Ground-level NO2 Concentrations Research Articles

Cng-term variations and trends of tropospheric and ground-level NO2 over typical coastal areas

Tropospheric and ground-level levels of nitrogen dioxide (NO2) may have changed due to environmental policies and technological changes. Here, multiyear ground-based measurement (2014–2022) and satellite-derived retrieval (October 2004 to May 2023) datasets of NO2 concentrations are used to study the long-term ground-level and tropospheric NO2 patterns and trends at four time scales (interannual, quarterly, monthly, and hourly) over the three regions with the highest global NO2 concentrations. Several tropospheric NO2 hotspots are observed over industrialized and highly populated coastal zone areas. Over the past 18 years, these hotpots have generally decreased due to the implementation of strong and effective environmental protection policies. A significant trend reversal was observed in 2011 in East-central China. The maximum values of tropospheric NO2 occurred in winter over East-central China, Western Europe and East-central USA, with concentrations of 13.010, 4.243 and 4.149 × 1015 molecules cm−2, respectively. The Bohai Sea exhibited disproportionately high tropospheric NO2 levels, suggesting it as a significant contributor to regional nitrogen pollution. Ground-level NO2 shows similar temporal patterns to tropospheric concentrations, with over 82.8 % of sites in East-central China exceeding 30 μg m−3, compared to 12.8 % in Western Europe and 3.8 % in East-central USA. The analysis of diurnal variations in ground-level NO2 concentrations shows a significantly decrease during the daytime compared to nighttime, reflecting that the peak occurs at approximately 08:00 and 20:00 LT, while the lowest concentrations at around 12:00 LT. These insights clarify the impacts of environmental policies on NO2 levels and identify critical areas for targeted pollution control, enhancing our understanding of NO2 dynamics in high-pollution regions globally.

Comparison and Optimization of Ground-Level NO2 Concentration Estimation in China Based on TROPOMI and OMI

Comparison and Optimization of Ground-Level NO2 Concentration Estimation in China Based on TROPOMI and OMI

Impacts of local soil and vehicle NOx emissions on ground-level NO2 concentrations on a university campus in the city of Shenyang, China.

Nitrogen dioxide (NO2) is a ubiquitous atmospheric pollutant, and fossil fuel combustion is generally considered its predominant source in and around urban areas. As the total nitrogen deposition is high over here, soil NOx emissions from urban green space might also be an important local source of ground-level NO2. In this study, Willems badge samplers were employed to monitor the spatial and seasonal variations of 2-week mean atmospheric NO2 concentrations at a height of 1.7 m on an urban campus in Northeast China from November 2020 to December 2021. We found considerable small-scale spatial variations of ground-level NO2 concentrations on the campus during the growing season, with local soil NOx emissions as the main driver. According to its linear correlation with green space coverage, the increment in ground-level NO2 concentration was partitioned into two components, with one ascribed to the local soil source (referred to as NO2-Isoil) and the other the local vehicle source (NO2-Ivehicle). NO2-Isoil generally reached a maximum (as high as 25.6 μg/m3) during early spring, while its ratio to the background value generally reached a maximum (could be >1) during late spring and could reach 0.52 to 0.92 during summer. Therefore, soil NOx emissions were an important source of ground-level NO2 on the campus, with the contribution even higher than those of other anthropogenic sources during late spring. Even with light traffic on the campus, NO2-Ivehicle could reach 0.48 times the background value at a site with high frequencies of warm starts.

National ground-level NO2 predictions via satellite imagery driven convolutional neural networks

