sliding 8 h average concentration values and 24 h mean concentrations [40,41]. The vast dataset offers measurement data for assessing the air quality in different cities and regions, serving as valuable support for studies related to greenhouse gases, dust, and atmospheric particulate matter components.…”

Section: Ground-based Datamentioning

confidence: 99%

Analysis of Ozone Formation Sensitivity in Chinese Representative Regions Using Satellite and Ground-Based Data

Li,

Yu,

Tao

et al. 2024

Remote Sensing

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O3 poses a significant threat to human health and the ecological environment. In recent years, O3 pollution has become increasingly serious, making it difficult to accurately control O3 precursor emissions. Satellite indicator methods, such as the FNR (formaldehyde-to-nitrogen dioxide ratio (HCHO/NO2 ratio)), provide an effective way to identify ozone pollution control areas on a large geographical scale due to their simple acquisition of datasets. This can help determine the primary factors contributing to O3 pollution and assist in managing it. Based on TROPOMI data from May 2018 to December 2022, combined with ground-based monitoring data from the China National Environmental Monitoring Centre, we explored the uncertainty associated with using the HCHO/NO2 ratio (FNR) as an indicator in ozone control area determination. We focused on the four representative regions in China: Jing-Jin-Ji-Lu-Yu (JJJLY), Jiang-Zhe-Hu-Wan (JZHW), Chuan-Yu (CY), and South China. By using the statistical curve-fitting method, we found that the FNR thresholds were 3.5–5.1, 2.0–4.0, 2.5–4.2, and 1.7–3.5, respectively. Meanwhile, we analyzed the spatial and temporal characteristics of the HCHO, NO2, and O3 control areas. The HCHO concentrations and NO2 concentrations had obvious cyclical patterns, with higher HCHO column densities occurring in summer and higher NO2 concentrations in winter. These high values always appeared in areas with dense population activities and well-developed economies. The distribution characteristics of the ozone control areas indicated that during O3 pollution periods, the urban areas with industrial activities and high population densities were primarily controlled by VOCs, and the suburban areas gradually shifted from VOC-limited regimes to transitional regimes and eventually reverted back to VOC-limited regimes. In contrast, the rural and other remote areas with relatively less development were mainly controlled by NOx. The FNR also exhibited periodic variations, with higher values mostly appearing in summer and lower values appearing in winter. This study identifies the main factors contributing to O3 pollution in different regions of China and can serve as a valuable reference for O3 pollution control.

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“…Especially, deep learning algorithms have achieved significant breakthroughs in various domains. These advancements have also brought deep learning algorithms to the forefront of particulate matter prediction due to their exceptional self-learning capabilities and powerful nonlinear mapping abilities (Zhang et al, 2022). Among these algorithms, long short-term memory (LSTM) (Navare and Aznarte, 2020) and gated recurrent unit (GRU) ( Jiang et al,2020), are specifically designed for sequence prediction.…”

Section: Introductionmentioning

confidence: 99%

LightGBM-Based Stochastic Modeling for River Dust-Raising Alert

Ho,

Chang

2024

Preprint

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To enhance the accuracy of Taiwan's existing river dust-raising alert system, which exclusively depends on wind speed predictions, this study combines hydrological, meteorological, air quality information with LightGBM to establish a stochastic model for forecasting PM10 exceedance probabilities. The flexible probability information can effectively reduce the risk of poor decision-making caused by concentration deterministic forecast errors. LightGBM, a boosting-based ensemble learning algorithm, employs a depth-constrained leaf-wise growth strategy, speeding up training, reducing memory consumption, and shortening training time. The results of model training and validation demonstrate good performance in terms of accuracy, recall, and specificity metrics. This signifies that the model effectively predicts the occurrence of actual dust-raising events. In comparison to the current dust-raising alert mechanism, the model can significantly reduce unnecessary dust alerts and lightening the workforce's burden. Moreover, this model effectively forecasts dust events under low to moderate wind speed conditions, providing decision-makers with crucial support data for proactive dust control deployment.

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Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact

Cited by 15 publications

References 49 publications

Diagnosis of Ozone Formation Sensitivities in Different Height Layers via MAX‐DOAS Observations in Guangzhou

Diagnosis of Ozone Formation Sensitivities in Different Height Layers via MAX‐DOAS Observations in Guangzhou

Analysis of Ozone Formation Sensitivity in Chinese Representative Regions Using Satellite and Ground-Based Data

LightGBM-Based Stochastic Modeling for River Dust-Raising Alert

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