Comment on egusphere-2024-558 DOI Creative Commons
Naveed Ahmad, Changqing Lin, Alexis K.H. Lau

et al.

Published: April 10, 2024

Abstract. The major bridge linking satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) with ground-level concentration is theoretically the NO2 mixing height (NMH). Various meteorological parameters have been used as a proxy NMH in existing studies. This study developed nested machine learning model to convert VCDs into concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. was designed directly incorporate methodological framework and explore its impact on performance. inner predicted from parameters, which were then input main predict VCDs. inclusion significantly enhanced accuracy estimating concentration, reducing bias improving R² values 0.93 10-fold cross-validation 0.99 fully-trained model. Furthermore, identified second most important predictor variable, following NO2. Subsequently, data analyzed subregions varying geolocations urbanization levels. Highly populated areas typically experienced peak during early morning rush hours, whereas categorized lightly observed slight increase levels one or two hours later, likely due regional pollutant dispersion urban sources. underscores importance incorporating satellite measurements highlights significant advantages geostationary satellites providing detailed air pollution information at an hourly resolution.

Language: Английский

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador DOI Creative Commons
Danilo Mejía,

Gina Faican,

Rasa Žalakevičiūtė

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e28152 - e28152

Published: March 21, 2024

The concentration of gases in the atmosphere is a topic growing concern due to its effects on health, ecosystems etc. Its monitoring commonly carried out through ground stations which offer high precision and temporal resolution. However, countries with few stations, such as Ecuador, these data fail adequately describe spatial variability pollutant concentrations. Remote sensing have great potential solve this complication. This study evaluates spatiotemporal distribution nitrogen dioxide (NO2) ozone (O3) concentrations Quito Cuenca, using obtained from ground-based Sentinel-5 Precursor mission sources during years 2019 2020. Moreover, Linear Regression Model (LRM) was employed analyze correlation between satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE 0.18) NO2 0.25) Quito; 0.74, 0.23) NO2, 0.73, Cuenca. agreement datasets analyzed by employing intra-class coefficient (ICC), reflecting good them (ICC ≥0.57); Bland Altman coefficients, showed low bias that more than 95% differences are within limits agreement. Furthermore, investigated impact COVID-19 pandemic-related restrictions, social distancing isolation, atmospheric conditions. categorized into three periods 2020: before (from January 1st March 15th), 16th May 17th), after 18th December 31st). A 51% decrease recorded while experienced 14.7% decrease. tropospheric column decreased 27.3% Cuenca 15.1% Quito. an increasing trend, rising 0.42% 0.11% respectively, 14.4%. increase 10.5%. Finally, reduction chemical species consequence mobility restrictions highlighted. compared station Despite differing units preventing validation, it verified Sentinel-5P satellite's effectiveness anomaly detection. Our research's value lies applicability developing countries, may lack extensive networks, demonstrating use technology urban planning.

Language: Английский

Citations

8
Comparing the Influences on NO2 Changes in Terms of Inter-Annual and Seasonal Variations in Different Regions of China: Meteorological and Anthropogenic Contributions DOI Creative Commons
Xuehui Bai, Yi Wang,

Lu Gui

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 121 - 121

Published: Jan. 2, 2025

NO2 primarily originates from natural and anthropogenic emissions. Given China’s vast territory significant differences in topography meteorological conditions, a detailed understanding of the impacts weather human emissions different regions is essential. This study employs Kolmogorov–Zurbenko (KZ) filtering stepwise multiple linear regression to isolate effects conditions on tropospheric vertical column densities. Long term trends indicate an overall decline, with contribution rates exceeding 90% Shanghai, Changchun, Urumqi, Shijiazhuang, Wuhan, where interannual variations are driven by In Guangzhou, rate exceeds 100%, highlighting impact factors this region, although somewhat mitigate their effect NO2. Chengdu, also play role. Seasonal display U-shaped trend, there seasonal among regions. Meteorological Changchun Chengdu below 36.90% contributions exceed 63.10%. indicates that changes less influenced than activities, dominating. other regions, greater those activities.