Outdoor air pollution, specifically nitrogen dioxide (NO2), poses a global health risk. Land use regression (LUR) models are widely used to estimate ground-level NO2 concentrations by describing the satellite land use characteristics of a given location using buffer distance averages of variables. However, information may be leaked in this approach as averages ignore the variances within the averaged region. Therefore, in this study, we leverage a convolutional neural network (CNN) architecture to directly pass data grids of various satellite data for the prediction of U.S. national ground-level NO2. We designed CNN architectures of various complexity which inputs both satellite and meteorological reanalysis data, testing both high and low resolution data grids. Our resulting model accurately predicted NO2 concentrations at both daily (R2 = 0.892, RMSE = 2.259, MAE = 1.534) and annual (R2 = 0.952, RMSE = 0.988, MAE = 0.690) temporal scales, with coarse resolution imagery and simple CNN architectures displaying the best and most efficient performance. Furthermore, the CNN outperforms traditional buffer distance models, including random forest (RF), feedforward neural network (FNN), and multivariate linear regression (MLR) approaches, resulting in the MLR performing the poorest at daily (R2 = 0.625, RMSE = 4.281, MAE = 3.102) and annual (R2 = 0.758, RMSE = 2.218, MAE = 1.652) scales. With the success of the CNN in this approach, satellite land use variables continue to be useful for the prediction of NO2. Using this computationally inexpensive model, we encourage the globalization of advanced LUR models as a low-cost alternative to traditional NO2 monitoring.

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland

Land use regression modeling has commonly been used to model ambient air pollutant concentrations in environmental epidemiological studies. Recently, other statistical and machine-learning methods have also been applied to model air pollution, but their relative strengths and limitations have not been extensively investigated. In this study, we developed and compared land-use statistical and machine-learning models at annual, monthly and daily scales estimating ground-level NO2 concentrations across Switzerland (at high spatial resolution 100 × 100 m). Our study showed that the best model type varies with context, particularly with temporal resolution and training data size. Linear-regression-based models were useful in predicting long-term (annual, monthly) spatial distribution of NO2 and outperformed machine-learning models. However, linear-regression-based models were limited in representing short-term temporal variation even when predictor variables with temporal variability were provided. Machine-learning models showed high capability in predicting short-term temporal variation and outperformed linear-regression-based models for modeling NO2 variation at high temporal resolution (daily). However, the best performing models, XGBoost and LightGBM, constantly overfit on training data and may result in erratic patterns in the model-estimated concentration surfaces. Therefore, the temporal and spatial scale of the study is an important factor on which the choice of the suitable model type should be based and validation is required whatever approach is used.

Mitigating nitrogen dioxide air pollution: The roles and effect of national smart city pilots in China

The impacts of smart city policy on environmental efficiency and pollution are widely studied. However, we know very little about the roles and effect of smart city policy in nitrogen dioxide (NO2) air pollution. Using Chinese city-level panel data, we investigate the impact of national smart city pilots on satellite-derived ground level NO2 concentrations by Difference-in-Differences method. The results show that the smart city policy is conductive to mitigating the NO2 concentrations in pilot cities, especially for the cities with large population, high administrative hierarchies and located in eastern China. In particular, the mitigation effect of first round smart city pilots is greater than the second and third rounds. The mechanisms of smart city policy mitigating NO2 concentrations is to optimize energy structure and advance treatment technology, rather than directly reduce fossil energy consumption. Therefore, smart city policy may be a win-win choice for developing countries to break the trade-off between air pollution and economic development.

Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression

Predicting long-term ground-level nitrogen dioxide (NO2) is important globally to support environmental and public health research and to provide information to governments and society for air pollution control policies. The ozone monitoring instrument (OMI), onboard Aura Satellite, detects monthly global tropospheric column amounts (TrCA) of NO2 molecules. However, the relationship between the ground-level NO2 concentration and TrCA of NO2 molecules remains elusive because NO2 molecules in the air are not evenly distributed vertically. We use geographically weighted panel regression (GWPR) to examine the relationship between satellite-derived data, measured ground-level NO2 concentrations, and several controlling meteorological variables from January 2015 to October 2021. The GWPR can analyze unbalanced panel data and capture the spatial variability of the relationship. Based on the GWPR estimation, 82 monthly global ground-level NO2 concentrations are predicted from January 2015 to October 2021. The GWPR is reliable, as indicated by the 10-fold cross-validation. The accuracy of the raster prediction of global ground-level NO2 from January 2015 to October 2021 is 69.61%. The coefficient of correlation, root mean square error and mean absolute error between globally predicted and measured ground-level NO2 are 0.838, 7.84 μg/m3 and 4.07 μg/m3, respectively, while the mean of globally measured ground-level NO2 is 19.47 μg/m3. Overall, this research provides critical basic data to environmental and public health science and valuable information for governments and societies to make more reasonable policies.

