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

Observations of HONO and its precursors between urban and its surrounding agricultural fields: The vertical transports, sources and contribution to OH

The insufficient study on vertical observations of main atmospheric reactive nitrogen oxides (NO2 and HONO) posed a great challenge to evaluate their intertransport between urban and agricultural areas, and to further learn the atmospheric nitrogen chemistry and the atmospheric oxidation capacity at high altitudes. A stereoscopic measurement campaign (satellite remote sensing, hyperspectral unmanned aerial vehicle (UAV) remote sensing and MAX-DOAS observation) was performed in a typical inland city Hefei and its surrounding agricultural fields from June to October 2022. Average aerosol vertical profiles exhibited a Gaussian shape above 100 m with maximum values of 0.67 km-1 and 0.55 km-1 at 300-400 m layer at Anhui University (AHU) and Changfeng (CF), respectively. The distinct layered structure was mainly attributed to regional transport. Average H2O and NO2 vertical profiles all showed a Gaussian shape and an exponential shape at AHU and CF, respectively. Moreover, the diurnal evolution of H2O profiles performed one peak and bi-peak patterns at AHU and CF, respectively, whereas the diurnal evolution of NO2 at two stations all exhibited bi-peak patterns attributed to vehicle emissions. Average HONO vertical profiles showed an exponential shape and a Gaussian shape at AHU and CF, respectively. Higher HONO (> 0.05 ppb) above 1.0 km at 14:00-16:00 was observed at CF. The transport flux analysis showed that the northern transport flux always larger than southern transport flux for aerosol and H2O. The maximum northern transport fluxes appeared at 300 m and surface for aerosol and H2O, respectively. It indicated that surrounding agricultural fields was an important source of atmospheric H2O of city. The southern transport flux was larger than northern transport flux for NO2, with a maximum net transport flux of 9.20 ppb m s-1 at 100 m. It demonstrated that NO2 transported from urban areas was an important source of NO2 in agricultural fields. For HONO, the southern transport flux was larger than northern transport flux under 100 m, whereas it was opposite above 100 m. It indicated that the HONO distributed at high altitudes at agricultural fields had potential to enhance the atmospheric oxidation capacity of urban area. The net horizontal transport fluxes of HONO of our defined cropland were 5.25 μg m-2 s-1 and -3.65 μg m-2 s-1 during non-fertilization and fertilization periods, respectively. It indicated that the cropland could obviously export HONO to surrounding atmosphere during the fertilization period. Deducing the contribution of direct emission, heterogeneous process was a major source of HONO at urban and agricultural areas. The average surface conversion rate of NO2-to-HONO (CHONO) was 0.01467 h-1, and this value decreased with the increase of height at urban station. While average surface CHONO was 0.0322 h-1 at agricultural fields, which was ~1.2-2.8 times higher than that at urban area. The CHONO at agricultural fields significantly increased with the increase of height. The average CHONO at 1.0 km was ~2.0-3.6 times higher than that at surface. That suggested that the heterogeneous process was the main HONO source at high altitudes at CF, and this process obviously correlated with aerosol and H2O. The higher OH production from HONO (P(OH)HONO) occurred at 0-200 m and 100-400 m with averaged values of 0.31 ppb h-1 and 0.39 ppb h-1 at AHU and CF, respectively. The high P(OH)HONO above 1.0 km at CF from September to October was strongly correlated with high O3 (> 80 ppb). This study emphasized the importance of the stereoscopic of HONO on the analysis of its distribution, evolution, source and atmospheric oxidizing contribution.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.

Spatiotemporal variations of NO2 and its driving factors in the coastal ports of China

Nitrogen Dioxide (NO₂) is one of the major air pollutants in coastal ports of China. Understanding the spatiotemporal varying effects of driving factors of NO₂ is vital for the implementation of differentiated air pollution control measures for different port areas. Based on the Ozone Monitoring Instrument (OMI) satellite data, we adopted a Geographically and Temporally Weighted Regression (GTWR) model to explore the influences of meteorological and socioeconomic factors on the NO₂ Vertical Column Concentrations (VCDs) in coastal ports of China from 2015 to 2021. The results indicate that NO₂ VCD in most ports has decreased since 2016 and the ports with serious NO₂ pollution are mainly distributed in northern China. The associations between NO₂ VCD levels and their drivers exhibit obvious spatiotemporal heterogeneity. Higher wind speed and relative humidity are more helpful to alleviate NO₂ pollution in ports of the Bohai Rim and the Pearl River Delta. Cargo throughput has more closely associated with NO₂ pollution in Beibu Gulf in recent years, yet there is no significant association found for Shanghai ports. The positive relationship between transportation emissions and NO₂ VCD is more significant in southern ports. This work provides some implications for the formulation of targeted emission reduction policies for different ports along the Chinese coast.

Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial-temporal attention and residual learning

Accurate and reliable forecasting of PM_2.5 and PM₁₀ concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM_2.5 and PM₁₀ concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial-temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM_2.5 and PM₁₀ concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial-temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1-4 h ahead predictions in three major cities of PM_2.5 and PM₁₀, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.