Language: Английский

Citations

1
An environmental assessment through load capacity factor: the dynamic effects of technological cooperation grants and energy depletion in Pakistan DOI Creative Commons
Sami Ullah, Boqiang Lin

Frontiers in Sustainable Energy Policy, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 7, 2025

The global phenomenon of environmental deterioration often signifies the increase in ecological footprint and emissions levels that adversely affect earth's biocapacity. This results from use substantial fossil fuels energy sources, industrialization, extensive economic activities developing countries. In this context, study examine impact depletion, technical cooperation grants, on load capacity factor Pakistan 1970 to 2022. To accomplish this, employs innovative dynamic autoregressive distributed lag (ARDL) simulation approach, providing fresh insights contrast with earlier conclusions. authors contribute focusing supply-side dynamics indicators, namely capacity, viewpoint Pakistan, distinguishing our research existing academic publications. Our results, however, demonstrate a markedly favorable effect grants enhancing safety. Furthermore, depletion industrialization dynamics, exacerbating deterioration. Moreover, conducts sensitivity analysis by comparing obtained using those derived footprints. Consequently, we advocate for development realistic policies mitigate adverse impacts via effective sources preserve biodiversity.

Language: Английский

Citations

1
Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013–2020 DOI
Lei Zhang, Lili Wang, Boya Liu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 903, P. 166182 - 166182

Published: Aug. 9, 2023

Language: Английский

Citations

14
Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model DOI Creative Commons
Naveed Ahmad, Changqing Lin, Alexis K.H. Lau

et al.

Atmospheric chemistry and physics, Journal Year: 2024, Volume and Issue: 24(16), P. 9645 - 9665

Published: Aug. 30, 2024

Abstract. The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) and ground-level concentrations is theoretically the NO2 mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed nested XGBoost machine learning model to convert VCDs into across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. was designed directly incorporate methodological framework estimate concentrations. inner predicted from parameters, which were then input main predict its VCDs. inclusion significantly enhanced accuracy concentration estimates; i.e., R2 values improved 0.73 0.93 10-fold cross-validation 0.88 0.99 fully trained model. Furthermore, identified second most important predictor variable, following NO2. Subsequently, data analyzed subregions with varying geographic locations urbanization levels. Highly populated areas typically experienced peak during early morning rush hour, whereas categorized lightly observed slight increase levels 1 or 2 h later, likely due regional pollutant dispersion urban sources. underscores importance incorporating estimating satellite measurements highlights significant advantages geostationary satellites providing detailed air pollution information at an hourly resolution.

Language: Английский

Citations

5
Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019 DOI
Yifan Tang, Yuchen Wang,

Xuwu Chen

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 929, P. 172591 - 172591

Published: April 23, 2024

Language: Английский

Citations

4
Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China DOI Creative Commons
Chunsheng Fang, Xiaowei Zhou,

Yuxuan Cai

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 45 - 45

Published: Jan. 3, 2025

The increasingly severe nocturnal ozone enhancement (NOE) event pollution is widely concerning. Therefore, based on the observed hourly O3 concentrations from 2015 to 2023, this study analyzes characteristics of NOE events over Putian City. analysis results show that frequency City high, at about 127 days annually, with a high in April and low July August. Most corresponded peak concentration (NOP) <120 μg/m3. Moreover, they mainly occurred between 1:00–3:00 7:00. physicochemical processes April, October, November 2020 were simulated using Weather Research Forecasting (WRF, version 4.3.3) model coupled Community Multiscale Air Quality (CMAQ, 5.4) model. suggest transport, especially horizontal transport eastern sea Zhejiang Province vertical upper atmosphere, could be major cause Furthermore, movement zone, along aggregation due weakened dry deposition influence stable boundary layer obstructed by mountain terrain, significantly influenced overall concentration. Thus, stem interaction among these processes. emphasize importance control implementation strict joint regional measures for improve air quality.

Language: Английский

Citations

0
Temporal and spatial heterogeneity of tropospheric O3 and NO2 and health impact analysis in Shaanxi, Gansu, and Ningxia regions of China DOI
Cong Guan, Minxia Liu, Jianyang Shi

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(4)

Published: March 17, 2025

Language: Английский

Citations

0
Investigations of spatial-temporal distribution and regional transport in typical section of NO2 in eastern China using Mobile-DOAS DOI

Zhidong Zhang,

Pinhua Xie, Ang Li

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 973, P. 179174 - 179174

Published: March 23, 2025

Language: Английский

Citations

0
An interpretable physics-informed deep learning model for estimating multiple air pollutants DOI Creative Commons
Binjie Chen,

Jiacong Hu,

Yumiao Wang

et al.

GIScience & Remote Sensing, Journal Year: 2025, Volume and Issue: 62(1)

Published: March 25, 2025

Language: Английский

Citations

0