Assessment of NO2 population exposure from 2005 to 2020 in China.

Nitrogen dioxide (NO2) is a major air pollutant with serious environmental and human health impacts. A random forest model was developed to estimate ground-level NO2 concentrations in China at a monthly time scale based on ground-level observed NO2 concentrations, tropospheric NO2 column concentration data from the Ozone Monitoring Instrument (OMI), and meteorological covariates (the MAE, RMSE, and R2 of the model were 4.16 µg/m3, 5.79 µg/m3, and 0.79, respectively, and the MAE, RMSE, and R2 of the cross-validation were 4.3 µg/m3, 5.82 µg/m3, and 0.77, respectively). On this basis, this article analyzed the spatial and temporal variation in NO2 population exposure in China from 2005 to 2020, which effectively filled the gap in the long-term NO2 population exposure assessment in China. NO2 population exposure over China has significant spatial aggregation, with high values mainly distributed in large urban clusters in the north, east, south, and provincial capitals in the west. The NO2 population exposure in China shows a continuous increasing trend before 2012 and a continuous decreasing trend after 2012. The change in NO2 population exposure in western and southern cities is more influenced by population density compared to northern cities. NO2 pollution in China has substantially improved from 2013 to 2020, but Urumqi, Lanzhou, and Chengdu still maintain high NO2 population exposure. In these cities, the Environmental Protection Agency (EPA) could reduce NO2 population exposure through more monitoring instruments and limiting factory emissions.

Long-term variations of ground-level NO2 concentrations along coastal areas in China

The long-term trends of the ground-level NO2 concentrations over coastal areas in China were analysed from 2007 to 2019 in this study, based on the measurements from Ozone Monitoring Instrument (OMI) and the simulations from the Model for Ozone and Related Chemical Tracers (MOZART)-4. Multiple Gaussian functions were used to simulate the atmospheric profiles of NO2 concentrations from the MOZART-4, and the ratio of the ground-level concentrations to the columns estimated from this Gaussian function was used to convert the OMI NO2 columns to that at the ground level. The estimation results showed that the ground-level NO2 concentrations were well consistent with those of ground measurements in 2017 (the correlation coefficients ranging from 0.84 to 0.87 in the four seasons). The NO2 concentrations presented an increasing rate of 0.6 μg/m3/yr during 2007–2011 and then a decreasing rate of 0.5 μg/m3/yr until 2019. Most areas in the China Sea suffered from the maximum NO2 concentrations during 2011–2013. The temporal variations of NO2 over coastal lands were consistent with those over the ocean during 2007–2016, but they presented different after the implementation of coastal emission control area (ECA) policies in 2016. The ECA policies significantly decreased the NO2 concentrations in the Yangtze River Delta (the YRD) and the Bohai Rim, and they inhibited the increase of NO2 in the Pearl River Delta (the PRD). These results might be helpful in understanding the current situation of ground-level NO2 pollution over oceans and coastal land in China and provide guidance for further development of ECA policies.

Global fine-scale changes in ambient NO2 during COVID-19 lockdowns

Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1–9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910–20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Machine learning-based estimation of ground-level NO2 concentrations over China

Most current scientific research on NO2 remote sensing focuses on tropospheric NO2 column concentrations rather than ground-level NO2 concentrations; however, ground-level NO2 concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO2 and its influencing factors. The R2 values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO2 concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO2 concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO2 concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO2 concentrations indicates that pollution decreased continuously from 2018 to 2020.

Ground-level NO2 concentration estimation based on OMI tropospheric NO2 and its spatiotemporal characteristics in typical regions of China

Although the ground-level NO2 measurement from air quality monitoring sites is relatively accurate, it is a challenge to obtain continuous spatial coverage due to the discrete distribution of sites. Thus, the tropospheric column NO2 amount from satellites with wide spatial and temporal coverage and higher resolution has been increasingly used to estimate ground-level NO2. However, most estimation methods were performed spontaneously using a simple linear model throughout the study period. These simplified models improve the efficiency of model development and enhance the generality of the model application, but they ignore the fact that contributors to changes of ground-level NO2 are not always consistent with time. This study considered the fixed and random effects of influencing factors and developed a mixed effect model (MEM) to estimate the ground-level NO2. By using the data of tropospheric NO2 in China from January 1, 2014 to June 30, 2020 and other multivariate auxiliary data such as meteorological elements and terrain elevation, the reliability of daily ground-level NO2 in typical populated areas of China estimated by the MEM was evaluated. The average of monthly R2 of 10-fold CV in each study area during 2014–2020 is greater than 0.60 and the proportion of R2 greater than 0.7 is about 71%, suggesting the reliability of MEM. It is found that the ground-level NO2 distribution characteristics of each study area are more distinct, and the influential factors are also different. In addition, associated with the air quality control policies and emission reduction measures in various regions, the ground-level NO2 in each study area has shown an overall downward trend during 2014–2019. The uncertainty of daily-scale meteorological elements and boundary layer conditions can lead to varying degrees of deviations in daily-scale predictions of ground-level NO2. Validation with the station NO2 observations demonstrates that the ground-level NO2 prediction at seasonal time scale (R2 = 0.81, RMSE = 3.86 μg/m3) performs better than those at time scales of daily and monthly (R2 = 0.65, and 0.75, RMSE = 7.92, and 6.24 μg/m3). Therefore, the method of averaging can be used to improve the accuracy of ground-level NO2 predictions on individual dates. In summary, this study shows that MEM is a promising ground-level NO2 modeling method, and is effective for air pollution mapping in a large geographic region.

Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks

Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects.

Comparison of Satellite-retrieved NO2 Vertical Column Density with Ground-level NO2 concentration in a provincial scale region

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu Province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).

Estimating the spatial and temporal variability of the ground-level NO2 concentration in China during 2005–2019 based on satellite remote sensing

Based on the ground-level observed NO2 concentration, satellite-observed NO2 column concentration from the Ozone Monitoring Instrument (OMI) and meteorological parameters, we comprehensively consider the seasonal and regional differences in the relationship between NO2 column concentration and measured NO2 concentration and establish a two-stage combined ground NO2 concentration estimation (TSCE-NO2) model using a support vector machine for regression (SVR) and a genetic algorithm optimized back propagation neural network (GABP). On this basis, the spatial-temporal variation in the modelled ground-level NO2 concentration over China during the period of 2005–2019 was analysed. The results show that the TSCE-NO2 model proposed in this study provides a reliable estimation of the modelled ground-level NO2 concentration over China, effectively filling the spatial and temporal gaps in China's air quality ground monitoring network (the model's correlation coefficient, R, is 0.92, the mean absolute error, MAE, is 3.62 μg/m3, the mean square percentage error, MSPE, is 0.72%, and the root-mean-square error, RMSE, is 5.93 μg/m3). The analysis results of the spatial and temporal variation indicate that (1) the perennial ground-level NO2 concentration over China is high in the eastern area and low in the western area, and the high values are mainly distributed along the northern coast, the eastern coast, the middle reaches of the Yangtze River, the middle reaches of the Yellow River, the Pearl River Delta and the Sichuan Basin. (2) The modelled ground-level NO2 concentrations over China are highest in winter, followed by those in autumn and spring, and they are lowest in summer. Before 2011, the ground-level NO2 concentration over China increased at a rate of 0.348 ± 0.132 μg/(m3∙a) but decreased at a rate of 0.312 ± 0.188 μg/(m3∙a) after 2011. (3) From 2011 to 2019, measures such as energy savings and emission reductions alleviated NO2 pollution on the premise of ensuring sustained China's GDP growth.

Remote Sensing Estimation of Regional NO2 via Space-Time Neural Networks

Nitrogen dioxide (NO2) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO2 in this study by integrating ground NO2 station measurements, satellite NO2 products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO2 and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R2 value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO2 estimation framework will be of great use for remote sensing of ground-level NO2 concentrations.